Abstract
The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.
[1] Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N. and Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 28 (2000) 235–242. Search in Google Scholar
[2] Ginalski, K., von Grotthuss, M., Grishin, N.V. and Rychlewski, L. Detecting distant homology with Meta-BASIC. Nucleic Acids Res. 32 (2004) W576–W581. 10.1093/nar/gkh370Search in Google Scholar PubMed PubMed Central
[3] Sprinzak, E., Sattath, S. and Margalit, H. How reliable are experimental protein-protein interaction data? J. Mol. Biol. 327 (2003) 919–923. Search in Google Scholar
[4] von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S. and Bork, P. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417 (2002) 399–403. Search in Google Scholar
[5] Carter, P., Lesk, V.I., Islam, S.A. and Sternberg, M.J. Protein-protein docking using 3D-Dock in rounds 3, 4, and 5 of CAPRI. Proteins 60 (2005) 281–288. 10.1002/prot.20571Search in Google Scholar PubMed
[6] Fariselli, P., Pazos, F., Valencia, A. and Casadio, R. Prediction of protein-protein interaction sites in heterocomplexes with neural networks. Eur. J. Biochem. 269 (2002) 1356–1361. Search in Google Scholar
[7] Hoskins, J., Lovell, S. and Blundell, T.L. An algorithm for predicting protein-protein interaction sites: Abnormally exposed amino acid residues and secondary structure elements. Protein Sci. 15 (2006) 1017–1029. Search in Google Scholar
[8] Jothi, R., Cherukuri, P.F., Tasneem, A. and Przytycka, T.M. Coevolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions. J. Mol. Biol. 362 (2006) 861–875. Search in Google Scholar
[9] Tan, K., Shlomi, T., Feizi, H., Ideker, T. and Sharan, R. Transcriptional regulation of protein complexes within and across species. Proc. Natl. Acad. Sci. USA 104 (2007) 1283–1288. Search in Google Scholar
[10] Teichmann, S.A. Principles of protein-protein interactions. Bioinformatics 18 Suppl 2 (2002) S249. 10.1093/bioinformatics/18.suppl_2.S249Search in Google Scholar PubMed
[11] Cusick, M.E., Klitgord, N., Vidal, M. and Hill, D.E. Interactome: gateway into systems biology. Hum. Mol. Genet. 14 Spec No. 2 (2005) R171–R181. 10.1093/hmg/ddi335Search in Google Scholar PubMed
[12] Goh, C.S. and Cohen, F.E. Co-evolutionary analysis reveals insights into protein-protein interactions. J. Mol. Biol. 324 (2002) 177–192. Search in Google Scholar
[13] Sharan, R., Ideker, T., Kelley, B., Shamir, R. and Karp, R.M. Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. J. Comput. Biol. 12 (2005) 835–846. Search in Google Scholar
[14] Barash, Y., Elidan, G., Kaplan, T. and Friedman, N. CIS: compound importance sampling method for protein-DNA binding site p-value estimation. Bioinformatics 21 (2005) 596–600. Search in Google Scholar
[15] Sharan, R., Suthram, S., Kelley, R.M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R.M. and Ideker, T. Conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. USA 102 (2005) 1974–1979. Search in Google Scholar
[16] Kelley, B.P., Sharan, R., Karp, R.M., Sittler, T., Root, D.E., Stockwell, B.R. and Ideker, T. Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc. Natl. Acad. Sci. USA 100 (2003) 11394–11399. Search in Google Scholar
[17] Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U. and Eisenberg, D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32 (2004) D449–D451. 10.1093/nar/gkh086Search in Google Scholar PubMed PubMed Central
[18] Alfarano, C., Andrade, C.E., Anthony, K., Bahroos, N., Bajec, M., Bantoft, K., Betel, D., Bobechko, B., Boutilier, K., Burgess, E., Buzadzija, K., Cavero, R., D’Abreo, C., Donaldson, I., Dorairajoo, D., Dumontier, M.J., Dumontier, M.R., Earles, V., Farrall, R., Feldman, H., Garderman, E., Gong, Y., Gonzaga, R., Grytsan, V., Gryz, E., Gu, V., Haldorsen, E., Halupa, A., Haw, R., Hrvojic, A., Hurrell, L., Isserlin, R., Jack, F., Juma, F., Khan, A., Kon, T., Konopinsky, S., Le, V., Lee, E., Ling, S., Magidin, M., Moniakis, J., Montojo, J., Moore, S., Muskat, B., Ng, I., Paraiso, J.P., Parker, B., Pintilie, G., Pirone, R., Salama, J.J., Sgro, S., Shan, T., Shu, Y., Siew, J., Skinner, D., Snyder, K., Stasiuk, R., Strumpf, D., Tuekam, B., Tao, S., Wang, Z., White, M., Willis, R., Wolting, C., Wong, S., Wrong, A., Xin, C., Yao, R., Yates, B., Zhang, S., Zheng, K., Pawson, T., Ouellette, B.F. and Hogue, C.W. The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 33 (2005) D418–D424. Search in Google Scholar
[19] Chatr-Aryamontri, A., Ceol, A., Palazzi, L.M., Nardelli, G., Schneider, M.V., Castagnoli, L. and Cesareni, G. MINT: the Molecular INTeraction database. Nucleic Acids Res. 35 (2007) D572–574. 10.1093/nar/gkl950Search in Google Scholar PubMed PubMed Central
