Machine Learning Methods for Protein Structure Prediction

IEEE Reviews in Biomedical Engineering

Volumn 1, Issue , 2008, Pages 41-49

  Cheng, Jianlin ^a   Tegge, Allison N ^b   Baldi, Pierre ^c  

^a University of Missouri   (United States)

^b UNIVERSITY OF MISSOURI   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bioinformatics; machine learning; protein folding; protein structure prediction

Indexed keywords

PROTEIN;

ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CHEMICAL STRUCTURE; CHEMISTRY; GENETICS; METHODOLOGY; PROBABILITY; PROTEIN CONFORMATION; REVIEW; SEQUENCE ANALYSIS;

BAYES THEOREM; MARKOV CHAINS; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PROTEIN CONFORMATION; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 77952753917     PISSN: 19373333     EISSN: 19411189     Source Type: Journal    
DOI: 10.1109/RBME.2008.2008239     Document Type: Article

References (123)

1. F. Sanger and E. O. Thompson, “The amino-acid sequence in the glycyl chain of insulin. 1. The identification of lower peptides from partial hy-drolysates,” J. Biochem., vol. 53, no. 3, pp. 353–366, 1953a.
2. F. Sanger and E. O. Thompson, “The amino-acid sequence in the glycyl chain of insulin. II. The investigation of peptides from enzymic hy-drolysates,” J. Biochem., vol. 53, no. 3, pp. 366–374, 1953b.
3. L. Pauling and R. B. Corey, “The pleated sheet, a new layer config-uration of the polypeptide chain,” Proc. Nat. Acad. Sci., vol. 37, pp. 251–256, 1951.
4. L. Pauling, R. B. Corey, and H. R. Branson, “The structure of proteins: Two hydrogenbonded helical configurations of the polypeptide chain,” Proc. Nat. Acad. Sci., vol. 37, pp. 205–211, 1951.
5. J. C. Kendrew, R. E. Dickerson, B. E. Strandberg, R. J. Hart, D. R. Davies, D. C. Phillips, and V. C. Shore, “Structure of myoglobin: A three-dimensional Fourier synthesis at 2_a resolution,” Nature, vol. 185, pp. 422–427, 1960.
6. M. F. Perutz, M. G. Rossmann, A. F. Cullis, G. Muirhead, G. Will, and A. T. North, “Structure of haemoglobin: A three-dimensional Fourier synthesis at 5.5 Angstrom resolution, obtained by x-ray analysis,” Na-ture, vol. 185, pp. 416–422, 1960.
7. K. A. Dill, “Dominant forces in protein folding,” Biochemistry, vol. 31, pp. 7134–7155, 1990.
8. R. A. Laskowski, J. D. Watson, and J. M. Thornton, “From protein structure to biochemical function?,” J. Struct. Funct. Genomics, vol. 4, pp. 167–177, 2003.
9. A. Travers, “DNA conformation and protein binding,” Ann. Rev. Biochem., vol. 58, pp. 427–452, 1989.
10. P. J. Bjorkman and P. Parham, “Structure, function and diversity of class I major histocompatibility complex molecules,” Ann. Rev. Biochem., vol. 59, pp. 253–288, 1990.
11. L. Bragg, The Development of X-Ray Analysis. London, U.K.: G. Bell, 1975.
12. T. L. Blundell and L. H. Johnson, Protein Crystallography. New York: Academic, 1976.
13. K. Wuthrich, NMR of Proteins and Nucleic Acids. New York: Wiley, 1986.
14. E. N. Baldwin, I. T. Weber, R. S. Charles, J. Xuan, E. Appella, M. Yamada, K. Matsushima, B. F. P. Edwards, G. M. Clore, A. M. Gro-nenborn, and A. Wlodawar, “Crystal structure of interleukin 8: Sym-biosis of NMR and crystallography,” Proc. Nat. Acad. Sci., vol. 88, pp. 502–506, 1991.
