Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3d structure prediction method ASTRO-FOLD

Proteins: Structure, Function and Bioinformatics

Volumn 78, Issue 8, 2010, Pages 1825-1846

  Rajgaria, R ^a   Wei, Y ^a   Floudas, C A ^a  

^a Princeton University   (United States)

Author keywords

Bb; CASP8; CSA; Force field; Hydrophobic; Integer linear optimization

Indexed keywords

PROTEIN;

ACCURACY; ARTICLE; BETA SHEET; ENERGY TRANSFER; HYDROPHOBICITY; PREDICTION; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; STRUCTURE ANALYSIS; THREE DIMENSIONAL IMAGING;

ALGORITHMS; AMINO ACIDS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; HYDROPHOBICITY; MODELS, MOLECULAR; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS; SEQUENCE ALIGNMENT;

EID: 77953489790     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.22696     Document Type: Article

References (83)

1. Floudas CA, Fung HK, McAllister SR, Mönnigmann M, Rajgaria R. Advances in protein structure prediction and de novo protein design: A review. Chem. Eng Sc 2006; 61:966-988. (Pubitemid 41655853)
2. Floudas CA. Computational methods in protein structure prediction. Biotechnol Bioeng 2007; 97:207-213. (Pubitemid 46852917)
3. Zhang Y. Progress and challenges in protein structure prediction. Curr Opin Struct Biol 2008; 18:342-348.
4. Ortiz AR, Kolinski A, Skolnick J. Nativelike topology assembly of small proteins using predicted restraints in Monte Carlo folding simulations. Proc Nat Acad Sci USA 1998; 95:1020-1025. (Pubitemid 28124905)
5. Ortiz AR, Kolinski A, Skolnick J, Fold assembly of small proteins using Monte Carlo simulations driven by restraints derived from multiple sequence alignments. J Mol Biol 1998; 277:419-448. (Pubitemid 28174978)
6. Ortiz AR, Kolinski A, Skolnick J. Tertiary structure prediction of the KIX domain of CBP using Monte Carlo simulations driven by restraints derived from multiple sequence alignments. Proteins: Struct Funct Bioinform 1998; 30:287-294. (Pubitemid 28106863)
7. Olmea O, Rost B, Valencia A. Effective use of sequence correlation and. conservation in fold recognition. J Mol Biol 1999; 295:1221-1239.
8. Bonneau R, Ruczinski I, Tsai J, Baker D. Contact order and ab-initio protein structure prediction. Protein Science 2002; 11:1937-1944.
9. McAllister SR, Mickus BE, Klepeis JL, Floudas CA. A novel approach for alpha-helical topology prediction in globular proteins: generation of interhelical restraints. Proteins: Struct Funct Bioinform 2006; 65:930-952.
10. McAllister SR, Floudas CA. Alpha helical topology and tertiary structure prediction in globular proteins. Proc of 46th IEEE Conf Decis Control, 2007; 46:4551-4556.
11. Wako H, Scheraga HA. On the use of distance constraints to fold a protein. Macromolecules 1981; 14:961-969.
12. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993; 234:779-815.
13. Marti-Renom MA, Stuart A, Fiser A, Snchez R, Melo F, Sali A. Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 2000; 29:291-325. (Pubitemid 30599596)
14. Klepeis JL, Floudas CA. ASTRO-FOLD: A combinatorial and global optimization framework for ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. Biophys J 2003; 85:2119-2146.
15. Klepeis JL, Floudas CA, Morikis D, Lambris JD. Predicting peptide structures using NMR data and deterministic global optimization. J Comput Chem 1999; 20:1354-1370.
16. Klepeis JL, Wei Y, Hecht MH, Floudas CA. Ab initio prediction of the 3-dimeusioual structure of a de novo designed protein: a double blind case study. Proteins: Struct Funct Bioinform 2005; 58:560-570.
17. McAllister SR, Floudas CA. Enhancing bounding techniques to reduce the protein conformational search space. Optim Method Softw 2009; 24(4,5):837-855.
18. Vendruscolo M. Protein folding using inter-residue contacts. In: First International Symposium on 3D Data Processing Visualization Transmission, 3dpvt, Padova, Italy, 2002, pp 724-728.
19. Vassura ML, Margara L, Di Lena P, Medri P, Fariselli P, Casadio R. Reconstruction of 3D structures from protein contact maps. IEEEACM. Trans Comput Biol Bioinform 2003; 5:357-367.
20. Göbel U, Sander C, Schneider R, Valencia A. Correlated mutations and residue contacts in proteins. Proteins: Struct Funct Bioinform 1994; 18:309-317.
21. Olmea O, Valencia A. Improving contact prediction by the combination of correlated mutations and other sources of sequence information. Fold Des 1997; 2:S25-S32.
22. Singer MS, Vriend G, Bywater RP. Prediction of protein residue contacts with a PDB-derived likelihood matrix. Protein Eng 2002; 15:721-725.
