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MOLS 2.0: software package for peptide modeling and protein–ligand docking

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Abstract

We previously developed an algorithm to perform conformational searches of proteins and peptides, and to perform the docking of ligands to protein receptors. In order to identify optimal conformations and docked poses, this algorithm uses mutually orthogonal Latin squares (MOLS) to rationally sample the vast conformational (or docking) space, and then analyzes this relatively small sample using a variant of mean field theory. The conformational search part of the algorithm was denoted MOLS 1.0. The docking portion of the algorithm, which allows only “flexible ligand/rigid receptor” docking, was denoted MOLSDOCK. Both are FORTRAN-based command-line-only molecular docking computer programs, though a GUI was developed later for MOLS 1.0. Both the conformational search and the rigid receptor docking parts of the algorithm have been extensively validated. We have now further enhanced the capabilities of the program by incorporating “induced fit” side-chain receptor flexibility for docking peptide ligands. Benchmarking and extensive testing is now being carried out for the flexible receptor portion of the docking. Additionally, to make both the peptide conformational search and docking algorithms (the latter including both flexible ligand/rigid receptor and flexible ligand/flexible receptor techniques) more accessible to the research community, we have developed MOLS 2.0, which incorporates a new Java-based graphical user interface (GUI). Here, we give a detailed description of MOLS 2.0. The source code and binary for MOLS 2.0 are distributed free (under a GNU Lesser General Public License) to the scientific community. They are freely available for download at https://sourceforge.net/projects/mols2-0/files/.

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References

  1. Vengadesan K, Gautham N (2003) Enhanced sampling of the molecular potential energy surface using mutually orthogonal Latin squares: application to peptide structures. Biophys J 84:2897

  2. Koehl P, Delarue M (1996) Mean-field minimization methods for biological macromolecules. Curr Opin Struct Biol 6:222–226. doi:10.1016/S0959-440X(96)80078-9

    Article  CAS  Google Scholar 

  3. Olszewski KA, Piela L, Scheraga HA (1992) Mean field theory as a tool for intramolecular conformational optimization. 1. Tests on terminally-blocked alanine and met-enkephalin. J Phys Chem 96:4672–4676. doi:10.1021/j100190a096

    Article  CAS  Google Scholar 

  4. Arun Prasad P, Gautham N (2008) A new peptide docking strategy using a mean field technique with mutually orthogonal Latin square sampling. J Comput Aided Mol Des 22:815–829

    Article  CAS  Google Scholar 

  5. Viji SN, Prasad PA, Gautham N (2009) Protein-ligand docking using mutually orthogonal Latin squares (MOLSDOCK). J Chem Inf Model 49:2687–2694

  6. Kuntz ID (1992) Structure-based strategies for drug design and discovery. Science 257:1078–1082

    Article  CAS  Google Scholar 

  7. Prasad PA, Vengadesan K, Gautham N (2005) MOLS—a program to explore the potential energy surface of a peptide and locate its low energy conformations. In Silico Biol 5:401–405

  8. Koshland DE (1958) Application of a theory of enzyme specificity to protein synthesis. Proc Natl Acad Sci USA 44:98–104

  9. Monod J, Wyman J, Changeux J-P (1965) On the nature of allosteric transitions: a plausible model. J Mol Biol 12:88–118. doi:10.1016/S0022-2836(65)80285-6

  10. Monod J, Changeux J-P, Jacob F (1963) Allosteric proteins and cellular control systems. J Mol Biol 6:306–329. doi:10.1016/S0022-2836(63)80091-1

    Article  CAS  Google Scholar 

  11. Gutteridge A, Thornton J (2005) Conformational changes observed in enzyme crystal structures upon substrate binding. J Mol Biol 346:21–28. doi:10.1016/j.jmb.2004.11.013

  12. Benson M (2009) Binding MOAD (Mother of All Databases). Dissertation, University of Michigan, Ann Arbor

  13. Najmanovich R, Kuttner J, Sobolev V, Edelman M (2000) Side-chain flexibility in proteins upon ligand binding. Proteins 39:261–268. doi:10.1002/(SICI)1097-0134(20000515)39:3<261::AID-PROT90>3.0.CO;2-4

  14. Zavodszky MI (2005) Side-chain flexibility in protein–ligand binding: the minimal rotation hypothesis. Protein Sci 14:1104–1114. doi:10.1110/ps.041153605

  15. Antes I (2010) DynaDock: a new molecular dynamics-based algorithm for protein–peptide docking including receptor flexibility. Proteins 78:1084–1104. doi:10.1002/prot.22629

  16. Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385:381–392

    Article  CAS  Google Scholar 

  17. Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748. doi:10.1006/jmbi.1996.0897

    Article  CAS  Google Scholar 

  18. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. doi:10.1002/jcc.21256

  19. Ravindranath PA, Forli S, Goodsell DS et al (2015) AutoDockFR: advances in protein–ligand docking with explicitly specified binding site flexibility. PLoS Comput Biol 11:e1004586. doi:10.1371/journal.pcbi.1004586

  20. Sherman W, Beard HS, Farid R (2006) Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Des 67:83–84. doi:10.1111/j.1747-0285.2005.00327.x

