Patent Application
20110093204
The invention relates to the field of high-throughput analysis of samples with peptide content and especially computer implemented methods and a system implementing these methods for identifying and characterizing peptides and their functional relationships by use of measures of correlation.
The success of the Human Genome Project in mapping the human genetic code offers astonishing potential for medical research. A prerequisite for using this information, however, is the identification of gene products, especially proteins and peptides. Peptides are the family of molecules formed from the linking, in a defined order, of various amino acids. The link between one amino acid residue and the next is an amide bond, and is sometimes referred to as a peptide bond. Peptides occur in nature and are responsible for a variety of functions, many of which are not understood. They differ from proteins, which are also long chains of amino acids, by virtue of their size.
Parallel to the world-wide efforts in Genomics, a variety of discovery technologies have been developed for analyzing samples with peptide content. Just as Genomics focuses on decoding the human genome, these technologies strive for an comprehensive analysis of the myriad of biologically relevant proteins and peptides with a molecular mass between about 0.5 and 20 kDa, among which insulin is a prominent example.
Profiling of peptides and proteins of human body fluids and tissues by mass spectrometry reveals a large number of peptide signals. Such high-throughput analytical processes demand highly sophisticated bloinformatic approaches to understand and analyze biological and pharmaceutical coherences in huge sets of data.
Conventional computer implemented methods for assisting the mass spectrometric identification of peptides and small proteins interpret the spectra and generate proposals for the identity of the candidate peptide signal by determining the differences of masses of the fragments in one spectra and assigning these differences to missing amino acids. A string of missing amino acids is then composed to a proposed amino acid sequence that is thereafter queried in a huge database containing tens of thousands of the known protein sequences, such as the Swiss-Prot database. However, if the analyzed peptide or protein is not abundant and/or in a complex mixture, such an approach turns out to be not very effective and, thus, time consuming concentration or fractionation steps of the sample have to be performed.
More sophisticated approaches take the knowledge of a known sequence in a spectrum into consideration. Here, proteolytic digests of the known sequence are proposed “in silico”, and a hypothetic resulting spectrum is then correlated with the actually measured one. However, these approaches are successful only if the sources of the spectra contain only a few different analytes, as their fragment signals alter the calculations and lower the correlation coefficients of the hypothesized calculated spectra with the actually measured one. If many possible protein precursors exist for a given peptide, then creating such a hypothetic spectrum for each unknown peptide and each possible precursor, the correlation process of hypothetic and measured spectra often turn out to be quite laborious and at times even unsuccessful.
Eng et al (Journ. Am. Soc. Mass Spectrom. 5, 976-989, 1994) for instance describe a statistical scorer for tandem mass spectrometry, that relies on cross-correlating experimental spectra with predicted spectra of peptides from a database (Havilio et al, Anal. Chem. 75[3], 435-444, 2003). No additional information (e.g. enzyme specificity used to create the peptides) about the peptide except the mass of the peptide is used. In a first step the tandem mass spectrometry data is reduced, whereby all but the most abundant signals are removed. In a second step protein sequences are queried from a database for combinations of amino acids that match the mass of the peptide, wherein the search algorithm only considers mass changes typical for a post-translational modification at every occurrence of the modification site. In a third step, the preliminary matches are scored by summing the number of fragmented ions that match the ions observed in the spectrum immonium ions are considered if the sequence contains the amino acids Tyrosine, Tryptophan, Methionine or Phenylalanine. This and the sum of fragments are being taken into account in the scoring function. Finally, a spectrum is reconstructed from the putative amino acid sequences and the highest scoring predictions are assessed by a cross-correlational analysis. The cross-correlation function measures the coherence of the reconstructed and the measured spectrum signals by, in effect, translating one signal across the other. Well-known applications such as SEQUEST and Sonar make use of this approach. However, a disadvantage of this approach is that the peak intensity strongly depends on the ion type, the ion mass and other experimental parameters and that many factors are not fully understood yet that contribute to peptide fragmentation.
Perkins et al (Electrophoresis 20[18], 3551-3567, 1999) describe a statistical scorer that evaluates the probability of finding a collection of detected fragments in a protein database (Havilio et al, Anal. Chem. 75[3], 435-444, 2003). Applications such as Mascot, MOWSE, Protocall are based on this approach. However, a disadvantage of this approach is that the signal intensities of the measured spectra are not being considered for the data analysis.
Weinberger et al (United States Patent Application 2002/0182649) describe essentially two approaches. In the first approach a protein candidate is identified by providing the mass spectrum to a protein database mining tool which identifies at least one protein candidate for the test protein in the database based on a closeness-of-fit measure between the mass spectrum and the theoretically calculated mass spectra of proteins in the database. In the second approach, the protein candidate is directly sequenced using mass spectrometry. In this method the unknown peptide is directly fragmented during mass spectrometry and the masses of the generated fragments are determined by mass spectrometry and are used to calculate the sequence of the unknown peptide.
The approaches according to Eng et al and Weinberger et al have in common that a closeness-of-fit analysis or a cross-correlation is performed over all signals of two spectra, i.e. the measured spectrum and the predicted spectrum. A fundamental disadvantage of these methods is that they rely on predicted mass spectra.
Thus, all of the above approaches have their disadvantages in that at times they turn out to be not very effective, quite laborious, time consuming and often unsuccessful.
There is thus a need for methods for analyzing samples with peptide content and a system implementing these methods overcoming or at least mitigating the disadvantages associated with the prior art.
The following methods according to the present invention are based upon the concept of Correlation Associated Networks and peptide topologies as will be apparent from the detailed description in the sections further below.
According to the present invention a method based on CANs is provided for providing a representative, non-redundant overview of the peptide content of a sample type by analyzing a plurality of samples using its peptide topology, wherein the method comprises the steps of providing a respective mass spectrum for each sample of said plurality of samples, wherein signal intensity peaks correspond to potential peptides, computing the measures of correlation between the signal intensities of said potential peptides, grouping potential peptides together, which exhibit a degree of correlation among each other above a certain threshold, thereby providing a plurality of correlation associated networks of potential peptides, and assigning one representative potential peptide out of each correlation associated network as a representative peptide to said correlation associated network of said sample type.
Furthermore, a method based on CANs is provided for predicting the sequence of peptides using the peptide topology of a plurality of samples containing a peptide having a known precursor, wherein the method comprises the steps of providing a respective mass spectrum for each sample of said plurality of samples, wherein signal intensity peaks correspond to potential peptides, identifying said peptide having a known precursor using the mass of said peptide, wherein the sequence of the known precursor is known, computing the measures of correlation between the signal intensity of said peptide having a known precursor and the signal intensities of the other potential peptides, selecting potential peptides, which exhibit a degree of correlation with said peptide having a known precursor above a certain threshold, and predicting the sequence of the potential peptides by matching masses of putative fragments of the sequence of the known precursor with the masses of the potential peptides correlating with said peptide having a known precursor.
Still furthermore, a method based on CANs is provided for predicting the sequence of peptides using the peptide topology of a plurality of samples containing a peptide with a known sequence, wherein the method comprises the steps of providing a respective mass spectrum for each sample of said plurality of samples, wherein signal intensity peaks correspond to potential peptides, identifying a peptide with a known sequence using its mass, computing the measures of correlation between the signal intensity of said known peptide and the signal intensities of the potential peptides, selecting potential peptides, which exhibit a degree of correlation with the known peptide above a certain threshold, computing the mass differences between each of the potential peptides and the known peptide, and predicting the sequence and/or the biologically, chemically or physically modified sequence of the potential peptides by using data about mass differences caused by biological, chemical or physical processes matching the mass differences determined in the previous step.
Yet still furthermore, a method based on CANs is provided for identifying peptides suitable to be used as marker panels using the peptide topology of a plurality of samples taken from at least two different experimental groups representing a status A and a status B, wherein the method comprises the steps of providing a respective mass spectrum for each sample of said plurality of samples, wherein signal intensity peaks correspond to potential peptides, computing the measures of correlation between the signal intensities of said potential peptides for each plurality of samples within each experimental group separately, and selecting pairs of potential peptides, which exhibit a difference in the degree of correlation between the different experimental groups above a certain threshold, thereby providing peptides which are suitable to be used as marker panels for diagnostic purposes to distinguish between status A and status B.
Yet still furthermore, a method based on CANs is provided for identifying peptides suitable to be used as marker panels using the peptide topology of a plurality of samples taken from at least two different experimental groups representing a status A and a status B, wherein the method comprises the steps of providing a respective mass spectrum for each sample of said plurality of samples, wherein signal intensity peaks correspond to potential peptides, selecting potential peptides correlating with a parameter being representative of status A or status B, computing the measures of correlation between the signal intensities of said selected potential peptides for each plurality of samples, and selecting pairs of potential peptides which exhibit no correlation of their respective signal intensities above a certain threshold, thereby providing potential peptides which are suitable to be used as complementing peptides in a marker panel for diagnostic purposes to distinguish between status A and status B.
Finally, a method based on CANS is provided for identifying peptides suitable as a surrogate for a known peptide using the peptide topology of a plurality of samples, wherein the method comprises the steps of providing a respective mass spectrum for each sample of said plurality of samples, wherein signal intensity peaks correspond to potential peptides, computing the measures of correlation between the signal intensity of said known peptide and the signal intensities of potential peptides, and selecting potential peptides, which exhibit a degree of correlation with said known peptide above a certain threshold, thereby providing potential peptides suitable as a surrogate for said known peptide.
