Patent Application
20210372959
The present invention relates to a transformative concept based on nanopore technology, Sequencing-by-Hydrolysis, to identify the N-terminal amino acid and the length of each peptide fragment in a peptide ladder to reconstitute the sequence of a protein.
Nanopores in biological and synthetic membranes have been developed to detect and characterize a variety of analytes at the single-molecule level. While nanopores have shown great promise for sequencing of nucleic acid molecules, there is much more enthusiasm surrounding the idea of applying the nanopore technology to sequence proteins/peptides. However, existing studies only achieved identification of peptide or quadromers in its entirety.
Accordingly, it is an object of the present invention to overcome the above failings. The current disclosure provides a transformative concept based on nanopore technology, Sequencing-by-Hydrolysis, to identify the N-terminal amino acid and the length of each peptide fragment in a peptide ladder to reconstitute the sequence of a protein. Specifically, a protein/peptide analyte will be nonspecifically hydrolyzed to generate random fragments of the analyte that are different by one amino acid (ladder). The N-terminal amino acid of each fragment will be modified so it generates a distinguishable fingerprint signal when tested by nanopore. The length of the fragment can be estimated by characterizing its translocation signal to back calculate the location of the amino acid in the original analyte. This approach will significantly advance the nanopore technology with single amino acid resolution for protein/peptide sequencing.
Citation or identification of any document in this application is not an admission that such a document is available as prior art to the present disclosure.
The above objectives are accomplished according to the present disclosure by providing in a first embodiment, a method for identifying individual amino acids. The method may include employing a biosensing strategy using at least one nanopore, N-terminal derivatization of at least one amino acid to form an amino acid analyte, and differentiating individual amino acid analytes from one another via analysis of the at least one analyte interacting with the at least one nanopore. Further, the method may include developing a characteristic profile for each individual amino acid via a statistical description of each individual amino acid analyte's translocation process through the at least one nanopore. Still, the method may include analyzing blockade and dwell times for the individual amino acid analytes within the at least one nanopore. Yet again, the nanopore may be an α-hemolysin nanopore. Further again, the method may include employing an aromatic tag as part of the N-terminal derivatization. Moreover, N-terminal derivatization may employ derivatization reagents such as 2,3-naphthalenedicarboxaldehyde (NDA) and/or 2-naphthylisothiocyanate (NITC). Again yet, identifying at least one individual amino acid may be accomplished via analyzing current blockade induced via presence of the at least one amino acid analyte. Further still, identifying at least one individual amino acid may be accomplished via analyzing dwell time induced via the at least one amino acid analyte when analyzing current blockage is ineffective at identifying the at least one amino acid. Yet further, the method may include generating a signal on an electrical current trace characterized by current blockade and dwell time when the at least one individual amino acid analyte translocates the at least one nanopore.
In a further embodiment, a method for identifying individual amino acids is provided. The method may include inserting at least one nanopore into a phosphate lipid bilayer and the phosphate lipid bilayer separates cis and trans compartments in an electrolyte solution, applying an external positive voltage to the trans facing side of the bilayer, grounding the cis facing side of the bilayer, determining amino acid analyte insertion via an absolute value of open pore current under positive and negative voltages, and identifying at least one individual amino acid via interaction of an amino acid analyte with the at least one nanopore. Further, the method may include the tail of the at least one nanopore inserted into the phosphate lipid bilayer with the head of the at least one nanopore remaining in the cis compartment. Still yet, the at least one nanopore may comprise α-hemolysin nanopore. Again, the method may include introducing a sample of at least one individual amino acid analyte to the cis compartment. Moreover, introduction of at least one amino acid derivative in the cis compartment may induce transient events in an ionic current flowing through the at least one nanopore. Further yet, the method may characterize capture of at least one amino acid analyte via analysis of current blockade and blockade duration within the at least one nanopore. Still, identifying at least one individual amino acid may be accomplished via analyzing current blockade induced via presence of the at least one individual amino acid analyte. Furthermore, identifying at least one individual amino acid may be accomplished via analyzing dwell time induced via presence of the at least one individual amino acid analyte when analyzing current blockage is ineffective at identifying the at least one amino acid analyte. Still further, the method may generate a signal on an electrical current trace characterized by current blockade and dwell time when the at least one individual amino acid analyte translocates the at least one nanopore.
These and other aspects, objects, features, and advantages of the example embodiments will become apparent to those having ordinary skill in the art upon consideration of the following detailed description of example embodiments.
An understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure may be utilized, and the accompanying drawings of which:
The figures herein are for illustrative purposes only and are not necessarily drawn to scale.
Before the present disclosure is described in greater detail, it is to be understood that this disclosure is not limited to particular embodiments described, and as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Unless specifically stated, terms and phrases used in this document, and variations thereof, unless otherwise expressly stated, should be construed as open ended as opposed to limiting. Likewise, a group of items linked with the conjunction “and” should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as “and/or” unless expressly stated otherwise. Similarly, a group of items linked with the conjunction “or” should not be read as requiring mutual exclusivity among that group, but rather should also be read as “and/or” unless expressly stated otherwise.
Furthermore, although items, elements or components of the disclosure may be described or claimed in the singular, the plural is contemplated to be within the scope thereof unless limitation to the singular is explicitly stated. The presence of broadening words and phrases such as “one or more,” “at least,” “but not limited to” or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, the preferred methods and materials are now described.
All publications and patents cited in this specification are cited to disclose and describe the methods and/or materials in connection with which the publications are cited. All such publications and patents are herein incorporated by references as if each individual publication or patent were specifically and individually indicated to be incorporated by reference. Such incorporation by reference is expressly limited to the methods and/or materials described in the cited publications and patents and does not extend to any lexicographical definitions from the cited publications and patents. Any lexicographical definition in the publications and patents cited that is not also expressly repeated in the instant application should not be treated as such and should not be read as defining any terms appearing in the accompanying claims. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided could be different from the actual publication dates that may need to be independently confirmed.
As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.
Where a range is expressed, a further embodiment includes from the one particular value and/or to the other particular value. The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints. Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure. For example, where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure, e.g. the phrase “x to y” includes the range from ‘x’ to ‘y’ as well as the range greater than ‘x’ and less than ‘y’. The range can also be expressed as an upper limit, e.g. ‘about x, y, z, or less’ and should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of ‘less than x’, less than y’, and ‘less than z’. Likewise, the phrase ‘about x, y, z, or greater’ should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of ‘greater than x’, greater than y’, and ‘greater than z’. In addition, the phrase “about ‘x’ to ‘y’”, where ‘x’ and ‘y’ are numerical values, includes “about ‘x’ to about ‘y’”.
It should be noted that ratios, concentrations, amounts, and other numerical data can be expressed herein in a range format. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. Ranges can be expressed herein as from “about” one particular value, and/or to “about” another particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms a further aspect. For example, if the value “about 10” is disclosed, then “10” is also disclosed.
It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited. To illustrate, a numerical range of “about 0.1% to 5%” should be interpreted to include not only the explicitly recited values of about 0.1% to about 5%, but also include individual values (e.g., about 1%, about 2%, about 3%, and about 4%) and the sub-ranges (e.g., about 0.5% to about 1.1%; about 5% to about 2.4%; about 0.5% to about 3.2%, and about 0.5% to about 4.4%, and other possible sub-ranges) within the indicated range.
As used herein, the singular forms “a”, “an”, and “the” include both singular and plural referents unless the context clearly dictates otherwise.