[20] Hermjakob, H., Montecchi-Palazzi, L., Lewington, C., Mudali, S., Kerrien, S., Orchard, S., Vingron, M., Roechert, B., Roepstorff, P., Valencia, A., Margalit, H., Armstrong, J., Bairoch, A., Cesareni, G., Sherman, D. and Apweiler, R. IntAct: an open source molecular interaction database. Nucleic Acids Res. 32 (2004) D452–D455. Search in Google Scholar
[21] Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I., Bridge, A., Derow, C., Dimmer, E., Feuermann, M., Friedrichsen, A., Huntley, R., Kohler, C., Khadake, J., Leroy, C., Liban, A., Lieftink, C., Montecchi-Palazzi, L., Orchard, S., Risse, J., Robbe, K., Roechert, B., Thorneycroft, D., Zhang, Y., Apweiler, R. and Hermjakob, H. IntAct - open source resource for molecular interaction data. Nucleic Acids Res. 35 (2007) D561–565. 10.1093/nar/gkl958Search in Google Scholar PubMed PubMed Central
[22] Peri, S., Navarro, J.D., Amanchy, R., Kristiansen, T.Z., Jonnalagadda, C.K., Surendranath, V., Niranjan, V., Muthusamy, B., Gandhi, T.K., Gronborg, M., Ibarrola, N., Deshpande, N., Shanker, K., Shivashankar, H.N., Rashmi, B.P., Ramya, M.A., Zhao, Z., Chandrika, K.N., Padma, N., Harsha, H.C., Yatish, A.J., Kavitha, M.P., Menezes, M., Choudhury, D.R., Suresh, S., Ghosh, N., Saravana, R., Chandran, S., Krishna, S., Joy, M., Anand, S.K., Madavan, V., Joseph, A., Wong, G.W., Schiemann, W.P., Constantinescu, S.N., Huang, L., Khosravi-Far, R., Steen, H., Tewari, M., Ghaffari, S., Blobe, G.C., Dang, C.V., Garcia, J.G., Pevsner, J., Jensen, O.N., Roepstorff, P., Deshpande, K.S., Chinnaiyan, A.M., Hamosh, A., Chakravarti, A. and Pandey, A. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 13 (2003) 2363–2371. Search in Google Scholar
[23] Hoffmann, R. and Valencia, A. Implementing the iHOP concept for navigation of biomedical literature. Bioinformatics 21 Suppl 2 (2005) ii252–ii258. 10.1093/bioinformatics/bti1142Search in Google Scholar PubMed
[24] von Mering, C., Jensen, L.J., Snel, B., Hooper, S.D., Krupp, M., Foglierini, M., Jouffre, N., Huynen, M.A. and Bork, P. STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33 (2005) D433–D437. 10.1093/nar/gki005Search in Google Scholar PubMed PubMed Central
[25] Finn, R.D., Mistry, J., Schuster-Bockler, B., Griffiths-Jones, S., Hollich, V., Lassmann, T., Moxon, S., Marshall, M., Khanna, A., Durbin, R., Eddy, S.R., Sonnhammer, E.L. and Bateman, A. Pfam: clans, web tools and services. Nucleic Acids Res. 34 (2006) D247–D251. 10.1093/nar/gkj149Search in Google Scholar PubMed PubMed Central
[26] Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25 (1997) 3389–3402. Search in Google Scholar
[27] Jones, D.T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292 (1999) 195–202. Search in Google Scholar
[28] Tatusov, R.L., Fedorova, N.D., Jackson, J.D., Jacobs, A.R., Kiryutin, B., Koonin, E.V., Krylov, D.M., Mazumder, R., Mekhedov, S.L., Nikolskaya, A.N., Rao, B.S., Smirnov, S., Sverdlov, A.V., Vasudevan, S., Wolf, Y.I., Yin, J.J. and Natale, D.A. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4 (2003) 41. 10.1186/1471-2105-4-41Search in Google Scholar
[29] Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M. and Sherlock, G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25 (2000) 25–29. Search in Google Scholar
[30] Camon, E., Barrell, D., Lee, V., Dimmer, E. and Apweiler, R. The Gene Ontology Annotation (GOA) Database - an integrated resource of GO annotations to the UniProt Knowledgebase. In Silico Biol 4 (2004) 5–6. Search in Google Scholar
[31] Camon, E., Magrane, M., Barrell, D., Binns, D., Fleischmann, W., Kersey, P., Mulder, N., Oinn, T., Maslen, J., Cox, A. and Apweiler, R. The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res. 13 (2003) 662–672. Search in Google Scholar
[32] Mao, X., Cai, T., Olyarchuk, J.G. and Wei, L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 21 (2005) 3787–3793. Search in Google Scholar
[33] Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. and Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32 (2004) D277–D280. 10.1093/nar/gkh063Search in Google Scholar
[34] Jeong, H., Mason, S.P., Barabasi, A.L. and Oltvai, Z.N. Lethality and centrality in protein networks. Nature 411 (2001) 41–42. Search in Google Scholar
[35] Sprinzak, E., Altuvia, Y. and Margalit, H. Characterization and prediction of protein-protein interactions within and between complexes. Proc. Natl. Acad. Sci. USA 103 (2006) 14718–14723. Search in Google Scholar
[36] Brown, K.R. and Jurisica, I. Online predicted human interaction database. Bioinformatics 21 (2005) 2076–2082. Search in Google Scholar