Preferred embodiments of the present invention are disclosed in the dependent claims.
a shows a flow chart schematizing the process of checking whether a predicted sequence matches the experimental properties of an unknown peptide.
b shows a flow chart exemplifying the generation of sequence predictions, which are checked according to
c shows a flow chart schematizing the query of all unknown peptides P2 which are related to a known peptide P1. Sequence predictions are generated for any unknown peptide P2 according to
d shows a flow chart exemplifying the iteration of the process as demonstrated in
a shows a table with amino acids and their empirically derived occurrence before the N-terminal cleavage position (start position) of a peptide in a precursor sequence, the respective overall occurrence of the given amino acid in all determined sequences and the ratio thereof.
b shows a table with amino acids and their empirically derived occurrence after the N-terminal cleavage position (start position) of a peptide in a precursor sequence, the respective overall occurrence of the given amino acid in all determined sequences and the ratio thereof.
c shows a table with amino acids and their empirically derived occurrence before the C-terminal cleavage position (end position) of a peptide in a precursor sequence, the respective overall occurrence of the given amino acid in all determined sequences and the ratio thereof.
d shows a table with amino acids and their empirically derived occurrence after the C-terminal cleavage position (end position) of a peptide in a precursor sequence, the respective overall occurrence of the given amino acid in all determined sequences and the ratio thereof.
a and 18b show a table of the signal intensity values of the peptides with coordinates Fraction 54; m/z 2743.0, Fraction 54; m/z 1371.5, Fraction 56; m/z 2927.2 and Fraction 20; m/z 1114.3 taken from 74 samples. Furthermore, the number of related peptides k with a Spearman's Rank Order Correlation Coefficient threshold of |r|≧0.8 is shown.
a and 23b show a table of the signal intensity values of the peptides with coordinates Fraction 54; m/z 1371.5, Fraction 56; m/z 2927.2 and Fraction 20; m/z 1114.3 of 74 samples after removal of the variance of the signal intensity of the peptide with coordinates Fraction 54; m/z 2743.0. Furthermore, the number of related peptides k with a Spearman's Rank Order Correlation Coefficient threshold of |r|≧0.8 after the removal of said variance is shown.
a shows a graph plotting the signal Intensity of the peptide in fraction 54 with a mass-to-charge-ratio of 2743.0 (F 54; m/z 2743.0) versus the signal intensity of the peptide in fraction 20 with a mass-to-charge-ratio of 1114.3 (F 20; m/z 1114.3). This plot exemplifies a pair of peptides showing no correlation.
b shows a graph plotting the signal intensity of the peptide in fraction 54 with a mass-to-charge-ratio of 2743.0 (F 54; m/z 2743.0) versus the signal intensity of the peptide in the same fraction with a mass-to-charge-ratio of 1371.5 (F 54; m/z 1371.5). This plot exemplifies a correlation between a peptide-to-peptide pair consisting of a single charged and a double charged peptide ion.
c shows a graph plotting the signal intensity of the peptide in fraction 54 with a mass-to-charge-ratio of 2743.0 (F 54; m/z 2743.0) versus the signal intensity of the peptide in fraction 56 with a mass-to-charge-ratio of 2927.2 (F 56; m/z 2927.2). This plot exemplifies a peptide-to-peptide pair exhibiting a functional relation.
a shows a graph plotting the studentized signal intensity of the peptide in fraction 54 with a mass-to-charge-ratio of 2743.0 (F 54; m/z 2743.0) versus the studentized signal intensity of the peptide in fraction 20 with a mass-to-charge-ratio of 1114.3 (F 20; m/z 1114.3), i.e. the peptide pair of
b shows a graph plotting the studentized signal intensity of the peptide in fraction 54 with a mass-to-charge-ratio of 2743.0 (F 54; m/z 2743.0) versus the studentized signal intensity of the peptide in the same fraction with a mass-to-charge-ratio of 1371.5 (F 54; m/z 1371.5), i.e. the peptide pair of
c shows a graph plotting the studentized signal intensity of the peptide in fraction 54 with a mass-to-charge-ratio of 2743.0 (F 54; m/z 2743.0) versus the studentized signal intensity of the peptide in fraction 56 with a mass-to-charge-ratio of 29272 (F 56; m/z 2927.2), i.e. the peptide pair of
Prior to giving a detailed albeit exemplary description of embodiments of the present invention the following definitions are provided to establish how the technical terms are to be understood herein.
Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this invention belongs. As used herein, the following terms have the meaning ascribed to them unless specified otherwise.
“Sample” refers to any material, substance or the like containing or potentially containing peptides.
“Peptides” refers to polymers of amino acids coupled by peptide bonds comprising at least two amino acids. These amino acids can be the twenty standard amino acids and additionally unusual amino acids as known in the arts including D- and L-amino acids. Peptides can contain additional modifications such as posttranslational, enzymatic and/or chemical modifications.
“Status of a sample or an organism” means that the status or type of a sample at the time of the generation of the sample, e.g. the drawing of blood, is reflected by the contents and the activities of the sample. The actual status of an organism at the time of sample generation (such as the drawing of blood) is reflected in the contents and activities present in the sample. The sample conserves the status similar to a snap shot picture. A status for example can represent the presence or absence of a certain disease, the presence or absence of pregnancy, the sex of the individual from which the sample originated, the presence of a certain genetic variation such as the knockout of a gene or a polymorphism, the over expression or increased activity of a certain gene or gene product (for example as a consequence of a drug or of the transfection of the gene coding for the gene product or by direct addition of the gene product, etc.), the suppression of the expression or activity of a certain gene or gene product (for example as a consequence of a drug, anti-sense nucleotides, RNAi (RNA interface) nucleotides, ribozymes, triplex-forming nucleotides, antibodies, etc.), the presence of genetic modified ingredients in food, cosmetics or other products, the age of the organism from which the sample originated, the species of the organism from which the sample originated, a certain treatment of the organism from which the sample originated (for example with a therapeutically active substance a food ingredient or substance present in cosmetics, treatment with insecticides, pesticides or other toxic substances, etc.), the geographic origin of the sample, the development stage of the organism from which the sample originated (for example the stage of a fertilized egg, an embryo, an adult, intracellular/extracellular bacteria/virus, egg/larva/pupil/adult-stage of for example butterflies, different development stages of plasmodium, etc.), the metabolic state of the organism from which the sample originated (for example hibernation, stages of the circadian rhythm, etc.), the point of time before, during or after the treatment of an organism with a substance, the localization (or tissue) within the organism, from where the sample was taken, and the like.
“Measurement parameter of a peptide” refers to any parameter known to or measurable by the investigator such as the molecular mass of the peptide, the mass/charge ratio of the peptide, the signal intensity of the measured peptide, the actual concentration of the measured peptide, the fraction-number in which the peptide is present as a consequence of a certain separation protocol subjected to the sample, or the measured activity of the peptide.
“Correlation” or “relation” refers to a hypothesized mutual dependency of at least one parameter of two peptides, may this dependency be symmetric or asymmetric, known or not known, statistically significant or not. Relations of two peptides can be caused by chemical and biochemical reactions from one peptide to the other, by concerted gene regulation of the analytes, by common precursor peptides and so on.
“Measure of correlation”, “correlation measure” or “measure of association” refers to statistical means to describe the symmetric or asymmetric statistical dependency of measurement parameters of pairs of peptides in terms of their “relation”. Examples for measures of correlation are: “Pearson Product-Moment Correlation Coefficient”, “Spearman's Rank-Order Correlation Coefficient”, “Kendall's Tau”, “Kendall's Coefficient of Concordance”, “Goodman and Kruskal's Gamma”, “Manhattan distance”, “Euclidean distance” and “Minimal Spanning Tree Diameter”.
“Correlation associated network (CAN)” refers to the complete network of all measures of correlation identified within samples representing one status or identified within different groups of samples representing different statuses. It is possible that more than two peptides correlate to each other and a CAN contains at least two peptides correlating to each other. It should be noted that the peptide CAN based on “a sample” does not necessarily comprise results obtained from a single experiment. Rather, to completely determine a peptide CAN, multiple experiments are often needed, and the combined results of which are used to construct the peptide CAN for that particular sample. The results of the calculation of a CAN (CAN of first order) can be used for another round of calculation of measures of correlation and so on. The results of these kind of calculations are also termed CANs or more specifically CANs of second or higher order.
“Peptide-topology” refers to the entirety of measured and computed peptide data of a sample (“measurement parameter of peptides”) comprising the masses of the peptides, the signal intensity of the peptides (preferably measured by mass spectrometry or another measurement method suitable to quantify peptides), the fraction number (If the sample was fractionated prior to mass spectrometry) and measures of correlation calculated using these data.
“Groups of samples” refers to a set of samples corresponding to a certain status. A group of samples for example could comprise 10 plasma samples of diabetic patients. The samples of a group need not to be of exactly the same origin. For example a group of samples may also comprise 5 plasma samples of diabetic patients and 5 urine samples of diabetic patients. The reason for this being that many peptides present in plasma are also present in urine and for example the same diabetes-specific peptides may be present in plasma and urine, as long as the sample originates from a diabetic patient.
“Known peptide” means that the peptide with that particular sequence or part of a sequence in the sample is known to the user of the invention. An unknown peptide is a peptide whose sequence is not known to the user of the invention, although the sequence of the peptide may be known from the literature or other sources of information such as sequence data bases.
“Potential peptide” refers to a mass spectrometric signal which most likely represents a peptide.
“Precursor of a peptide” refers to the longest amino acid sequence present in nature comprising the sequence of the peptide, i.e. form which the peptide can originate.
“Coordinate(s) of a peptide” refer to the mass-to-charge ratio and optionally further specific measurable properties obtainable by a detection or identification process that are involved in the identification and/or quantification of the said peptide/peptide ion. In the examples of this invention the peptide coordinates are the elution time/fraction number of a chromatographic process and the mass-to-charge ratio, thus comprising of two coordinates. In this invention, these coordinates often are written in a short form, such as “F 56; m/z 2873.0”, which identifies the signal of a peptide found in fraction 56 with the mass-to-charge ratio 2873.0. Of course, further dimensions can be necessary, such as a previous capillary electrophoresis, or a downstream second mass spectrometric process. “Coordinates of a peptide”, “signal coordinates” or “peptide” are often used synonymously.