15. H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne, “The protein data bank,” Nucl. Acids Res., vol. 28, pp. 235–242, 2000.
16. J. M. Chandonia and S. E. Brenner, “The impact of structural genomics: Expectations and outcomes,” Science, vol. 311, pp. 347–351, 2006.
17. C. B. Anfinsen, “Principles that govern the folding of protein chains,” Science, vol. 181, pp. 223–230, 1973.
18. D. Petrey and B. Honig, “Protein structure prediction: Inroads to bi-ology,” Mol. Cell., vol. 20, pp. 811–819, 2005.
19. M. Jacobson and A. Sali, “Comparative protein structure modeling and its applications to drug discovery,” in Annual Reports in Medical Chemistry, J. Overington, Ed. London, U.K.: Academic, 2004, pp. 259–276.
20. B. Rost, J. Liu, D. Przybylski, R. Nair, K. O. Wrzeszczynski, H. Bigelow, and Y. Ofran, “Prediction of protein structure through evolution,” in Handbook of Chemoinformatics - From Data to Knowl-edge, J. Gasteiger and T. Engel, Eds. New York: Wiley, 2003, pp. 1789–1811.
21. P. Baldi and S. Brunak, Bioinformatics: The Machine Learning Ap-proach, 2nd ed. Cambridge, MA: MIT Press, 2001.
22. B. Rost and C. Sander, “Improved prediction of protein secondary structure by use of sequence profiles and neural networks,” Proc. Nat. Acad. Sci., vol. 90, no. 16, pp. 7558–7562, 1993a.
23. B. Rost and C. Sander, “Prediction of protein secondary structure at better than 70% accuracy,” J. Mol. Bio., vol. 232, no. 2, pp. 584–599, 1993b.
24. D. T. Jones, “Protein secondary structure prediction based on position-specific scoring matrices,” J. Mol. Bio., vol. 292, pp. 195–202, 1999b.
25. G. Pollastri, D. Przybylski, B. Rost, and P. Baldi, “Improving the pre-diction of protein secondary structure in three and eight classes using recurrent neural networks and profiles,” Proteins, vol. 47, pp. 228–235, 2002a.
26. B. Rost and C. Sander, “Conservation and prediction of solvent acces-sibility in protein families,” Proteins, vol. 20, no. 3, pp. 216-226,1994.
27. G. Pollastri, P. Baldi, P. Fariselli, and R. Casadio, “Prediction of co-ordination number and relative solvent accessibility in proteins,” Pro-teins, vol. 47, pp. 142–153, 2002b.
28. P. Fariselli, O. Olmea, A. Valencia, and R. Casadio, “Prediction of con-tact maps with neural networks and correlated mutations,” Prot. Eng., vol. 13, pp. 835–843, 2001.
29. G. Pollastri and P. Baldi, “Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners.” Bioinfo., vol. 18. no. Suool 1. pp. S62-S70, 2002.
30. P. Fariselli and R. Casadio, “Prediction of disulfide connectivity in pro-teins,” Bioinfo., vol. 17, pp. 957–964, 2004.
31. A. Vullo and P. Frasconi, “A recursive connectionist approach for pre-dicting disulfide connectivity in proteins,” in Proc. 18th Annu. ACM Symp. Applied Computing, 2003, pp. 67–71.
32. P. Baldi, J. Cheng, and A. Vullo, “Large-scale prediction of disulphide bond connectivity,” in Advances in Neural Information Processing Sys-tems, L. Bottou, L. Saul, and Y. Weiss, Eds. Cambridge, MA: MIT Press, 2005, vol. 17, NIPS04 Conf., pp. 97–104.
33. J. Cheng, H. Saigo, and P. Baldi, “Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural net-works, and weighted graph matching,” Proteins: Structure, Function, Bioinformatics, vol. 62, no. 3, pp. 617–629, 2006b.