As used herein, “about,” “approximately,” “substantially,” and the like, when used in connection with a measurable variable such as a parameter, an amount, a temporal duration, and the like, are meant to encompass variations of and from the specified value including those within experimental error (which can be determined by e.g. given data set, art accepted standard, and/or with e.g. a given confidence interval (e.g. 90%, 95%, or more confidence interval from the mean), such as variations of +/−10% or less, +/−5% or less, +/−1% or less, and +/−0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosure. As used herein, the terms “about,” “approximate,” “at or about,” and “substantially” can mean that the amount or value in question can be the exact value or a value that provides equivalent results or effects as recited in the claims or taught herein. That is, it is understood that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and/or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art such that equivalent results or effects are obtained. In some circumstances, the value that provides equivalent results or effects cannot be reasonably determined. In general, an amount, size, formulation, parameter or other quantity or characteristic is “about,” “approximate,” or “at or about” whether or not expressly stated to be such. It is understood that where “about,” “approximate,” or “at or about” is used before a quantitative value, the parameter also includes the specific quantitative value itself, unless specifically stated otherwise.
The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.
As used interchangeably herein, the terms “sufficient” and “effective,” can refer to an amount (e.g. mass, volume, dosage, concentration, and/or time period) needed to achieve one or more desired and/or stated result(s). For example, a therapeutically effective amount refers to an amount needed to achieve one or more therapeutic effects.
Various embodiments are described hereinafter. It should be noted that the specific embodiments are not intended as an exhaustive description or as a limitation to the broader aspects discussed herein. One aspect described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced with any other embodiment(s). Reference throughout this specification to “one embodiment”, “an embodiment,” “an example embodiment,” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the disclosure. For example, in the appended claims, any of the claimed embodiments can be used in any combination.
All patents, patent applications, published applications, and publications, databases, websites and other published materials cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.
Nanopore technology is a promising alternative proteomic tool for protein sequencing in point-of-care and resource-limited settings. Existing nanopore studies have only achieved identification of oligopeptides in their entirety due to the lack of single amino acid distinguishability. The current disclosure provides a Sequencing-by-Hydrolysis (SBH) method to develop a nanopore-based protein/peptide sequencer with single amino acid resolution towards de novo sequencing.
The primary sequence of a protein or a peptide is essential to its identification and function. In the area of personalized diagnosis and therapeutics, accurate proteomic information of proteins or peptides as biomarkers can much better reflect an individual's health status than the genomic information. Classical proteomics techniques, such as mass spectrometry (MS), are less sensitive and reproducible when detecting low abundance proteins/peptides, and are also time consuming and too expensive for medical diagnostics. Nanopore technology is a promising alternative because of its single-molecule analysis capacity and simplicity.
However, while the technology is maturing in DNA detection and sequencing, it still cannot sequence protein/peptide due to the lack of single amino acid distinguishability. To address this issue, the current disclosures provides an innovative Sequencing-by-Hydrolysis (SBH) nanopore-based method to identify the N-terminal amino acid of each peptide fragment in a peptide ladder generated from a peptide analyte to reconstitute its full-length sequence. The project will focus on the proof-of-concept of identifying the N-terminal amino acid and the length of different oligopeptides as the first step towards de novo protein/peptide sequencing in clinical, point-of-care, and resource-limited settings by nanopore sequencers.
This approach has several unique features that distinguish it from other known research efforts. First, a crucial point for nanopore sensing is the effective diameter and length of the sensing region (i.e. the constriction region). In existing nanopore technologies, several amino acid residues usually engage the sensing region of the pore at the same time, leading to a joint effect to the measurement, which prevents single amino acid resolution. Instead of trying to resolute each amino acid on a peptide sequentially, we propose to identify only the N-terminal amino acid on every hydrolyzed fragment from a peptide ladder at first, and then reconstitute the sequence of the peptide. The current disclosure functionalizes (derivatize) the N-terminal end of each oligopeptide with an optimized tag to generate distinguishable fingerprint signals. Second, efficient conjugation chemistry will be employed to in situ derivatize the N-terminal end of amino acids or peptides to control the net charge distribution, ensure the anisotropic structural feature and prolong the interaction with the lumen face of the nanopore. Third, the nanopore biosensor detects each peptide fragment translocation at single molecular level to identify its N-terminal amino acid and length by analyzing signal amplitude, dwell time, and roughness, etc. using automated algorithms. The output results can be readily input to bioinformatic analysis for sequencing. Fourth, the current disclosure provides nanopore devices that may be fabricated with precision and reproducibility suitable for clinical applications. The use of a hemolysin-based nanopore allows the adoption of a large variety of well-established protocols to ensure fabrication quality. These processes produce highly consistent devices to ensure reproducible results.
Nanopore technology has been employed as a powerful tool for DNA sequencing and analysis. To extend this method to peptide sequencing, a necessary step is to profile individual amino acids (AAs) through their nanopore stochastic signals, which remains a great challenge due to the low signal-to-noise ratio and unpredictable conformational changes of AAs during their translocation through nanopores. We showed that the combination of an N-terminal derivatization strategy of AAs with nanopore technology could lead to effective in situ differentiation of AAs. Four different derivatization reactions have been tested with five selected AAs, i.e. Ala, Phe, Tyr, His and Asp. Using an α-Hemolysin (α-HL) nanopore, we demonstrated the feasibility of derivatization-assisted identification of AAs regardless of their charge composition and polarity. The method was further applied to discriminate each individual AA in testing datasets using their established nanopore profiles from training datasets. We envision this proof-of-concept study will not only pave a way for identification of individual AAs but also lead to future applications in protein/peptide sequencing using the nanopore technology.
Emerging resistive pulse nanopore sensing technology, ranging from biological protein to artificial solid-state nanometer-scale pores, makes it possible to detect, analyze, manipulate and characterize a variety of analytes at the single-molecule level. In general, nanopore sensing operates on a basic structure with a thin membrane containing a single nanopore that separates an ionic solution into two compartments. A transmembrane bias is applied to capture and transport analytes from one side of the membrane to the other through the nanopore. The entry of a molecule into a nanopore could cause a reduction in the latter's ionic conductance. The resulting ionic current blockade depth and the residence time have been shown to provide detailed information on the size, adsorbed charge, and other properties of the molecule. Consequently, nanopore based nucleic acid sequencing technology has been successfully commercialized with single base resolution, label-free detection, and long-read capability. However, to extend this method to peptide sequencing, a large hurdle is to differentiate individual amino acids (AAs) through their nanopore stochastic signals due to the low signal-to-noise ratio and unpredictable conformational changes of AAs during their translocation through nanopores.
To mitigate these challenges, different measurement methods on various nanopores have been developed in attempt to achieve higher sensitivity. In some pioneer studies, the identification of some specific AAs or short peptides by monitoring the ion translocation in perpendicular nanochannel, in recognition tunneling, and in sputtered sub-nanometer pores made on solid-state materials were first demonstrated. Later on, identification of peptides with different lengths by one AA was achieved with wild-type aerolysin nanopore, α-Hemolysin (α-HL) nanopore and viral DNA packaging motor, whereas recognition of proteins and peptides with minor sequence differences was accomplished using FraC nanopores.
Despite the solid foundation laid by these investigations, identification of AAs by nanopore technology is still limited by the lack of characteristics in the interaction with nanopores because of the much smaller size and the fast translocation rate of the AAs. To utilize the robust structure of biological nanopores, alternative methods such as decreasing the diameter of the pore lumen, or increasing the volume of AAs by efficient and versatile chemical modifications were used to achieve more AA-pore interactions during translocation. A simulation study proposes that nanoporous single-layer MoS2 can detect individual AAs in a polypeptide chain, but the results have not been experimentally proven. An elegant design by Bayley and others incorporated the usage of metal-organic complexes into the biological nanopore, which could effectively differentiate amino acid enantiomers. Recently, aerolysin with a narrow constriction of ˜1.0 nm was favored as a biological nanopore for recognizing AAs due to its highly charged sensing interface. Lu et al. found that the cyclization of cysteine and homocysteine into thiazolidines could enhance the signal differentiation through an aerolysin nanopore. Ying et al. first reported the detection of a single cysteine molecule using the interaction between the aerolysin sensing interface and the analyte. An encouraging study by Oukhaled et al. reported identification of all proteinogenic AAs using an aerolysin nanopore with the help of a short peptide carrier. However, translation of this method to practical protein sequencing seems overwhelmingly challenging.