“Fitness value” refers to an assessment of a predicted sequence based on the experimental properties of an unknown peptide. Any predicted sequence gains points for properties that match the experimental properties, such as the correct prediction of the fraction number. The higher the “fitness value”, the more probable the correctness of the predicted sequence. According to the present invention fitness values are manually or automatically suggested for each sample type and empirically tested for suitability.
“Landmark peptides” refers to peptides that are related to numerous other peptide signals and least related to each other. The identification, e.g. sequencing, of these landmark peptides should be prioritized to gain a rapid overview about the peptide composition of a sample.
Each bar in
Similar 2D gel-like maps are produced for every sample out of the set of samples to be analyzed. These maps can be averaged yielding an averaged peptide mass fingerprint map as shown in
In order to obtain measurement data that is suitable for a correlation analysis and that gives meaningful results preferably a pre-processing of the data is performed using methods such as baseline correction, spectra normalization, outlier detection and the like. Methods for baseline correction are well known in the art (e.g. Fuller et al, Applied Spectroscopy, 42, 217 1988). In a preferred embodiment the pre-processing of the data is performed by applying the baseline correction being part of the software RAZOR Library 4.0, Spectrum Square Associates, Ithaca N.Y., USA. Optionally a normalization of the mass spectra can be performed by using the signal intensifies or the integrated mass spectra. Outlier samples can be identified by means of a principal component analysis as provided by the commercially available software package Pirouette 3.0, Infometrix Inc., WA, USA. Based on this principal component analysis individual mass spectra or even whole samples that exhibit a Mahalanobis distance MD above a critical threshold value of should not be considered for a further analysis and thus be discarded in the examples described further below a Mahalanobis distance of MD>11.5 was chosen for 74 samples.
The preprocessing, processing and display of the data according to the present invention can be performed e.g. on a Apple G4 Computer, wherein the CPU consists of 2 processors with 800 MHz each and the memory size is 1.25 Gigabyte. The local data storage of peptide-to-peptide relations (measures of correlation, coordinates of peptides) is accomplished by a local Valentina Database system (Valentina 1.9 for Realbasic, Paradigma Software, Beaverton, Oreg., USA). The peptide sequence information is provided by a proprietary Interbase Server (Interbase 6, Borland Software Corp., Scotts Valley, Calif., USA). Microsoft Internet Explorer 5.1 for Apple computer systems can be used for representation of results from Internet resources. The CAN software launches the Internet explorer with a specific address that contained the keywords for querying the Swiss-Prot, the PubMed, and the US Patent database. Visualization of three-dimensional objects can be performed using a Realbasic RB3D engine (Realbasic 3.5, RealSoft, Austin, Tex., USA).
Other digital computer system configurations can also be employed to perform the methods of the present invention, and to the extent that a particular system configuration is capable of performing the method of this invention, it is equivalent to the representative digital computer system schematically shown in
Computer programs implementing the methods according to the present invention will commonly be distributed to users on a distribution medium such as floppy disk or CD-ROM. From there, they will often be copied to a hard disk or a similar intermediate storage medium. When the programs are to be run, they will be loaded either from their distribution medium or their intermediate storage medium into the execution memory of the computer, configuring the computer to act in accordance with the method of this invention. All these operations are well-known to those skilled in the art of computer systems. The term “computer-readable medium” encompasses distribution media, intermediate storage media, execution memory of a computer, and any other medium or device capable of storing a computer program implementing the methods of this invention for later access by a computer.
As exemplified by the arrows in
Measures of correlation can be used to represent the degree of relationship between two variables throughout many observations. These variables can be either correlated, not correlated or anti-correlated. In the context of the present invention measures of correlation are used to detect such correlated, not correlated or anti-correlated peptides in a set of samples. This can be done e.g. by calculating Spearman's rank-order correlation coefficient of the signal intensities of two peptides measured in several samples. Preferably this is done for all pairs of peptides. Once these measures of correlation have been calculated only those pairs of peptides are selected that exhibit a certain behaviour, i.e. a certain degree of correlation, a certain degree of anti-correlation or a certain degree of no correlation at all. The parameters of such selected peptide pairs, e.g. the coordinates of the two peptides of each peptide pair, the measure of correlation, etc., can be stored, displayed on a display device or further processed. Preferably the data is stored in a database, as a text file or in another computer-readable form. Alternative measures of correlation to Spearman's rank order correlation coefficient are Pearson Product-Moment Correlation Coefficient, Kendall's Tau, KendeIrs Coefficient of Concordance, Goodman and Kruskal's Gamma and Minimal Spanning Tree diameters.
A Minimal Spanning Tree (MST), also known as Minimum Spanning Tree, is defined by the collection of edges that joins together all points in a connected set of data points, with the minimum possible sum of edge values (e.g. Evan, Graph Algorithms, Computer Science Press, 1979). An edge can be graphically displayed by a line connecting two data points. A MST can be graphically displayed by a set of points (data points) connected by a minimum of lines to each other. Examples of MSTs are shown in
As already mentioned pairs of peptides are tested for their degree of correlation by estimating e.g. Spearman's rank order correlation coefficients between their signal intensities throughout many observations. It turns out that pairs of peptides which are biologically or functionally related surprisingly often exhibit correlation coefficients that are much higher than correlation coefficients that would be expected by chance. Unrelated pairs of peptides have low absolute values of correlation coefficients.
Using the results of the above described computations of measures of correlation so called correlation associated networks (CANs) can be defined. A CAN, i.e. a network of peptide relations, comprises a peptide of interest, the so called hub peptide, and all those peptides and sample parameters that correlate to a certain degree with the hub peptide. The term hub is used in a similar manner in the theory of network topology and is to characterize the resemblance of a hub peptide to the hub of a wheel, the hub peptide being at the center of the spokes representing the peptide-to-peptide relations and the correlating peptides being at the respective ends of the spokes. In practice, the composition of a CAN is highly dependent on the threshold of correlation as selected by the user. This threshold is chosen according to the goal of a user. If a user is searching for peptides that strongly relate to a peptide of interest, such as peptides stemming from the same precursor, then he will select a threshold that will cause a selection of only the upper 5% of the strongest correlations with the peptide of interest. The threshold value to be chosen for e.g. the Spearman's rank order correlation coefficient depends for the thus selected subset on the number of samples and the peptide of interest. In case the user is interested in finding functionally related peptides, such as e.g. peptides being co-secreted from vesicles, the user will choose a threshold value, that will select e.g. the upper 10% of the strongest correlations.
The hub peptide and the peptides related thereto and selected as described above represent a CAN of first order. Depending on the objective it can be necessary to compute CANs of higher order due to the complexity of biological networks and pathways. As explained above, CANs connect directly related peptides which exhibit a high degree of correlation. Adjusting the threshold to lower values results in including more loosely related peptides into the network as well as increasing the probability of predicting false relations. For this reason a preferred embodiment of the present invention contemplates the computation of CANs of higher orders, such as e.g. second and third order. Since the direct members of a network of interest constitute the first order neighborhood, all these members are potential starting points for the calculation of second order neighborhoods as shown in
For any kind of sample the composition of peptides varies, novel peptide coordinates emerge, others disappear and many peptide coordinates have a different peptide sequence aligned to it. This results in dealing with many unknown peptide coordinates when operating with novel sample sources (types of samples). In order to accelerate analysis of an interest list or more general, to analyze the overall peptide composition of a sample, according to the present invention it is possible using CANs to accelerate the identification of peptides in complex biological samples by defining a list of representative peptides, so called landmark peptides, for further analysis such as peptide sequencing based on CANs described further below. The method comprises the following steps, which are shown in
It is contemplated that a such generated interest list of prioritized landmark peptides will contain a set of n peptide coordinates, and for any peptide z the number of relations, which the peptide z has at a defined threshold r, kz,r, will be determined. The peptide z with the highest value of kz,r will be defined as y and be rank 1 etc. on the prioritization list. Then the variance of signal intensities of that determined peptide coordinate y will be removed from the signal intensities of the related peptides x in a data matrix, for example by a combination of formulas 1, 2 and 3 shown below. Then this peptide will be removed from the data matrix. Calculations of any k and r start from the beginning to determine the representative peptide ranked number 2 in the prioritization list, and so on. Calculations end for example when the data matrix contains no more peptide coordinates, or no peptide has more than zero relations, or the number of peptide coordinates desired has been reached.
X
VR,p
=X
p
−a
xy
−b
xy
Y
p (1)
where
XVR,p: Signal intensity of peptide x at observation p, Variance of peptide y removed
Xp: Signal intensity of peptide x at observation p
Yp: Signal intensity of peptide y at observation p
m: number of observations
It is further contemplated that peptides being part of a CAN preferably are represented by graphical objects such as e.g. bullets and their mutual relations by lines connecting these bullets. In order to enable a more intuitive analysis of the results, this network can be projected onto a peptide map as shown in
The coordinates or measurement parameters of related peptides can be queried in public, commercial and/or proprietary databases in order to identify further data about the potential identity, function or use of the corresponding peptides. Suitable public databases include e.g. the PubMed literature database, the OMIM disease database, the NCBI-Sequence database (all provided by the US National Library of Medicine, Md., USA), the Swiss-Prot and TrEMBL Sequence database, enzyme database, Swiss 3D image database, Prosite protein family and domain database (all provided the Swiss Institute of Bioinformatics, Switzerland), patent databases of the US, European, Japanese, German patent offices, the Gene Cards database of the Weizmann Institute, etc. Suitable commercial databases are for instance commercial patent databases containing patented amino acid or nucleic acid sequences such as DGENE (Thomson Derwent, USA) or REGISTRY (Chemical Abstracts Service, USA). A suitable proprietary database is the database of the user containing peptide sequences from various sources and species. This combination of the visualization of peptide networks and the connection to many sources of information alleviates the evaluation of the identified peptides for potential uses such as their use as therapeutic peptides or as biomarkers as will be described in more detail further below.
