Characterizing nucleic acid and amino acid sequences in silico

Abstract

The invention relates generally to molecular biology and bioinformatics. In particular, the invention related to in silico methods of characterizing nucleic acid and amino acid sequences. In addition, the invention relates to identifying conserved residues and producing an evolutionary profile.

Description

FIELD OF THE INVENTION

[0002] The invention relates generally to molecular biology and bioinformatics. In particular, the invention relates to in silico methods of characterizing nucleic acid and amino acid sequences. In addition, the invention relates to identifying conserved residues and producing an evolutionary profile.

BACKGROUND

[0003] The interaction between proteins is fundamental to a broad spectrum of biological function including regulation of metabolic pathways, immunological responses, DNA replications, and protein synthesis (Gough et al., Bioinformatics, 17: 455-60 (2001)). Current techniques in elucidating protein-protein interactions and protein functions are tedious and often involved experimental techniques such as the yeast-two-hybrid system. In addition, while efforts from the Human Genome Project and other sequencing efforts continue to identify genes, the function of the genes and resulting proteins is lacking. For example, the budding yeast Sacchromyces cerevisiae was fully sequenced in April 1996 however, one-third of the predicted open reading frames (ORFs) are still classified as unknown function (Uetz et al., Nature, 403: 623-627 (2000)). In contrast to current techniques, the present invention provides the means to identify protein-protein interactions based on primary sequence and structure. In another embodiment, the invention provides a method of identifying the same using solely primary sequence.

DESCRIPTION OF FIGURES

[0004] FIG. 1 depicts the flowchart of the methodology described herein.

[0005] FIG. 2 shows a diagram of a system for identifying protein-protein relationships.

[0006] FIG. 3 shows a flow diagram describing a method for identifying protein-protein relationships.

[0007] FIG. 4 shows the protein relationship of an amino acid biosynthesis protein.

SUMMARY OF INVENTION

[0008] The invention relates to a method of identifying a protein-protein interaction and protein function in silico. Such method includes: i.) compiling a database of sequences; ii.) comparing a reference sequence to at least one sequence in the database; iii.) identifying conserved residues between the reference sequence and at least one sequence in the database sequences; iv.) comparing the conserved residues between the reference sequence and the database sequences; and v.) identifying the protein-protein relationship based on the comparison.

[0009] In another embodiment the invention relates to: i.) compiling a database of sequences; ii.) comparing a reference sequence to the database;identifying conserved residues between the reference sequence and the database sequences; iii.) compiling the conserved residues across the reference sequence and the database sequences into a positional vector; iv.) calculating a score for each positional vector; v.) grouping the positional vectors into evolutionary clusters based on the score; vi.) comparing each conserved residue between the reference sequence and database sequences of the evolutionary cluster; vii.) establishing a score at each conserved residue position across the evolutionary cluster; viii.) forming an evolutionary profile based on the scores of the evolutionary clusters; and ix.) based on the evolutionary profile, identifying the protein-protein relationship.

[0010] In yet another emobodiment, the invention relates to using the structure the primary sequence to identify the protein-protein interaction and function including: i.) compiling a database of sequences; ii.) comparing a reference sequence to at least one sequence in the database; iii.) identifying conserved residues between the reference sequence and at least one sequence in the database sequences; iv.) compiling conserved residues based on location in structure; v.) forming an evolutionary cluster based on the compiled residues; vi.) comparing each conserved residue between the reference sequence and database sequences of the evolutionary cluster; vii.) establishing a score at each conserved residue position across the evolutionary cluster; viii.) forming an evolutionary profile based on the scores of the evolutionary clusters; and ix.) based on the evolutionary profile, identifying the protein-protein relationship.

DETAILED DESCRIPTION OF THE INVENTION

[0011] Definitions

[0012] To aid in the understanding of the specification and claims, the following definitions are provided.

[0013] Protein-protein interaction or protein-protein relationship generally refer to at least two proteins that are functionally related which form part of the same or similar biochemical pathway or biological process. The terms also refer to proteins that share similar structure.

[0014] Assembled-sequence refers to a sequence composed of at least one non-overlapping segment of sequence. The sequence can comprise, for example, nucleic acid or amino acid sequences.

[0015] Conserved Residue refers to a substitution in an amino acid sequence which does not substantially alter the polypeptide's structure and/or activity. These conserved residues are ones which may not be important for protein acitivity or a substitution of an amino acid with a residue having similar properties (acidity, charge, polarity, etc.) such that the substitution may be a critical amino acid but it does not substantially alter the structure and/or activity. Examples of such conserved residues include, but are not limited to Table 1.

TABLE 1
Original Residue Conservative Substitution(s)
Ala              Ser
Arg              Lys
Asn              Gln, His
Asp              Glu
Cys              Ser
Gln              Asn
Glu              Asp
Gly              Pro
His              Asn, Gln
Ile              Leu, Val
Leu              Ile, Val
Lys              Arg, Gln, Glu
Met              Len, Ile
Phe              Met, Leu, Tyr
Ser              Thr
Thr              Ser
Trp              Tyr
Tyr              Trp, Phe
Val              Ile, Leu

[0016] Conserved Bases refer nucleic acid bases which encode for conserved amino acid bases. Conserved Bases also refer to nucleic acid substitutions which do not alter the resulting amino acid sequence. For example, a codon consist of three (3) nucleic acid bases which encode for one (1) amino acid. Due to the degeneracy of the code, one (1) or more of the three (3) nucleic acid bases could be substituted or altered and encode for the same amino acid. For example as in the codons that encode for valine which include GUU, GUA, GUC.

[0017] Conserved sequence refers to at least six (6) bases for nucleic acid sequences or two (2) residues for amino acid sequences which are conserved between two (2) or more sequences.

[0018] Positional Vector refers to a mathematical description of the conserved residues of the reference sequence and the database sequences. In some instances, the positional vector refers to a matrix that is linearized into one-dimensional vector of length N2, where N is the number of sequences in the alignment.

[0019] Evolutionary Cluster refers to at least two (2) conserved residues between the reference sequence and the database sequences.

[0020] Evolutionary Profile refers to the mathematical description of an evolutionary cluster based on the statistical scoring of conserved residues.

[0021] The invention described herein relates to a means of elucidating protein-protein relationships and protein function in silico. One could identify proteins which are essential or proteins which are involved in essential pathway of an organism. This type of information could be used to identify certain drug targets. For example, a protein that is identified as being essential in a bacteria or pathogen could be used in antibiotic screening and discovery. In addition, for instance, an interactor in the inflammatory system of a human could be identified and used in screening agents that prevent inflammatory diseases such as asthma. Additionally, the invention can help target certain active site regions to aid in drug discovery. Other uses include helping group a protein-coding gene into its proper functional unit, and providing 3-D structure validation by showing high homology to proteins of known structure.