    Article  CAS  Google Scholar 

  21. Sherman W, Day T, Jacobson MP et al (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49:534–553

  22. Vengadesan K (2004) Sampling the molecular potential energy surface using mutually orthogonal latin squares and application to peptide structures. Dissertation, University of Madras, Chennai

  23. Vengadesan K, Gautham N (2004) Energy landscape of Met-enkephalin and Leu-enkephalin drawn using mutually orthogonal Latin squares sampling. J Phys Chem B 108:11196–11205

    Article  CAS  Google Scholar 

  24. Vengadesan K, Gautham N (2004) Conformational studies on enkephalins using the MOLS technique. Biopolymers 74:476–494

    Article  CAS  Google Scholar 

  25. Purisima EO, Scheraga HA (1987) An approach to the multiple-minima problem in protein folding by relaxing dimensionality. J Mol Biol 196:697–709. doi:10.1016/0022-2836(87)90041-6

    Article  CAS  Google Scholar 

  26. Li Z, Scheraga HA (1987) Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc Natl Acad Sci USA 84:6611–6615

  27. Griewank AO (1981) Generalized descent for global optimization. J Optim Theory Appl 34:11–39. doi:10.1007/BF00933356

    Article  Google Scholar 

  28. Kirkpatrick S, Gelatt CDJ, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680. doi:10.1126/science.220.4598.671

    Article  CAS  Google Scholar 

  29. Meirovitch H, Vásquez M, Scheraga HA (1990) Free energy and stability of macromolecules studied by the double scanning simulation procedure. J Chem Phys 92:1248–1257. doi:10.1063/1.458134

    Article  CAS  Google Scholar 

  30. Piela L, Kostrowicki J, Scheraga HA (1989) On the multiple-minima problem in the conformational analysis of molecules: deformation of the potential energy hypersurface by the diffusion equation method. J Phys Chem 93:3339–3346. doi:10.1021/j100345a090

    Article  CAS  Google Scholar 

  31. Ito K (1987) Encyclopedic dictionary of mathematics. MIT Press, Cambridge

  32. Viji SN, Balaji N, Gautham N (2012) Molecular docking studies of protein–nucleotide complexes using MOLSDOCK (mutually orthogonal Latin squares DOCK). J Mol Model 18(8):3705–3722

  33. Vengadesan K, Anbupalam T, Gautham N (2004) An application of experimental design using mutually orthogonal Latin squares in conformational studies of peptides. Biochem Biophys Res Commun 316:731–737

    Article  CAS  Google Scholar 

  34. Sinko W, Lindert S, McCammon JA (2013) Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design. Chem Biol Drug Des 81:41–49. doi:10.1111/cbdd.12051

  35. Sousa SF, Ribeiro AJM, Coimbra JTS et al (2013) Protein–ligand docking in the new millennium—a retrospective of 10 years in the field. Curr Med Chem 20:2296–2314

  36. Feixas F, Lindert S, Sinko W, McCammon JA (2014) Exploring the role of receptor flexibility in structure-based drug discovery. Biophys Chem 186:31–45. doi:10.1016/j.bpc.2013.10.007

    Article  CAS  Google Scholar 

  37. London N, Movshovitz-Attias D (1993) Schueler-Furman O (2010) The structural basis of peptide–protein binding strategies. Struct Lond Engl 18:188–199. doi:10.1016/j.str.2009.11.012

  38. Biosym (1995) Biosym/MSI release 95.0. Biosym, San Diego

  39. Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285:1735–1747

    Article  CAS  Google Scholar 

  40. Cornell WD, Cieplak P, Bayly CI et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197

    Article  CAS  Google Scholar 

  41. Gehlhaar DK, Verkhivker GM, Rejto PA et al (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol 2:317–324

    Article  CAS  Google Scholar 

  42. Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168

    Article  Google Scholar 

  43. O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminformatics 3:33. doi:10.1186/1758-2946-3-33

  44. Tuffery P, Etchebest C, Hazout S (1997) Prediction of protein side chain conformations: a study on the influence of backbone accuracy on conformation stability in the rotamer space. Protein Eng 10:361–372

    Article  CAS  Google Scholar 

  45. Nemethy G, Gibson KD, Palmer KA et al (1992) 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 96:6472–6484. doi:10.1021/j100194a068

    Article  CAS  Google Scholar 

  46. Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519

    Article  CAS  Google Scholar 

  47. Harel M, Su CT, Frolow F et al (1991) Gamma-chymotrypsin is a complex of alpha-chymotrypsin with its own autolysis products. Biochemistry (Mosc) 30:5217–5225

    Article  CAS  Google Scholar 

  48. Chung SY, Subbiah S (1996) How similar must a template protein be for homology modeling by side-chain packing methods? Pac Symp Biocomput 126–141

  49. Abagyan RA, Totrov MM (1997) Contact area difference (CAD): a robust measure to evaluate accuracy of protein models. J Mol Biol 268:678–685

    Article  CAS  Google Scholar 

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Acknowledgements

We thank the Department of Science and Technology, Government of India, for financial support. We also thank the University Grants Commission for support under the CAS program.

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Correspondence to N. Gautham.

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Paul, D.S., Gautham, N. MOLS 2.0: software package for peptide modeling and protein–ligand docking. J Mol Model 22, 239 (2016). https://doi.org/10.1007/s00894-016-3106-x

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  • DOI: https://doi.org/10.1007/s00894-016-3106-x

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