[37] Cagney, G., Uetz, P. and Fields, S. High-throughput screening for protein-protein interactions using two-hybrid assay. Methods Enzymol. 328 (2000) 3–14. Search in Google Scholar
[38] Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., Qureshi-Emili, A., Li, Y., Godwin, B., Conover, D., Kalbfleisch, T., Vijayadamodar, G., Yang, M., Johnston, M., Fields, S. and Rothberg, J.M. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403 (2000) 623–627. Search in Google Scholar
[39] Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M. and Sakaki, Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA 98 (2001) 4569–4574. Search in Google Scholar
[40] Ito, T., Chiba, T. and Yoshida, M. Exploring the protein interactome using comprehensive two-hybrid projects. Trends Biotechnol. 19 (2001) S23–S27. 10.1016/S0167-7799(01)00005-1Search in Google Scholar
[41] Rigaut, G., Shevchenko, A., Rutz, B., Wilm, M., Mann, M. and Seraphin, B. A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 17 (1999) 1030–1032. Search in Google Scholar
[42] Bader, G.D. and Hogue, C.W. Analyzing yeast protein-protein interaction data obtained from different sources. Nat. Biotechnol. 20 (2002) 991–997 Search in Google Scholar
[43] Chen, T., Jaffe, J.D. and Church, G.M. Algorithms for identifying protein cross-links via tandem mass spectrometry. J. Comput. Biol. 8 (2001) 571–583. Search in Google Scholar
[44] Ito, T., Ota, K., Kubota, H., Yamaguchi, Y., Chiba, T., Sakuraba, K. and Yoshida, M. Roles for the two-hybrid system in exploration of the yeast protein interactome. Mol. Cell. Proteomics 1 (2002) 561–566. Search in Google Scholar
[45] McDermott, J., Bumgarner, R. and Samudrala, R. Functional annotation from predicted protein interaction networks. Bioinformatics 21 (2005) 3217–3226. Search in Google Scholar
[46] Morrison, J.L., Breitling, R., Higham, D.J. and Gilbert, D.R. A lock-and-key model for protein-protein interactions. Bioinformatics 22 (2006) 2012–2019. Search in Google Scholar
[47] Schweitzer, B., Predki, P. and Snyder, M. Microarrays to characterize protein interactions on a whole-proteome scale. Proteomics 3 (2003) 2190–2199. Search in Google Scholar
[48] Tong, A.H., Drees, B., Nardelli, G., Bader, G.D., Brannetti, B., Castagnoli, L., Evangelista, M., Ferracuti, S., Nelson, B., Paoluzi, S., Quondam, M., Zucconi, A., Hogue, C.W., Fields, S., Boone, C. and Cesareni, G. A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science 295 (2002) 321–324. Search in Google Scholar
[49] Walhout, A.J., Boulton, S.J. and Vidal, M. Yeast two-hybrid systems and protein interaction mapping projects for yeast and worm. Yeast 17 (2000) 88–94. Search in Google Scholar
[50] Wehr, M.C., Laage, R., Bolz, U., Fischer, T.M., Grunewald, S., Scheek, S., Bach, A., Nave, K.A. and Rossner, M.J. Monitoring regulated protein-protein interactions using split TEV. Nat. Methods 3 (2006) 985–993. Search in Google Scholar
[51] Wu, X., Zhu, L., Guo, J., Zhang, D.Y. and Lin, K. Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations. Nucleic Acids Res. 34 (2006) 2137–2150. Search in Google Scholar
[52] Yarmush, M.L. and Jayaraman, A. Advances in proteomic technologies. Annu. Rev. Biomed. Eng. 4 (2002) 349–373. 10.1146/annurev.bioeng.4.020702.153443Search in Google Scholar
[53] Marcotte, E.M., Pellegrini, M., Ng, H.L., Rice, D.W., Yeates, T.O. and Eisenberg, D. Detecting protein function and protein-protein interactions from genome sequences. Science 285 (1999) 751–753. Search in Google Scholar
[54] Troyanskaya, O.G., Dolinski, K., Owen, A.B., Altman, R.B. and Botstein, D. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl. Acad. Sci. USA 100 (2003) 8348–8353. Search in Google Scholar
[55] Lu, L., Lu, H. and Skolnick, J. MULTIPROSPECTOR: an algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins 49 (2002) 350–364. Search in Google Scholar
[56] Smith, G.R. and Sternberg, M.J. Prediction of protein-protein interactions by docking methods. Curr. Opin. Struct. Biol. 12 (2002) 28–35. Search in Google Scholar
[57] Wodak, S.J. and Mendez, R. Prediction of protein-protein interactions: the CAPRI experiment, its evaluation and implications. Curr. Opin. Struct. Biol. 14 (2004) 242–249. 10.1016/j.sbi.2004.02.003Search in Google Scholar
[58] Jones, S. and Thornton, J.M. Analysis of protein-protein interaction sites using surface patches. J. Mol. Biol. 272 (1997) 121–132. Search in Google Scholar
[59] Lo Conte, L., Chothia, C. and Janin, J. The atomic structure of protein-protein recognition sites. J. Mol. Biol. 285 (1999) 2177–2198. Search in Google Scholar
[60] Glaser, F., Steinberg, D.M., Vakser, I.A. and Ben-Tal, N. Residue frequencies and pairing preferences at protein-protein interfaces. Proteins 43 (2001) 89–102. Search in Google Scholar