[0022] The invention provides a method of compiling nucleic acid and amino acid sequences (See FIG. 1). The compilation could include nucleic acids or amino acid sequences. Preferably, the nucleic acid sequences contains an open reading frames (ORFs). Even more preferably, the sequences include amino acid sequences of the ORFs. The sequences can be derived from eukaryotes, prokaryotes or a combinations thereof. In one embodiment the database contains bacteria sequences. For example the bacteria could be E. coli.

[0023] FIG. 2 shows a flow diagram describing a method for identifying protein-protein relationships. In referring to FIG. 2, in step 100, a database containing the structure of proteins can also be created by the following: A subset of the PDB database (Berman, et al., Nucleic Acid Res., 28:235-242 (2000)) containing a set of unique structures with 99% but more prefereably <95% sequence identity is created and those structures separated into individual chains. The sequence identity cutoff used in the creation of the subset database can also be set to 20% but more preferably <30% identity to further lower the redundancy in the dataset.

[0024] The reference sequence is the sequence in which the analysis is performed to determine the protein-protein interactions. The reference sequence could be a nucleic acid sequence or an amino acid sequence. The reference sequence could also be combination thereof. Preferably, the reference sequence contains a partial open reading frame or is an expressed sequence tag (EST). More preferably, the sequence contains a full length open reading frame. If the reference sequence is a nucleic acid sequence, the reference sequence would contain at least 10 bases. Alternatively, the reference sequence could be an amino acid sequences, containing at least 5 residues. There may also be more than one reference sequence used in the methodology.

[0025] In step 110, in comparing the reference sequence to the database, various algorithms are used including optimal alignment of sequences for comparison can be conducted, e.g., by the local homology algorithm of Smith & Waterman (Adv. Appl. Math., 2:482 (1981)), by the homology alignment algorithm of Needleman & Wunsch (J. Mol. Biol., 48:443 (1970)), by the search for similarity method of Pearson & Lipman (Proc. Natl. Acad. Sci. USA, 85:2444 (1988)) by computerized implementations of these algorithms (CLUSTAL, GAP, BESTFIT, FASTA (Pearson Proc. Tanl. Acad. Sci. USA, 85(8):2444-2448 (1998)) and TFASTA in the Wisconsin Genetics Softare Package, Genteics Computer Group, 575 Science Drive, Madison, Wis.) and BLITZ (Altschul J. Mol. Biol., 215:403-410 (1990)), or by manual alignment and visual inspection.

[0026] For example, in comparing a reference sequence which is an amino acid sequence, the algorithm could be BLASTP2. In comparing a reference sequence which is a nucleic acid sequence, BLASTN could be used. In addition, in comparing a reference sequence which is an amino acid sequence, TBLASTN2 could be used against a database of translated nucleotide sequences. In determining the database sequence(s) which contain conserved sequences, a subset of database sequences are chosen based on parameters that one skilled in the art would recognize for such a comparison. For instance in using the the BLAST algorithm, statistical methods can be used to judge the significance of possible matches. The statistical significance of an alignment score is described by the probability, P, of obtaining a higher score when the sequences are shuffled. One way to compute P value threshold is to first consider the total number of sequence comparisons that are to be performed. For example, if there are N proteins in E. coli and M in all other genomes this number is N×M. If a comparison of this number of random sequences would result in one pair to yield a P value of 1/ NM by chance this then is set as the threshold. In the preferred emobodiment, the P-value is <10−5.

[0027] In step 120, in identifying the conserved residues or bases between the database sequences and the reference sequence, additional algorithms are utilized, including but not limited to, Clustal W program (Thompson, Nuc. Acids Res., 22:4673-4680 (1994); Higgins, Methods Enzymol, 266:383-402 (1996)) and PileUp (Devereaux, Nuc. Acids Res., 12:387-395 (1984)). Variations can also be used, such as CLUSTAL X (Jeanmougin, Trends Biochem Sci., 23:403-405 (1998); Thompson, Nucleic Acids Res., 25:4876-4882 (1997)). In the preferred embodiment, the sequences are aligned automatically in a multiple sequence alignment using ClustalW using small gap penalties with the following parameters “-PWGAPOPEN=2.5-GAPOPEN =2.5-PWGAPEXT=0-GAPEXT=0-MAXDIV=20%”. One skilled in the art would appreciate that these parameters can be varied empirically depending on the subset of sequences obtained in the comparison. Every base or residue position from the reference sequence is then scored and compared to all other sequences using an evolutionary scoring matrix such as BLOSUM 62 (Henikoff, Proc. Natl. Acad. Sci. USA, 89:100915 (1989)) or PAM250 and a conservation score for each position is defined as the sum of all scores.

[0028] High scoring residues (“conserved residues” for amino acids and “conserved bases” for nucleic acids that encode for conserved residues) are selected and clustered based on structural or evolutionary space as follows: In cases in which the structure of the chain is available, the atoms surrounding the conserved residue or base are investigated further. The distance from the could be 1 Angstrom (Å) or up to 10 Å. More preferably, the distance from the conserved residue or base in between 3 to 7 Å. This area surrounding the conserved residue or base is called the sphere. If there are bases or residues within the sphere which are also conserved, the atoms are grouped together. These residues can then be clustered using an algorithm biased towards surface/exposed clusters by counting all atoms within 1 to 20 Å, more preferable 3 to 7Å from each residue, and concentrating on those residues with fewer atoms around them. This type of clustering identifies important structural motifs where there is some evolutionary pressure to conserve structural and functional characteristics.

[0029] When analyzing sequences with no known structures, a positional vector is formed or compiled. In step 130, a matrix of values is calculated using BLOSUM, PAM or Dayhoff algorithm of all possible pairwise comparisons using the evolutionary scoring matrix amongst all species for each high-scoring residue (“conserved residue”) from the original sequence. The matrix is then linearized into N2-dimensional vectors, also known as “positional vectors”, where N is the number of sequences in the alignment, and calculated correlation and euclidean distances amongst all those vectors. Positional vector pairs that have a correlation coefficient of anywhere from 1 to >0.5 and/or were deemed as close in euclidean space are grouped together into “evolutionary clusters.” The exact metric for the euclidean cutoff is determined at runtime with the sole requirement being that the euclidean cutoff is a positive number, to ensure that it is possible to group vectors based on euclidean distance, in addition to correlation. Other distance methods could also be used, such as correlation distance or Manhattan correlation. Initial groups identified in this manner are then merged if they have members in common and their correlation/euclidean distance is above the desired threshold. The merging of these positional vectors into evolutionary clusters can also be achieved using other techniques such as K-means clustering, Self-Organizing maps or Hierarchical Clustering.