The current disclosure envisions an efficient universal conjugation strategy of AAs could augment the interaction of AA derivatives with the pore lumen surface, and may be readily applicable towards sequencing. Considering the size mismatch between AAs and the α-HL nanopore, N-terminal conjugation can increase the aspect ratio of AAs, leading to a prolonged interaction with the nanopore. Meanwhile, most positively charged AA or peptides can be neutralize by N-terminal conjugation to form more negatively charged final conjugates. Moreover, for future applications in nanopore protein sequencing, the quantitative nature of the N-terminal conjugation method with similar reactivity towards different amino acids and peptides is desired to avoid introducing interfering impurities and any further purification. Among various N-terminal derivatization methods of AAs, in situ ortho-phthalaldehyde (OPA) and phenyl isothiocyanate (PITC) derivatizations are two of the most widely studied due to their high reaction rate and efficiency. As shown in
Material and Methods
Materials.
Derivative reagents o-phthalaldehyde (OPA), phenyl isothiocyanate (PITC), naphthalene isothiocyanate (NITC) and all amino acids were purchased from Sigma-Aldrich and used without further purification. The derivative reagent 2,3-naphthalenedicarboxaldehyde (NDA) was synthesized according to the reference method, see Mallouli, A.; Lepage, Y. CONVENIENT SYNTHESES OF NAPHTHALENE-2,3-DICARBOXALDEHYDES, ANTHRACENE-2,3-DICARBOXALDEHYDES, AND NAPHTHACENE-2,3-DICARBOXALDEHYDES. Synthesis-Stuttgart 1980, 689-689. The KCl working solution was prepared using deionized water from a Milli-Q water purification system (resistivity of 18.2 MΩ/cm, 25° C., Millipore Corporation) and was filtered through 0.02 μm filter before use. α-HL from Staphylococcus aureus (lyophilized powder, Protein ˜60% by Lowry, ≥10,000 units/mg protein) was purchased from Sigma-Alrich.
General Procedure for Preparing of PITC/NITC Derivatives.
PITC/NITC derivatives were synthesized according to the route i as shown in
General Procedure for Preparing of OPA/NDA Derivatives.
OPA/NDA derivatives were synthesized according to the route ii as shown in
Characterization of amino acid derivatives.
The 1H and 13C NMR spectra were recorded at 298 K in deuterated solvents using Bruker Avance 400 MHz spectrometer. Data are represented as follows: chemical shift, multiplicity (s=singlet, d=doublet, t=triplet, q=quartet, m=multiplet), coupling constants in Hertz (Hz), integration. High-Resolution-Mass-Spectra (HRMS) of amino acid derivative were recorded on Thermo Velos Pro Orbitrap Liquid Chromatography-Mass Spectrometry (LCMS). High performance liquid chromatography (HPLC) of amino acid derivative were recorded on an Agilent 1100 HPLC equipped with a ZORBAX SB-C18 column, see
Nanopore Fabrication and Low-Noise Electrical Recording.
All electrophysiology experiments were performed on the Planar Lipid Bilayer workstation (Warner Instruments) at room temperature (˜23° C.). Fabrication of α-HL nanopore devices follows traditional method previously reported. Briefly, an orifice (200 μm in diameter) punctured on a 25 μm thick Delrin wall that separates the cis (grounded) and the trans chambers of the flow cell was precoated with 1:10 hexadecane/pentane (Sigma-Aldrich). Then both chambers were filled with 1 mL of 3 M KCl solution buffered in 10 mM Tris-HCl (pH 8). To form a lipid bilayer membrane in the orifice, 20 μL (10 mg/mL) 1,2 diphytanoyl-sn-glycero-3-phosphocholine (Avanti Polar Lipids) dissolved in pentane (Sigma-Aldrich) was added to the cis side of chambers to allow self-assembly. Following this, electrical potential was applied to the trans side using Ag/AgCl electrodes and slowly ramped up to examine the stability of the membrane at ±200 mV. The membrane capacitance was maintained between 160-170 pF with various voltage bias values throughout each experiment.
To insert a single nanopore channel into the lipid bilayer, trans voltage was changed to 100 mV and a small amount (˜0.05 μg) of α-HL protein (Sigma-Aldrich) were added from a monomeric stock solution made in 3 M KCl to the cis compartment. To ensure consistency of testing conditions, the direction of each α-HL nanopore was examined by comparing the value of the channel current under positive and negative voltages after its insertion into the lipid bilayer. A properly inserted α-HL pore exhibits larger ionic current under a positive trans voltage than it is under a negative voltage, see
Data Collection and Analysis.
Ionic current recordings were collected using a patch clamp amplifier (Warner Instruments) with a built-in high-pass Bessel filter (cutoff: 5 kHz) at a holding potential of 100 mV. After sample addition to the cis chamber, magnetic stirring was used to disperse the sample before characteristic signal was recorded. The magnetic stirring was performed at the bottom of cis side to avoid any impact on the stability of the membrane and the nanopore, see
A fresh α-HL protein nanopore was used for each replicate. The raw data was analyzed using an in-house Matlab based algorithm to find the current blockade and the dwell time of each eligible event, which are two commonly used properties for discriminating different molecules when they translocate nanopores. The current blockade that represents the capture of single molecules and their translocation through the nanopore is defined as I/I0 (I=I0−Ib, Ib: the average current measured with the molecules inside the pore; I0: the average baseline current in absence of analytes). Dwell time (i.e. duration) represents the effective interaction time between nanopore and single molecule analyst, see
Statistical Analysis.
To profile each analyte, current blockade was plotted against dwell time using Python. The python modules used for scatter plots and contour plots were Matplotlib and Seaborn's bivariate kernel density estimator. Contours were created according to the density of the data points in the logarithmic duration fractional blockade space, based on a kernel density function whereby every data point contributes a two-dimensional Gaussian to the cumulative contour, which was then normalized in z such that the entire volume of all of the contributing data integrated to one.
For discriminant analysis, multiple parallel experiments were performed for each NITC-AA respectively to collect >1000 events as the training datasets, which are used to calculate the similarity relationships with testing datasets of each NITC-AAs. All analyses were performed using Mahalanobis Distance matrices and histogram binning methods with an in-house MATLAB based program, see
Molecular Modeling.
Geometry optimization of amino acids and their derivatives was calculated using Q-chem 4.3. Unrestricted B3LYP function was employed to describe our system, making use of the 6-31++G basis sets for C, H, O, N and S atoms. Solvent effects (KCl aqueous solution with a dielectric constant of 55) were included using the PCM implicit solvation model. The VDW radius was calculated by Multiwfn program, see Lu, T.; Chen, F. W. Multiwfn: A multifunctional wavefunction analyzer. Journal of Computational Chemistry 2012, 33, 580-592. 10.1002/jcc.22885, in which VDW surface is defined by the lengths of the three sides of the cube.
