Towards creating complete proteomic structural databases of whole organisms

Current Bioinformatics

Volumn 7, Issue 4, 2012, Pages 424-435

  Jayaram, B ^a   Dhingra, Priyanka ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

Author keywords

Critical assessment of protein structure prediction (CASP); Database; Homology modeling; Protein structure; Root mean square deviation (RMSD); Template based modelling

Indexed keywords

CONTROLLED DRUG DELIVERY; DRUG INTERACTIONS; FORECASTING; PROTEINS; QUERY PROCESSING;

CRITICAL ASSESSMENT; CRITICAL ASSESSMENT OF PROTEIN STRUCTURE PREDICTION; HOMOLOGY MODELING; PROTEIN SEQUENCES; PROTEIN STRUCTURE PREDICTION; PROTEINS STRUCTURES; ROOT MEAN SQUARE DEVIATION; ROOT-MEAN-SQUARE DEVIATIONS; STRUCTURE PREDICTION; TEMPLATE-BASED MODELING;

DATABASE SYSTEMS;

AB INITIO CALCULATION; ALGORITHM; COMPARATIVE MODELING; COMPUTER PROGRAM; HOMOLOGY MODELING; MODEL; PREDICTION; PRIORITY JOURNAL; PROTEIN DATABASE; PROTEIN TERTIARY STRUCTURE; PROTEOMICS; REVIEW; TEMPLATE BASED MODELING;

EID: 84874838850     PISSN: 15748936     EISSN: None     Source Type: Journal    
DOI: 10.2174/157489312803900992     Document Type: Review

References (179)