[0030] In analyzing these evolutionary clusters a pairwise scores are calculated amongst species consisting of the sum of the BLOSUM scores or its equivant for every position in the evolutionary cluster to create a symmetrical N×N matrix. In step 140, this matrix is then linearized using the top half to create or compile a N(N−1)/2 dimensional vector known as an “evolutionary profile”. The evolutionary profile is then normalized to between 0 and 100 with “−100” indicating a missing value. One of ordinary skill in the art would recognize that other normalization methods may be employed as long as they result in a common range for all vectors from a dataset.

[0031] This procedure is repeated for every sequence and structure in the dataset. Each evolutionary profile (10-20,000 from an average dataset) is then compared against all other profiles in the dataset and those that have a correlation coefficient of 0.1 or higher, but more preferably 0.5 or higher (or 0.5 or lower) are ranked based on their euclidean distance from the sequence of interest. One skilled in the art would be able to identify other changes and “cutoffs” which could be varied to relax or increase the stringency of the clustering. In addition, other clustering methods such K-means, Hierarchical clustering, Self-Organizing Maps or Principal Component Analysis can be used to analyze the data.

[0032] In step 150, to identify the protein-protein relationship of the reference sequence, the evolutionary profiles which result from the ranking using euclidean distances, absolute correlation, Manhattan distance, or other related means, are compiled. The closest “neighbors” based on the compilation of reference sequence's evolutionary profile to the database sequence's evolutionary profiles are then listed on a file and/or written to a database for further analysis and validation.

[0033] By examining its closest neighbors, the reference sequence protein-protein interaction can be inferred. In addition, the function and pathways of the reference sequence can also be determined by the compilation. For example, if an ORF has neighbors that are consistently involved in translation, the inference is that it is related to the translation machinery. For more information, see Example 1.

[0034] In another embodiment, the invention compiles a database of sequences. Preferably, the database contains sequence information for many different organisms. The reference sequence is compared with the sequences of the database. Segments from the sequences of the database, which closely match the reference sequence, are identified. Preferably, segments are identified using BLAST. Even more preferably, all the non-overlapping segments are identified for each organism in the database. Usually the number of segments identified for an organism depends on the nature of the sequences. For example, if the sequence information of the organism contains introns, non coding sequences, then the BLAST algorithm will return multiple segments for each organism. However, if the sequence information does not contain any introns then only one segment may be identified per organism. The non-overlapping segments are assembled to form an assembled-sequence to be used for analysis. Preferably, one assembled-sequence is created for each organism of the database. The invention identifies the conserved residues between the reference sequence and the assembled-sequences. Subsequently, the conserved residues are compared between the reference sequence and the assembled-sequences. Preferably, an evolutionary profile is created from the comparison. Based on the comparison, protein-protein relationships are identified. Preferably, the protein-protein relationships are identified by comparing evolutionary profiles. FIG. 3 shows a flow diagram describing a method for identifying protein-protein relationships. In referring to FIG. 3, the system 200 includes several modules: a database 210, which contains a plurality of sequences; a comparison module 220, which compares a reference sequence with sequences in the database 210; an identification module 230, which identifies conserved residues shared between the reference sequence and sequences in the database 210; a computational module 240, which computes a value based on the number of conserved residues shared between two sequences; a profiler module 250, which assembles a series of values to form an evolutionary profile, a storage module 260, which stores the evolutionary profile; and a selector module 270, which identifies protein-protein relationships by comparing two evolutionary profiles. Although the system 200 is described to run on a UNIX workstation, the system 200 can be run on other machines including the Macintosh, Windows, Linux, Sun, DOS and others.

[0035] A system 200 used for identifying at least one protein-protein relationship will now be described with reference to FIG. 3. The system 200 comprises a database 210 containing a plurality of sequences. The database 210 may include either nucleic acid or amino acid sequences. Preferably, the nucleic acid sequences contain open reading frames (ORFs). Even more preferably, the sequences could include amino acid sequences of the ORFs. The sequences can be derived from eukaryotes, prokaryotes or a combination thereof. The database 210 may contain ORFs from prokayotes and eukayotes. The database 210 may contain ORFs from bacteria. The database 210 may contain ORFs from E. coli.

[0036] In the comparison module 220, a reference sequence may be compared with sequences in the database 210 of sequences. Different algorithms may be used to compare the reference sequence with the sequences of the database 210. The comparison module 220 may incorporate different algorithms when analyzing the sequences of the database to find the closest matching sequence. Preferably, sequences of multiple organisms are stored in the database and comparison module 220 finds the closest matching sequence for each organism. For example, if the database 210 contained the entire sequence for 87 different organisms, the comparison module would return a subset containing the 87 closest matching sequences with one matching sequence for each organism. The algorithm used to compare the sequences and identify the closest match could be any one of the following BLAST, FASTA, or its equivalent. The algorithm may weigh sequence matches differently based on the nature of the sequence.

[0037] After the subset of the sequences is identified, an identification module 230 identifies conserved residues between the reference sequence and subset of the sequences. Preferably, the identification module 230 identifies only the most highly conserved residues of the subset. More preferably, the residues should not be all weighted equally. The algorithm used to identify the conserved residues includes ClustalW, PileUp or its equivalent. Preferably, the algorithm performs a pair wise comparison between the residues for the members of the subset. Even more preferably, as a result of the pair wise comparisons, the scoring of the residues is calculated using BLOSUM, PAM, Dayhoff, or its equivalent. A table containing the weight of different comparisons may be used to score each pair wise comparison. The conserved residue positions with the highest score beyond a certain cutoff will be saved for further analysis.

[0038] Once the conserved residues are identified, the computational module 240 computes a value based on all certain conserved residues shared between the reference sequence and sequences of the subset. The set of conserved residues to be analyzed is called an evolutionary cluster. A reference sequence may contain more than one evolutionary cluster. Based on comparing the evolutionary clusters between two different sequences, a value is computed. Preferably, a value is computed by comparing a sequence with another sequence in the subset of sequences. As a result, the computational module 240 would calculate up to N2 values based on N where N is the number of sequences in the subset of sequences. Preferably, N is equivalent to the number of organisms in the database 210 of sequences. Even more preferably, the computational module would create a matrix of N×N values.

[0039] A profiler module 250 creates an evolutionary profile grouping together a set of values into a vector. The values that make up the evolutionary profile are based on the calculations of conserved residues of the evolutionary cluster shared between a first sequence of a subset of sequences of the database 210 with a second sequence of the subset. Preferably, the evolutionary profile consists of a vector of values up to a length of N2 where N is the number of sequences in the subset. More preferably, assuming the calculations are redundant, the evolutionary profile will consist of values from the top half of the matrix to form a linearized vector of up to N*(N−1)/2 in length.