As is apparent from the above, correlation associated networks can be used to generate hypotheses about relations between structurally and/or biologically related peptides. These hypotheses are based on a correlational analysis of signal intensities and corresponding relative peptide concentrations from independent samples. The examples described in the sections further below will demonstrate that correlation associated networks are powerful tools for the systematic analysis and interpretation of large peptidomic and proteomic data in order to reveal functional relationships governing protein synthesis, posttranslational modifications and degradation. CANs support the discovery of novel bioactive and diagnostic peptides leading beyond the mere comparison of peptide concentration changes caused by a disease.
According to the present invention the CAN Module 42 is interacting with several application modules 44 comprising a Sequence Network Module 46, a Differential Network Module 48, a Marker Panel Network Module 50 and a Surrogate Network Module 52 as shown in
The interaction of the Sequence Network Module with the fundamental CAN Module according to the present invention allows to predict the amino acid sequences of unknown peptides with or without modifications of the sequence and/or to predict unknown modifications of a known or unknown peptide sequence. Although the identity of the peptide is unknown, certain physicochemical and biochemical properties of the signal of an unknown peptide are known and can be exploited for amino acid sequence prediction such as the mass-to-charge ratio (m/z) or the chromatographic behaviour (fraction number/retention time). Furthermore bioinformatic support data shown at 56 in
Alternatively after step 92 the mass differences between each of the potential peptides and the known peptide can be computed at step 96, and thereafter the sequence and/or the biologically, chemically or physically modified sequence of the potential peptides predicted at step 98 by using data about mass differences caused by biological, chemical or physical processes matching the mass differences determined in step 96.
The first of the above approaches is more comprehensive, since all plausible putative sequences are generated from the precursor sequence of the known peptide (steps 90-98). The second approach (steps 90-96, 100-102) generates fewer but more reliable predictions. It has been observed that related peptides very often have very similar sequences/sequence modifications, and these predictions are promoted by the second approach. Nevertheless, since both approaches have steps 90-96 in common, computational power is “saved” if both approaches are combined in one operation, as contemplated in the present invention.
Mass differences may result from addition or removal of N- or C-terminal amino acid residues or of postranslational modifications of amino acid side chains such as phosphorylation, amidation, sulfatation, glycosylation, fatty acids or Ubiquitin modification, and the like or chemical modifications such as oxidation, disulfide bonding, and the like or N- or C-terminal modifications such as pyroglutamate modifications and the like. All of these modifications result in distinct increases or decreases of the molecular mass of the corresponding peptide. Also internal insertions or deletions or the exchange of one amino acid for another, so called point mutations, result in exactly predictable mass changes of the peptide.
According to the present invention the prediction of sequences is possible regardless of whether the identity of one of the related peptides is known or not. Especially if the identity of one peptide is known, mass differences corresponding to the molecular masses of amino acid residues allow to directly predict the complete sequence of the unknown peptide with high reliability. If the identity of no peptide is known, than for example it can be predicted that the unknown peptide 1 and the unknown peptide 2 are identical, except that for example peptide 2 contains an additional amino acid residue, for example a Tyrosine residue, or for example peptide 2 is the same peptide as peptide 1 except that it is phosphorylated, etc. The prediction is not always correct, but the more independent information is accessible, the more reliable the prediction becomes. For example if the mass difference fits to the addition of an Tyrosine amino acid residue and the peptide is present in a fraction, which fits to the prediction of the fraction-shift of a peptide with an additional Tyrosine residue, the overall reliability of the prediction increases.
For this embodiment the use of proprietary and/or commercial and/or public databases is possible. Suitable databases are for example databases containing amino acid or nucleic acid sequence Information such as the NCBI sequence data base, Swiss-Prot, the EMBEL sequence data base, the DNA data base of Japan, data bases of patented sequences, and the like, data bases containing Information about the structure of carbohydrates, such as PROSITE (Faiquet et al, Nucleic Acids Res., 30, 235-238, 2002), data bases containing information about postranslational, enzymatic or chemical peptide modifications such as phosphorylation sites of peptides, glycosylation sites of peptides, positions of unusual amino acids such as hydroxy-proline or hydroxy-lysine within peptides, databases containing information about recognitions sites of proteases, ligases, phophatases, kinases, and the like within peptide sequences, databases containing information about the susceptibility of certain amino acids or sequences of amino acids towards chemical modifications such as oxidation, reduction, intra-molecular rearrangement, data bases containing data about three-dimensional structures about peptides, carbohydrates or other biological structures, and the like (Faiquet et al, Nucleic Acids Res., 30, 235-238, 2002). All of these different kinds of databases enable to predict the structural difference between peptides, based on certain incremental increased or decreased molecular masses of these peptides. For example:
The prediction of physicochemical and biochemical properties of putative amino acid sequences fit surprisingly well to experimentally determined properties. This approach can be extended by utilizing knowledge about precursor amino acid sequences and posttranslational, chemical and enzymatic modifications of known related peptides as provided by the support data 56 shown in
The invention comprises specific rules, which determine if a putative amino acid sequence derived according to one of the methods described above fits to the peptide signal coordinates of an unknown peptide. These rules which are schematically shown in
This rule applies formula 4 (shown below) to check, whether the unknown peptide coordinate is an n-fold charged ion of the known peptide coordinate by the following condition, where n can be an integer number greater than 1, m/zunknownpeptide is the m/z ratio of the unknown peptide, m/zunknownpeptide is the m/z ratio of the known peptide and MassThreshold is a maximum difference of the calculated mass from the measured mass. A preferable MassThreshold equals the mass precision of the instrument and the subsequent data processing routines. If this condition is met, the proposal is rewarded with a high fitness value and the proposal that the unknown peptide is the n-fold charged ion of the known peptide can be stored.
Formula 4: Check for N Times Charged Peptide Ions
|n*(m/zunknownpeptide−1)−m/zknownpeptide|=Massdeviation≦Massthreshold
wherein the asterix (*) indicates the mathematical operation of multiplication.
If the difference of the masses of a known hub peptide P1 and a related peptide P2 corresponds to a mass of an post-translational modification, as listed for example in the table “Mass Changes Due to Post-translational Modifications of Peptides and Proteins” shown in
Putative sequences or putative fragments are generated from potential amino- and carboxy-terminal truncations or additions of amino acids of the known precursor sequence of the hub peptide and are checked whether they match the found m/z ratio of the unknown peptide coordinate. A putative sequence is generated by systematically and iteratively defining start- and end-positions, i and j, in the given precursor sequence of the hub peptide, as exemplified in
M
CALC
=n
A
*M
A
+n
R
*M
R
+n
N
*M
N
+n
D
*M
D
+n
c
*M
c
+n
E
*M
E
+n
Q
*M
Q
+n
G
*M
G
+n
H
*M
H
*n
I
*M
I
+n
L
*M
L
+n
K
+M
K
*n
M
*M
M
+n
F
*M
F
+n
P
*M
P
+n
S
*M
S
+n
T
*M
T
+n
W
*M
W
+n
Y
*M
Y
+n
Y
*M
Y
+M
N-Terminal Group
+M
Modifications
wherein:
MCALC is the calculated mass of the peptide with the given/putative sequence,
MONE LETTER AMINO ACID CODE is the mass of the appropriate amino acid,
nONE LETTER AMINO ACID CODE is the number of the appropriate amino acid in the given/putative sequence,
MN-Terminal Group is the mass of the N-terminal group,
MC-Terminal Group is the mass of the C-terminal group, and
MModifications is the mass change by modification(s), in the case of no modification
The number and the identity of amino acids influence the elution time/fraction number, depending on the size and the kind of the chromatography column used and the chromatography conditions. The fraction number/elution time of a peptide can be surprisingly well predicted on the basis of its amino acid sequence by the so called Group Method of Data (GMDH, e.g. Mueller and Lemke, Self-Organising Data Mining Extracting Knowledge From Data, Trafford Publishing, 2003), multiple regression or comparable mathematic methods with a training set of peptides with known sequences, which are separated under the same chromatographic conditions as exemplified in Formula 6 shown below. In the said training set, the number of any amino acid residue type of a peptide is the independent variable whereas the fraction number of the peptide is the dependent variable. If the calculated fraction number (e.g. Formula 6) of the predicted amino acid sequence matches the derived fraction number of the unknown peptide within a given error tolerance, then the model fitness points are increased. If the mass differences are proposed to be resulting from distinct amino acid deletions/additions and if the differences in fraction number can be matched with these said amino acid sequence differences (see
F
CALC=35.89−0.45*nS+0.47*nE+2.86*nI−3.82*nH+5.15*nL+5.54*nF+2.92=nY−1.72*nK−0.85*nQ+5.35*nw+2.20*nV
wherein:
FCALC is the calculated Fraction number of the given sequence, and
nONE LETTER AMINO ACID CODE is the number of the appropriate amino acid in the given sequence.
If the N-terminal position of the predicted amino acid sequence is the same as the N-terminal position of the known peptide, the fitness value is increased. This is because the known peptide and the unknown peptide of the underlying signals are related via a C-terminal proteolytic reaction, which is observed surprisingly often.
If the C-terminal position of the predicted amino acid sequence is the same as the C-terminal position of the known peptide signal, the fitness value is increased. This is because the known peptide and the unknown peptide of the underlying signals are related via an N-terminal proteolytic reaction, which is observed surprisingly often.