34. J. Moult, K. Fidelis, A. Kryshtafovych, B. Rost, T. Hubbard, and A. Tramontano, “Critical assessment of methods of protein structure pre-diction-Round VII,” Proteins, vol. 29, pp. 179–187, 2007.
35. S. J. Wodak, “From the Mediterranean coast to the shores of Lake On-tario: CAPRI's premiere on the American continent,” Proteins, vol. 69, pp. 687–698, 2007.
36. P. Baldi, Y. Chauvin, T. Hunkapillar, and M. McClure, “Hidden Markov models of biological primary sequence information,” Proc. Nat. Acad. Sci., vol. 91, no. 3, pp. 1059–1063, 1994.
37. A. Krogh, M. Brown, I. S. Mian, K. Sjolander, and D. Haussler, “Hidden Markov models in computational biology: Applications to protein modeling,” J. Mol. Biol., vol. 235, pp. 1501–1531, 1994.
38. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning repre-sentations by back-propagating error,” Nature, vol. 323, pp. 533–536, 1986.
39. V. Vapnik, The Nature of Statistical Learning Theory. Berlin, Ger-many: Springer-Verlag, 1995.
40. K. Bryson, D. Cozzetto, and D. T. Jones, “Computer-assisted protein domain boundary prediction using the DomPred server,” Curr Protein Pept Sci., vol. 8, pp. 181–188, 2007.
41. M. Tress, J. Cheng, P. Baldi, K. Joo, J. Lee, J. H. Seo, J. Lee, D. Baker, D. Chivian, D. Kim, A. Valencia, and I. Ezkurdia, “Assessment of predictions submitted for the CASP7 domain prediction category,” Proteins: Structure, Function and Bioinformatics, vol. 68, no. S8, pp. 137-151,2007.
42. J. Cheng, “DOMAC: An accurate, hybrid protein domain prediction server,” Nucleic Acids Res., vol. 35, pp. w354-w356, 2007.
43. Z. Obradovic, K. Peng, S. Vucetic, P. Radivojac, and A. K. Dunker, “Exploiting heterogeneous sequence properties improves prediction of protein disorder,” Proteins, vol. 61, no. Suppl 1, pp. 176–182, 2005.
44. J. J. Ward, L. J. McGuffm, K. Bryson, B. F. Buxton, and D. T. Jones, “The DISOPRED server for the prediction of protein disorder,” Bioinfo., vol. 20, pp. 2138–2139, 2004.
45. J. Cheng, M. J. Sweredoski, and P. Baldi, “Accurate prediction of pro-tein disordered regions by mining protein structure data,” Data Mining Knowledge Discovery, vol. 11, pp. 213–222, 2005.
46. A. Krogh, B. Larsson, G. von Heijne, and E. L. L. Sonnhammer, “Pre-dicting transmembrane protein topology with a hidden Markov model: Application to complete genomes,” J. Mol. Biol., vol. 305, no. 3, pp. 567–580, 2001.
47. P. Y. Chou and G. D. Fasman, “Prediction of the secondary structure of proteins from their amino acid sequence,” Adv. Enzymol., vol. 47, pp. 45–148, 1978.
48. N. Qian and T. J. Sejnowski, “Predicting the secondary structure of globular proteins using neural network models,” J. Mol. Biol., vol. 202, pp. 265–884, 1988.
49. P. Baldi, S. Brunak, P. Frasconi, G. Soda, and G. Pollastri, “Exploiting the past and the future in protein secondary structure prediction,” Bioin-formatics, vol. 15, no. 11, pp. 937–946, 1999.
50. I. P. Crawford, T. Niermann, and K. Kirchner, “Prediction of secondary structure by evolutionary comparison: Application to the a subunit of tryptophan synthase,” Proteins, vol. 2, pp. 118–129, 1987.
51. G. J. Barton, R. H. Newman, P. S. Freemont, and M. J. Crumpton, “Amino acid sequence analysis of the annexin supergene family of pro-teins,” Eur. J. Biochem., vol. 198, pp. 749–760, 1991.