Amino Acids Derivatization and Characterization
Five characteristic AAs with different size, charge, polarity, and hydrophobicity were selected in our initial study: Alanine (Ala), Phenylalanine (Phe), Tyrosine (Tyr), Aspartic Acid (Asp), and Histidine (His). Each unmodified AA was first analyzed using an α-HL nanopore for extended recording time, and no obvious current blockade signal was observed at applied potential bias, see
To increase effective interaction between the nanopore and analytes, the N-terminals of the aforementioned AAs were readily modified with PITC, OPA, NITC and NDA, respectively, see
Translocation Profiles of Amino Acid Derivatives
To assess the ability of PITC derivatization for distinguishing the five different AAs, a contour plot was generated for each of the five PITC-derivatives from its average current blockade and dwell time values to profile its translocation behavior, see
We next tested the NITC derivatization, which adds one more benzene ring to the structures. A clear differentiation among the five AAs could be observed through current blockade and dwell time analysis, see
The current blockade and dwell time profiles of NDA-derivatives were similarly measured, see
Finally, to assess the discriminatory power of our method for the differentiation of AAs, three different AAs (Ala, His, and Tyr) derivatized with NITC were added to the cis compartment of the nanop ore flow chamber sequentially at the same final concentration (200 μM) while translocation signals were recorded simultaneously. As shown in
Revealing the primary sequence of a protein or peptide is essential to its identification and function. Traditionally, the most common method for protein sequencing is MS, a technique that involves fractionating the protein into many smaller peptides and then obtaining the mass-to-charge ratio of each new peptide from the mass spectrometer. However, sequencing is sometimes impossible with this technology due to low abundance of precursor peptide and poor fractionation efficiency. The sensitivity of MS also varies among different analytes and between instrument models. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS suffers from significant reduced sensitivity on samples with high concentration of salts. Although many high-resolution MS have been developed recently and the combination with high performance liquid chromatography may improve the sensitivity, it is still a laborious process to profile the complete sequence of an unknown protein. In addition, MS instruments are too costly and complex to be developed into portable devices. A portable, accurate, and easy-to-use protein sequencer will engender future implications in personalized medicine, especially in self-testing, resource-limited settings, disease outbreaks, and novel theranostics concepts. Resistive pulse sensing using biological nanopores shows atomic precision due to the extremely small dimension of their sensing regions in the pore lumen (1˜4 nm), and has been demonstrated with excellent sensitivity and accuracy in DNA and RNA sequencing. Recently, the focus of efforts has been directed towards amino acid identification and sequencing of proteins and peptides, which holds great promise for the advancement of proteomics. Comparing to MS, the nanopore technology has several advantages: (1) long-reads that are not limited by precursor peptide fractionation; (2) high tolerance to contaminations such as salts and polymers; (3) simplicity and cost efficiency.
Pioneering studies have demonstrated that a nanopore is able to detect certain single AA, and differentiate certain peptides with one AA difference in length. However, sequencing of random peptides with single AA resolution is still extremely challenging to realize, likely due to the zwitterionic form, the non-uniform translocation rate, as well as low signal-to-noise ratio and low distinguishability of most AAs caused by the mismatch between the diameter of AAs (0.6-0.8 nm) and the nanopore (1.4-3 nm). Inspired by the Edman degradation, the current disclosure developed a new nanopore method to identify a single AA using its N-terminal derivative as a surrogate. Four derivative regents were employed in this study to modify five AAs. Our results indicate that the derivatization afforded a fingerprint on stochastic signals when each AA translocating the α-HL nanopore due to the increased interaction. Importantly, these derivatization methods were efficient and reproducible under simple one-pot mild reaction conditions, affording a reliable strategy for the formation of a structurally diverse array of AA derivatives.
The current disclosure assessed four series of AA derivatives for their translocation behaviors through the α-HL nanopore, including PITC-derivatives, OPA-derivatives, NITC-derivatives, and NDA-derivatives of five different AAs (Ala, Asp, Phe, Tyr, and His) with various polarity, charge and size, representing different types of twenty AAs. Significantly increased translocation event frequency of derivatives comparing to unmodified AAs indicates enhanced interaction between the nanopore lumen and analytes. Detailed investigation on the translocation signals of all derivatives by estimating the distribution of current blockade and dwell time revealed best distinguishability among Ala, Asp, Phe, Tyr, and His by the NITC-derivatization. As confirmed by the molecular structure modeling for each AA and AA derivative, see
The current disclosure clearly demonstrates that derivatization is a feasible way to identify single AAs with biological nanopores, we do recognize the overall complexity of protein sequencing using nanopore. Although the technology has been proven successful for nucleic acid sequencing, it is considerably more challenging to differentiate 20 AAs than 4 nucleotides. In addition to sensitivity enhancement via different modifications to nanopores and analytes through biochemistry methods, more advanced data analysis technology (e.g. machine learning, pattern recognition, etc.) is in urgent need to improve resolution through novel characteristics other than the traditional blockade and dwell time from the stochastic signals. Similar to the MS technology, the data readout from nanopore also needs sophisticated bioinformatics database and algorithm to be interpreted into sequences and protein identifications. Therefore, the success of nanopore based protein sequencing no doubt requires multidisciplinary efforts.
Inspired by the protein ladder sequencing technique and the identification of single AA differences in length of peptides, the current disclosure provides a potential “Sequencing-by-Hydrolysis” method, in which a nanopore will be used to identify the N-terminal AA of each peptide fragment in a peptide ladder generated from a peptide analyte, and then bioinformatics methods will be applied to reconstitute its full-length sequence.
The current disclosure has demonstrated that N-terminus derivatization is an effective way to differentiate individual AAs using α-HL nanopore technology. Among four derivatization reagents applied in our work, NITC-derivatization of five typical AAs afforded significantly enhanced distinguishability based on the translocation signals. While we are working on developing more effective N-terminus modification strategies and optimizing the modifier's structure, more advanced data analysis technology is in urgent need to improve resolution through novel characteristics other than the traditional blockade and dwell time from the stochastic signals. Finally, further simulation work is undergoing to better model the conformational changes of each derivatives inside the lumen of the α-HL, and to understand the complexity of the interactions between each amino acid derivative and the lumen of the α-HL protein.
Supporting Information
1) Change of open pore current value when changing voltage direction after one α-HL nanopore inserted into the lipid membrane; and the current trace during the stirring process. 2) Representative current trace of the translocation of the raw materials through α-HL nanopores; 3) The space-filling structures of different AAs and AA derivatives calculated using the Q-chem 4.3 software package; 4) Reproducibility study of NITC-derivatives; 5) Table including structure of different amino acid derivatives. 6) General procedure and characterization of different derivatives.
Detailed Procedure for Preparing Different Derivatives
Derivatization of amino acid with phenyl isothiocyanate (PITC): PITC (4 mmol), amino acid (4.8 mmol), Na2CO3 (0.1 M, 10 mL) and acetonitrile (20 mL) were stirred under reflux condition overnight. The resulting precipitate was collected, washed with 1 M HCl and methanol, and dried to afford the corresponding product. The average yields are 70-85%.
Derivatization of amino acid with 2-naphthaleneisothiocyanate (NITC): NITC (1 mmol), amino acid (1.1 mmol), Na2CO3 (0.1 M, 4 mL) and acetonitrile (8 mL) under reflux condition overnight. The resulting precipitate was collected, washed with 1 M HCl and methanol, and dried to afford the corresponding product. The average yields are 60-81%.
Derivatization of amino acid with o-phthalaldehyde (OPA): OPA (3.6 mmol), amino acid (3 mmol), trifluoroacetate (4.2 mmol) and acetonitrile (30 mL) were stirred under reflux condition for 3 h. The precipitate was washed with acetonitrile to afford the corresponding product. The average yields are 50-70%.
Derivatization of amino acid with 2,3-naphthalenedicarboxaldehyde (NDA): NDA (0.7 mmol), amino acid (0.6 mmol), trifluoroacetate (0.8 mmol) and acetonitrile (10 mL) were stirred under reflux condition for 3 h. The precipitate was washed with acetonitrile to afford the corresponding product. The average yields are 45-79%.
Characterization of Derivatives by High Performance Liquid Chromatography (HPLC)
Samples were desolved in DMSO/methanol (5:95), injected 5 uL in an Agilent 1100 HPLC equipped with a ZORBAX SB-C18 column. HPLC parameters were as follows: 25° C.; solvent A, 1% acetic acid in water; solvent B, methanol; gradient, 10% B for 2 min; then, from 10% B to 100% B over 32 min; then, from 100% B to 10% B over 10 min, flow rate, 0.5 mL/min. Detection of the products was by UV absorbance at 254 nm or 286 nm.