If the start position and/or the end-position of the predicted sequence is preceded or followed by sites of infrequent proteolytic events, the fitness value of this proposal is decreased. If the start position and/or the end-position of the predicted sequence is preceded or followed by sites of frequent proteolytic events, the fitness value of this proposal is increased. This is because it has been observed that peptides are often products of specific and/or unspecific proteases. Depending on the source and preparation procedure of the samples, proteases and intra-molecular rearrangements, such as disulfide bonding, can vary. With for example liquor cerebrospinalis (CSF) as sample source, the sequences “R-R” or ° R-K″ are frequently preceding a peptide's N-terminal position in a precursor as they are recognition sites of the prohormone convertase PC2 in CSF. Next to known enzyme recognition sites, some amino acids are more frequently and others are less frequent. Positions preceding or following N- and C-terminal positions of peptides can be predicted on the basis of their mere percentage occurrence in a particular sample treated in that particular way. This kind of Information can easily be determined empirically and an example for peptides present in human liquor cerebrospinalis is shown in the tables in
If the mass difference between the peptide coordinates of a known and an unknown peptide can be explained by the loss of one or more distinct N- or C-terminal amino acids, the fitness value of this prediction is increased.
If a prediction has been generated by one of the rules b to h or a combination thereof, proposing that the unknown peptide is a reactant or a product of a post-translational modification of the known peptide, this proposal is tested by determining in terms of accessibility of the reaction site within the protein sequence by an enzyme performing the given post-translational modification. Thus, if a look-up in a database storing three-dimensional data of peptides or proteins reveals that the proposed site is on the surface of the protein and/or its conformation sterically allows action of that enzyme, the fitness value of that prediction is increased. In the same way, if a region of a sequence is proposed to be modified by a post-translational modification process, the accessibility of that sequence region to enzymes is assessed by means of algorithms estimating the hydrophobicity of that particular region (Engelman et al, Ann. Rev, Biophys. Chem., 15, 321, 1986; von Heijne, Eur. J. Biochem., 116, 419, 1981). For example, a highly hydrophilic sequence region is more likely to be accessible by enzymes performing post-translational modifications than a hydrophobic sequence region, thus the fitness value of that prediction is increased.
The results computed by applying rules a to i and optionally additional rules can be stored in a list or a database in computer readable format and/or can be printed or displayed via an appropriate user interface such as a monitor. If more than one prediction for an unknown amino add sequence fits the results obtained with the rules described above, then the predicted sequence can be ranked with the best fitting sequence for the unknown peptide on top as shown at step 148 in
According to the present invention the interaction of the Differential Network Module with the fundamental CAN Module allows to identify peptides which independently from each other distinguish between a sample A and a sample B. A status can be young, old, healthy, diseased, sweet taste, bitter taste, transfected, non-transfected, yellow, green, male female, pregnant, non pregnant, smoker, non smoker or any other criterion defining a group or a subgroup of samples or organisms from which samples are derived. Optionally the Differential Network Module is linked with various databases, containing data such as the status of the samples, as well as with the other modules of the present invention and especially the basic CAN Module as shown in
The results of the Differential Network Module allow statements about the different relations of peptides within samples of status A compared to status B as follows: If the difference of correlation coefficients of peptide I with peptide II in status A minus the corresponding correlation coefficient in status B is greater than a given threshold, signal coordinates of the peptide pairs, their mutual distance within the observed status A and status B or the degree of difference or combinations of the latter information are stored in a database or list. The Differential Network Module optionally provides the same visualization methods as the other modules, that means peptide coordinates and their relations can be represented as bullets connected by lines, respectively, as shown in
Another use of this aspect of the present invention is the comparison of the molecular masses of peptides present in at least three samples, representing one or at least two different states, status A with corresponding samples and status B with corresponding samples. For example samples from individuals with a certain disease versus samples from individuals without that certain disease, samples from pregnant versus samples from non-pregnant individuals, samples from bacteria transformed with an expression vector versus samples from non-transformed bacteria, samples from yoghurt with a strong acidic taste versus samples from yoghurt with a mild acidic taste, etc. might be compared by computing the correlation measures of peptides present in these samples. The comparison of measurement parameters of a peptide within two samples corresponding to two different states A and B may also indicate that the peptide is present only in samples of state A but not in samples of state B. Also in this case the measurement parameters of this peptide in status A and status B possibly can be related by a measure of correlation. If at least two different peptides, e.g. peptide I and peptide II, are identified, the measurement values of the parameters for peptide I and peptide II can be combined. Using measurement values of at least three samples being representative of status A and three samples being representative of status B, a mathematical function can be computed. This mathematical function describes the correlation-network of peptide I and peptide II. It is possible to include more than two different peptides in one correlation-network, e.g. to include more than two different peptides in one mathematical function describing a correlation-network. The resulting mathematical function describes which combinations of measures of correlation of at least two peptides (peptide I and peptide II) allow to distinguish status A from status B.
Furthermore, another use of this aspect of the present invention comprises the automated identification of sets of peptides that allow a prediction of a status of a sample by a regression model. The invention detects relations between at least two peptides, where the relations are representative for a given status A. In a next step, a linear or non-linear regression model is set up that uses input parameters of the found peptides, such as their respective MALDI signal intensities, and that fits these input parameters to an end point parameter, such as the diagnosis (yes/no=1/0), or that fits to another parameter of a peptide of this derived set.
In order to check whether a sample of unknown status is a member of the status A, the input parameters of these peptides from that sample are applied to the derived model. If the output value obtained from that sample deviates in the range as other samples from status A from an expected value obtained by means of the determined function, than this unknown sample can be considered to be from status A. Otherwise, the sample most likely has another status.
According to the present invention the interaction of the Marker Panel Network Module with the fundamental CAN Module allows to identify peptides which independently from each other distinguish between a sample representing status A and a sample representing status B. For example a disease is caused by different factors such as inflammation and an increased heart beat rate. Each of these disease factors might result in altered concentrations of distinct peptides in for example blood plasma of the patient. If a panel of for example two peptide markers is used for diagnosis of the disease it would be useful if one of the peptide markers indicates inflammation and the other peptide marker indicates increased heart beat rate. The combination of these two markers would increase the specificity and sensitivity of the marker panel to detect the disease caused by a combination of inflammation and increased heart beat rate. The Marker Panel Network Module selects those potential peptides which are related to the disease but are most likely associated to different disease factors (in this hypothetical case inflammation and increased heart beat rate), since these peptide coordinates have a low measure of correlation to each other but both have a high correlation to the disease. Thus the specificity and sensitivity of a diagnostic test can be improved by combining these complementary peptide coordinates to a marker panel.
For example a disease 1 (status A) which is associated with Inflammation has to be distinguished from another disease 2 (status B) which is not associated with inflammation. There are, for example, four peptides found, which distinguish disease 1 form disease 2. Peptide 1 and peptide 2 are fragments from the same protein, for example from TNF-alpha, peptide 3 is, for example, a fragment of IL-6 and peptide 4 is a fragment of an unknown protein. All of these four peptides differentiate between disease 1 and disease 2 by a measure of correlation, but peptide 1 and 2 correlate to each other, which is not surprising, as they originate from the same molecule (TNF-alpha). Additionally peptide 1 and peptide 3 correlate to each other, which is also not surprising, as TNF-alpha and IL-6 have similar pro-inflammatory functions. Consequently there are two groups of peptides, peptides 1, 2 and 3 belong to one group and peptide 4 represents the second group. To obtain a diagnostic test, with improved specificity and/or sensitivity combination of the detection of peptide 1 and 2 or 1 and 3 or 2 and 3 would not increase the specificity and/or sensitivity as much as combination of peptide 1 and 4 or 2 and 4 or 3 and 4 would do. This method allows to identify panels of peptides with additive or synergistic value (diagnostic, therapeutic, functional, etc.).
In other words, the Marker Panel Network Module selects potential peptides which correlate with a parameter being representative for status A or status B. The Marker Panel Network Module then queries the Correlation Associated Network (CAN) Module for those pairs of selected peptide coordinates, which have a very low measure of correlation of their respective signal intensities to each other. The result are pairs of peptides which are related to the status A or B but not directly related to each other and can be combined for a marker panel to distinguish between status A and B. It is possible to combine two or more peptides to a marker panel.
The Differential Network Module described in the previous section discovers combinations of peptides, whose ratio of concentration indicate a certain state and deviations from that ratio indicate a different state. It is mandatory to measure the signal intensity (e.g. concentration) of both/any peptide to calculate said ratio. The relations between two peptides may be present only in state A, whereas the relations between the same two peptides may be different or absent in state B.
In contrast, any peptide found by the Marker Panel Network Module described in the present section could serve as a diagnostic marker alone, but a combination of both markers improves the sensitivity/specificity etc: of the diagnostic test. The members of a marker panel ideally should not correlate with each other in any of both states. If the members of a marker panel correlate with each other their combination most likely would not improve the sensitivity/specifity of the diagnosis.
The Surrogate Network Module relates to the identification of peptides (so called surrogate peptides) that can replace or complement established diagnostic or therapeutic peptides or peptides of other use. If for instance it is discovered that peptides correlate with known bioactive therapeutic peptides, these peptides might serve as surrogates for therapeutic measures or even may exhibit a higher/larger potency, efficacy, specificity, selectivity and/or less undesirable side effects. These kind of peptides can be found using the Surrogate Network Module in combination with the CAN Module according to the present invention by applying the steps shown in
For example a plasma sample is known to contain the peptide insulin and a potentially unknown peptide X within the same plasma sample correlates with the peptide insulin. In this case peptide X might have the same function as insulin, as its correlation measure indicates that it is related to insulin. The reason for this could be that peptide X is a derivative of insulin, for example a glycosylated form of insulin, or another peptide which is completely different from the amino acid sequence of insulin but which is involved in the same functional or metabolic cycles as insulin. In both cases peptide X could serve as an alternative to the use of insulin for example in treating diabetes. It might also turn out that peptide X in combination with insulin improves the therapeutic effect of insulin by itself.