[61] Hu, Z., Ma, B., Wolfson, H. and Nussinov, R. Conservation of polar residues as hot spots at protein interfaces. Proteins 39 (2000) 331–342. Search in Google Scholar
[62] DeLano, W.L. Unraveling hot spots in binding interfaces: progress and challenges. Curr. Opin. Struct. Biol. 12 (2002) 14–20. 10.1016/S0959-440X(02)00283-XSearch in Google Scholar
[63] Pellegrini, M., Marcotte, E.M. and Yeates, T.O. A fast algorithm for genome-wide analysis of proteins with repeated sequences. Proteins 35 (1999) 440–446. Search in Google Scholar
[64] Eisen, M.B., Spellman, P.T., Brown, P.O. and Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95 (1998) 14863–14868. Search in Google Scholar
[65] Sprinzak, E. and Margalit, H. Correlated sequence-signatures as markers of protein-protein interaction. J. Mol. Biol. 311 (2001) 681–692. Search in Google Scholar
[66] Bock, J.R. and Gough, D.A. Predicting protein-protein interactions from primary structure. Bioinformatics 17 (2001) 455–460. Search in Google Scholar
[67] Gallet, X., Charloteaux, B., Thomas, A. and Brasseur, R. A fast method to predict protein interaction sites from sequences. J. Mol. Biol. 302 (2000) 917–926. Search in Google Scholar
[68] Ofran, Y. and Rost, B. Predicted protein-protein interaction sites from local sequence information. FEBS Lett. 544 (2003) 236–239. Search in Google Scholar
[69] Jones, S. and Thornton, J.M. Principles of protein-protein interactions. Proc. Natl. Acad. Sci. USA 93 (1996) 13–20. Search in Google Scholar
[70] Nooren, I.M. and Thornton, J.M. Diversity of protein-protein interactions. Embo J. 22 (2003) 3486–3492. Search in Google Scholar
[71] Nooren, I.M. and Thornton, J.M. Structural characterisation and functional significance of transient protein-protein interactions. J. Mol. Biol. 325 (2003) 991–1018. Search in Google Scholar
[72] Bahadur, R.P., Chakrabarti, P., Rodier, F. and Janin, J. A dissection of specific and non-specific protein-protein interfaces. J. Mol. Biol. 336 (2004) 943–955. Search in Google Scholar
[73] Ofran, Y. and Rost, B. Analysing six types of protein-protein interfaces. J. Mol. Biol. 325 (2003) 377–387. Search in Google Scholar
[74] Saha, R.P., Bahadur, R.P. and Chakrabarti, P. Interresidue contacts in proteins and protein-protein interfaces and their use in characterizing the homodimeric interface. J. Proteome Res. 4 (2005) 1600–1609. Search in Google Scholar
[75] Bordner, A.J. and Abagyan, R. Statistical analysis and prediction of protein-protein interfaces. Proteins 60 (2005) 353–366. Search in Google Scholar
[76] Neuvirth, H., Raz, R. and Schreiber, G. ProMate: a structure based prediction program to identify the location of protein-protein binding sites. J. Mol. Biol. 338 (2004) 181–199. Search in Google Scholar
[77] Chung, J.L., Wang, W. and Bourne, P.E. Exploiting sequence and structure homologs to identify protein-protein binding sites. Proteins 62 (2006) 630–640. Search in Google Scholar
[78] Valdar, W.S. and Thornton, J.M. Protein-protein interfaces: analysis of amino acid conservation in homodimers. Proteins 42 (2001) 108–124. Search in Google Scholar
[79] Yao, H., Kristensen, D.M., Mihalek, I., Sowa, M.E., Shaw, C., Kimmel, M., Kavraki, L. and Lichtarge, O. An accurate, sensitive, and scalable method to identify functional sites in protein structures. J. Mol. Biol. 326 (2003) 255–261. Search in Google Scholar
[80] Aloy, P., Querol, E., Aviles, F.X. and Sternberg, M.J. Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking. J. Mol. Biol. 311 (2001) 395–408. Search in Google Scholar
[81] Berezin, C., Glaser, F., Rosenberg, J., Paz, I., Pupko, T., Fariselli, P., Casadio, R. and Ben-Tal, N. ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics 20 (2004) 1322–1324. Search in Google Scholar
[82] Caffrey, D.R., Somaroo, S., Hughes, J.D., Mintseris, J. and Huang, E.S. Are protein-protein interfaces more conserved in sequence than the rest of the protein surface? Protein Sci. 13 (2004) 190–202. Search in Google Scholar
[83] Yan, C., Dobbs, D. and Honavar, V. A two-stage classifier for identification of protein-protein interface residues. Bioinformatics 20 Suppl 1 (2004) I371–I378. 10.1093/bioinformatics/bth920Search in Google Scholar PubMed
[84] Porollo, A. and Meller, J. Prediction-based fingerprints of protein-protein interactions. Proteins 66 (2006) 630–645. Search in Google Scholar
[85] Koike, A. and Takagi, T. Prediction of protein-protein interaction sites using support vector machines. Protein Eng. Des. Sel. 17 (2004) 165–173. Search in Google Scholar
[86] Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F. and Gerstein, M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302 (2003) 449–453. Search in Google Scholar