1. Pandey A, Mann M. Proteomics to study genes and genomes. Nature 2000; 405: 837-46.
2. O'Farrell, P. H. High resolution two-dimensional electrophoresis of proteins. J Biol Chem 1975; 250: 4007-21.
3. Fields S. Proteomics. Proteomics in genomeland. Science 2001; 291: 1221-4.
4. Burley SK, Almo SC, Bonanno JB, et al. Structural Genomics: beyond the Human Genome Project. Nat Genet 1999; 23: 151-57.
5. Vukmirovic OG, Tilghman SM. Exploring genome space. Nature 2000; 405: 820-22.
6. Skolnick J. Protein Structure Prediction. Encylopedia Life Sci 2007; 1-7.
7. Fischman SG, Honig B. Structural genomics: Computational methods for structure analysis. Protein Sci 2003; 12: 1813-21.
8. Venter JC, Adams MD, Myers EW, et al. The Sequence of the Human Genome. Science 2001; 291: 1304-51.
9. Müller A, MacCallum RM, Sternberg MJE. Structural characterization of the human proteome. Genome Res 2002; 12: 1625-41.
10. McEntyre Jo, Ostell J. The NCBI handbook National Library of Medicine (US), National Center for Biotechnology Information. Internet: Bethesda (MD) National Center for Biotechnology Information US 2002. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Books.
11. Pruitt KD, Tatusova T, Klimke W, Maglott DR. NCBI Reference Sequence: current status, policy and new initiatives. Nucleic Acids Res 2009; v.39: D52-57.
12. Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Res 2000; 28: 235-42. www.pdb.org
13. Boeckmann B, Bairoch A, Apweiler R, et al. The Swiss-Prot Protein Knowledgebase and its supplement TrEMBL. Nucleic Acids Res 2003; 31:365-70.
14. Browne WJ, North AC, Phillips DC, Brew K, Vanaman TC, Hill RL. A possible three-dimensional structure of bovine alpha-lactalbumin based on that of hen's egg-white lysozyme. J Mol Biol 1969; 42: 65-86.
15. Chandonia JM, Brenner SE. The impact of structural genomics: expectations and outcomes. Science 2006; 311: 347-51.
16. Sali A. 100,000 protein structures for the biologist. Nature Structural Biology 1998; 5: 1029-32.
17. Torsten Schwede, Andrej Sali, Barry Honig, et al. Outcome of a workshop on applications of protein models in biomedical research. Structure 2009; 17: 151-59.
18. Lee D, de Beer TA, Laskowski RA, Thornton JM, Orengo CA. 1,000 structures and more from MCSG. BMC Struct Biol 2011; 11: 2.
19. Jones DT. Protein structure prediction in the postgenomic era. Curr Opin Struct Biol 2000; 3: 371-9.
20. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995; 247: 536-40.
21. Andreeva A, Howorth D, Chandonia J-M, et al. Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res 2008; 36: D419-25.
22. Skolnick J, Kihara D. The PDB is covering set of small protein structures. J Mol Biol 2003; 334: 793-802.
23. Zhang Y. Progress and challenges in protein structure prediction. Curr Opin Struct Biol 2008; 18: 342-8.
24. Murzin AG. Progress in protein structure prediction. Nat Struct Biol 2001; 8: 110-12.
25. Zhang Y, Skolnick J. Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci 2004; 101: 7594-99.
26. Petrey D, Honig B. Protein structure prediction: Inroads to Biology. Mol Cell 2005; 20: 811-9.
27. McGuffin LJ. In: Schwede T, Peitsch MC, Ed. Protein fold recognition and threading. World Scientific Co. 2008; 37-60.
28. Jones DT, Taylor WR, Thornton JM. A new approach to protein fold prediction. Nature 1992; 358: 86-9.
29. Anfinsen CB. Principles that govern the folding of protein chains. Science 1973; 181: 223-30.
30. Chothia C, Lesk AM. The relation between the divergence of sequence and structure in proteins. EMBO J. 1986; 5: 823-6.
31. Sander C, Schneider R. Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 1991; 9: 56-68.
32. Vitkup D, Melamud E, Moult J, Sander C. Completeness in Structural Genomics. Nature Struct Biol 2001; 8: 559-66.
33. Eddy SR. Profile hidden Markov models. Bioinformatics 1998; 14: 755-53.
34. Altschul SF, Madden TL, Schäffer AA, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389-402.
35. Eddy SR. Hidden Markov models. Curr Opin Struct Biol 1996; 6: 361-5.
36. Karplus K, Barrett C, Hughey R. Hidden Markov models for detecting remote protein homologs. Bioinformatics 1998; 14: 846-56.
37. Karplus K, Karchin R, Barrett C, et al. What is the value added by human intervention in protein structure prediction?. Proteins 2001; Suppl 5: 86-91.
38. Söding J. Protein homology detection by HMM-HMM comparison. Bioinformatics 2005; 21: 951-60.
39. Karplus K, Sjölander K, Barrett C, et al. Predicting protein structure using hidden Markov models. Proteins. 1997; 1:134-9.
40. Krogh A, Brown M, Mian IS, Sjölander K, Haussler D. Hidden Markov models in computational biology. Applications to protein modeling. J Mol Biol 1994; 235: 1501-31.