52. B. Rost and C. Sander, “Improved prediction of protein secondary structure by use of sequence profiles and neural networks,” Proc. Nat. Acad. Sci., vol. 90, pp. 7558–7562, 1993.
53. A. Bairoch, R. Apweiler, C. H. Wu, W. C. Barker, B. Boeckmann, S. Ferro, E. Gasteiger, H. Huang, R. Lopez, M. Magrane, M. J. Martin, D. A. Natale, C. O'Donovan, N. Redaschi, and L. S. Yeh, “The universal protein resource (UniProt),“ Nucleic Acids Res., vol. 33, pp. D154-159, 2005.
54. S. F. Altschul, T. L. Madden, A. A. Scha_er, J. Zhang, Z. Zhang, W. Miller, and D. J. Lipman, “Gapped BLAST and PSI-BLAST: A new generation of protein database search programs,” Nuc. As. Res., vol. 25, no. 17, pp. 3389–3402, 1997.
55. G. Pollastri and A. McLysaght, “Porter: A new, accurate server for protein secondary structure prediction,” Bioinfo., vol. 21, no. 8, pp. 1719–20, 2005.
56. J. Cheng, A. Z. Randall, M. J. Sweredoski, and P. Baldi, “SCRATCH: A protein structure and structural feature prediction server,” Nuc. As. Res., vol. 33, pp. 72–76, 2005.
57. R. Bondugula and D. Xu, “MUPRED: A tool for bridging the gap be-tween template based methods and sequence profile based methods for protein secondary structure prediction,” Proteins, vol. 66, no. 3, pp. 664–670, 2007.
58. J. J. Ward, L. J. McGuffin, B. F. Buxton, and D. T. Jones, “Secondary structure prediction using support vector machines,” Bioinfo., vol. 19, pp. 1650–1655, 2003.
59. Randall, J. Cheng, M. Sweredoski, and P. Baldi, “TMBpro: Secondary structure, beta-contact, and tertiary structure prediction of transmem-brane beta-barrel proteins,” Bioinfo., vol. 24, pp. 513–520, 2008.
60. B. Rost, “Rising accuracy of protein secondary structure prediction,” in Protein Structure Determination, Analysis, and Modeling for Drug Discovery, D. Chasman, Ed. New York: Marcel Dekker, 2003, pp. 207–249.
61. J. Cheng, M. Sweredoski, and P. Baldi, “DOMpro: Protein domain prediction using profiles, secondary structure, relative solvent acces-sibility, and recursive neural networks,” Data Mining Knowledge Dis-covery, vol. 13, pp. 1–10, 2006.
62. Y. Freund, “Boosting a weak learning algorithm by majority,” in Proc. Third Annu. Workshop Computational Learning Theory, 1990.
63. O. Olmea and A. Valencia, “Improving contact predictions by the com-bination of correlated mutations and other sources of sequence infor-mation,” Fold Des, vol. 2, pp. s25-s32, 1997.
64. P. Baldi and G. Pollastri, “A machine learning strategy for protein anal-ysis,” IEEE Intelligent Systems, Special Issue Intelligent Systems in Bi-ology, vol. 17, no. 2, pp. 28–35, Feb. 2002.
65. A. Aszodi, M. Gradwell, and W. Taylor, “Global fold determination from a small number of distance restraints,” J. Mol. Biol., vol. 251, pp. 308–326, 1995.
66. M. Vendruscolo, E. Kussell, and E. Domany, “Recovery of protein structure from contact maps,” Folding Design, vol. 2, pp. 295–306, 1997.
67. J. Skolnick, A. Kolinski, and A. Ortiz, “MONSSTER: A method for folding globular proteins with a small number of distance restraints,” J Mol. Biol., vol. 265, pp. 217–241, 1997.