[87] Liu, X., Zhang, L.M. and Zheng, W.M. Prediction of protein secondary structure based on residue pairs. J. Bioinform. Comput. Biol. 2 (2004) 343–352. Search in Google Scholar
[88] Zhang, L.V., Wong, S.L., King, O.D. and Roth, F.P. Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 5 (2004) 38. Search in Google Scholar
[89] Zhou, H.X. and Shan, Y. Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins 44 (2001) 336–343 Search in Google Scholar
[90] Hesse, H. and Hoefgen, R. On the way to understand biological complexity in plants: S-nutrition as a case study for systems biology. Cell. Mol. Biol. Lett. 11 (2006) 37–56. Search in Google Scholar
[91] Hsieh, C.J., Chen, M.J., Liao, Y.L. and Liao, T.N. Polymorphisms of the uridine-diphosphoglucuronosyltransferase 1A1 gene and coronary artery disease. Cell. Mol. Biol. Lett. 13 (2008) 1–10. Search in Google Scholar
[92] Huang, B., Chu, C.H., Chen, S.L., Juan, H.F. and Chen, Y.M. A proteomics study of the mung bean epicotyl regulated by brassinosteroids under conditions of chilling stress. Cell. Mol. Biol. Lett. 11 (2006) 264–278. Search in Google Scholar
[93] Knizewski, L., Steczkiewicz, K., Kuchta, K., Wyrwicz, L., Plewczynski, D., Kolinski, A., Rychlewski, L. and Ginalski, K. Uncharacterized DUF1574 leptospira proteins are SGNH hydrolases. Cell Cycle 7 (2008) 542–544. Search in Google Scholar
[94] Korohoda, W. and Wilk, A. Cell electrophoresis - a method for cell separation and research into cell surface properties. Cell. Mol. Biol. Lett. 13 (2008) 312–326. Search in Google Scholar
[95] Li, J., Ji, C., Zheng, H., Fei, X., Zheng, M., Dai, J., Gu, S., Xie, Y. and Mao, Y. Molecular cloning and characterization of a novel human gene containing 4 ankyrin repeat domains. Cell. Mol. Biol. Lett. 10 (2005) 185–193. Search in Google Scholar
[96] Liu, S.J., Zhang, D.Q., Sui, X.M., Zhang, L., Cai, Z.W., Sun, L.Q., Liu, Y.J., Xue, Y. and Hu, G.F. The inhibition of in vivo tumorigenesis of osteosarcoma (OS)-732 cells by antisense human osteopontin RNA. Cell. Mol. Biol. Lett. 13 (2008) 11–19. Search in Google Scholar
[97] Miyamato, T., Sato, H., Yogev, L., Kleiman, S., Namiki, M., Koh, E., Sakugawa, N., Hayashi, H., Ishikawa, M., Lamb, D.J. and Sengoku, K. Is a genetic defect in Fkbp6 a common cause of azoospermia in humans? Cell. Mol. Biol. Lett. 11 (2006) 557–569. Search in Google Scholar
[98] Wisniewska, A., Draus, J. and Subczynski, W.K. Is a fluid-mosaic model of biological membranes fully relevant? Studies on lipid organization in model and biological membranes. Cell. Mol. Biol. Lett. 8 (2003) 147–159. Search in Google Scholar
[99] Wladyka, B. and Pustelny, K. Regulation of bacterial protease activity. Cell. Mol. Biol. Lett. 13 (2008) 212–229. Search in Google Scholar
[100] Cottage, A., Mullan, L., Portela, M.B., Hellen, E., Carver, T., Patel, S., Vavouri, T., Elgar, G. and Edwards, Y.J. Molecular characterisation of the SAND protein family: a study based on comparative genomics, structural bioinformatics and phylogeny. Cell. Mol. Biol. Lett. 9 (2004) 739–753 Search in Google Scholar
[101] Gronemeyer, H. and Miturski, R. Molecular mechanisms of retinoid action. Cell. Mol. Biol. Lett. 6 (2001) 3–52. Search in Google Scholar
[102] Agoston, V., Cemazar, M., Kajan, L. and Pongor, S. Graph-representation of oxidative folding pathways. BMC Bioinformatics 6 (2005) 19. Search in Google Scholar
[103] Kajan, L., Kertesz-Farkas, A., Franklin, D., Ivanova, N., Kocsor, A. and Pongor, S. Application of a simple likelihood ratio approximant to protein sequence classification. Bioinformatics 22 (2006) 2865–2869. Search in Google Scholar
[104] Kocsor, A., Kertesz-Farkas, A., Kajan, L. and Pongor, S. Application of compression-based distance measures to protein sequence classification: a methodological study. Bioinformatics 22 (2006) 407–412. Search in Google Scholar
[105] Vlahovicek, K., Kajan, L., Agoston, V. and Pongor, S. The SBASE domain sequence resource, release 12: prediction of protein domain-architecture using support vector machines. Nucleic Acids Res. 33 (2005) D223–D225. 10.1093/nar/gki112Search in Google Scholar PubMed PubMed Central
[106] Vlahovicek, K., Kajan, L., Murvai, J., Hegedus, Z. and Pongor, S. The SBASE domain sequence library, release 10: domain architecture prediction. Nucleic Acids Res. 31 (2003) 403–405. 10.1093/nar/gkg098Search in Google Scholar PubMed PubMed Central
[107] von Grotthuss, M., Plewczynski, D., Ginalski, K., Rychlewski, L. and Shakhnovich, E.I. PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics. BMC Bioinformatics 7 (2006) 53. Search in Google Scholar