41. th ed. Cambridge Univ. Press: 2000.
42. Finn RD, Mistry J, Tate J, et al. The Pfam protein families database. Nucleic Acids Res 2010; 38:D211-22.
43. Richardson JS. The Anatomy & Taxonomy of Protein Structure. Protein Chemistry 1981; 34: 167-339.
44. Levitt M, Chothia C. Structural patterns in globular proteins. Nature. 1976; 261: 552-8
45. Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. CATH--a hierarchic classification of protein domain structures. Structure 1997; 5: 1093-108.
46. Müller A, MacCallum RM, Sternberg MJ. Benchmarking PSI-BLAST in genome annotation. J Mol Biol.;293:1257-71.
47. Lobley A, Sadowski MI, Jones DT. pGenTHREADER and pDomTHREADER: a new methods for improved protein fold recognition and superfamily discrimination. Bioinformatics 2009; 25: 1761-7.
48. Shi J, Blundell TL, Mizuguchi K. FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol 2001; 310: 243-57.
49. Cheng J, Baldi P. A machine learning information retrieval approach to protein fold recognition. Bioinformatics 2006; 22: 1456-63.
50. Lundström J, Rychlewski L, Bujnicki J, Elofsson A. Pcons: a neural-network-based consensus predictor that improves fold recognition. Protein Sci 2001; 10:2354-62.
51. Wallner B, Fang H, Elofsson A. Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller. http://www.ncbi.nlm.nih.gov/pubmed/14579343Proteins 2003; 53(Suppl 6): 534-41.
52. Wallner B, Elofsson A. Pcons5: combining consensus, structural evaluation and fold recognition scores. Bioinformatics 2005; 21: 4248-54.
53. Wu S, Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res 2007; 35: 3375-82.
54. Ginalski K, Elofsson A, Fischer D, Rychlewski L. 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 2003; 19: 1015-8.
55. Sánchez R, Sali A. Evaluation of comparative protein structure modeling by Modeller-3. Proteins 1997; Suppl 1: 50-8.
56. Venclovas C, Margelevicius M. Comparative modeling in CASP6 using consensus approach to template selection, sequence-structure alignment, and structure assessment. Proteins 2005; 61: 99-105.
57. Sali A, Blundell TL. Comparative protein modeling by satisfaction of spatial restraints. J. Mol. Biol. 1993; 234: 779-815.
58. Rohl CA, Strauss CE, Misura KM, Baker D. Protein structure prediction using Rosetta. Methods Enzymol 2004; 383: 66-93.
59. Fernandez-Fuentes N, Rai BK, Madrid-Aliste CJ, Fajardo JE, Fiser A. Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments. Bioinformatics 2007; 23:2558-65.
60. Fernandez-Fuentes N, Madrid-Aliste CJ, Rai BK, Fajardo JE, Fiser A. M4T: a comparative protein structure modeling server. Nucleic Acids Res 2007; 35:W363-8.
61. Rykunov D, Steinberger E, Madrid-Aliste CJ, Fiser A. Improved scoring function for comparative modeling using the M4T method. J Struct Funct Genomics 2009; 10: 95-9.
62. Petrey D, Xiang Z, Tang CL, et al. Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling. Proteins 2003; 53:430-5.
63. Cheng J, Randall AZ, Sweredoski MJ, Baldi P. SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 2005; 33: W72-6.
64. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protocols 2010; 5: 725-38.
65. Ogata K, Umeyama H. An automatic homology modeling method consisting of database searches and simulated annealing. J Mol Graph Model 2000; 18: 258-72.
66. Iwadate M, Ebisawa K, Umeyama H. Comparative modeling of CAFASP2 competition. Chem-Bio Info 2001; 1: 136-48.
67. Zhou H, Pandit SB, Skolnick J. Performance of the Pro-sp3-TASSER server in CASP8. Proteins 2009; 77:123-7.
68. Pei J, Kim BH, Grishin NV. PROMALS3D: a tool for multiple protein sequence and structural alignments. Nucleic Acids Res 2008; 36: 2295-300.
69. Do CB, Mahabhashyam MS, Brudno M, Batzoglou S. ProbCons: Probabilistic consistency-based multiple sequence alignment. Genome Res 2005; 15: 330-40.
70. Moretti S, Armougom F, Wallace IM, Higgins DG, Jongeneel CV, Notredame C. The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods. Nucleic Acids Res 2007; 35:W645-8.
71. Martí-Renom MA, Stuart AC, Fiser A, Sánchez R, Melo F, Sali A. Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 2000; 29: 291-325.
72. Greer J. Model for haptoglobin heavy chain based upon structural homology. Proc Natl Acad Sci 1980; 77: 3393-97.
73. Srinivasan N, Blundell TL. An evaluation of the performance of an automated procedure for comparative modelling of protein tertiary structure. Protein Eng 1993; 6:501-12.
74. Lee RH. Protein model building using structural homology. Nature 1992; 356: 543-44.
75. Greer J. Comparative model-building of the mammalian serine proteases. J Mol Biol 1981; 153:1027-42.