23. Hamilton N, Burrage K, Ragan MA, Huber T. Protein contact prediction using patterns of correlation. Proteins: Struct Funct Bioinform 2004; 56:679-684.
24. Vicatos S, Reddy BVB, Kaznessis Y. Prediction of distant residue contacts with the use of evolutionary information. Proteins: Struct Funct Bioinform 2005; 58:935-949.
25. Kundrotas P, Alexov EG. Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives. Bioinformatics 2006; 7:503-512.
26. Fariselli P, Casadio R. A neural network based predictor of residue contacts in proteins. Protein Eng 1999; 12:15-21.
27. Fariselli P, Olmea O, Valencia A, Casadio R. Prediction of contact maps with, neural networks and correlated mutations. Protein Eng 2001; 13:835-843.
28. Fariselli P, Olmea O, Valencia A, Casadio R. Progress in Predicting Inter-Residue Contacts of Proteins With Neural Networks and Correlated Mutations. Proteins: Struct Funct Bioinform 2001; 5:157-162.
29. Lund O, Frimand K, Gorodkin J, Bohr H, Bohr J, Hansen J, Brunak S. Protein distance constraints predicted by neural networks and probability density functions. Protein Eng 1997; 10:1241-1248.
30. Zhao Y, Karypis G. Prediction of contact maps using support vector machines. In Proceedings of the IEEE Symposium on Bioinformatics Bioengineering, 2003, pp 26-36.
31. Shao Y, Bystroff C. Predicting interresidue contacts using templates and pathways. Proteins: Struct Funct Bioinform 2003;53: 497-502.
32. Zhang GZ, Huang DS. Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme. J Comput-Aided Mol Des 2004; 18:797-810.
33. Punta M, Rost B. PROFcon: novel prediction of long-range contacts. Bioinformatics 2005; 21:2960-2968. (Pubitemid 40916420)
34. Vullo A, Walsh I, Pollastri G. A two-stage approach for improved prediction of residue contact maps. Bioinformatics 2006; 7:180-192.
35. Cheng J, Baldi P. Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics 2007; 8:113-121.
36. Shackelford G, Karplus K. Contact prediction using mutual information and neural nets. Proteins: Struct Funct Bioinform 2007; 69: 159-164.
37. Wu S, Zhang Y. A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics 2008; 24:924-931.
38. Klepeis JL, Floudas CA. Prediction of beta-sheet topology and disulfide bridges in polypeptides. J Comput Chem 2003; 24:191-208.
39. Rajgaria R, McAllister SR, Floudas CA. Towards accurate residueresidue hydrophobic contact prediction for alpha helical proteins via integer linear optimization. Proteins: Struct Funct Bioinform 2009; 74:929-947.
40. Taylor WR, Hatrick K. Compensating changes in protein multiple sequence alignments. Protein Eng 1994; 7:341-348.
41. Horner DS, Pirovano W, Pesole G. Correlated substitution analysis and the prediction of amino acid structural contacts. Brief Bioinform 2008; 9:46-56.
42. Pollastri G, Baldi P. Prediction of contact maps by recurrent neural network architecture and hidden context propagation from all cardinal corners. Bioinformatics 2002; 18:S62-S70.
43. Gr̃na O, Baker D, MacCallum R, Meiler J, Punta M, Rost B, Tress M, Valencia A. CASP6: assessment of contact predictions. Proteins: Struct Funct Bioinform 2005; 61:214-224.
44. Izarzugaza JMG, Grana O, Trees ML, Valencia A, Clarke ND. Assessment of intramolecular contact predictions for CASP7. Proteins: Struct Funct Bioinform 2007; 18:152-158.
45. Frenkel-Morgenstern M, Magid R, Eyal E, Pietrokovski S. Refining intra-protein contact by graph analysis. BMC Bioinformatics 2007; 8:S56-S60.
46. Vicatos S, Kaznessis YN. Separating true positive predicted residue contacts from false positive ones in mainly α proteins, using constrained metropolis MC simulations. Proteins: Struct Funct Bioinform 2008; 70:539-552.
47. Klepeis JL, Floudas CA. ASTRO-FOLD: Ab initio secondary and tertiary structure prediction in protein folding. In: European Symposium on Computer Aided Process Engineering-12: The Hague, The Netherlands, 2002.
48. Klepeis JL, Floudas CA. Ab initio tertiary structure prediction of proteins. J Global Optim 2003; 25:113-140.
49. Klepeis JL, Pieja MT, Flouda. CA. A new class of hybrid global optimization algorithms for peptide structure prediction: integrated hybrids. Comput Phys Commun 2003; 151:121-140.
50. Klepeis JL, Pieja MT, Flouda CA. A new class of hybrid global optimization algorithms for peptide structure predic tion: alternating hybrids and application fo met-enkephalin and melittin. Biophys J 2003; 84:869-882.
51. Kabsch W, Sander C. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983; 22:2577-2637.
52. α distance dependent force field based on a high quality decoy set. Proteins: Struct Funct Bioinform 2006; 65:726-741.