68. K. Plaxco, K. Simons, and D. Baker, “Contact order, transition state placement and the refolding rates of single domain proteins,” J. Mol. Biol., vol. 277, pp. 985–994, 1998.
69. M. Punta and B. Rost, “Protein folding rates estimated from contact predictions,” J. Mol. Biol., pp. 507–512, 2005a.
70. P. Baldi and G. Pollastri, “The principled design of large-scale re-cursive neural network architectures—DAG-RNNs and the protein structure prediction problem,” J. Machine Learning Res., vol. 4, pp. 575–602, 2003.
71. M. Punta and B. Rost, “PROFcon: Novel prediction of long-range con-tacts,” Bioinfo., vol. 21, pp. 2960–2968, 2005b.
72. G. Shackelford and K. Karplus, “Contact prediction using mutual in-formation and neural nets,” Proteins, vol. 69, pp. 159–164, 2007.
73. R. MacCallum, “Striped sheets and protein contact prediction,” Bioinfo., vol. 20, no. Supplement 1, pp. i224—i231, 2004.
74. J. Cheng and P. Baldi, “Improved residue contact prediction using sup-port vector machines and a large feature set,” BMC Bioinformatics, vol. 8, p. 113, 2007.
75. J. M. G. Izarzugaza, O. Grana, M. L. Tress, A. Valencia, and N. D. Clarke, “Assessment of intramolecular contact predictions for CASP7,” Proteins, vol. 69, pp. 152–158.
76. S. Wu and Y. Zhang, “A comprehensive assessment of sequence-based and template-based methods for protein contact prediction,” Bioinfo., 2008, to be published.
77. J. Cheng and P. Baldi, “Three-stage prediction of protein beta-sheets by neural networks, alignments, and graph algorithms,” Bioinfo., vol. 21, pp. i75–i84, 2005.
78. P. Fariselli, P. Riccobelli, and R. Casadio, “Role of evolutionary infor-mation in predicting the disulfide-bonding state of cysteine in proteins,” Proteins, vol. 36, pp. 340–346, 1999.
79. A. Vullo and P. Frasconi, “Disulfide connectivity prediction using re-cursive neural networks and evolutionary information,” Bioinfo., vol. 20, pp. 653–659, 2004.
80. J. Cheng, H. Saigo, and P. Baldi, “Farge-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural net-works, and weighted graph matching,” Proteins: Structure, Function, Bioinformatics, vol. 62, no. 3, pp. 617–629, 2006b.
81. A. Z. Randall, J. Cheng, M. Sweredoski, and P. Baldi, “TMBpro: Sec-ondary structure, beta-contact, and tertiary structure prediction of trans-membrane beta-barrel proteins,” Bioinfo., vol. 24, pp. 513–520, 2008.
82. M. Vassura, L. Margara, P. Di Lena, F. Medri, P. Fariselli, and R. Casadio, “FT-COMAR: Fault tolerant three-dimensional structure reconstruction from protein contact maps,” Bioinfo., vol. 24, pp. 1313–1315, 2008.
83. C. A. Rohl and D. Baker, “De novo determination of protein back-bone structure from residual dipolar couplings using Rosetta,” J. Amer. Chemical Soc., vol. 124, pp. 2723–2729, 2004.
84. P. M. Bowers, C. E. Strauss, and D. Baker, “De novo protein structure determination using sparse NMR data,” J. Biomol. NMR, vol. 18, no. 4, pp. 311–318, 2000.
85. Y. Zhang and J. Skolnick, “Automated structure prediction of weakly homologous proteins on a genomic scale,” Proc Nat. Acad. Sci., vol. 101, no. 20, pp. 7594–7599, 2004a.
86. D. T. Jones, “GenTHREADER: An efficient and reliable protein fold recognition method for genomic sequences,” J. Mol. Biol., vol. 287, pp. 797–815, 1999a.