76. Arnold K, Bordoli L, Kopp J, Schwede T. The SWISS-MODEL workspace: a web-based environment for protein structure homology modeling. Bioinformatics 2006; 22: 195-201.
77. Bates PA, Kelley LA, MacCallum RM, Sternberg MJ. Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM. Proteins 2002; Suppl 5: 39-46.
78. Levitt M. Accurate modeling of protein conformation by automatic segment matching. J Mol Biol 1992; 226: 507-33.
79. Jones TA, Thirup S. Using known substructures in protein model building and crystallography. EMBO J 1986; 5: 819-822.
80. Chung SY, Subbiah S. A structural explaination for the twilight zone of protein sequence homology. Structure 1996; 4: 1123-27.
81. Dunbrack RL Jr. Rotamer libraries in the 21st century. Curr Opin Struct Biol 2002; 12:431-40.
82. Dunbrack RL Jr, Karplus M. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J Mol Biol 1993; 230: 543-74.
83. Ponder JW, Richards FM. Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes. J Mol Biol 1987; 193: 775-91.
84. Xiang Z, Honig B. Extending the accuracy limits of prediction for side-chain conformations. J Mol Biol 2001; 311:421-30.
85. Holm L, Sander C. Fast and simple Monte Carlo algorithm for side chain optimization in proteins: application to model building by homology. Proteins 1992; 14: 213-23.
86. Levitt M, Gerstein M, Huang E, Subbiah S, Tsai J. Protein folding: the endgame. Annu Rev Biochem 1997; 66: 549-79.
87. Samudrala R, Moult J. Determinants of side chain conformational preferences in protein structures. Protein Eng 1998; 11: 991-7.
88. Vásquez M. Modeling side-chain conformation. Curr Opin Struct Biol 1996; 6: 217-21.
89. Petrella RJ, Lazaridis T, Karplus M. Protein sidechain conformer prediction: a test of the energy function. Fold Des 1998; 3: 353-77.
90. Mendes J, Soares CM, Carrondo MA. Improvement of side-chain modeling in proteins with the self-consistent mean field theory method based on an analysis of the factors influencing prediction. Biopolymers 1999; 50:111-31.
91. Jacobson MP, Pincus DL, Rapp CS, et al. A hierarchical approach to all-atom protein loop prediction. Proteins 2004; 55: 351-67.
92. Looger LL, Dwyer MA, Smith JJ, Hellinga HW. Computational design of receptor and sensor proteins with novel functions. Nature 2003; 423: 185-190.
93. Lee MR, Baker D, Kollman PA. 2.1 and 1.8 Å Average C RMSD Structure Prediction on Two Small Proteins, HP-36 and S15. J Am Chem Soc 2001; 123: 1040-46.
94. Lu H, Skolnick J. Application of statistical potentials to protein structure refinement from low resolution ab initio models. Biopolymers 2003; 70: 575-84.
95. Fan H, Mark AE. Mimicking the action of folding chaperones in molecular dynamics simulations: Application to the refinement of homology-based protein structures. Protein Sci 2004; 13: 992-9.
96. Fan H, Mark AE. Refinement of homology-based protein structures by molecular dynamics simulation techniques. Protein Sci 2004; 13: 211-20.
97. Flohil JA, Vriend G, Berendsen HJ. Completion and refinement of 3-D homology models with restricted molecular dynamics: application to targets 47, 58, and 111 in the CASP modeling competition and posterior analysis. Proteins 2002; 48: 593-604.
98. Krieger E, Koraimann G, Vriend G. Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field. Proteins 2002; 47: 393-402.
99. Chen J, Brooks CL 3rd. Can molecular dynamics simulations provide high-resolution refinement of protein structure? Proteins 2007; 67: 922-30.
100. Im W, Lee MS, Brooks CL 3rd. Generalized born model with a simple smoothing function. J Comput Chem 2003; 24 1691-702.
101. Chen J, Im W, Brooks CL 3rd. Balancing solvation and intramolecular interactions: toward a consistent generalized Born force field. J Am Chem Soc 2006; 128: 3728-36.
102. Zhu J, Fan H, Periole X, Honig B, Mark AE. Refining homology models by combining replica-exchange molecular dynamics and statistical potentials. Proteins 2008; 72: 1171-88.
103. Xu D, Zhang J, Roy A, Zhang Y. Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement. Proteins: Structure, Function and Bioinformatics 2011; http://onlinelibrary.wiley.com/doi/10.1002/prot.23111/pdf
104. Misura KM, Baker D. Progress and challenges in high-resolution refinement of protein structure models. Proteins 2005; 59: 15-29.
105. Zhang Y, Arakaki AK, Skolnick J. TASSER: an automated method for the prediction of protein tertiary structures in CASP6. Proteins 2005; 61: 91-8.
106. Grimm V, Zhang Y, Skolnick J. Benchmarking of dimeric threading and structure refinement. Proteins 2006; 63: 457-65.
107. Baker D, Sali A. Protein structure prediction and structural genomics. Science 2001; 294: 93-6.
108. Peitsch MC. About the use of protein models. Bioinformatics 2002; 18: 934-38.