In a further example a tissue sample of a prostate cancer patient contains the prostate-specific antigen (PSA) peptide, which is a known marker for prostate cancer. Another potentially unknown peptide Y is related by a correlation measure to the PSA peptide and consequently peptide Y might have the same diagnostic value as a biomarker for prostate cancer as the PSA peptide or the measurement of peptide Y might complement the prostate cancer diagnosis by PSA measurements.
Though any of the modules described above can be used independently, any combination of these modules can be used and potentially can synergistically improve the result of one or more of the modules.
For example results of the Surrogate Network Module can be analyzed by the Sequence Network Module. In case the Surrogate Network Module yields peptide signals, which are not yet sequenced, a prediction of the sequence can give early hints for biological interpretation, thus accelerating validation processes of for example therapeutic or diagnostic peptides. However, a subsequent identification of these peptides by sequencing is recommended.
Results of the Differential Network Module can be analyzed with the Surrogate Network Module. If the Differential Network Module yields for example potential biomarkers, it is highly desirable to identify possible surrogate markers that show a similar behavior and therefore are of interest as well. Therefore a combination of the Surrogate Network Module with the Differential Network Module accelerates the discovery of novel therapeutic, diagnostic or other peptides and is highly synergistic.
Furthermore, results of the Differential Network Module can be analyzed with the Sequence Network Module. If the Differential Network Module yields peptide signals, which have not been sequenced yet, the prediction of the sequences of the unknown peptides can give early hints for biological interpretation, thus accelerating validation processes of potential therapeutic, diagnostic or other peptides. However, the later identification of these peptides by sequencing is recommended.
The following examples are intended to describe how the methods according to the present invention can be applied to real data. For the sake of a clarity only a limited number of exemplary measurement parameters are calculated and presented in the figures. However, as is readily observable by the person skilled in the art, the advantages of the methods according to the present invention become even more obvious when applied to large sets of data. On present computer systems commonly measures of correlation for data sets consisting of up to 6.000 potential peptides are commonly calculated and without undue effort data sets of up to 100.000 potential peptides can be analyzed by means of the methods according to the present invention.
The basic CAN Module calculates to what extent a potential peptide for each individual potential peptide measured in a sample correlates to every other potential peptide in that sample. The CAN Module determines a network of correlations among the peptides which in case of some degree of correlation supposedly are related to each other for certain reasons such as a common precursor as the origin of the peptides or the same biological function of the different precursors of the correlating peptides.
In the present example the set of data, i.e. the data matrix, consists of 444.000 values comprising measurement parameters, in this case signal intensities, of 74 independent samples, each sample resulting in 6.000 peptide coordinates. The tables shown in
Most probable true positive relations can be found where the area under the curve is small, while the maxima of the curves represent the correlations coefficients which are most likely false positive relations. In case Spearman's rank order correlation coefficient is chosen as measure of correlation and |tthreshold|≧0.8 is chosen as threshold for a definition of a peptide-to-peptide relation, the peptide coordinate Fraction 20; m/z 1114.3 is not related to the peptide coordinate Fraction 54; m/z 2743.0 (see table shown in
Assuming that one is interested in finding surrogate markers for Chromogranin A in hypothetical prostate cancer patients and that some of the 74 samples described above originated from healthy male persons and some samples originated from prostate cancer patients. Under the further assumption that a peptide originating from Chromogranin A, amino acids 97-131, had been identified, the Surrogate Network Module would now query the basic CAN Module for peptide coordinates that are highly related by a correlation measure with the hub-peptide Chromogranin A, 97-131. This could be done for example by defining that the Spearman's rank order correlation coefficient of peptide-to-peptide relations has to comply with the relation |r|≧0.67. Then the Surrogate Network Module would instruct the CAN Module to query the Valentina Database, and report that there are about 14 peptide coordinates matching this condition. These peptide coordinates are searched in databases for any known peptide fitting to these coordinates. In this way it would be found that three peptides known from the database and present in the list of 14 peptides belong to the Chromogranin/Secretogranin family as illustrated in the table shown in
In an exemplary hypothetic serum dataset 48 samples are derived from patients before prostatectomy and 26 samples from patients after prostatectomy. For the Differential Network Module a correlation measure, e.g. the Spearman's rank order correlation coefficient r, between the peptides is calculated for samples from patients before prostatectomy and for samples from patients after prostatectomy separately. The correlation coefficient of Chromogranin A 97-131 and Secretogranin I 88-132 for all 74 samples is r=0.67, for those patients before prostatectomy is r=0.23 and for those after prostatectomy is r=0.97 (see
This section exemplifies the identification of representative peptides, also called “landmark peptides” and also refers to the given data matrix of 74 observations of 6.000 peptide coordinates already discussed in a previous example.
Two peptide coordinates are considered as related if the Spearman's rank order correlation of their signal intensities is above |r|>0.8. The number of relations k a respective peptide has with different peptide coordinate is shown in the second row of the table shown in
In this example, the signal intensities of four fictive peptide coordinates of 74 samples, their respective mass-to-charge ratio and their fraction numbers are given (see table shown in
The measure of correlation of the four unknown peptide coordinates with HP 25-48 has been calculated in the CAN Module by means of Spearman's rank order correlation coefficient
As can be seen in
In the same manner, the MST diameter was calculated as a measure of correlation:
MST diameter (HP 25-48 and F 20; m/z 1114.3)=29 (see
MST diameter (HP 25-48 and F 54; m/z 1371.5)=50 (see
MST diameter (HP 25-48 and F 56; m/z 2927.3)=38
MST diameter (HP 25-48 and F 53; m/z 2823.0)=40 (see
In contrast, peptide coordinates F 54; m/z 1371.5, F 53; m/z 2823.0 and F 56; m/z 2927.3 are highly related to HP 25-48 (see
Rule a determines whether the related peptide coordinate is a n-charged ion of HP 25-48. The calculation of MassDeviation is exemplified with n=1, 2, 3 or 4 and the mass-to charge ratios of F 54; m/z 1371.5 and F 56; m/z 2927.26 given in the table shown in
Rules b to l will now be applied to F 53; m/z 2823.0 and F 56; m/z 2927.3. Rule b assumes that the relation of the hub peptide P1 in fraction F 54; m/z 2743.029 of known identity with the unknown peptide P2 (peptide coordinate F 53; m/z 2823.0) is derived from a post-translational modification. In this case, the mass difference of the hub peptide P1 and the unknown peptide P2 MDIFF=|MP1−MP2|=79.971 might be caused by phosphorylation or sulphation (see table shown in
As stated before, if the unknown peptide and the known hub peptide are related, it is hypothesized that the unknown peptide is derived from the same precursor protein and thus has the same precursor sequence as the known hub peptide. An algorithm systematically defines putative start and end positions, I an E, in the precursor sequence of the hub peptide P1 proposing a putative sequence fragment potentially derived from the precursor sequence, that could be the sequence of the unknown peptide P2 (see
With HP 25-48 as the hub peptide and P2 having the peptide coordinate Fraction 56; m/z 2927.3 the Sequence Network Module searches for possible sets of start and end positions in the protein precursor sequence of HP as defined in
One possible combination is a start position at amino acid No. 25 and an end position at amino acid No. 50 of HP resulting in the potential peptide HP 25-50:
nD=2, nA=4, nH=2, nK=2, nS=1, nE=3, nV=2, nR=1, nF=t nL=3, nG=1, nI=1, nN=1, nT=1, nY=1 in Formula 5 results In MCALC==2927.337
This proposal is added to the list of proposals for P2.
The Sequence Network Module will now address the evaluation of the proposal HP 25-50 for P2 by applying rules c to i. In rule d, the chromatographic fraction of the proposed sequence FCALC is estimated and compared with the found peptide coordinate of P2 (FFOUND). If FCALC deviates from FFOUND by less than the threshold for fractionation (TFRACTION) then the proposal is awarded with 2 model fitness points. If Formula 6, “Estimation of fraction number based on proposed sequence”, is applied to HP 25-50, the calculated Fraction results in FCALC=56. As P2 HP 25-50 is found in fraction 56, the number of model fitness points for this proposal is increased by two points. Formula 6 was generated empirically from a mathematical model using data originating from Liquor cerebrospinalis samples separated using a specific HPLC-column (as described in the patent application WO 03/048775 A2) using a specific software. Of course, for different types of samples and different separation methods other empirically determined models can be calculated in the same way.
Rule e rewards those proposals for P2, whose start-positions match the start positions of the hub peptide P1. In the case of HP 25-48 as the P1 hub peptide and HP 25-50 as the proposal for the related peptide P2, the proposal HP 25-50 will be rewarded with 3 model fitness points.
Rule f rewards those proposals for P2, whose end-positions equal the end-position of the hub peptide P1. This is not the case with HP 25-50 as a proposal, therefore this rule does not increase the model fitness points of this proposal for P2.
Rule g will increase the model fitness points of the proposal HP 25-50 by three points as the start position 25 is preceded by the amino acid sequence “R-R” (written in 1-letter amino acid code). The sequence “R-R” is a recognition site of prohormone convertases, which commonly cleave after the second “R”. In addition, rule g will increase the model fitness points for this proposal by another 3 points, as the RD-A″ sequence is one of the preferred starts for peptide sequences present in liquor cerebrospinalis. Further sites of frequent proteolytic cleavage sites at start positions awarded by rule f are well known in the art.