[0040] A storage module 260 stores the evolutionary profile for comparison with other evolutionary profiles. The storage module may reside in RAM, in hard disk, or on another networked computer.

[0041] A selector module 270 identifies protein-protein relationships based on a comparison between the evolutionary profile and other evolutionary profiles. The comparison measures the correlation coefficient between the evolutionary profile and the other evolutionary profiles. If the correlation coefficient reaches a cutoff point, for example 0.5, that evolutionary profile is saved. The saved evolutionary profiles are ranked utilizing the Euclidean distance or the Manhattan distance from the evolutionary profile. Based on the Euclidean distance or the Manhattan distance, the reference sequence protein-protein relationship can be inferred.

EXAMPLE

[0042] The example as set forth herein are meant to exemplify the various aspects of the present invention and are not intended to limit the invention in any way.

[0043] Following the flowchart in FIG. 1, a database was compiled containing FASTA sequences consisting of all stop-stop open reading frames (ORFs) from sixty-four fully sequenced organisms and all predicted proteins from S. cerevisiae, C. elegans and D.melanogaster was constructed from public and propietary genomes including Genome Therapeutics Corporation PathoGenome™ Database (genomecorp.com) and TIGR's microbial database (tigr.org/tdb/mdb/mdb.html). This resulted in a database consisting of 67 organisms. This database is expected to grow as more complete genomes become available. The current database contains the following species (followed by number of ORFs).

A AEOLICUS           8089
A BAUMANNII          12038
A FULGIDUS           13476
A FUMIGATUS          175346
A PERNIX             10173
A ANTHRACIS          18565
B BURGDORFERI        1253
B FRAGILIS           26977
B HALODURURANS       18307
B SP                 1401
B SUBTILIS           4099
C ACETOBUTYLICUM     12353
C ALBICANS           44462
C CRESCENTUS         35325
C ELEGANS            18424
C JEJUNI             5779
C MURIDARUM          818
C PNEUMONIAE         1529
C PSITTACI           1388
C TEPIDUM            449
C TRACHOMATIS        887
D ETHENEGENES        9066
D MELANOGASTER       18032
D RADIODURANS        28101
D VULGARIS           39046
E CLOACAE            33289
E COLI               4257
E FAECALIS           17477
E FAECIUM            11346
G SULFURREDUCENS     36705
H INFLUENZA          1706
H PYLORI             2994
H SP                 21444
K PNEUMONIAE         40677
L LACTIS             7229
M AVIUM              55417
M CATARRHALIS        7426
M GENITALIUM         467
M JANNASCHII         1714
M LEPRAE             27491
M LOTI               68565
M PNEUMONIAE         677
M THERMOATOTROPHICUM 1868
M TUBERCULOSIS       3881
N MENINGIDITIS       20677
P ABYSSI             7714
P AERUGINOSA         60987
P HORIKOSHII         6762
P MIRABILIS          11191
P MULTOCIDA          7738
P PUTIDA             53095
R PROWAZEKII         834
S AUREUS             7108
S CEREVISIAE         6401
S EPIDERMIDIS        5949
S PCC                17839
S PNEUMONIAE         13560
S PUTREFACIENS       19096
S PYOGENES           5558
T ACIDOPHILUM        9201
T FERROOXIDANS       26286
T MARITIMA           12700
T PALLIDU            1030
T VOLCANIUM          6548
U UREALYTICUM        611
V CHOLERA            3781
X FASTIDIOSA         18374

[0044] Using TBLASTN2 as the comparison algorithm, one could then compare the reference sequence against a database of sequences of different organisms. When multiple sequences from an organism have segments that show a similarity to a segment of the reference sequence, one can assemble the non-overlapping segments into a larger sequence to maximize the similarity to the reference sequence. This method is especially beneficial for sequences of organisms that contain introns. In addition, one can then minimize the chance of problems caused by missasembled regions within the sequences. The reference database used in this case contains 85 different genomes from Prokaryotes and Eukaryotes available in the public domain in addition to those included in the Pathogenome™ Database.

[0045] The list of species included the following (shown as the first letter of the Genus plus up to the first five characters from the species name:

[0046] AAEOLI

[0047] ABAUMA

[0048] AFULGI

[0049] AFUMIG

[0050] APERNI

[0051] ATHALI

[0052] ATUMEF

[0053] BANTHR

[0054] BBURGD

[0055] BFRAGI

[0056] BHALOD

[0057] BSPAPS

[0058] BSUBTI

[0059] CACETO

[0060] CALBIC

[0061] CCRESC

[0062] CELEGA

[0063] CJEJUN

[0064] CMURID

[0065] CNEOFO

[0066] CPNEUM

[0067] CPSITT

[0068] CTEPID

[0069] CTRACH

[0070] DETHEN

[0071] DMELAN

[0072] DRADIO

[0073] DVULGA

[0074] ECLOAC

[0075] ECOLI_

[0076] ECUNIC

[0077] EFAECA

[0078] EFAECI

[0079] GSULFU

[0080] HINFLU

[0081] HPYLOR

[0082] HSAPIE

[0083] HSP

[0084] KPNEUM

[0085] LINNOC

[0086] LLACTI

[0087] LMONOC

[0088] MAVIUM

[0089] MCATAR

[0090] MGENIT

[0091] MJANNA

[0092] MLEPRA

[0093] MLOTI

[0094] MMUSCU

[0095] MPNEUM

[0096] MPULMO

[0097] MTHERM

[0098] MTUBER

[0099] NCRASS

[0100] NMENIN

[0101] PABYSS

[0102] PAERUG

[0103] PFALCI

[0104] PHORIK

[0105] PMIRAB

[0106] PMULTO

[0107] PPUTID

[0108] RCONOR

[0109] RPROWA

[0110] SAUREU

[0111] SCEREV

[0112] SEPIDE

[0113] SMELIL

[0114] SPCC68

[0115] SPNEUM

[0116] SPOMBE

[0117] SPUTRE

[0118] SPYOGE

[0119] SSOLFA

[0120] STOKOD

[0121] STYPHI

[0122] TACIDO

[0123] TFERRO

[0124] TMARIT

[0125] TPALLI

[0126] TVOLCA

[0127] UUREAL

[0128] VCHOLE

[0129] XFASTI

[0130] YPESTI

[0131] Tables 2 through 6 show sample results from the methodology described herein. The dataset comprises ˜1500 randomly selected ORFs from E.coli. The ORFs were compared against each other using Evolutionary Profiles and the closest euclidean neighbors for each ORF ranked by distance. Annotation information was extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG); (Nucleic Acids Res. 28, 29-34 (2000)).