109. Eramian D, Eswar N, Shen MY, Sali A. How well can the accuracy of comparative protein structure models be predicted? Protein Sci 2008; 17: 1881-93.
110. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007; 35: W407-10.
111. Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins 1993; 17: 355-62.
112. Zhou H, Skolnick J. Protein model quality assessment prediction by combining fragment comparisons and a consensus C contact potential. Proteins 2008; 71: 1211-18.
113. Paluszewski M, Karplus K. Model quality assessment using distance constraints from alignments. Proteins 2009; 75: 540-9.
114. Narang P, Bhushan K, Bose S, Jayaram B. Protein structure evaluation using an all-atom energy based empirical scoring function. J Biomol Str Dyn 2006; 23: 385-406
115. Arora N, Jayaram B, Strength of hydrogen bonds in alpha helices. J Comput Chem 1997; 18; 1245-1252.
116. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004; 57: 702-10.
117. Xu J, Zhang Y. How significant is protein structure similarity with TM-score=0.5. Bioinformatics 2010; 26: 889-895.
118. Wallner B, Elofsson A. Can correct protein models be identified? Protein Sci 2003; 12: 1073-86.
119. Larsson P, Skwark MJ, Wallner B, Elofsson A. Assessment of global and local model quality in CASP8 using Pcons and ProQ. Proteins 2009; 77: 167-72.
120. Benkert P, Tosatto SC, Schomburg D. QMEAN: A comprehensive scoring function for model quality assessment. Proteins 2008; 71: 261-77.
121. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 2011; 27: 343-50.
122. Benkert P, Schwede T, Tosatto SC. QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information. BMC Struct Biol 2009; 9: 35.
123. Benkert P, Künzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res 2009; 37: W510-4.
124. McGuffin LJ. Prediction of global and local model quality in CASP8 using the ModFOLD server. Proteins 2009; 77: 185-90.
125. Wang Z, Eickholt J, Cheng J. APOLLO: a quality assessment service for single and multiple protein models. Bioinformatics 2011; 27: 1715-16.
126. Lüthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992; 356: 83-5.
127. Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three dimensional structure. Science 1991; 253: 164-170.
128. Vriend G. WHAT IF: a molecular modeling and drug design program. J Mol Graph 1990; 8: 52-6.
129. Laskowski RA, MacArthur M.W., Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 1993; 26: 283-91.
130. Kota P, Ding F, Ramachandran S, Dokholyan NV. Gaia: automated quality assessment of protein structure models. Bioinformatics 2011; 27: 2209-15.
131. Adamczak R, Pillardy J, Vallat BK, Meller J. Fast Geometric Consensus Approach for Protein Model Quality Assessment. J Comput Biol 2011[Epub ahead of print].
132. Cozzetto D, Kryshtafovych A, Ceriani M, Tramontano A. Assessment of predictions in the model quality assessment category. Proteins 2007; 69: 175-83.
133. Söding J, Biegert A, Lupas AN. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 2005; 33: W244-8.
134. Kelley LA, Sternberg MJ. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 2009; 4: 363-71.
135. Wallner B, Larsson P, Elofsson A. Pcons.net: protein structure prediction meta server. Nucleic Acids Res 2007; 35: W369-73.
136. Cheng J. A multi-template combination algorithm for protein comparative modeling. BMC Struct Biol 2008; 8: 18.
137. Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using Rosetta server. Nucleic Acids Res 2004; 32: W526-31.
138. Kurowski MA, Bujnicki JM. GeneSilico protein structure prediction meta-server. Nucleic Acids Res 2003; 31: 3305-7.
139. Kreiger E, Joo K, Lee J, et al. Improving physical realism, stereochemistry and side-chain accuracy in homology modeling: four approaches that performed well in CASP9. Proteins 2009; 77: 114-122.
140. Xu J, Li M, Kim D, Xu Y. RAPTOR: optimal protein threading by linear programming. J Bioinform Comput Biol 2003; 1: 95-117.
141. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 2008; 40: 1-8.
142. Eswar N, John B, Mirkovic N, et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res 2003; 31: 3375-80.
143. Eswar N, Webb B, Marti-Renom MA, et al. Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 2007; Unit 2.9.
144. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292: 195-202.
145. Combet C, Jambon M, Deléage G, Geourjon C. Geno3D: automatic comparative molecular modelling of protein. Bioinformatics 2002; 18: 213-4.
146. Zhou H, Skolnick J. Ab initio protein structure prediction using chunk-TASSER. Biophys J 2007; 93: 1510-8.
147. Zhou H, Skolnick J. Protein structure prediction by pro-Sp3-TASSER. Biophys J 2009; 96: 2119-27.
