Use of genetic algorithms and solvation potential to study peptides structure

Applied Mathematics and Computation

Volumn 195, Issue 2, 2008, Pages 515-522

  Scott, Luis P B ^a   Chahine, Jorge ^b   Ruggiero, José R ^b  

^a ABC   (Brazil)

^b SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

Bioinformatics; Genetic algorithms; Optimization; Peptide structure; Prediction; Rotamer library

Indexed keywords

BIOINFORMATICS; CONFORMATIONS; GENETIC ALGORITHMS; OPTIMIZATION; SOLVATION;

PEPTIDE STRUCTURE; ROTAMER LIBRARY; SOLVATION POTENTIAL;

PEPTIDES;

EID: 37349005438     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2007.05.002     Document Type: Article

References (43)

1. Baldi P., et al. Exploiting the past and the future in protein secondary structure prediction. Bioninformatics 15 11 (1999) 937-946
2. Dill, et al. Principles of protein folding - a perspective form simple exact models. Protein Sci. 4 (1995) 561-602
3. Cui Y. Protein folding simulation with genetic algorithm and supersecondary structure constraints. Proteins: Structure, Functions Genetics 31 (1998) 247-257
4. Kim P.S., and Baldwin R.L. Intermediates in the folding reactions of small proteins. Annu. Rev Biochem. 50 631 (1990) 660
5. Kollman P.A., Djam Y., and Lee M.R. State of the art in studying protein folding and protein structure prediction using molecular dynamics methods. J. Mol. Graph. Model. 19 I1 (2001) 146-149
6. Desjarlais J.R., and Handel T.M. Side-chain backbone flexibility in protein core design. J. Mol. Biol. 290 I (1999) 305-318
7. Leonhard K., et al. Solvent-amino acid interaction energies in three-dimensional-lattice Monte Carlo simulations of a model 27-mer protein: folding thermodynamics and kinetics. Protein Sci. May (2004) 358-369
8. Radja, et al. Conservation of statistical results under the reduction of pair-contact interactions to salvation interaction. Phys. Rev. E 72 (2005) 061915
9. Peterson R.W. Improved side-chain prediction accuracy using an ab initio potential energy function and very large rotamer library. Protein Sci. 13 (2004) 735-751
10. Wang J., Wang W., Huo S., Lee M., and Kollman A. Solvation model based on weighted solvent accessible surface area. J. Phys. Chem. B 105 (2001) 505-507
11. Rost B., and Sander C. Combining evolutionary information and neural networks to predict protein secondary structure. Proteins: Structure, Functions Genetics 19 (1999) 55-72
12. Ponder J.W., and Richards F.M. Tertiary templates for protein use packing criteria in the enumeration of allowed sequences for different structural classes. J. Mol. Biol. 193 (1987) 775-791
13. Pendersen T.J., and Moult J. Protein folding simulations with genetic algorithms and a detailed molecular description. J. Mol. Biol. 269 (1997) 240-259
14. Belda I., et al. ENPDA: an evolutionary structure-based de novo peptide design algorithm. J. Comput. Aid. Mol. Des. 19 (2005) 585-601
15. Amari S., Aizawa M., Zhang J., and Fukuzawa V. VISCANA: visualized cluster analysis of protein-ligand interaction based on the ab initio fragment molecular orbital method for virtual ligand screening. J. Chem. Inf. Model. 46 (2006) 221-230
16. Barton G. Protein secondary structure prediction. Curr. Opin. Struct. Biol. 5 (1995) 276-372
17. Bohr H., Bohr J., Brunak S., Cotterill R.M.J., Lautrup B., Nørskov L., Olsen O., and Petersen S. Protein secondary structure and homology by neural networks. FEBS Lett. 241 (1998) 223-228
18. Bonneau R., et al. Contact order and ab initio protein structure prediction. Protein Sci. 11 (2002) 1937-1944
19. Bottegoni G., Cavalli A., and Racnatinni M.A. Comparative study on the application of hierarchical-agglomerative clustering approaches to organize outputs of reiterated docking runs. J. Chem. Inf. Model. 46 (2006) 852-862
20. Bystroff C., and Baker D. Prediction of local structure in proteins using a library of sequence-structure motifs. J. Mol. Biol. 281 (1999) 565-577
21. Cai Y., Li Y., and Chou K.C. Using neural networks for prediction of domain structure. Biochim. Biophys. Acta 1476 (2000) 1-2
22. Canutescu A. A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 12 (2003) 2001-2004
23. Chandonia J.M., and Karplus M. New methods for accurate prediction of protein secondary structure. Proteins: Structure, Function Genetics 35 (1999) 293-306
24. Chen H., et al. On Evaluating molecular-docking methods for pose prediction and enrichment factors. J. Chem. Inf. Model. 46 (2006) 401-415
25. Compiani M., et al. An entropy criterion to detect minimally frustrated intermediates in native proteins. Proc. Natl. Acad. Sci. USA 95 (1998) 9290-9294
26. Dandekar T., and Argos P. Folding the main chain of small proteins with the genetic algorithm. J. Mol. Biol. 236 (1993) 844-861
27. Fang Q., and Shortle D. New fold methods: prediction reports. Protein: Structure, Function Genetics 53 S6 (2003) 486-490
28. Frishman D., and Argos P. Incorporation of non-local interactions in protein secondary structure prediction from amino acid sequence. Protein Eng. 9 2 (1996) 133-142
29. Gan H.H., Trospha A., and Schllick T. Generating folded protein structures with lattice growth algorithm. J. Chem. Phys. 113 (2000) 12
30. Gray J.F. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol. 331 (2003) 281-299
31. Jacobson M. Force field validation using protein side chain prediction. J. Phys. Chem. B 106 44 (2002) 11673-11680
32. Kono H., and Doi J. Energy minimization method using automata network for sequence and side-chain conformation prediction form given backbone geometry. Proteins: Structure, Function and Genetics 19 (1994) 244-255
33. Kolinsket A., et al. A reduced model of short range interactions in polypeptides chains. J. Chem. Phys. 103 10 (1995)
34. Liang S., and Grishin N.V. Side-chain modeling with an optimized scoring function. Protein Sci. 11 (2002) 322-331
35. Moult J., et al. Critical assessment of methods of protein structure prediction (CASP)-round 5. Proteins: Structure, Function Genetics 53 S6 (2003) 334-339
36. Ofran Y., and Rost B. Predicted protein-protein interaction sites from local sequence information. FEBS Lett. 544 (2003) 236-239
37. Qian H. Prediction of α-helices in proteins based on thermodynamic parameters form solution chemistry. J. Mol. Biol. 256 (1996) 663-666
38. Rost B. Protein secondary structure prediction continues to rise: http://cubic.bioc.columbia.edu/papers/2000_opinion, (2000).
39. Saunders C.T., and Bakers D. Evaluation of structural and evolutionary contributions to deleterious mutation prediction. J. Mol. Biol. 322 (2002) 891-901
40. Samudrala R., Huang S.E., Koehl P., and Levitt M. Constructing side chains on near-native main chains for ab initio protein structure prediction. Protein Eng. 13 7 (2000) 453-457
41. Sun S. A genetic algorithm that seeks native states of peptides and proteins. Biophys. J. 69 (1995) 340-355
42. Unger R., and Moult J. Genetic algorithms for protein folding simulations. J. Mol. Biol. 231 (1993) 75-81
43. Zhang Y., and Skolinick J. The protein structure prediction problem could be solved using the current PDB library. PNAS 102 4 (2005) 1029-1034