Improved docking of polypeptides with glide

Journal of Chemical Information and Modeling

Volumn 53, Issue 7, 2013, Pages 1689-1699

  Tubert Brohman, Ivan ^a   Sherman, Woody ^a   Repasky, Matt ^a   Beuming, Thijs ^a  

^a SCHRÖDINGER INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; MOLECULES; SOFTWARE TESTING;

FLEXIBLE LIGANDS; HELICAL PEPTIDE; IMPLICIT SOLVENTS; NATIVE PROTEINS; POSE PREDICTIONS; POST PROCESSING; PROTEIN-PROTEIN DOCKING; SMALL MOLECULES;

PEPTIDES;

PEPTIDE;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; BINDING SITE; CHEMISTRY; COMPUTER PROGRAM; DRUG DATABASE; METABOLISM; MOLECULAR DOCKING; PROTEIN CONFORMATION; SURFACE PROPERTY; TIME;

ALGORITHMS; AMINO ACID SEQUENCE; BINDING SITES; DATABASES, PHARMACEUTICAL; MOLECULAR DOCKING SIMULATION; PEPTIDES; PROTEIN CONFORMATION; SOFTWARE; SURFACE PROPERTIES; TIME FACTORS;

EID: 84880565980     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400128m     Document Type: Article

References (37)

1. Saladin, P.; Zhang, B.; Reichert, J. Current trends in the clinical development of peptide therapeutics IDrugs 2009, 12, 779-784
2. Vlieghe, P.; Lisowski, V.; Martinez, J.; Khrestchatisky, M. Synthetic therapeutic peptides: Science and market Drug Discovery Today 2010, 15, 40-56
3. Mocellin, S.; Pilati, P.; Nitti, D. Peptide-based anticancer vaccines: Recent advances and future perspectives Curr. Med. Chem. 2009, 16, 4779-4796
4. McGregor, D. P. Discovering and improving novel peptide therapeutics Curr. Opin. Pharmacol. 2008, 8, 616-619
5. Huang, C. Receptor-Fc fusion therapeutics, traps, and MIMETIBODY technology Curr. Opin. Biotechnol. 2009, 20, 692-699
6. Andersen, J. T.; Pehrson, R.; Tolmachev, V.; Daba, M. B.; Abrahmsén, L.; Ekblad, C. Extending half-life by indirect targeting of the neonatal Fc receptor (FcRn) using a minimal albumin binding domain J. Biol. Chem. 2011, 286, 5234-5241
7. Staneva, I.; Wallin, S. All-atom Monte Carlo approach to protein-peptide binding J. Mol. Biol. 2009, 393, 1118-1128
8. Bordner, A. J.; Abagyan, R. Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes Proteins 2006, 63, 512-526
9. Niv, M. Y.; Weinstein, H. A flexible docking procedure for the exploration of peptide binding selectivity to known structures and homology models of PDZ domains J. Am. Chem. Soc. 2005, 127, 14072-14079
10. Antes, I. DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility Proteins 2010, 78, 1084-1104
11. Raveh, B.; London, N.; Schueler-Furman, O. Sub-angstrom modeling of complexes between flexible peptides and globular proteins Proteins 2010, 78, 2029-2040
12. Raveh, B.; London, N.; Zimmerman, L.; Schueler-Furman, O. Rosetta FlexPepDock ab-initio: Simultaneous folding, docking and refinement of peptides onto their receptors PloS One 2011, 6, e18934
13. London, N.; Movshovitz-Attias, D.; Schueler-Furman, O. The structural basis of peptide-protein binding strategies Structure 2010, 18, 188-199
14. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening J. Med. Chem. 2004, 47, 1750-1759
15. Donsky, E.; Wolfson, H. J. PepCrawler: A fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide inhibitors Bioinformatics 2011, 27, 2836-2842
16. Trellet, M.; Melquiond, A. S.; Bonvin, A. M. A unified conformational selection and induced fit approach to protein-peptide docking PloS One 2013, 8, e58769
17. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy J. Med. Chem. 2004, 47, 1739-1749
18. SiteMap, version 2.7; Schrödinger, LLC: New York, 2012.
19. Perola, E.; Walters, W. P.; Charifson, P. S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance Proteins 2004, 56, 235-249
20. Zhou, Z.; Felts, A. K.; Friesner, R. A.; Levy, R. M. Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets J. Chem. Inf. Model. 2007, 47, 1599-1608
21. Watts, K. S.; Dalal, P.; Murphy, R. B.; Sherman, W.; Friesner, R. A.; Shelley, J. C. ConfGen: A conformational search method for efficient generation of bioactive conformers J. Chem. Inf. Model. 2010, 50, 534-546
22. Chen, I. J.; Foloppe, N. Drug-like bioactive structures and conformational coverage with the LigPrep/ConfGen suite: comparison to programs MOE and catalyst J. Chem. Inf. Model. 2010, 50, 822-839
23. Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field J. Chem. Theory and Comput. 2010, 6, 1509-1519
24. Jorgensen, W. L.; Tirado-Rives, J. The OPLS potential function for proteins. Energy minimizations for crystals of cyclic peptides and crambin J. Am. Chem. Soc. 1988, 110, 1657-1666
25. Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development andtesting of the OPLS all-atom force field on conformational energetics and properties of organic liquids J. Am. Chem. Soc. 1996, 118, 11225-11236
26. Feher, M.; Williams, C. I. Numerical errors and chaotic behavior in docking simulations J. Chem. Inf. Model. 2012, 52, 724-738
27. Labute, P. LowModeMD: Implicit low-mode velocity filtering applied to conformational search of macrocycles and protein loops J. Chem. Inf. Model. 2010, 50, 792-800
28. Prime, version 3.1; Schrödinger, LLC: New York, 2012.
29. Sherman, W.; Day, T.; Jacobson, M. P.; Friesner, R. A.; Farid, R. Novel procedure for modeling ligand/receptor induced fit effects J. Med. Chem. 2006, 49, 534-553
30. Thevenet, P.; Shen, Y.; Maupetit, J.; Guyon, F.; Derreumaux, P.; Tuffery, P. PEP-FOLD: An updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides Nucleic Acids Res. 2012, 40, W288-293
31. Kaur, H.; Garg, A.; Raghava, G. P. PEPstr: A de novo method for tertiary structure prediction of small bioactive peptides Protein Peptide Lett. 2007, 14, 626-631
32. Halgren, T. A. Identifying and characterizing binding sites and assessing druggability J. Chem. Inf. Model. 2009, 49, 377-389
33. Halgren, T. New method for fast and accurate binding-site identification and analysis Chem. Biol. Drug Des. 2007, 69, 146-148
34. Berman, H.; Henrick, K.; Nakamura, H. Announcing the worldwide Protein Data Bank Nat. Struct. Biol. 2003, 10, 980
35. Maestro, version 9.3; Schrödinger, LLC: New York: 2012.
36. Li, J.; Abel, R.; Zhu, K.; Cao, Y.; Zhao, S.; Friesner, R. A. The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling Proteins 2011, 79, 2794-2812
37. Wells, J. A.; McClendon, C. L. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces Nature 2007, 450, 1001-1009