Characterization of Derivatives by High Resolution Mass Spectrometry (HRMS) and 1H&13C Nuclear Magnetic Resonance (NMR)
1H NMR (DMSO-d6, 400 MHz): δ 10.68 (s, 1H), 9.33 (s, 1H), 7.42-7.37 (m, 3H), 6.99 (d, 2H, J=8.4 Hz), 6.82 (dd, 2H, J1=7.7 Hz, J2=1.8 Hz), 6.69 (d, 2H, J=8.5 Hz), 4.69-4.67 (m, 1H), 3.05-2.69 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 182.2, 173.6, 156.4, 133.2, 130.8, 128.6, 128.5, 128.4, 124.3, 114.9, 60.2, 35.3. HRMS: [M+H]+ m/z calcd for C16H16N2O3SH+, 317.0954; found, 317.0953.
1H NMR (DMSO-d6, 400 MHz): δ10.61 (s, 1H), 7.41-7.36 (m, 3H), 7.35-7.29 (m, 3H), 7.22-7.20 (m, 2H), 6.79-6.76 (m, 2H), 4.79-4.77 (m, 1H), 3.13 (d, 2H, J=4.5 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 182.2, 173.5, 134.5, 133.2, 129.8, 128.7, 128.6, 128.4, 124.2, 127.1, 60.2, 36.1. HRMS: [M+H]+ m/z calcd for C16H16N2O2SH+, 301.1005; found, 301.1005.
1H NMR (DMSO-d6, 400 MHz): δ10.54 (s, 1H), 7.51-7.40 (m, 3H), 7.31-7.28 (m, 2H), 4.47 (q, 1H, J=7.1 Hz), 1.40 (d, 2H, J=7.1 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 182.0, 175.0, 133.5, 128.8, 128.7, 128.5, 55.1, 16.2. HRMS: [M+H]+ m/z calcd for C10H12N2O2SH+, 225.0692; found, 225.0692.
1H NMR (DMSO-d6, 400 MHz): δ10.41 (s, 1H), 7.50-7.46 (m, 2H), 7.44-7.40 (m, 1H), 7.26-7.24 (m, 2H), 4.56 (t, 1H, J=4.4 Hz), 2.93/2.78 (q, 2H, J=7.3 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 183.1, 174.1, 170.7, 133.8, 128.7 (2C), 128.5, 55.8, 34.8. HRMS: [M−H]− m/z calcd for C11H11N2O4S−, 267.0445; found, 267.0452.
1H NMR (DMSO-d6, 400 MHz): δ 11.88 (s, 1H), 10.41 (s, 1H), 7.57 (s, 1H), 7.48-7.39 (m, 3H), 7.15 (d, 2H, J=7.4 Hz), 6.86 (s, 1H), 4.65 (t, 1H, J=4.7 Hz), 3.07 (d, 2H, J=4.7 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 183.0, 174.4, 135.3, 134.1, 133.1, 129.2, 129.1, 128.9, 116.2, 59.6, 29.3. HRMS: [M−H]− m/z calcd for C13H13N4O2S−, 289.0765; found, 289.0770.
1H NMR (DMSO-d6, 400 MHz): δ 10.06 (s, 1H), 9.62 (s, 1H), 7.94-7.88 (m, 2H), 7.84 (d, 1H, J=7.0 Hz), 7.57-7.55 (m, 2H), 7.25 (s, 1H), 7.02 (d, 2H, J=7.3 Hz), 6.86 (d, 1H, J=8.6 Hz), 6.73 (d, 2H, J=7.1 Hz), 4.70 (s, 1H), 3.05 (d, J=2.5 Hz, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 182.8, 174.4, 156.9, 132.89, 132.87, 131.4, 131.1, 128.8, 128.2, 128.1, 127.8, 127.6, 127.2, 126.5, 124.8, 115.4, 61.1, 35.8. HRMS: [M−H]− m/z calcd for C20H18N2O3S−, 365.0965; found, 365.0965.
1H NMR (DMSO-d6, 400 MHz): δ10.68 (s, 1H), 7.96-7.86 (m, 3H), 7.61-7.53 (m, 2H), 7.38-7.32 (m, 4H), 7.26-7.23 (m, 2H), 6.85 (dd, 1H, J1=10.6 Hz, J2=6.8 Hz), 4.83 (t, 1H, J=4.3 Hz), 3.16 (d, 2H, J=4.5 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 182.8, 174.1, 135.0, 133.0, 132.9, 131.1, 130.0, 128.69, 128.67, 128.3, 128.1, 127.8, 127.6, 127.4, 127.1, 126.5, 60.8, 36.7. HRMS: [M−H]− m/z calcd for C20H17N2O2S−, 349.1016; found, 349.1011.
1H NMR (DMSO-d6, 400 MHz): δ 10.62 (s, 1H), 8.03-8.00 (m, 3H), 7.89 (d, 1H, J=1.7 Hz), 7.64-7.57 (m, 2H), 7.43 (q, 1H, J=3.6 Hz), 4.57-4.50 (m, 1H), 1.45 (d, 3H, J=7.1 Hz). 13C NMR (DMSO-d6, 100 MHz): δ182.6, 175.6, 133.1, 133.0, 131.4, 128.7, 128.4, 128.2, 128.1, 127.4, 127.1, 127.0, 55.7, 16.7. HRMS: [M+H]+ m/z calcd for C14H14N2O2SH+, 275.0849; found, 275.0849.
1H NMR (DMSO-d6, 400 MHz): δ 12.8 (s, 1H), 10.41 (s, 1H), 8.03-7.97 (m, 3H), 7.83 (d, 1H, J=1.8 Hz), 7.64-7.57 (m, 2H), 7.40 (q, 1H, J=3.5 Hz), 4.68 (q, 1H, J=4.4 Hz), 3.00/2.84 (q, 2H, J=7.3 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 183.2, 174.2, 170.8, 132.7, 132.5, 131.3, 128.3, 128.0, 127.7, 127.6, 126.9, 126.6, 126.4, 55.9, 34.8. HRMS: [M+H]+ m/z calcd for C15H14N2O4SH+, 319.0747; found, 319.0747.
1H NMR (DMSO-d6, 400 MHz): δ 10.23 (s, 1H), 8.29 (d, 1H, J=8.7 Hz), 8.24 (s, 1H), 8.00 (s, 1H), 7.84 (d, 2H, J=8.7 Hz), 7.80 (d, 1H, J=8.0 Hz), 7.51-7.40 (m, 3H), 5.18 (q, 1H, J=5.0 Hz), 3.19-3.09 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 180.5, 172.4, 136.7, 134.3, 133.2, 131.6, 130.0, 128.2, 127.5, 127.4, 126.4, 125.2, 123.2, 119.2, 116.5, 56.4, 27.8. HRMS: [M−H]− m/z calcd for C17H15N4O2S−, 339.0921; found, 339.0917.
1H NMR (DMSO-d6, 400 MHz): δ 13.08 (s, 1H), 9.20 (s, 1H), 7.64-7.55 (m, 3H), 7.49-7.43 (m, 1H), 7.04 (d, 2H, J=8.4 Hz), 6.60 (d, 2H, J=8.4 Hz), 5.05 (q, 1H, J=5.4 Hz), 4.44 (s, 2H), 3.31-3.04 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 172.6, 168.3, 156.3, 142.4, 132.1, 132.0, 129.9, 128.4, 127.8, 123.9, 123.3, 115.6, 55.5, 47.8, 34.4. HRMS: [M+H]+ m/z calcd for C17H15NO4H+, 298.1074; found, 298.1074.