[108] Wyrwicz, L.S., Koczyk, G., Rychlewski, L. and Plewczynski, D. ProteinSplit: splitting of multi-domain proteins using prediction of ordered and disordered regions in protein sequences for virtual structural genomics. J. Phys. Condens. Matter 19 (2007) 285222. Search in Google Scholar
[109] Grabarkiewicz, T., Grobelny, P., Hoffmann, M. and Mielcarek, J. DFT study on hydroxy acid-lactone interconversion of statins: The case of fluvastatin. Org. Biomol. Chem. 4 (2006) 4299–4306. 10.1039/B612999BSearch in Google Scholar
[110] Grabarkiewicz, T. and Hoffmann, M. Syn- and anti-conformations of 5′- deoxy- and 5′-O-methyl-uridine 2′,3′-cyclic monophosphate. J. Mol. Model. 12 (2006) 205–212. 10.1007/s00894-005-0019-5Search in Google Scholar PubMed
[111] Hoffmann, M., Chrzanowska, M., Hermann, T. and Rychlewski, J. Modeling of purine derivatives transport across cell membranes based on their partition coefficient determination and quantum chemical calculations. J. Med. Chem. 48 (2005) 4482–4486. Search in Google Scholar
[112] Hoffmann, M. and Marciniec, B. Quantum chemical study of the mechanism of ethylene elimination in silylative coupling of olefins. J. Mol. Model. 13 (2007) 477–483. Search in Google Scholar
[113] Hoffmann, M., Plutecka, A., Rychlewska, U., Kucybala, Z., Paczkowski, J. and Pyszka, I. New type of bonding formed from an overlap between pi aromatic and pi C=O molecular orbitals stabilizes the coexistence in one molecule of the ionic and neutral meso-ionic forms of imidazopyridine. J. Phys. Chem. A Mol. Spectrosc. Kinet. Environ. Gen. Theory 109 (2005) 4568–4574. Search in Google Scholar
[114] Hoffmann, M. and Rychlewski, J. Effects of substituting a OH group by a F atom in D-glucose. Ab initio and DFT analysis. J. Am. Chem. Soc. 123 (2001) 2308–2316. Search in Google Scholar
[115] Hoffmann, M., Rychlewski, J., Chrzanowska, M. and Hermann, T. Mechanism of activation of an immunosuppressive drug: azathioprine. Quantum chemical study on the reaction of azathioprine with cysteine. J. Am. Chem. Soc. 123 (2001) 6404–6409. Search in Google Scholar
[116] Plutecka, A., Hoffmann, M., Rychlewska, U., Kucybala, Z., Paczkowski, J. and Pyszka, I. Relationship between structure and photoinitiating abilities of selected bromide salts of 2-oxo-2,3-dihydro-1H-imidazo[1,2-a]pyridine (IMP): influence of the solvent and the substitution in benzaldehyde on the course of its reaction with IMP. Acta Crystallogr. B 62 (2006) 135–142. Search in Google Scholar
[117] Hoffmann, M., Eitner, K., von Grotthuss, M., Rychlewski, L., Banachowicz, E., Grabarkiewicz, T., Szkoda, T. and Kolinski, A. Three dimensional model of severe acute respiratory syndrome coronavirus helicase ATPase catalytic domain and molecular design of severe acute respiratory syndrome coronavirus helicase inhibitors. J. Comput. Aided Mol. Des. 20 (2006) 305–319. Search in Google Scholar
[118] Ostrowski, J., Rubel, T., Wyrwicz, L.S., Mikula, M., Bielasik, A., Butruk, E. and Regula, J. Three clinical variants of gastroesophageal reflux disease form two distinct gene expression signatures. J. Mol. Med. 84 (2006) 872–882. Search in Google Scholar
[119] Paziewska, A., Wyrwicz, L.S., Bujnicki, J.M., Bomsztyk, K. and Ostrowski, J. Cooperative binding of the hnRNP K three KH domains to mRNA targets. FEBS Lett. 577 (2004) 134–140. Search in Google Scholar
[120] Paziewska, A., Wyrwicz, L.S. and Ostrowski, J. The binding activity of yeast RNAs to yeast Hek2p and mammalian hnRNP K proteins, determined using the three-hybrid system. Cell. Mol. Biol. Lett. 10 (2005) 227–235. Search in Google Scholar
[121] von Grotthuss, M., Koczyk, G., Pas, J., Wyrwicz, L.S. and Rychlewski, L. Ligand-Info small-molecule Meta-Database. Comb. Chem. High. Throughput Screen. 7 (2004) 757–761. 10.2174/1386207043328265Search in Google Scholar PubMed
[122] von Grotthuss, M., Pas, J. and Rychlewski, L. Ligand-Info, searching for similar small compounds using index profiles. Bioinformatics 19 (2003) 1041–1042. Search in Google Scholar
[123] von Grotthuss, M., Wyrwicz, L.S. and Rychlewski, L. mRNA cap-1 methyltransferase in the SARS genome. Cell 113 (2003) 701–702. Search in Google Scholar
[124] Wyrwicz, L.S. and Rychlewski, L. Herpes glycoprotein gL is distantly related to chemokine receptor ligands. Antiviral Res. 75 (2007) 83–86. Search in Google Scholar
[125] Zemojtel, T., Frohlich, A., Palmieri, M.C., Kolanczyk, M., Mikula, I., Wyrwicz, L.S., Wanker, E.E., Mundlos, S., Vingron, M., Martasek, P. and Durner, J. Plant nitric oxide synthase: a never-ending story? Trends Plant Sci. 11 (2006) 524–525; author reply 526-528. 10.1016/j.tplants.2006.09.008Search in Google Scholar PubMed