53. Cornette JL, Cease KB, Margalit H, Spouge JL, Berzofsky JA, DeList C. Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins. J Mol Biol 1987; 195:659-685.
54. Lifson S, Sander C. Antiparallel and parallel, β-strands differ in amino acid residue preferences. Nature .1979; 282:109-111.
55. Lifson S, Sander C. Specific recognition in the tertiary structure of beta-sheets of proteins. J Mol Biol 1980; 139:627-639.
56. ILOG.CPLEX user's Manual 9.0. 2003. ILOG S.A.: Gentilly, France.
57. 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; 247:536-540.
58. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292:195-202.
59. Rost B, Sander C, Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol 1994; 235:13-26. (Pubitemid 24049488)
60. Grana O, Gonzalez-Izarzugaza T, Tress M. CASP8 contact prediction. In: CASP8 Meeting, Sardinia, Italy, 2008.
61. Klepeis JL, Floudas CA. Ab initio prediction of helical segments in polypeptides. J Comput Chem 2002; 23:245-266.
62. Némethy G, Gibson KD, Palmer KA, Yoon CN, Paterlini G, Zagari A, Rumsey S, Scheraga HA. Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. J Phys Chem 1992; 96:6472-6484.
63. Klepeis JL, Floudas CA, Free energy calculations for peptides via deterministic global optimization. J Chem Phys 1999;110:7491-7512.
64. Subramani A, Floudas CA. HELIOS: a novel method for the prediction of a-helical regions of a protein, in press.
65. Subramani A, Floudas CA. BEST-PRED: a naive bayesian and first order markov model for the prediction of β-strand regions of a protein, in press.
66. Monnigmann M, Floudas CA. Protein loop structure prediction with flexible stem geometries. Proteins: Struct Funct Bioinform 2005; 61:748-762.
67. Adjiman CS, Androuiakis IP, Maranas CD, Floudas CA. A global optimization method, α BB, for process design. Comput Chem Eng 1996; 20:S419-S424.
68. Adjiman CS, Androulakis IP, Floudas CA. Global optimization of MINLP problems in process synthesis and design. Comput Chem Eng 1997; 21:S445-S450.
69. Adjiman CS, Androulakis IP, Floudas CA. A. global optimization method for general twice-differentiable NLPs - II. Implementation and computational results. Comput Chem Eng 1998a; 22:1159-1179.
70. Adjiman CS, Dallwig S, Floudas CA, Neumaier A. A global optimization method for general twice-differentiable NLPs - I. Theoretical advances. Comput Chem Eng 1998; 22:1137-1158.
71. Androulakis IP, Maranas CD, Floudas CA. αBB: A global optimization method for general constrained nonconvex problems. J Global Optim 1995; 7:337-363.
72. Floudas CA. Deterministic global optimization: Theory, methods and applications. Nonconvex. Optimization and its applications. Kluwer Academic Publishers: Dordrecht, The Netherlands, 2000.
73. Floudas CA, Pardalos PM. State of the art in global optimization: Computational methods and applications-preface. J Global Optim 1995; 7:iii.
74. Lee J, Scheraga HA, Rackovsky S. New optimization method for conformational energy calculations on polypeptides: conformational space annealing. J Comput Chem 1997; 18:1222-1232.
75. Lee J, Scheraga HA, Rackovsky S. Conformational analysis of the 20-residue membrane-bound portion of melittin by conformational space annealing. Biopolymers 1998; 46:103-115.
76. Lee J, Pillardy J, Czaplewski C, Arnautova Y, Ripoll DR, Liwo A, Gibson KD, Wawak RJ, Scheraga HA. Efficient parallel algorithms in global optimization of potential energy functions for peptides, proteins and crystals. Comput Phys Commun 2000; 128:399-411.
77. Lee J, Scheraga HA. Conformational space annealing by parallel computations: extensive conformational search of met-enkephalin and the 20-residue membrane-bound portion of melittin. Int J Quantum Chem 1999; 75:255-265.
78. Ripoll D, Liwo A, Scheraga HA. New developments of the electrostatically driven Monte Carlo method: tests on the membranebound portion of melittin. Biopolymers 1998; 46:117-126.
79. McAllister SR, Floudas CA. An improved hybrid global optimization method for protein tertiary structure prediction. Comput Optim Appl, in press.
80. Zemla A, Venclovas Č, Moult J, Fidelis K. Processing and analysis of CASP3 protein structure predictions. Proteins: Struct Funct Bioinform 1999; S3:22-29.
81. Zemla A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res 2003; 31:3370-3374.
82. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins: Struct Funct Bioinform 2004; 57:702-710.
83. Subramani A, DiMaggio P, Jr, Floudas C. Selecting high quality protein structures from diverse conformational ensembles. Biophys J 2009; 97:1728-1736.