TABLE 2
tufB, factor; Proteins—translation and,
protein chain elongation factor EF-Tu
1.  tufA, factor; Proteins—translation and, protein chain elongation
    factor EF-Tu
2.  pyrG, enzyme; Central intermediary metabolism:, CTP synthetase
3.  fliI, enzyme; Surface structures, flagellum-specific ATP synthase
4.  infB, factor; Proteins—translation and, protein chain initiation factor
    IF-2
5.  rplB, structural component; Ribosomal proteins—, 50S ribosomal
    subunit protein L2
6.  hflB, enzyme; Degradation of proteins peptides, sigma32 integral
    membrane peptidase
7.  atpA, enzyme; ATP-proton motive force, membrane-bound ATP
    synthase F1 sector
8.  thrS, enzyme; Aminoacyl tRNA synthetases tRNA, threonine tRNA
    synthetase
9.  lysS, enzyme; Aminoacyl tRNA synthetases tRNA, lysine tRNA
    synthetase
10. lysU, enzyme; Aminoacyl tRNA synthetases tRNA, lysine tRNA
    synthetase; heat shock
11. fusA, factor; Proteins—translation and, GTP-binding protein chain
    elongation factor
12. atpD, enzyme; ATP-proton motive force, membrane-bound ATP
    synthase F1 sector
13. ftsY, membrane; Cell division, cell division membrane protein
14. eno, enzyme; Energy metabolism carbon: Glycolysis, enolase
15. rpsK, structural component; Ribosomal proteins—, 30S ribosomal
    subunit protein S11
16. selB, factor; Proteins—translation and, selenocysteinyl-tRNA-specific
    translation
17. metG, enzyme; Aminoacyl tRNA synthetases tRNA, methionine
    tRNA synthetase
18. lepA, factor; Proteins—translation and, GTP-binding elongation
    factor may be inner
19. ygjD, putative enzyme; Not classified, putative O-sialoglycoprotein
    endopeptidase
20. rpsE, structural component; Ribosomal proteins—, 30S ribosomal
    subunit protein S5
21. valS, enzyme; Aminoacyl tRNA synthetases tRNA, valine tRNA
    synthetase
22. rpsL, structural component; Ribosomal proteins—, 30S ribosomal
    subunit protein S12
23. rpoB, enzyme; RNA synthesis modification DNA, RNA polymerase
    beta subunit
24. rplC, structural component; Ribosomal proteins—, 50S ribosomal
    subunit protein L3
25. aspS, enzyme; Aminoacyl tRNA synthetases tRNA, aspartate tRNA
    synthetase
26. rpoC, enzyme; RNA synthesis modification DNA, RNA polymerase
    beta prime subunit
27. rplM, structural component; Ribosomal proteins—, 50S

[0132]

TABLE 3
fliG, structural component; Surface structures,
flagellar biosynthesis component of motor
1.  flgB, structural component; Surface structures, flagellar biosynthesis
    cell-proximal portion of
2.  fliC, structural component; Surface structures, flagellar biosynthesis;
    flagellin filament
3.  fliG, structural component; Surface structures, flagellar biosynthesis
    component of motor
4.  fliN, structural component; Surface structures, flagellar biosynthesis
    component of motor
5.  fliM, structural component; Surface structures, flagellar biosynthesis
    component of motor
6.  flgE, structural component; Surface structures, flagellar biosynthesis
    hook protein
7.  flgF, structural component; Surface structures, flagellar biosynthesis
    cell-proximal portion
8.  flgL, structural component; Surface structures, flagellar biosynthesis;
    hook-filament junction
9.  flgC, structural component; Surface structures, flagellar biosynthesis
    cell-proximal portion of
10. motA, phenotype; Chemotaxis and mobility, proton conductor
    component of motor; no effect
11. cheA, enzyme; Chemotaxis and mobility, sensory transducer kinase
    between chemo-signal
12. ybiS, orf; Unknown, orf hypothetical protein
13. fliR, putative enzyme; Surface structures, flagellar biosynthesis
14. fhiA, putative enzyme; Surface structures, flagellar biosynthesis
15. ycgB, putative factor; Not classified, putative sporulation protein
16. ybgA, orf; Unknown, orf hypothetical protein
17. aer, regulator; Degradation of small molecules:, aerotaxis sensor
    receptor flavoprotein
18. tar, regulator; Chemotaxis and mobility, methyl-accepting chemotaxis
    protein II
19. ynhG, orf; Unknown, orf hypothetical protein
20. btuB, membrane; Outer membrane constituents, outer membrane
    receptor for transport of vitamin

[0133]

TABLE 4
rep [DNA-replication repair,
rep helicase, single-stranded DNA dependent]
1.  uvrD, DNA—replication repair, DNA-dependent ATPase I and
    helicase II
2.  ruvB, DNA—replication repair, Holliday junction helicase subunit A;
    branch
3.  ybeX, putative transport; Not classified, putative transport protein
4.  polA, DNA—replication repair, DNA polymerase
    I 3′—5′ polymerase 5′—
5.  mfd, DNA—replication repair, transcription-repair coupling factor;
    mutation
6.  murF, Murein sacculus peptidoglycan, D-alanine:D-alanine-adding
    enzyme
7.  thdF, Detoxification, GTP-binding protein in thiophene and furan
8.  yhdG, Not classified, putative dehydrogenase
9.  mraY, Murein sacculus peptidoglycan, phospho-N-acetylmuramoyl-
    pentapeptide
10. yqcB, hypothetical protein
11. sfhB, Not classified, suppressor of ftsH mutation
12. yceC, hypothetical protein
13. yjfG, Not classified, putative ligase
14. yabO, hypothetical protein
15. ddlA, Murein sacculus peptidoglycan, D-alanine-D-alanine ligase A
16. murE, Murein sacculus peptidoglycan, meso-diaminopimelate-adding
    enzyme
17. rnc, Degradation of RNA, RNase III ds RNA
18. gyrB, DNA—replication repair, DNA gyrase subunit B type II
    topoisomerase
19. ddlB, Murein sacculus peptidoglycan, D-alanine-D-alanine ligase B
    affects cell
20. rpoS, Global regulatory functions, RNA polymerase sigma S
    (sigma38) factor
21. dnaX, DNA—replication repair, DNA polymerase III tau and gamma
    subunits; DNA

[0134]