[126] Plewczynski, D., Hoffmann, M., von Grotthuss, M., Ginalski, K. and Rychewski, L. In silico prediction of SARS protease inhibitors by virtual high throughput screening. Chem. Biol. Drug Design 69 (2007) 269–279. Search in Google Scholar
[127] Plewczynski, D., Hoffmann, M., von Grotthuss, M., Knizewski, L., Rychewski, L., Eitner, K. and Ginalski, K. Modelling of potentially promising SARS protease inhibitors. J. Phys. Condens. Matter 19 (2007) 285207. Search in Google Scholar
[128] Feder, M., Pas, J., Wyrwicz, L.S. and Bujnicki, J.M. Molecular phylogenetics of the RrmJ/fibrillarin superfamily of ribose 2′-Omethyltransferases. Gene 302 (2003) 129–138. Search in Google Scholar
[129] Ginalski, K., Pas, J., Wyrwicz, L.S., von Grotthuss, M., Bujnicki, J.M. and Rychlewski, L. ORFeus: Detection of distant homology using sequence profiles and predicted secondary structure. Nucleic Acids Res. 31 (2003) 3804–3807. Search in Google Scholar
[130] Klimek-Tomczak, K., Mikula, M., Dzwonek, A., Paziewska, A., Wyrwicz, L.S., Hennig, E.E. and Ostrowski, J. Mitochondria-associated satellite I RNA binds to hnRNP K protein. Acta Biochim. Pol. 53 (2006) 169–178. Search in Google Scholar
[131] Klimek-Tomczak, K., Wyrwicz, L.S., Jain, S., Bomsztyk, K. and Ostrowski, J. Characterization of hnRNP K protein-RNA interactions. J. Mol. Biol. 342 (2004) 1131–1141. Search in Google Scholar
[132] Pas, J., von Grotthuss, M., Wyrwicz, L.S., Rychlewski, L. and Barciszewski, J. Structure prediction, evolution and ligand interaction of CHASE domain. FEBS Lett. 576 (2004) 287–290. Search in Google Scholar
[133] von Grotthuss, M., Pas, J., Wyrwicz, L., Ginalski, K. and Rychlewski, L. Application of 3D-Jury, GRDB, and Verify3D in fold recognition. Proteins 53 Suppl 6 (2003) 418–423. 10.1002/prot.10547Search in Google Scholar PubMed
[134] von Grotthuss, M., Plewczynski, D., Ginalski, K., Rychlewski, L. and Shakhnovich, E.I. PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics. BMC Bioinformatics 7 (2006) 53. Search in Google Scholar
[135] von Grotthuss, M., Wyrwicz, L.S., Pas, J. and Rychlewski, L. Predicting protein structures accurately. Science 304 (2004) 1597–1599; author reply 1597–1599. Search in Google Scholar
[136] Wyrwicz, L.S., von Grotthuss, M., Pas, J. and Rychlewski, L. How unique is the rice transcriptome? Science 303 (2004) 168; author reply 168. Search in Google Scholar
[137] Plewczynski, D., Jaroszewski, L., Godzik, A., Kloczkowski, A. and Rychlewski, L. Molecular modeling of phosphorylation sites in proteins using a database of local structure segments. J. Mol. Mod. 11 (2005) 431–438. Search in Google Scholar
[138] Plewczynski, D., Tkacz, A., Godzik, A. and Rychlewski, L. A support vector machine approach to the identification of phosphorylation sites. Cell. Mol. Biol. Lett. 10 (2005) 73–89. Search in Google Scholar
[139] Plewczynski, D., Tkacz, A., Wyrwicz, L., Godzik, A., Kloczkowski, A. and Rychlewski, L. Support-vector-machine classification of linear functional motifs in proteins. J. Mol. Mod. 12 (2006) 453–461. Search in Google Scholar
[140] Plewczynski, D., Tkacz, A., Wyrwicz, L.S. and Rychlewski, L. AutoMotif server: prediction of single residue post-translational modifications in proteins. Bioinformatics 21 (2005) 2525–2527. Search in Google Scholar
[141] Plewczynski, D., Tkacz, A., Wyrwicz, L.S., Rychlewski, L. and Ginalski, K. AutoMotif Server for prediction of phosphorylation sites in proteins using support vector machine: 2007 update. J. Mol. Mod. 14 (2008) 69–76. Search in Google Scholar
[142] Plewczynski, D., Slabinski, L., Tkacz, A., Kajan, L., Holm, L., Ginalski, K. and Rychlewski, L. The RPSP: Web server for prediction of signal peptides. Polymer 48 (2007) 5493–5496. Search in Google Scholar
[143] Fernandez-Ballester, G. and Serrano, L. Prediction of protein-protein interaction based on structure. Methods Mol. Biol. 340 (2006) 207–234 Search in Google Scholar
[144] Plewczynski, D., Pas, J., von Grotthuss, M. and Rychlewski, L. Comparison of proteins based on segments structural similarity). Acta Bioch. Pol. 51 (2004) 161–172. Search in Google Scholar
[145] Plewczynski, D., Rychlewski, L., Ye, Y.Z., Jaroszewski, L. and Godzik, A. Integrated web service for improving alignment quality based on segments comparison. BMC Bioinformatics 5 (2004) 98. Search in Google Scholar
[146] Kinch, L.N., Ginalski, K., Rychlewski, L. and Grishin, N.V. Identification of novel restriction endonuclease-like fold families among hypothetical proteins. Nucleic Acids Res. 33 (2005) 3598–3605. Search in Google Scholar
[147] Ginalski, K., Elofsson, A., Fischer, D. and Rychlewski, L. 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19 (2003) 1015–1018. Search in Google Scholar