87. D. Kim, D. Xu, J. Guo, K. Ellrott, and Y. Xu, “PROSPECT II: Pro-tein structure prediction method for genome-scale applications,” Pro-tein Eng., vol. 16, no. 9, pp. 641–650, 2003.
88. J. Cheng and P. Baldi, “A machine learning information retrieval approach to protein fold recognition,” Bioinfo., vol. 22, no. 12, pp. 1456–1463, 2006.
89. K. T. Simons, C. Kooperberg, E. Huang, and D. Baker, “Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions,” J. Mol. Biol., vol. 268, pp. 209–225, 1997.
90. B. Wallner and A. Elofsson, “Prediction of global and local model quality in CASP7 using Peons and ProQ,” Proteins, vol. 69, pp. 184–193, 2007.
91. J. Qiu, W. Sheffler, D. Baker, and W. S. Noble, “Ranking predicted protein structures with support vector regression,” Proteins, vol. 71, pp. 1175–1182, 2007.
92. K. Karplus, C. Barrett, and R. Hughey, “Hidden Markov models for detecting remote protein homologies,” Bioinfo., vol. 14, no. 10, pp. 846–856, 1998.
93. S. R. Eddy, “Profile hidden Markov models,” Bioinfo., vol. 14, pp. 755–763, 1998.
94. J. Soeding, “Protein homology detection by HMM-HMM compar-ison,” Bioinfo., vol. 21, pp. 951–960, 2005.
95. A. Sali and T. L. Blundell, “Comparative protein modelling by satis-faction of spatial restraints,” J. Mol. Biol., vol. 234, pp. 779–815, 1993.
96. Y. Zhang and J. Skolnick, “SPICKER: A clustering approach to iden-tify near-native protein folds,” J. Comp. Chem., vol. 25, pp. 865–871, 2004b.
97. D. Cozzetto, A. Kryshtafovych, M. Ceriani, and A. Tramontano, “As-sessment of predictions in the model quality assessment category,” Pro-teins, vol. 69, no. S8, pp. 175–183, 2007.
98. P. Aloy, G. Moont, H. A. Gabb, E. Querol, F. X. Aviles, and M. J. E. Sternberg, “Modelling protein docking using shape complemen-tarity, electrostatics and biochemical information,” Proteins, vol. 33, pp. 535–549, 1998.
99. A. J. Bordner and A. A. Gorin, “Protein docking using surface matching and supervised machine learning,” Proteins, vol. 68, pp. 488–502, 2007.
100. V. Chelliah, T. L. Blundell, and J. Fernandez-Recio, “Efficient re-straints for protein-protein docking by comparison of observed amino acid substitution patterns with those predicted from local environ- ment” J.Mol. Biol., vol. 357. pp. 1669–1682, 2006.
101. R. Chen, L. Li, and Z. Weng, “ZDOCK: An initial-stage protein docking algorithm,” Proteins, vol. 52, pp. 80–87, 2003.
102. S. R. Comeau, D. W. Gatchell, S. Vajda, and C. J. Camacho, “ClusPro: An automated docking and discrimination method for the prediction of protein complexes,” Bioinfo., vol. 20, pp. 45–50, 2004.
103. M. D. Daily, D. Masica, A. Sivasubramanian, S. Somarouthu, and J. J. Gray, “CAPRI rounds 3–5 reveal promising successes and future challenges for RosettaDock,” Proteins, vol. 60, pp. 181–186, 2005.
104. C. Dominguez, R. Boelens, and A. Bonvin, “HADDOCK: A protein-protein docking approach based on biochemical or biophysical infor-mation,” J. Amer. Chem. Soc., vol. 125, pp. 1731–1737, 2003.
105. H. A. Gabb, R. M. Jackson, and M. J. E. Sternberg, “Modelling protein docking using shape complementarity, electrostatics, and biochemical information,” J. Mol. Biol., vol. 272, pp. 106–120, 1997.