TABLE 5
trpC, enzyme; Amino acid biosynthesis: Tryptophan,
N-(5-phosphoribosyl)anthranilate isomerase
1.  trpA, enzyme; Amino acid biosynthesis: Tryptophan, tryptophan
    synthase alpha protein
2.  trpB, enzyme; Amino acid biosynthesis: Tryptophan, tryptophan
    synthase beta protein
3.  trpE, enzyme; Amino acid biosynthesis: Tryptophan, anthranilate
    synthase component I
4.  pabB, enzyme; Biosynthesis of cofactors carriers:, p-aminobenzoate
    synthetase component I
5.  hisB, enzyme; Amino acid biosynthesis: Histidine,
    imidazoleglycerolphosphate dehydratase and
6.  ilvD, enzyme; Amino acid biosynthesis: Isoleucine, dihydroxyacid
    dehydratase
7.  hisC, enzyme; Amino acid biosynthesis: Histidine, histidinol-
    phosphate aminotransferase
8.  edd, enzyme; Central intermediary metabolism:, 6-phosphogluconate
    dehydratase
9.  hisD, enzyme; Amino acid biosynthesis: Histidine,
    L-histidinal:NAD+ oxidoreductase
10. ribH, enzyme; Biosynthesis of cofactors carriers:, riboflavin synthase
    beta chain
11. leuB, enzyme; Amino acid biosynthesis: Leucine, 3-isopropylmalate
    dehydrogenase
12. aroA, enzyme; Amino acid biosynthesis: Chorismate, 5-enolpyruvyl-
    shikimate-3-phosphate synthetase
13. leuD, enzyme; Amino acid biosynthesis: Leucine, isopropylmalate
    isomerase subunit
14. pheA, enzyme; Amino acid biosynthesis: Phenylalanine, chorismate
    mutase-P and prephenate dehydratase
15. argD, enzyme; Amino acid biosynthesis: Arginine, acetylornithine
    deltaaminotransferase
16. goaG, enzyme; Central intermediary metabolism: Pool, 4-amino-
    butyrate aminotransferase
17. ilvC, enzyme; Amino acid biosynthesis: Isoleucine, ketol-acid
    reductoisomerase
18. lysA, enzyme; Amino acid biosynthesis: Lysine, diaminopimelate
    decarboxylase
19. leuA, enzyme; Amino acid biosynthesis: Leucine, 2-isopropylmalate
    synthase
20. leuC, enzyme; Amino acid biosynthesis: Leucine, 3-isopropylmalate
    isomerase (dehydratase)
21. aroE, enzyme; Amino acid biosynthesis: Chorismate, dehydro-
    shikimate reductase
22. glnA, enzyme; Amino acid biosynthesis: Glutamine, glutamine
    synthetase

[0135] FIG. 4 shows rank percentages for all proteins in the dataset with “Amino Acid Biosynthesis”. The data of FIG. 4 also reflects the information of Table 5. We show the percent occurence of a similar annotation at that rank position based on the methodology described herein. For example, for proteins with “Amino Acid Biosynthesis” in their description, other proteins with the same annotation >60% of the time are related, while none of the other annotations we looked at show up at more than 5% frequency.

TABLE 6
narV, enzyme; Energy metabolism carbon: Anaerobic,
cryptic nitrate reductase 2 gamma subunit
1. narV, enzyme; Energy metabolism carbon: Anaerobic, cryptic nitrate
   reductase 2 gamma subunit
2. narI, enzyme; Energy metabolism carbon: Anaerobic, nitrate
   reductase 1 cytochrome b(NR) gamma
3. narJ, enzyme; Energy metabolism carbon: Anaerobic, nitrate
   reductase 1 delta subunit assembly
4. narW, enzyme; Energy metabolism carbon: Anaerobic, cryptic nitrate
   reductase 2 delta subunit
5. narZ, enzyme; Energy metabolism carbon: Anaerobic, cryptic nitrate
   reductase 2 alpha subunit
6. narY, enzyme; Energy metabolism carbon: Anaerobic, cryptic nitrate
   reductase 2 beta subunit
7. narH, enzyme; Energy metabolism carbon: Anaerobic, nitrate
   reductase 1 beta subunit
8. narG, enzyme; Energy metabolism carbon: Anaerobic, nitrate
   reductase 1 alpha subunit

[0136] Table 7 shows representative results from the method using a dataset comprising about 3,700 Saccaromyces cerevisiae genes processed against the genome database containing 85 genomes. This approach used TBLASTN2 to assemble to non-overlapping high-scoring segments from each organism. This example thus shows the protein-protein relationships which result from the invention described herein.

TABLE 7
RPL11A, Ribosomal subunit/Ribosomal subunit/RNA-binding protein
1.  RPL11B, Ribosomal subunit/RNA-binding protein
2.  RPS9A, /Ribosomal subunit/RNA-binding protein
3.  RPL10, /RNA-binding protein/Ribosomal subunit
4.  RAD51, /DNA-binding protein/ATPase
5.  RPS9B, /Ribosomal subunit/RNA-binding protein
6.  RPL15A, /Ribosomal subunit/RNA-binding protein
7.  SCL1, /Proteasome subunit
8.  DMC1, /ATPase/DNA-binding protein
9.  RPL43B, /RNA-binding protein/Ribosomal subunit
10. PRE6, /Proteasome subunit/Proteasome subunit
11. PRE9, /Proteasome subunit
12. PUP2, /Proteasome subunit
13. RPL4A, /Ribosomal subunit/RNA-binding protein
14. RPL4B, /Ribosomal subunit/RNA-binding protein
15. DYS1, /Oxidoreductase
16. RPL19B, /Ribosomal subunit/RNA-binding protein
17. RPS18B, /Ribosomal subunit/Ribosomal subunit/RNA-binding
    protein
18. MCM3, /DNA-binding protein/ATPase/Hydrolase
19. CDC46, /DNA-binding protein/ATPase/Hydrolase
20. RPB10, /RNA polymerase subunit
21. PRE10, /Proteasome subunit/Proteasome subunit
22. RPO21, /Transferase/RNA polymerase subunit/RNA polymerase
    subunit
23. CDC47, /ATPase/Hydrolase/DNA-binding protein
24. PRE8, /Proteasome subunit
25. RPL19A, /Ribosomal subunit/RNA-binding protein
26. RPL43A, /RNA-binding protein/Ribosomal subunit
27. RPS18A, /Ribosomal subunit/RNA-binding protein
28. RPS13, /Ribosomal subunit/RNA-binding protein

[0137] Equivalents

[0138] The disclosure of each of the patents, patent applications, and publications cited in the specification is hereby incorporated by reference herein in its entirety for all purposes.

[0139] Although the invention has been set forth in detail, one skilled in the art will recognize that numerous changes and modifications can be made, and that such changes and modifications may be made without departing from the spirit and scope of the invention.

Claims

1. A method of identifying at least one protein-protein relationship comprising: a. compiling a database of sequences; b. comparing a reference sequence to at least one sequence in the database; c. identifying conserved residues between the reference sequence and at least one sequence in the database; d. comparing the conserved residues between the reference sequence and at least one sequence in the database; and e. identifying the protein-protein relationship based on the comparison.