Rule g assumes that the unknown peptide P2 is a product of N- or C-terminal proteolysis of the known hub peptide P1 or vice versa. The mass difference of P1 and P2 MDIFF=|MP1−MP2| is determined and aligned with the masses of the amino acids preceding and following the start- and end positions of P2 in the precursor sequence HP. In the example of HP 25-48 as P1 and HP 28-50 as P2 the mass difference is MDIFF=184.2 and can be explained by the amino acids “I-A” (MI+MA=184.2) which are following the end position of P1. Therefore P2 fits the model and the model fitness points for this proposal for P2 are increased by 3 points.
Obviously, rules c to i can be examined in any order, and rules can be left out for biological considerations, but still any combinations and any omissions of these rules are within the scope of this invention.
The process described above can be repeated for all unknown peptides coordinates P2, which are related with HP 25-48.
This example demonstrates the advantages associated with the methods according to the present invention by combining Correlation-Associated Peptide Networks with recognition of probable cleavage sites for peptidases and proteases in cerebrospinal fluid, resulting in a model able to predict the sequence of unknown peptides with high accuracy. On the basis of this approach, for instance the identification of peptide coordinates can be prioritized, and a rapid overview of the peptide content of a novel sample source can be obtained.
Cerebrospinal fluid (CSF) is in dose contact with many parts of the brain. CSF aids to maintain a stable chemical environment for the central nervous system and is a route to remove products of brain metabolism. CSF distributes a multitude of biologically active substances within the central nervous system. It is acceptable to assume that CSF mirrors the physiological and pathophysiological status of the brain and, therefore, peptides from CSF represent a source of potential diagnostic and therapeutic target molecules.
Here the correlational behavior of peptides from CSF is analyzed, derived from the same protein precursor in more detail and correlational dependencies for the prediction of putative sequences of unknown peptides are exploited. If one assumes that a known peptide and an unknown peptide signal of a peptide-to-peptide pair might have a common protein precursor, the known protein precursor sequence is analyzed for proteolytic cleavage sites which could explain the generation of a signal with a mass corresponding to that of the unknown peptide. It will be shown that the combination of statistical analysis (CAN) and recognition of possible cleavage sites for peptidases and proteases in CSF results in a model with high predictive power for correct assignment of an unknown peptide signal to a protein precursor or even a sequence, thus reducing the number of peptides to be sequenced.
After approval by the local ethics committees, written Informed consent was obtained from patients involved in this study. Human CSF was collected by lumbar puncture from neurological patients without cognitive impairment (n=39) and from patients suffering from dementia such as vascular dementia, Lewy-body dementia, frontotemporal dementia or Parkinson's disease (n=27). All CSF samples were prepared using mild conditions minimizing risk of sample alteration: The fluid was collected without aspiration and avoiding blood contamination. Samples were centrifuged for 10 min at 2000 g and the supernatant was stored at −80° C. until analysis.
Peptides were separated using reversed-phase 018 chromatography. 300 to 1500 μL CSF was diluted 1:3.75 with water and the pH was adjusted to 2-3. Samples were loaded onto RP silica columns (250×4 mm column, Vydac, Hesperia, Calif., USA; HP-ChemStation 1100 Agilent Technologies, Palo Alto, Calif., USA). Retained peptides were eluted using an acetonitrile gradient (4 to 80%) in 0.05% trifluoroacetic acid, collected into 96 fractions and Lyophilized. Elution was monitored by UV detection. The retention time of major peptide peaks from repeatedly loaded extracts was used to confirm the reproducibility of the method.
After lyophilization, each HPLC fraction was resuspended in matrix solution (mixture of α-cyano-4-hydroxycinnamic acid and L-fucose (co-matrix) in 0.1% acetonitrile/trifluoroacetic acid (1:1 v/v) and applied to a matrix-assisted laser-desorption/ionization (MALDI) target, followed by ambient temperature air drying. Sample ionization was performed by application of repeated single laser shots over a representative area of the sample spot. The accelerated ions were analyzed in a time-of-flight (ToF) mass spectrometer (Voyager-DE STR, Applied Biosystems, Framingham, Mass., USA) in linear mode.
Peptides of interest were identified by mass-spectrometric sequencing using nanoESI-qTOF-MS/MS (QSTAR pulsar, Sciex, Toronto, Canada) with subsequent protein database searching. The resulting peptide fragment spectra were acquired in product ion scan mode (spray voltage 950 V, collision energy 20-40 eV). Up to 200 scans per sample were accumulated. Data processing previous to database searching included charge-state de-convolution (Bayesian reconstruct tool of the BioAnaiyst program package, Sciex, Concord, Canada) and de-isotoping (customized Analyst QS macro; Sciex, Concord, Canada). The resulting spectra were saved in MASCOT (Matrix Science, London, UK) generic file format and submitted to the MASCOT search engine. Cascading searches including several posttranslational modifications in Swiss-Prot (Version 39 or higher, www.expasy.ch) and MSDB (Version 030212 or higher, EBI, Cambridge, UK) were performed by MASCOT DAEMON client (Version 1.9, Matrix Science) that allows, beside sequence determination, identification of modified amino acids as well as determination of their position within the peptide's sequence.
All mass spectra with the same fraction number of chromatography were baseline-corrected averaged, and all 96 averaged mass spectra fractions were visualized in a “2D gel-like” format (peptide display), yielding an averaged peptide display (see
Data pre-processing of the acquired MALDI-ToF-mass spectra was performed applying baseline correction (RAZOR Library 4.0, Spectrum Square Associates, Ithaca, N.Y., USA) in combination with normalization of the mass spectra to a constant integral value. For the benefit of simplicity and uniformity, all m/z-ratios were stated as average Masses of the uncharged analyte. Wherever necessary, data was made available for the model by transformation of m/z-ratio data into this format.
For the analysis of all peptide-to-peptide relations, calculations of correlation were performed with the signal intensities (i.e. relative peptide quantity) of all present (unknown) peptide coordinate data sets to any known peptide coordinate in peptide displays of patient samples: Any pair-wise relation of two peptides was rated by Spearman's rank order correlation of their respective signal intensities in all samples. Peptide pairs in combination with m/z ratios, chromatographic fraction and Spearman' rank order coefficients of correlation were stored in a local Peptide-to-Peptide database.
In an automated approach, all peptide coordinates were individually queried in a peptide sequence database. The following rules were applied
All members of this list of identifications are now utilized as “hub peptides” for subsequent correlational analysis.
The determination of the bonus points is explained further below.
a) If the amino add residue before/after amino-terminal/carboxy-terminal cleavage site of the putative peptide sequence on the precursor sequence corresponds to the following amino acid residues (one letter code), the proposal is awarded with the respective bonus points (bpt):
b) If the pairs of amino acids before/after amino-terminal/carboxy-terminal cleavage site of the putative peptide sequence on the precursor sequence correspond to the following amino add pairs, the proposal is awarded with the respective bonus points (bpt):
c) If the putative sequence has the same start position as the known hub peptide, the proposal of this sequence is awarded with 69 bonus points. If the putative sequence has the same end position as the known hub peptide, the proposal of this sequence was awarded with 63 bonus points.
The determination of the bonus points is explained further below.
The peptidome of 66 independent CSF samples was analyzed using a combination of chromatographic separation (96 fractions) and subsequent mass spectrometry, leading to a database containing 7104 MALDI-ToF pre-processed mass spectra. All mass spectra with the same fraction number were averaged, yielding an averaged peptide display (see
As described in detail further above, a network is defined as a collective of a peptide of interest, the so-called hub peptide, and peptides highly correlating with this peptide, selected from all peptides by exceeding an arbitrarily defined correlational threshold. This concept is exemplified by two networks of VGF and albumin peptides: The network of VGF 26-58 as a hub peptide (see
It was assumed that any member of a network is derived from the same protein precursor without experimental determination of the sequence of the unknown signal coordinate. Thus, start- and end positions on that precursor protein sequence were systematically permuted and iterated resulting in putative peptide sequences.
Peptides are generated by cleavage of peptide bounds by proteolytic enzymes. These proteases recognize specific sites (amino acid sequence motifs), where a cleavage occurs. The probability of cleavage as a function of a particular amino acid and the position of the amino acid regarding the cleavage site was investigated and compared with the occurrence of the respective amino acid at any position in all precursor sequences. The table in
Besides investigations of single amino acids, pairs of amino acids were investigated for their influence on an increased probability of cleavage. The probability of cleavage as a function of a pair of amino acids and the position of such a pair with respect to the cleavage site was investigated and compared with the occurrence of the respective pair of amino acids at any position in all precursor sequences (see table in
Many related peptides were found to have the same start position on a precursor protein as exemplified by VGF 26-58, VGF 26-59, VGF 26-61 and VGF 26-62 (see
The application of the above described rules is exemplified by two proposals (see table in
In order to asses the power of prediction of the described model, 139 peptides identified by ESI-MS/MS were split into a group of 70 peptides, that were used for the diction of a second group of 69 peptides, whose sequence identity was suppressed during the prediction process (see
The above example thus demonstrates that related peptides are automatically grouped by CANs. The underlying algorithm exploits the fact that concentrations of peptides from different steps of the processing chain can display a conserved ratio, as shown previously for CSF-derived peptides. These conserved ratios of related peptides were reliably found by Spearman's rank order correlation analysis, which is the basis for the definition of CAN relations. The results show that CANs can be used to automatically group intermediate products of peptide processing. At high thresholds of correlation coefficient, the number of predictions is low, but each having a high degree of accuracy. Decreasing thresholds delivers a growing number of predictions with false ones, finally outbalancing the correct predictions. The present example was based on the assumption/condition that a strict threshold delivers a network whose members are solely derived from the same protein precursor. This was the fundamental basis for the prediction of the sequence of unknown network members. Since the network was based on mass spectrometric data, all peptide signals were characterized by their mass-to-charge ratio. By iterating start and downstream end position on the protein precursor sequence of the previously sequenced hub peptide, putative peptide sequences were generated that matched experimental molecular masses of selected unknown MALDI mass spectrometric peptide signals. The mass accuracy of less than 500 ppm of the MALDI-ToF measurement in linear mode is sufficient to reduce the overwhelming number of theoretical combinations of start- and stop positions of a precursor to a concise selection. The putative sequences were evaluated by models based on the presumed protein precursor's sequence in combination with found proteolytic cleavage patterns in human CSF. In this approach, posttranslational modifications were not considered, considerably reducing the degree of freedom for possible predictions. However posttranslational, as well as other modifications in general can be used to search for correlation in peptide signals.