106. J. J. Gray, S. E. Moughan, C. Wang, O. Schueler-Furman, B. Kuhlman, C. A. Rohl, and D. Baker, “Protein-protein docking with simultaneous optimization of rigid body displacement and side chain conforma-tions,” J. Mol. Biol., vol. 331, pp. 281–299, 2003.
107. S. J. Wodak and R. Mendez, “Prediction of protein-protein interactions: The CAPRI experiment, its evaluation and implications,” Curr. Opin. Struct. Biol., vol. 14, pp. 242–249, 2004.
108. H. Lu, L. Lu, and J. Skolnick, “Development of unified statistical potentials describing protein-protein interactions,” Biophysical J., vol. 84, pp. 1895–1901, 2003.
109. J. Mintseris, B. Pierce, K. Wiehe, R. Anderson, R. Chen, and Z. Weng, “Integrating statistical pair potentials into protein complex prediction,” Proteins, vol. 69, pp. 511–520, 2007.
110. G. Moont, H. A. Gabb, and M. J. Sternberg, “Use of pair potentials across protein interfaces in screening predicted docked complexes,” Proteins, vol. 35, pp. 364–373, 1999.
111. E. Katchalski-Katzir,I. Shariv, M. Eisenstein, A. A. Friesem, C. Aflalo, and I. A. Vakser, “Molecular surface recognition: Determination of geometric fit between proteins and their ligands by correlation tech-niques,” Proc. Nat. Acad. Sci., vol. 89, pp. 2195–2199, 1992.
112. S. Lorenzen and Y. Zhang, “Identification of near-native structures by clustering protein docking conformations,” Proteins, vol. 68, pp. 187–194, 2007.
113. H. X. Zhou and S. Qin, “Interaction-site prediction for protein complexes: A critical assessment,” Bioinfo., vol. 23, no. 17, pp. 2203–2209, 2007.
114. H. X. Zhou and Y. Shan, “Prediction of protein interaction sites from sequence profile and residue neighbor list,” Proteins, vol. 44, pp. 336–343, 2001.
115. P. Smialowski, A. J. Martin-Galiano, A. Mikolajka, T. Girschick, T. A. Holak, and D. Frishman, “Protein solubility: Sequence based prediction and experimental verification,” Bioinformatics, vol. 23, pp. 2536–2542, 2007.
116. J. Cheng, A. Randall, and P. Baldi, “Prediction of protein stability changes for single site mutations using support vector machines,” Pro-teins, vol. 62, no. 4, pp. 1125–1132, 2006c.
117. O. Emanuelsson, S. Brunak, G. V. Heijne, and H. Nielsen, “Locating proteins in the cell using TargetP, SignalP, and related tools,” Nature Protocols, vol. 2, pp. 953–971, 2007.
118. J. D. Bendtsen, H. Nielsen, G. V. Heijne, and S. Brunak, “Improved prediction of signal peptides: SignalP 3.0,” J. Mol. Biol., vol. 340, pp. 783–795, 2004.
119. N. Blom, S. Gammeltoft, and S. Brunak, “Sequence- and structure-based prediction of eukaryotic protein phosphorylation sites,” J. Molec-ular Biol., vol. 294, pp. 1351–1362, 1999.
120. P. H. Andersen, M. Nielsen, and O. Lund, “Prediction of residues in discontinuous B-cell epitopes using protein 3D structures,” Protein Sci., vol. 15, pp. 2558–2567, 2006.
121. J. Larsen, O. Lund, and M. Nielsen, “Improved method for predicting linear B-cell epitopes,” Immunome Res., vol. 2, p. 2, 2006.
122. J. Sweredoski and P. Baldi, “PEPITO: Improved discontinuous B-cell epitope prediction using multiple distance thresholds and half-sphere exposure,” Bioinformatics, vol. 24, pp. 1459–1460, 2008a.
123. J. Sweredoski and P. Baldi, COBEpro: A Novel System for Predicting Continuous B-Cell Epitopes, 2008, submitted for publication.