148. Jayaram B, Bhushan K, Shenoy SR, et al. Bhageerath: An energy based web enabled computer software suite for limiting the search space of tertiary structures of small globular proteins. Nucl. Acids Res 2006; 34: 6195-6204.
149. Narang P, Bhushan K, Bose S, Jayaram B. A computational pathway for bracketing native-like structures fo small alpha helical globular proteins. Phys Chem Chem Phys 2005; 7: 2364-75.
150. Thukral L, Shenoy SR, Bhushan K, Jayaram B. ProRegIn: a regularity index for the selection of native-like tertiary structures of proteins. J Biosci 2007; 32: 71-81.
151. Jayaram B, Dhingra P, Lakhani B, Shekhar S. Bhageerath-Targeting the near impossible: Pushing the frontiers of atomic models for protein tertiary structure prediction. J Chem Sci 2011; 124: 83-91.
152. Shenoy SR, Jayaram B. Proteins: sequence to structure and function--current status. Curr Protein Pept Sci 2010; 11: 498-514.
153. Bhageerath H: A homology ab initio hybrid server for protein tertiary structure prediction. (http://www.scfbioiitd.res.in/bhageerath/bhageerath_h.jsp).
154. Fischer D, Barret C, Bryson K, et al. CAFASP-1: critical assessment of fully automated structure prediction methods. Proteins 1999; Suppl 3: 209-17.
155. Wu S, Zhang Y. MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 2008; 72: 547-56.
156. Zhou H, Zhou Y. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 2005; 58: 321-8.
157. Jaroszewski L, Rychlewski L, Li Z, Li W, Godzik A. FFAS03: a server for profile--profile sequence alignments. Nucleic Acids Res 2005; 33: W284-8.
158. Sadreyev RI, Tang M, Kim BH, Grishin NV. COMPASS server for homology detection: improved statistical accuracy, speed and functionality. Nucleic Acids Res 2009; 37: W90-4.
159. Cheng J, Baldi P. A machine learning information retrieval approach to protein fold recognition. Bioinformatics 2006; 22: 1456-63.
160. Vallat BK, Pillardy J, Májek P, et al. Building and assessing atomic models of proteins from structural templates: learning and benchmarks. Proteins 2009; 76: 930-45.
161. Vallat BK, Pillardy J, Elber R. A template-finding algorithm and a comprehensive benchmark for homology modeling of proteins. Proteins 2008; 72: 910-28.
162. Teodorescu O, Galor T, Pillardy J, Elber R. Enriching the sequence substitution matrix by structural information. Proteins 2004; 54: 41-8.
163. Tobi D, Elber R. Distance-dependent, pair potential for protein folding: results from linear optimization. Proteins 2000; 41: 40-6.
164. Skolnick J, Kihara D, Zhang Y. Develpoment of large scale benchmark testing of the PROSPECTOR_3 threading algorithm. Proteins Struct Funct Bioinform 2004; 56: 502-18.
165. Zhang Y, Skolnick J. Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci USA 2004; 101: 7594-9.
166. Moult J, Pedersen JT, Judson R, Fidelis K. A large scale experiment to assess protein structure prediction methods. Protiens 1995; 23: 2-5.
167. Floudas CA. Computational methods in protein structure prediction. Biotechnol Bioeng 2007; 97: 207-13.
168. Moult J, Fidelis K, Kryshtafovych A, Rost B, Tramontano A. Critical assessment of methods of protein structure prediction-Round VIII. Proteins 2009; 77: 1-4.
169. Bowie JU, Lüthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991; 253: 164-70.
170. Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997; 268: 209-25.
171. Das R, Qian B, Raman S, et al. Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins 2007; 69: 118-28.
172. Raman S, Vernon R, Thompson J, et al. Structure prediction for CASP8 with all-atom refinement using Rosetta. Proteins 2009; 77: 89-99.
173. Zhang Y, Skolnick J. SPICKER: a clustering approach to identify near-native protein folds. J Comput Chem 2004; 25: 865-71.
174. http://predictioncenter.org/casp9/
175. Mittal A, Jayaram B. Backbones of folded proteins reveal novel invariant amino acid neighborhoods. J Biomol Struct Dyn 2011; 28: 443-54.
176. Mittal A, Jayaram B. The Newest View on Protein Folding: Stoichiometric and Spatial Unity in Structural and Functional Diversity. J Biomol Struct Dyn 2011; 28: 443-674.
177. Mittal A, Jayaram B, Shenoy S, Bawa TS. A stoichiometry driven universal spatial organization of backbones of folded proteins: are there Chargaff's rules for protein folding? J Biomol Struct Dyn 2010; 28: 133-42.
178. Shaikh SA, Jain T, Sandhu G, Latha N, Jayaram B. From drug target to leads--sketching a physicochemical pathway for lead molecule design in silico. Curr Pharm Des 2007; 13: 3454-70.
179. Singh T, Biswas D, Jayaram B. AADS-An automated active site identification, docking and scoring protocol for protein targets based on physico-chemical descriptors. J Chem Inf Model 2011; 51(10): 2515-27.