1H NMR (DMSO-d6, 400 MHz): δ 7.64-7.56 (m, 3H), 7.48-7.44 (m, 1H), 7.28-7.21 (m, 4H), 7.16-7.13 (m, 1H), 5.16 (q, 1H, J=5.4 Hz), 4.49-4.40 (M, 2H), 3.43-3.18 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 172.6, 168.3, 156.3, 142.4, 132.1, 132.0, 129.9, 128.4, 127.8, 123.9, 123.3, 115.6, 55.5, 47.7, 34.4. HRMS: [M+H]+ m/z calcd for C17H15NO3H+, 282.1125; found, 282.1123.
1H NMR (DMSO-d6, 400 MHz): δ 12.9 (s, 1H), 7.71 (d, J=7.5 Hz, 1H), 7.66-7.61 (m, 2H), 7.54-7.48 (m, 1H), 4.84 (q, 1H, J=7.5 Hz), 4.52 (dd, 2H, J1=17.3 Hz, J2=7.5 Hz), 1.51 (d, 3H, J=7.5 Hz). 13C NMR (DMSO-d6, 100 MHz): δ173.5, 168.0, 142.6, 132.4, 132.0, 128.4, 124.0, 123.3, 49.6, 47.3, 15.8. HRMS: [M+H]+ m/z calcd for C11H11NO3H+, 206.0812; found, 206.0812.
1H NMR (DMSO-d6, 400 MHz): δ 12.7 (s, 1H), 7.71 (d, 1H, J=7.5 Hz), 7.66-7.61 (m, 2H), 7.54-7.48 (m, 1H), 5.10 (dd, 1H, J1=6.4 Hz, J2=1.5 Hz), 4.49 (s, 2H), 3.06-2.86 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 172.2, 171.8, 168.0, 142.5, 132.2, 132.1, 128.4, 124.0, 123.4, 51.4, 48.4, 35.0. HRMS: [M+H]+ m/z calcd for C12H11NO5H+, 250.0710; found, 250.0711.
1H NMR (DMSO-d6, 400 MHz): δ 7.68-7.62 (m, 1H), 7.61-7.59 (m, 2H), 7.54 (d, 1H, J=1.0 Hz), 7.49-7.47 (m, 1H), 6.84 (s, 1H), 5.06 (q, 1H, J=5.2 Hz), 4.54 (q, 2H, J=19.6 Hz), 3.28-3.12 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ172.5, 168.3, 142.6, 135.3, 134.5, 132.2, 132.0, 128.3, 123.9, 123.3, 116.3, 54.6, 47.6, 27.8. HRMS: [M−H]− m/z calcd for C14H12N3O3−, 270.0884; found, 270.0883.
1H NMR (DMSO-d6, 400 MHz): δ 9.46 (s, 1H), 8.23 (s, 1H), 8.06 (d, 1H, J=8.2 Hz), 8.02 (s, 1H), 8.00 (d, 1H, J=8.1 Hz), 7.63-7.53 (m, 2H), 7.05 (d, 2H, J=8.4 Hz), 6.58 (d, 2H, J=8.4 Hz), 5.11 (q, 1H, J=5.4 Hz), 4.56 (dd, 2H, J1=17.2 Hz, J2=3.8 Hz), 3.30/3.10 (q, 2H, J1=6.4 Hz, J2=8.7 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 171.9, 167.4, 155.8, 136.6, 134.6, 132.3, 129.9, 129.5, 129.3, 127.9, 127.8, 127.3, 126.2, 123.0, 121.9, 115.2, 55.2, 50.0, 33.8. HRMS: [M−H]− m/z calcd for C21H16NO4−, 346.1085; found, 346.1083.
1H NMR (DMSO-d6, 400 MHz): δ 13.2 (s, 1H), 8.27 (s, 1H), 8.11 (d, 1H, J=8.1 Hz), 8.05 (s, 1H), 8.01 (d, 1H, J=8.1 Hz), 7.64-7.55 (m, 2H), 7.28 (d, 2H, J=7.2 Hz), 7.21 (t, 2H, J=7.5 Hz), 7.12 (t, 1H, J=7.2 Hz), 5.21 (q, 1H, J=5.4 Hz), 4.60 (q, 2H, J=14.6 Hz), 3.45-3.16 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 172.3, 167.9, 137.9, 137.0, 135.1, 132.7, 130.2, 129.8, 129.0, 128.8, 128.4, 128.1, 126.9, 126.7, 123.6, 122.5, 55.5, 47.8, 35.0. HRMS: [M+H]+ m/z calcd for C21H17NO3H+, 332.1281; found, 332.1279.
1H NMR (DMSO-d6, 400 MHz): δ 8.36 (s, 1H), 8.16 (d, 1H, J=7.9 Hz), 8.11 (s, 1H), 8.05 (d, 1H, J=8.1 Hz), 7.73-7.56 (m, 2H), 4.92 (q, 1H, J=7.5 Hz), 4.66 (dd, 2H, J1=17.3 Hz, J2=4.3 Hz), 1.55 (d, 3H, J=7.5 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 173.0, 167.4, 136.9, 134.8, 132.4, 130.3, 129.4, 128.1, 127.8, 126.3, 123.1, 122.1, 49.4, 46.6, 15.3. HRMS: [M−H]− m/z calcd for C15H12NO3−, 254.0823; found, 254.0825.
1H NMR (DMSO-d6, 400 MHz): δ 12.51 (s, 1H), 8.36 (s, 1H), 8.14 (d, 1H, J=8.0 Hz), 8.10 (s, 1H), 8.03 (d, 1H, J=8.1 Hz), 7.65-7.56 (m, 2H), 5.16 (q, 1H, J=4.7 Hz), 4.60 (dd, 2H, J1=17.1 Hz, J2=1.6 Hz), 3.04/2.93 (q, 2H, J=7.6 Hz, J=8.6 Hz). 13C NMR (DMSO-d6, 100 MHz): δ 171.8, 171.3, 167.3, 136.8, 134.8, 132.4, 129.9, 129.4, 128.0, 127.8, 126.3, 123.2, 122.1, 51.2, 47.7, 34.5. HRMS: [M−H]− m/z calcd for C16H12NO5−, 298.0721; found, 298.0717.
1H NMR (DMSO-d6, 400 MHz): δ 14.33 (s, 1H), 8.99 (s, 1H), 8.31 (s, 1H), 8.13 (d, 1H, J=8.2 Hz), 8.11 (s, 1H), 8.04 (d, 1H, J=8.2 Hz), 7.66-7.57 (m, 2H), 7.42 (s, 1H), 5.32 (q, 1H, J=5.2 Hz), 4.81/4.81 (d, 2H, J=16.6 Hz), 3.50-3.40 (m, 2H). 13C NMR (DMSO-d6, 100 MHz): δ 170.7, 167.7, 136.6, 134.8, 133.8, 132.3, 129.5, 129.4, 129.3, 128.0, 127.9, 126.3, 123.3, 122.2, 116.8, 53.5, 46.9, 24.3. HRMS: [M−H]− m/z calcd for C18H15N3O3−, 320.1041; found, 320.1038.
1H and 13C NMR Spectra
HRMS Spectra
Samples were analyzed by infusion using a 50/50 mixture of MS grade acetonitrile/water with 0.1% formic acid in the mobile phase. The injection volume was 10 μL. The ion source was a heated electrospray source (H-ESI type II) performing in positive or negative mode. HRMS full scans were acquired from m/z 100-800 Da. The automatic gain control and mass resolution were set at 1×106 ions and 70,000 (m/z=200), respectively.
Identification of amino acids (AA) with nanopore technology remains a great challenge due to the low signal-to-noise ratio and unpredictable conformational changes of AAs during their translocation through nanopores. Here, we showed that the combination of an N-terminal derivatization strategy of AAs with nanopore technology could lead to effective in situ differentiation of AAs by testing four series of derivatives of five selected AAs, i.e. Ala, Asp, Phe, Tyr, and His using an α-Hemolysin nanopore. Results demonstrated the feasibility of derivatization-assisted identification of AAs regardless of their charge composition and polarity. The method was further applied to discriminate each individual AA in testing datasets using their established nanopore profiles from training datasets. This will not only pave a way for identification of individual AAs but also lead to future applications in protein/peptide sequencing using the nanopore technology.