2. The method of claim 1, wherein the database contains nucleic acids sequences.

3. The method of claim 1, wherein the database contains amino acid sequences.

4. The method of claim 1, wherein the database contains open reading frame sequences.

5. The method of claim 1, wherein the database contains open reading frame sequences from prokaryotes and eukaryotes.

6. The method of claim 1, wherein the database contains open reading frame sequences from bacteria.

7. The method of claim 1, wherein the database contains open reading frame sequences from E. coli.

8. The method of claim 1, wherein comparing the reference sequence to the database includes the algorithm BLAST, FASTA or its equivalent.

9. The method of claim 1, wherein the identifying of conserved residues includes the algorithm ClustalW, PileUp or its equivalent.

10. The method of claim 1, wherein the identifying of conserved residues includes a pairwise comparison of the reference sequence and the database sequences.

11. The method of claim 10, wherein the identifying of conserved residues further comprises scoring the conserved residues using BLOSUM, PAM, Dayhoff or its equivalent.

12. The method of claim 1, wherein the comparing of conserved residues includes measuring Euclidean distances.

13. The method of claim 1, wherein the comparing of conserved residues includes measuring absolute correlation of the conserved residues.

14. A method of identifying at least one protein-protein relationship comprising: a. compiling a database of sequences; b. comparing a reference sequence to at least one sequence in the database; c. identifying conserved residues between the reference sequence and at least one sequence in the database; d. comparing the conserved residues between the reference sequence and at least one sequence in the database; e. grouping the conserved residues; and f. identifying the protein-protein relationship based on the grouping.

15. The method of claim 14, wherein the database contains nucleic acids sequences.

16. The method of claim 14, wherein the database contains amino acid sequences.

17. The method of claim 14, wherein the database contains open reading frame sequences.

18. The method of claim 14, wherein the database contains open reading frame sequences from prokaryotes and eukaryotes.

19. The method of claim 14, wherein the database contains open reading frame sequences from bacteria.

20. The method of claim 14, wherein the database contains open reading frame sequences from E. coli.

21. The method of claim 14, wherein comparing the reference sequence to the database includes the algorithm BLAST, FASTA or its equivalent.

22. The method of claim 14, wherein the identifying of conserved residues includes the algorithm ClustalW, PileUp or its equivalent.

23. The method of claim 14, wherein the identifying of conserved residues includes a pairwise comparison of the reference sequence and the database sequences.

24. The method of claim 23, wherein the identifying of conserved residues further comprises scoring the residues using BLOSUM, PAM, Dayhoff or its equivalent

25. The method of claim 14, wherein the comparing of conserved residues includes measuring Euclidean distances.

26. The method of claim 14, wherein the comparing of conserved residues includes measuring absolute correlation of the conserved residues.

27. The method of claim 14, wherein the grouping includes combining based on Euclidean distance and absolute correlation measurements of the conserved bases.

28. A method of identifying at least one protein-protein relationship comprising: a. compiling a database of sequences; b. comparing a reference sequence to at least one sequence in the database; c. identifying conserved residues between the reference sequence and at least one sequence in the database; d. forming a positional vector containing the conserved residues; e. grouping the positional vectors into evolutionary clusters; f. compiling an evolutionary profile based on the evolutionary clusters; and g. identifying the protein-protein relationship based on the evolutionary profiles.

29. The method of claim 28, wherein the database contains nucleic acids sequences.

30. The method of claim 28, wherein the database contains amino acid sequences.

31. The method of claim 28, wherein the database contains open reading frame sequences.

32. The method of claim 28, wherein the database contains open reading frame sequences from prokaryotes and eukaryotes.

33. The method of claim 28, wherein the database contains open reading frame sequences from bacteria.

34. The method of claim 28, wherein the database contains open reading frame sequences from E. coli.

35. The method of claim 28, wherein comparing the reference sequence to the database includes the algorithm BLAST, FASTA or its equivalent.

36. The method of claim 28, wherein the identifying of conserved residues includes the algorithm ClustalW, PileUp or its equivalent.

37. The method of claim 28, wherein the identifying of conserved residues includes a pairwise comparison of the reference sequence and the database sequence.

38. The method of claim 37, wherein the identifying of conserved residues further comprises a scoring the residues using BLOSUM, PAM, Dayhoff or its equivalent.

39. The method of claim 28, wherein the forming of positional vectors includes compiling conserved residues at each position within the reference sequence.

40. The method of claim 28, wherein the grouping of positional vectors includes measuring Euclidean distances.

41. The method of claim 28, wherein the grouping of positional vectors includes measuring absolute correlation of conserved residues.

42. The method of claim 28, wherein the grouping includes combining positional vectors based on Euclidean distances and absolute correlation of conserved residues.

43. The method of claim 28, wherein the compiling of evolutionary profiles includes a pairwise comparison of each position of the evolutionary cluster.

44. The method of claim 43, further comprising using the algorithm BLOSUM, PAM, Dayhoff or its equivalent

45. A method of identifying at least one protein-protein relationship comprising: a. compiling a database of sequences; b. comparing a reference sequence to at least one sequence in the database; c. identifying conserved residues between the reference sequence and at least one sequence in the database; d. compiling the conserved residues across the reference sequence and the database sequences into a positional vector; e. calculating a score for each positional vector; f. grouping the positional vectors into evolutionary clusters based on the score; g. comparing each conserved residue between the reference sequence and at least one sequence in database of the evolutionary cluster; h. forming an evolutionary profile based on the evolutionary clusters; and i. based on comparing each evolutionary profile, identifying the protein-protein relationship.

46. The method of claim 45, wherein the database contains nucleic acids sequences.

47. The method of claim 45, wherein the database contains amino acid sequences.

48. The method of claim 45, wherein the database contains open reading frame sequences.

49. The method of claim 45, wherein the database contains open reading frame sequences from prokaryotes and eukaryotes.

50. The method of claim 45, wherein the database contains open reading frame sequences from bacteria.

51. The method of claim 45, wherein the database contains open reading frame sequences from E. coli.

52. The method of claim 45, wherein comparing the reference sequence to the database includes the algorithm BLAST, FASTA or its equivalent.

53. The method of claim 45, wherein the identifying of conserved residues includes the algorithm ClustalW, PileUp or its equivalent.

54. The method of claim 45, wherein calculating the score for each positional vector includes a pairwise comparison of the reference sequence and the database sequences.

55. The method of claim 54, wherein calculating the score for each positional vector further comprising comparing conserved residues using BLOSUM, PAM, Dayhoff or its equivalent.

56. The method of claim 45, wherein the grouping of positional vectors includes measuring Euclidean distances.

57. The method of claim 45, wherein the grouping of positional vectors includes measuring absolute correlation of the conserved residues.