[148] Ginalski, K. and Rychlewski, L. Detection of reliable and unexpected protein fold predictions using 3D-Jury. Nucleic Acids Res. 31 (2003) 3291–3292. Search in Google Scholar
[149] Bu, D., Zhao, Y., Cai, L., Xue, H., Zhu, X., Lu, H., Zhang, J., Sun, S., Ling, L., Zhang, N., Li, G. and Chen, R. Topological structure analysis of the protein-protein interaction network in budding yeast. Nucleic Acids Res. 31 (2003) 2443–2450. Search in Google Scholar
[150] Sen, T.Z., Kloczkowski, A. and Jernigan, R.L. Functional clustering of yeast proteins from the protein-protein interaction network. BMC Bioinformatics 7 (2006) 355. Search in Google Scholar
[151] Ogmen, U., Keskin, O., Aytuna, A.S., Nussinov, R. and Gursoy, A. PRISM: protein interactions by structural matching. Nucleic Acids Res. 33 (2005) W331–W336. 10.1093/nar/gki585Search in Google Scholar PubMed PubMed Central
[152] Aytuna, A.S., Gursoy, A. and Keskin, O. Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces. Bioinformatics 21 (2005) 2850–2855. Search in Google Scholar
[153] Aloy, P., Bottcher, B., Ceulemans, H., Leutwein, C., Mellwig, C., Fischer, S., Gavin, A.C., Bork, P., Superti-Furga, G., Serrano, L. and Russell, R.B. Structure-based assembly of protein complexes in yeast. Science 303 (2004) 2026-2029. Search in Google Scholar
[154] Aloy, P. and Russell, R.B. Interrogating protein interaction networks through structural biology. Proc. Natl. Acad. Sci. USA 99 (2002) 5896–5901. 10.1073/pnas.092147999Search in Google Scholar PubMed PubMed Central
[155] Aloy, P. and Russell, R.B. InterPreTS: protein interaction prediction through tertiary structure. Bioinformatics 19 (2003) 161–162. Search in Google Scholar
[156] Ben-Hur, A. and Noble, W.S. Kernel methods for predicting protein-protein interactions. Bioinformatics 21 Suppl 1 (2005) i38–46. 10.1093/bioinformatics/bti1016Search in Google Scholar PubMed
[157] Ben-Hur, A. and Noble, W.S. Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinformatics 7 Suppl 1 (2006) S2. 10.1186/1471-2105-7-S1-S2Search in Google Scholar PubMed PubMed Central
[158] Gomez, S.M., Noble, W.S. and Rzhetsky, A. Learning to predict proteinprotein interactions from protein sequences. Bioinformatics 19 (2003) 1875–1881. Search in Google Scholar
[159] Nanni, L. and Lumini, A. An ensemble of K-local hyperplanes for predicting protein-protein interactions. Bioinformatics 22 (2006) 1207–1210. Search in Google Scholar
[160] Sun, S., Zhao, Y., Jiao, Y., Yin, Y., Cai, L., Zhang, Y., Lu, H., Chen, R. and Bu, D. Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm. FEBS Lett. 580 (2006) 1891–1896. Search in Google Scholar
[161] Bordner, A.J. and Abagyan, R.A. Large-scale prediction of protein geometry and stability changes for arbitrary single point mutations. Proteins 57 (2004) 400–413. Search in Google Scholar
[162] Lu, H., Zhu, X., Liu, H., Skogerbo, G., Zhang, J., Zhang, Y., Cai, L., Zhao, Y., Sun, S., Xu, J., Bu, D. and Chen, R. The interactome as a tree-an attempt to visualize the protein-protein interaction network in yeast. Nucleic Acids Res. 32 (2004) 4804–4811. Search in Google Scholar
[163] Plewczynski, D., Spieser, S.A.H. and Koch, U. Assessing different classification methods for virtual screening. J. Chem. Inf. Mod. 46 (2006) 1098–1106. Search in Google Scholar
[164] Plewczynski, D., von Grotthuss, M., Spieser, S.A.H., Rychlewski, L., Wyrwicz, L.S., Ginalski, K. and Koch, U. Target specific compound identification using a support vector machine. Comb. Chem. High Throughput Screen. 10 (2007) 189–196. Search in Google Scholar
[165] Plewczynski, D., Spieser, S.A. and Koch, U. Assessing different classification methods for virtual screening. J. Chem. Inf. Model. 46 (2006) 1098–1106. Search in Google Scholar
[166] Sen, T.Z., Kloczkowski, A., Jernigan, R.L., Yan, C., Honavar, V., Ho, K.M., Wang, C.Z., Ihm, Y., Cao, H., Gu, X. and Dobbs, D. Predicting binding sites of hydrolase-inhibitor complexes by combining several methods. BMC Bioinformatics 5 (2004) 205. Search in Google Scholar
[167] Donald, J.E., Hubner, I.A., Rotemberg, V.M., Shakhnovich, E.I. and Mirny, L.A. CoC: a database of universally conserved residues in protein folds. Bioinformatics 21 (2005) 2539–2540. Search in Google Scholar
[168] Mirny, L.A., Abkevich, V.I. and Shakhnovich, E.I. How evolution makes proteins fold quickly. Proc. Natl. Acad. Sci. USA 95 (1998) 4976–4981. 10.1073/pnas.95.9.4976Search in Google Scholar PubMed PubMed Central
[169] Mirny, L.A. and Shakhnovich, E.I. Universally conserved positions in protein folds: reading evolutionary signals about stability, folding kinetics and function. J. Mol. Biol. 291 (1999) 177–196. Search in Google Scholar
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