In a further aspect, the current disclosure provides nanopore technology for sensing individual amino acids by a derivatization strategy and can provide methods for combining nanopore biosensing and N-terminal derivatization of amino acids to effectively differentiate individual amino acids with similar properties for potential future applications in protein sequencing.
Nanopore technology holds remarkable promise for sequencing proteins and peptides. To achieve this, it is necessary to establish a characteristic profile for each individual amino acid through the statistical description of its translocation process. However, the subtle molecular differences among all twenty amino acids along with their unpredictable conformational changes at the nanopore sensing region result in very low distinguishability. Here, the current disclosure provides the electrical sensing of individual amino acids using an α-hemolysin nanopore based on a derivatization strategy. Using derivatized amino acids as detection surrogates not only prolongs their interactions with the sensing region, but also improves their conformational variation.
Furthermore, we show that distinct characteristics including current blockades and dwell times can be observed among all three classes of amino acids after 2,3-naphthalenedicarboxaldehyde (NDA)- and 2-naphthylisothiocyanate (NITC)-derivatization, respectively. These observable characteristics were applied towards the identification and differentiation of 9 of the 20 natural amino acids using their NITC derivatives. The method demonstrated herein will pave the way for the identification of all amino acids and further protein and peptide sequencing.
The primary structure of proteins and peptides plays a significant role in their structural folding and functions. Very often subtle changes in the primary sequence of a protein can lead to debilitating pathologies. Traditional methods for proteome analysis and sequencing, such as mass spectrometry and Edman degradation, suffer from high cost, short reads, long turnaround time, and lack of sensitivity; so alternative approaches are sought. Nanopores made of either biological or inorganic materials with orifices of nanometer diameters and depth have been exploited as an exceptionally sensitive tool for the analysis of individual biomolecules in real time without the potential bias associated with signal amplification. See, J. Im, S. Lindsay, X. Wang and P. Zhang, ACS Nano, 2019, 13, 6308-6318 and M. Waugh, K. Briggs, D. Gunn, M. Gibeault, S. King, Q. Ingram, A. M. Jimenez, S. Berryman, D. Lomovtsev, L. Andrzejewski and V. Tabard-Cossa, Nat. Protoc., 2020, 15, 122-143. As a result, significant progress towards DNA and RNA sequencing has been realized through nanopore technology. Various other applications for nanopores in single molecular sensing have also been demonstrated. See, J. Nivala, D. B. Marks and M. Akeson, Nat. Biotechnol., 2013, 31, 247-250, S. Benner, R. J. A. Chen, N. A. Wilson, R. Abu-Shumays, N. Hurt, K. R. Lieberman, D. W. Deamer, W. B. Dunbar and M. Akeson, Nat. Nanotechnol., 2007, 2, 718-724, and E. V. B. Wallace, D. Stoddart, A. J. Heron, E. Mikhailova, G. Maglia, T. J. Donohoe and H. Bayley, Chem. Commun., 2010, 46, 8195-8197.
Recently, the focus of efforts has been directed towards amino acid identification and sequencing of proteins and peptides, which holds great promise for the advancement of proteomics. Research into protein and peptide nanopore applications has been reported using the ionic current blockade signatures generated by their nanopore translocation. See, G. Huang, A. Voet and G. Maglia, Nat. Commun., 2019, 10, 835. Various nanopore methods including ionic current blockade measurement in biological nanopores (i.e. bacteriophage T7 DNA packaging motor, Z. Ji, X. Kang, S. Wang and P. Guo, Biomaterials, 2018, 182, 227-233, FraC nanopores, see G. Huang, K. Willems, M. Soskine, C. Wloka and G. Maglia, Nat. Commun., 2017, 8, 935, L. Restrepo-Perez, G. Huang, P. R. Bohlander, N. Worp, R. Eelkema, G. Maglia, C. Joo and C. Dekker, ACS Nano, 2019, 13, 13668-13676, aerolysin, see L. Restrepo-Perez, G. Huang, P. R. Bohlander, N. Worp, R. Eelkema, G. Maglia, C. Joo and C. Dekker, ACS Nano, 2019, 13, 13668-13676, F. Piguet, H. Ouldali, M. Pastoriza-Gallego, P. Manivet, J. Pelta and A. Oukhaled, Nat. Commun., 2018, 9, 966., and C. Cao, N. Cirauqui, M. J. Marcaida, E. Buglakova, A. Duperrex, A. Radenovic and M. Dal Peraro, Nat. Commun., 2019, 10, 4918, and α-hemolysin, see, G. Di Muccio, A. E. Rossini, D. Di Marino, G. Zollo and M. Chinappi, Sci. Rep., 2019, 9, 6440, and inorganic perpendicular nanochannels, see P. Boynton and M. Di Ventra, Sci. Rep., 2016, 6, 25232, and recognition by tunneling current, Y. A. Zhao, B. Ashcroft, P. M. Zhang, H. Liu, S. M. Sen, W. Song, J. Im, B. Gyarfas, S. Manna, S. Biswas, C. Borges and S. Lindsay, Nat. Nanotechnol., 2014, 9, 466-473, have been used to identify proteins and peptides. Nonetheless, nanopore sequencing of proteins and peptides still faces formidable open challenges, especially the feasibility of distinguishing individual amino acids. Recently, a small group of researchers have focused on nanopore sensing of individual amino acids. See, A. J. Boersma and H. Bayley, Angew. Chem. Int. Ed., 2012, 51, 9606-9609. Y. Guo, A. Niu, F. Jian, Y. Wang, F. Yao, Y. Wei, L. Tian and X. Kang, Analyst, 2017, 142, 1048-1053, and A. Asandei, A. E. Rossini, M. Chinappi, Y. Park and T. Luchian, Langmuir, 2017, 33, 14451-14459. For example, a fingerprinting scheme has been reported in which only a subset of amino acids was labeled and detected. See, L. Restrepo-Perez, G. Huang, P. R. Bohlander, N. Worp, R. Eelkema, G. Maglia, C. Joo and C. Dekker, ACS Nano, 2019, 13, 13668-13676. In another study, an elegant approach has been developed to detect 13 of 20 proteinogenic amino acids in an aerolysin nanopore with the help of a short peptide tag. See, H. Ouldali, K. Sarthak, T. Ensslen, F. Piguet, P. Manivet, J. Pelta, J. C. Behrends, A. Aksimentiev and A. Oukhaled, Nat. Biotechnol., 2020, 38, 176-181. However, all the reported methods are unfeasible for the derivatization and differentiation of amino acids in situ.
Based on the Edman peptide degradation reaction, see P. Edman, Acta Chem. Scand., 1950, 4, 283-293, P. Edman, Arch. Biochem., 1949, 22, 475-476, I. Molnar-Perl, in Quantitation of Amino Acids and Amines by Chromatography: Methods and Protocols, ed. I. MolnarPerl, 2005, vol. 70, pp. 163-198, and R. Checa-Moreno, E. Manzano, G. Miron and L. F. Capitan-Vallvey, J. Sep. Sci., 2008, 31, 3817-3828, herein we demonstrate that the efficient N-terminal derivatization of amino acids using aromatic tags can augment the distinguishability of different amino acids when they translocate through α-hemolysin (α-HL) nanopores. A panel of nine amino acids, including nonpolar, polar and charged ones, could be discriminated individually.