Six models were tested that were built on different sets of rules and combinations thereof. Since the cleavage of protein precursors is sequence- and tissue-specific, the sequence specificity of proteases in human CSF was investigated: Pairs of amino adds, the ‘motifs’, at the four positions, before and after amino-terminal and carboxy-terminal cleavage site, were differentiated for cleavage pattern analysis (
The described rules applied to sequence prediction are generic since they are based on the x-fold increase of the probability for the given event, and scoring the respective proposals with x bonus points. Combining the individual rules by summing the bonus points substantially increased the accuracy of prediction. This confirms that the rules of the different approaches are complementary and not contradictive. The magnitude of the bonus points was significantly different due the individual definition for every single parameter.
However, if the algorithm is applied to other sample matrices, the presented rules most likely will have to be redefined. The rules can be determined empirically using these other sample matrices. It is also recommended to test the parameters r and bonus points in a given data set with known peptides to determine the false positive rate prior to use for prediction of peptide sequences. The parameters should be readjusted until the false positive rate and prediction number match the design and requirements of the experimental purposes.
As a result of the combination of statistical analysis and peptide biology for the definition of a set of specific rules, a promising model is envisaged that predicts the sequence of peptides with high accuracy. A system of bonus points was used to select the prediction, which fitted the model best. Proposals with the highest sore of bonus points were compared with ESI-MS/MS identifications, and were found to be correctly predicting 85% of protein precursors, start- and end positions and 89% of precursor protein only. A further improvement is expected by using MALDI-ToF measurements in the reflectron mode with a mass accuracy of less than 30 ppm, and with broader sequence coverage to redefine the model.
As a consequence of the promising results of this proof-of-concept study the following procedure is suggested, in case a rapid overview of a peptide content of a new sample source has to be obtained: The peptide coordinates of a novel samples source are defined based on a representative peptide display. Thereafter, the related peptide coordinates are determined by calculating the CAN of any peptide coordinate: Hubs with the most network members are considered as related to a multitude of other peptide coordinates, thus being the most representative ones (Lamerz et al., 2005, Proteomics, 5:x-xx). These peptide coordinates should be identified first. On the basis of these identifications, CAN is used to predict sequence of the remaining not identified peptide coordinates. The identification of peptide signal coordinates that are adequately described by the model can be postponed or discarded from the identification list, leaving more resources for the identification of less abundant peptides or peptides unsatisfactorily described by the model. This procedure can be repeated several times with the additional sequence information generated during the process, resulting in a reduction of MS/MS Identification work while achieving a comparably deep insight in the content of the novel sample source.
Posttranslational and other modifications of peptide sequences can be included by scanning the sequence of the hub peptide for specific motifs characteristic or susceptible for such modifications, such as phosphorylation, dephosphorylation, oxidation, reduction, glycosylation, deglycosilation, acetylation and other modifications known for peptides. Subsequently, the mass difference between the hub peptide and its related peptides can be analyzed to assess whether the mass difference corresponds to the respective posttranslational modification. This implies the implementation of many, even thousands of motifs, as documented in PROSITE (Falquet et al., 2002, Nucleic Acids Res., 30:235-238), and the scanning process can be computationally laborious.
CANs were also exploited for the discovery of surrogates of biomarkers. The whole albumin molecule is routinely used in diagnosis as gold standard to determine the integrity of the brain barriers (blood-brain barrier, blood-CSF barrier). The ratio of albumin concentration in CSF and blood, the ‘albumin ratio’, correlates with the extent of a barrier disruption (Reiber et al., 1980, J. Neurobiol., 224:89-99), leading to the increased transfer of “blood born” peptides and proteins into the CSF. Previous work shows, that the albumin peptide representing amino acids 25-48 of human albumin can serve as a marker for an impaired brain barrier (Heine et al., 2002, J. Chromatogr. B Analyt. Technol. Biomed. Life. Sci. 782(1-2):353-61). By way of this example (see
This was tested in an independent experimental set-up using well-documented CSF samples taken from patients with different severe disruptions of the blood-CSF barrier. Subsequently to the identification of these potential surrogates for albumin 25-48, the surrogates identified were searched in the original dataset described in a previous work (Heine et al., 2002, J. Chromatogr. B Analyt. Technol. Biomed. Life. Sci. 782(1-2):353-61). It was confirmed, that the surrogates proposed (see
The samples used for the study performed with the present invention were e.g. human CSF collected by lumbar puncture from 74 patients suffering from vascular dementia, Lewy-body dementia, frontotemporal dementia, Parkinson's disease, depression, lumbago, facial paresis, vertigo, polyneuropathy or optic neuritis.
These samples were analysed by reversed-phase chromatography and MALDI mass spectrometry under the same conditions as the samples in example 6. Albumin 25-48 as the hub peptide displayed strong correlations (|r|>0.75) with 25 different peptide signals and, most importantly, a significant correlation with the albumin ratio in the new sample set, which was determined using standard albumin-ELISA tests, as known in the art (|r|=0.73). It was found that all network members correlated positively with the albumin quotient, and 16 out of 25 reached a significant level (|r|>0.7, n=9, p<0.05). This positive correlation with the established and accepted albumin ratio as a measure for blood-CSF barrier disruption indicate the correctness of the predicted peptide-to-peptide relations in CSF. Five prominent network members were identified subsequently as structurally similar amino-terminal fragments of albumin, namely albumin 25-48, albumin 25-50, albumin 25-51 and albumin 27-50 by sequencing. The novel peptide alpha-1-antitrypsin 397-418 of the albumin 25-48 CAN correlated even stronger to the albumin quotient (|r|=0.83) than the albumin fragment itself (|r|=0.73). The identification of the alpha 1-antitrypsin 397-418 peptide as a member of the albumin CAN highlights the power of the claimed methodology for the identification of new chemically unrelated peptide surrogates with a high diagnostic potential. Interestingly, alpha 1-antitrypsin as whole protein is already described as a protein directly correlating with disturbances in the blood-brain barrier determined by assessment of the ratio of albumin in CSF to that in serum (Pearl et al., 1985, Arch. Neurolo. 42:775-777) further supporting that CANs are suitable to predict surrogates of known markers.
The person skilled in the art will appreciate form the foregoing that the range of application of CANs is expandable to any proteomic approach that allows a semi-quantitative analysis of components, for example data from two-dimensional gels (2D-gels). In such cases pair wise correlation coefficients of the components can be calculated, but it is of utmost importance to verify spot identity, avoiding inclusion of spots deriving from contaminating proteins. There, the precision of the two dimensions of peptidomics CAN, i.e. chromatographic fraction in RP-HPLC (usually better than 1%) and in MALDI-MS (usually better than 100 ppm) is highly superior to separations obtained by 2D-gel electrophoresis (Schulz-Knappe et al., 2001, Comb. Chem. High Throughput. Screen., 4:207-217). On the other hand, CANs based on the approach described in the examples of the present invention are restricted to proteins <15 kDa, while CAN based on 2D-gels can also address networks of larger proteins.
CANs are also applicable to peptide and protein quantification data from Isotope-Coded Affinity Tag (ICAT) mass spectrometric experiments. In ICAT experiments peptides and proteins present in the samples are isotopically labelled through a reactive group that specifically binds to cysteine residues. In a low molecular weight (peptidome) region, the number of peptides and small proteins that do not contain cysteins needed for ICAT labelling is higher compared to the proteomics field, thus decreasing the efficiency of ICAT. Novel labels such as ITRAQ which is an amine specific isotope labelling technique developed by Applied Biosystems, Foster City, Calif., USA, will allow detection of all small proteins/peptides in CAN experiments.
It is envisaged that CANs will also support the interpretation of data from tryptic digestions of protein or peptide containing samples. Although the CAN methods presented here are based on undigested, native peptides, a similar clustering of different peptide species derived from the same precursor after tryptic digestion is possible. While this Invention has been described with reference to preferred embodiments, it will be understood by those skilled in the art that various changes or modifications in form and detail may be made without departing from the scope of the Invention as defined in the following claims.
For example it is readily apparent that the present invention can advantageously be utilized basically with all kinds of samples potentially containing peptides, such as samples from animals, plants, fungi, humans, parasites, microorganisms, such as bacteria, yeasts, viruses, and the like, samples from food or other agricultural materials such as meat, milk, grain, vegetables, wool, cotton, silk, samples from cosmetic products or other products containing peptides such as cleaning agents (often containing proteolytic enzymes), etc. Samples for example can be plasma, serum, hemo-filtrate, whole blood, blood cells, tissues samples, in vitro grown cells, cell culture supernatants, urine, cerebrospinal fluid, lymph fluid, sputum, tear fluid, ascites, preparations of cell organelles, tissue homogenate or homogenates of a virus, a microorganism, a parasite, a multi-cellular organism, an animal, a fungus or a plant and the like or combinations thereof. Examples of combinations are in vitro cultured cells infected with a microorganism or treated with pharmaceutical substances, tissue samples of humans infected with a microorganism, food products containing microorganisms, tissue culture supernatants of cells treated with peptides or mixtures of peptides present in food or cosmetic products, and the like.
| Number | Date | Country | Kind |
|---|---|---|---|
| 04000170.3 | Jan 2004 | EP | regional |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/EP2005/000090 | 1/7/2005 | WO | 00 | 4/25/2007 |