58. The method of claim 45, wherein the grouping includes combining the positional vectors based on Euclidean distance and absolute correlation of the conserved residues.

59. The method of claim 45, wherein the comparing of conserved residues includes a pairwise comparison of each residue at each position of the evolutionary cluster whereby each database sequence and reference sequence is compared to each other.

60. The method of claim 59, further comprising using the algorithm BLOSUM, PAM, Dayhoff or its equivalent.

61. A method of identifying at least one protein-protein relationship comprising: a. compiling a database of sequences; b. comparing a reference sequence to at least one sequence in the database; c. identifying conserved residues between the reference sequence and at least one sequence in the database; d. compiling the conserved residues across the reference sequence and at least one sequence in the database into a positional vector; e. calculating a score for each positional vector; f. grouping the positional vectors into evolutionary clusters based on the score; g. comparing each conserved residue between the reference sequence and at least one sequence in database of the evolutionary cluster; h. establishing a score at each conserved residue position across the evolutionary cluster; i. forming an evolutionary profile based on the scores of the evolutionary clusters; and j. based on the evolutionary profile, identifying the protein-protein relationship.

62. The method of claim 61, wherein the database contains nucleic acids sequences.

63. The method of claim 61, wherein the database contains amino acid sequences.

64. The method of claim 61, wherein the database contains open reading frame sequences.

65. The method of claim 61, wherein the database contains open reading frame sequences from prokaryotes and eukaryotes.

66. The method of claim 61, wherein the database contains open reading frame sequences from bacteria.

67. The method of claim 61, wherein the database contains open reading frame sequences from E. coli.

68. The method of claim 61, wherein comparing the reference sequence to the database includes the algorithm BLAST, FASTA or its equivalent.

69. The method of claim 61, wherein the identifying of conserved residues includes the algorithm ClustalW, PileUp or its equivalent.

70. The method of claim 61, wherein calculating the score for each positional vector includes a pairwise comparison of the reference sequence and the database sequences.

71. The method of claim 61, wherein calculating the score for each positional vector further comprising comparing conserved residues using BLOSUM, PAM, Dayhoff or its equivalent.

72. The method of claim 61, wherein the grouping of positional vectors includes measuring Euclidean distances.

73. The method of claim 61, wherein the grouping of positional vectors includes measuring absolute correlation of the conserved residues.

74. The method of claim 61, wherein the grouping includes combining the positional vectors based on Euclidean distance and absolute correlation of the conserved residues.

75. The method of claim 61, wherein the comparing of conserved residues includes a pairwise comparison of each residue at each position of the evolutionary cluster whereby each database sequence and reference sequence is compared to each other.

76. The method of claim 75, further comprising using the algorithm BLOSUM, PAM, Dayhoff or its equivalent.

77. A method of identifying at least one protein-protein relationship comprising: a) compiling a database of sequences; b) comparing a reference sequence to at least one sequence in the database; c) identifying conserved residues between the reference sequence and at least one sequence in the database; d) compiling conserved residues based on location in structure; e) forming an evolutionary cluster based on the compiled residues; f) comparing each conserved residue between the reference sequence and at least one sequence in the database of the evolutionary cluster; g) establishing a score at each conserved residue position across the evolutionary cluster; h) forming an evolutionary profile based on the scores of the evolutionary clusters; and i) based on the evolutionary profile, identifying the protein-protein relationship.

78. The method of claim 77, wherein the database contains nucleic acids sequences.

79. The method of claim 77, wherein the database contains amino acid sequences.

80. The method of claim 77, wherein the database contains open reading frame sequences.

81. The method of claim 77, wherein the database contains open reading frame sequences from prokaryotes and eukaryotes.

82. The method of claim 77, wherein the database contains open reading frame sequences from bacteria.

83. The method of claim 77, wherein the database contains open reading frame sequences from E. coli.

84. The method of claim 77, wherein comparing the reference sequence to the database includes the algorithm BLAST, FASTA or its equivalent.

85. The method of claim 77, wherein the identifying of conserved residues includes the algorithm ClustalW, PileUp or its equivalent.

86. The method of claim 77, wherein the compiling of conserved residues is based on the location between the conserved residues measured in Ångstroms.

87. The method in claim 86, wherein the location distance between residues is 3 to 7 Ångstroms.

88. The method of claim 77, wherein the comparing of conserved residues includes a pairwise comparison of each residue at each position of the evolutionary cluster whereby each database sequence and reference sequence is compared to each other.

89. The method of claim 88, further comprising using the algorithm BLOSUM, PAM, Dayhoff or its equivalent.

90. A method of identifying at least one protein-protein relationship comprising: a. compiling a database of sequences; b. comparing a reference sequence to at least one sequence in the database; c. identifying at least one segment of a sequence within a set of sequences of the database; d. assembling a set of segments to create an assembled-sequence; e. identifying conserved residues between the reference sequence and at least one assembled-sequence in a set of assembled-sequences; f. comparing the conserved residues between the reference sequence and at least one assembled-sequence in the set of assembled-sequences; and g. identifying the protein-protein relationship based on the comparison.

91. A system of identifying at least one protein-protein relationship comprising: a database of a plurality of sequences; a Comparison module used to compare a reference sequence to at least one sequence in the database of sequences; an Identification module to identify conserved residues between the reference sequence and at least one sequence in the database of sequences; a Calculation module used to compare the conserved residues between the reference sequence and at least one sequence in the database of sequences; and an Selector module to identify the protein-protein relationship based on the comparison.

92. The system of claim 91, wherein the database contains nucleic acids sequences.

93. The system of claim 91, wherein the database contains amino acid sequences.

94. The system of claim 91, wherein the database contains open reading frame sequences.

95. The system of claim 91, wherein the database contains open reading frame sequences from prokaryotes and eukaryotes.

96. The system of claim 91, wherein the database contains open reading frame sequences from bacteria.

97. The system of claim 91, wherein the database contains open reading frame sequences from E. coli.

98. The system of claim 91, wherein the comparison module further comprises using the algorithm BLAST, FASTA or its equivalent.

99. The system of claim 91, wherein the identification module further comprises identifying of conserved residues using the algorithm ClustalW, PileUp or its equivalent.

100. The system of claim 91 further comprising a profiler module to calculate an evolutionary profile.

101. The system of claim 91 further comprising a storage module to store evolutionary profiles.

102. A computer readable medium, which when executed by a microprocessor, performs a method of identifying at least one protein-protein relationship comprising: a. compiling a database of sequences; b. comparing a reference sequence to at least one sequence in the database; c. identifying conserved residues between the reference sequence and at least one sequence in the database; d. comparing the conserved residues between the reference sequence and at least one sequence in the database; and e. identifying the protein-protein relationship based on the comparison.