This method can potentially be employed in the development of nanopore sequencing of protein or peptide analytes. The derivatization reagents, 2,3-naphthalenedicarboxaldehyde (NDA) and 2-naphthylisothiocyanate (NITC) were chosen due to their wide usage along with their high reaction rate and efficiency with most natural amino acids. See, I. Molnar-Perl, in Quantitation of Amino Acids and Amines by Chromatography: Methods and Protocols, ed. I. MolnarPerl, 2005, vol. 70, pp. 163-198, R. Checa-Moreno, E. Manzano, G. Miron and L. F. Capitan-Vallvey, J. Sep. Sci., 2008, 31, 3817-3828, M. Fountoulakis and H. W. Lahm, J. Chromatogr. A, 1998, 826, 109-134, and K. L. Woo and Y. K. Ahan, J. Chromatogr. A, 1996, 740, 41-50.
Nine amino acids were randomly selected from each of the three classes for derivatization with NDA and NITC, respectively, see
In a typical experiment, a single α-HL nanopore is inserted into a phosphate lipid bilayer that separates cis and trans compartments in an electrolyte solution. An external positive voltage (100 mV) is applied to the trans side of the bilayer, while the cis side is electrically grounded, see
Each blockade corresponding to the capture of an individual derivative in the pore is characterized by two parameters: current blockade I/I0(I=I0−Ib, Ib indicates the residual current induced by the analyte) and the blockade duration (dwell time) that represents the effective interaction time between the pore and the analyte, see
For the identification of individual amino acids, current blockade (I/I0) is used as the primary criterion as it can reflect the variation of the spatial structure of the molecule directly before and after modification, as confirmed previously. See, F. Piguet, H. Ouldali, M. Pastoriza-Gallego, P. Manivet, J. Pelta and A. Oukhaled, Nat. Commun., 2018, 9, 966. Dwell time is used as the secondary identification criterion when the current blockade is noneffective.
For instance, although two populations—corresponding to members (Y, and S) of the polar family,
Compared with the polar family, NDA derivatives of the charged family H, E, and D exhibit wider distributions of I/I0, with the mean I/I0 of 0.084±0.037, 0.154±0.077, and 0.267±0.063, respectively, as shown in
After geometrically optimizing the NDA amino acid derivatives to gain an accurate measurement of hydrodynamic volume (
However, subpopulations located beside the main peaks (tagged with asterisks in
To improve the discrimination between amino acids, we further modified these 9 amino acids with NITC. An increase in spatial structure complexity of the NITC-derivatives is confirmed by the wider volume range, i.e., 734-1264 Å3, which is expected to lead to higher discriminatory power and less uncertainty of the spatial orientation of derivatives inside the nanopore.
Results of all NITC derivatives confirm effective interactions with the nanopore by characteristic distribution of I/I0 and dwell time for each derivative. Although with some notable exceptions, the superimposed histograms of each family of derivatives exhibit well-separated populations with narrow distributions, see
While identification of all the 9 amino acids can be achieved by using the mean I/I0 (primary criterion) only, their dwell time distribution (secondary criterion) was also analyzed to further enhance the identification accuracy, see
Previous studies have demonstrated that an aerolysin nanopore with a narrower constriction of B1.0 nm is able to detect a bare cysteine, see B. Yuan, S. Li, Y. L. Ying and Y. T. Long, Analyst, 2020, 145, 1179-1183, and differentiate certain peptides with one amino acid difference in length. See, F. Piguet, H. Ouldali, M. Pastoriza-Gallego, P. Manivet, J. Pelta and A. Oukhaled, Nat. Commun., 2018, 9, 966. A recent advancement demonstrates detection of more types of amino acids using a peptide as the carrier, resulting in various I/I0 distributions around 0.4 with only slight shifts for different modified amino acids, see H. Ouldali, K. Sarthak, T. Ensslen, F. Piguet, P. Manivet, J. Pelta, J. C. Behrends, A. Aksimentiev and A. Oukhaled, Nat. Biotechnol., 2020, 38, 176-181, whereas the NITC derivatization produced larger difference between I/I0 distributions of different amino acids (0.1-0.5), indicating improved sensitivity using small molecules as amino acid modifiers. In addition, it is overwhelmingly challenging to apply the peptide carrier method to practical protein sequencing due to various reaction conditions for modifying different amino acids. As demonstrated in the Edman degradation, N-terminal derivatization can efficiently increase the spatial size of all amino acids with similar reactivity within the same reaction, and thus can be readily applied to recognize all the amino acids towards protein sequencing.
When an analyte translocates the nanopore under the applied voltage and the diffusion effect, a signal on the electrical current trace characterized by a current blockade and a dwell time can be generated as a result of the transient occupation of the nanopore lumen by the analyte and the interaction between the analyte and the nanopore. The low frequencies of translocation events for the selected amino acids demonstrate their weak interactions with the lumen of the α-HL nanopore, due to the smaller van der Waals radii of the amino acids (B0.3-0.4 nm) compared to the dimension of the α-HL nanopore constriction region (1.4 nm). See, J. J. Kasianowicz, E. Brandin, D. Branton and D. W. Deamer, Proc. Natl. Acad. Sci. U.S.A., 1996, 93, 13770-13773, J. Wilson, L. Sloman, Z. He and A. Aksimentiev, Adv. Funct. Mater., 2016, 26, 4830-4838, and E. Kennedy, Z. Dong, C. Tennant and G. Timp, Nat. Nanotechnol., 2016, 11, 968.
As confirmed by the molecular structure modeling results, most NITC derivatives produced current blockades that positively correlate to their spatial size. Although exceptions (i.e. D, and F) were observed, the general mechanism of electrical sensing of individual amino acids is to increase their spatial size by derivatization to promote interactions with the nanopore sensing region, thereby improving the signal-to-noise ratio. Further investigation using other types of biological nanopores is warranted to probe possible explanations for these exceptions, such as intra- and inter-molecular interactions between analytes. See, X. Y. Zhang, C. C. Gong, O. U. Akakuru, Z. Q. Su, A. G. Wu and G. Wei, Chem. Soc. Rev., 2019, 48, 5564-5595.
In conclusion, we have demonstrated a derivatization strategy for reliable identification of individual amino acids using an α-HL nanopore. Compared to bare amino acids, both NDA-derived and NITC-derived amino acids can produce obvious fingerprint signals when translocating the nanopore. Furthermore, the amino acids S, Y, D, E, H, G, A, F, and V can be effectively identified with improved discriminatory power by NITC derivatization. While promising results were obtained for 9 amino acids, we do recognize the overall complexity of identifying all 20 amino acids. In particular, we need to develop more effective conjugation chemistry to derivatize proline, which does not have a primary amino group like the others. Additionally, an in-depth analysis is needed to better understand the interactions between amino acid derivatives and the lumen surface of biological nanopores. Novel characterizations of stochastic signals other than the traditional blockade and dwell time must be explored, and more advanced data analysis technology (e.g. machine learning, pattern recognition, etc.) should be applied to achieve even greater resolution.
Nonetheless, compared to previous efforts on amino acid identification using nanopores, the presented method is readily applicable to future protein sequencing. We provide a “sequencing-by-hydrolysis” method, in which a nanopore will be used to identify the N-terminal amino acid of each peptide fragment in a peptide ladder generated from a peptide analyte, and then bioinformatics methods will be applied to reconstitute its full-length sequence. See, H. Y. Zhong, Y. Zhang, Z. H. Wen and L. Li, Nat. Biotechnol., 2004, 22, 1291-1296.
All patents, patent applications, published applications, and publications, databases, websites and other published materials referred to throughout the entire disclosure herein, unless noted otherwise, are incorporated herein by reference in their entirety.
Various modifications and variations of the described methods of the disclosure will be apparent to those skilled in the art without departing from the scope and spirit of the disclosure. Although the disclosure has been described in connection with specific embodiments, it will be understood that it is capable of further modifications and that the disclosure as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the disclosure that are obvious to those skilled in the art are intended to be within the scope of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure come within known customary practice within the art to which the disclosure pertains and may be applied to the essential features herein before set forth.
This disclosure was made with government support under K22 AI136686 by the National Institute of Health. The government may have certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 63029764 | May 2020 | US |
