Investigating Protein–Peptide Interactions Using the Schrödinger Computational Suite

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1561)

Abstract

The Schrödinger software suite contains a broad array of computational chemistry and molecular modeling tools that can be used to study the interaction of peptides with proteins. These include molecular docking using Glide and Piper, relative binding free energy predictions with FEP+, conformational searches using MacroModel and Desmond, and structural refinement using Prime and PrimeX. In this review we provide a comprehensive overview of these tools and describe their potential application in the identification and optimization of peptide ligands for proteins.

Key words

Peptides Docking Glide Prime Piper Free Energy Conformational search Molecular dynamics Protein refinement 

References

  1. 1.
    Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner RA (2012) Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. J Comput Aided Mol Des 26(6):787–799. doi: 10.1007/s10822-012-9575-9 CrossRefPubMedGoogle Scholar
  2. 2.
    Tubert-Brohman I, Sherman W, Repasky M, Beuming T (2013) Improved docking of polypeptides with Glide. J Chem Inf Model 53(7):1689–1699. doi: 10.1021/ci400128m CrossRefPubMedGoogle Scholar
  3. 3.
    Bioluminate 2.1 (2015) Schrödinger, Inc., Portland, ORGoogle Scholar
  4. 4.
    Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234. doi: 10.1007/s10822-013-9644-8 CrossRefPubMedGoogle Scholar
  5. 5.
    Bas DC, Rogers DM, Jensen JH (2008) Very fast prediction and rationalization of pKa values for protein-ligand complexes. Proteins 73(3):765–783. doi: 10.1002/prot.22102 CrossRefPubMedGoogle Scholar
  6. 6.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749CrossRefPubMedGoogle Scholar
  7. 7.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49(21):6177–6196CrossRefPubMedGoogle Scholar
  8. 8.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759CrossRefPubMedGoogle Scholar
  9. 9.
    Feher M, Williams CI (2012) Numerical errors and chaotic behavior in docking simulations. J Chem Inf Model 52(3):724–738. doi: 10.1021/ci200598m CrossRefPubMedGoogle Scholar
  10. 10.
    Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49(2):534–553CrossRefPubMedGoogle Scholar
  11. 11.
    Prime 4.2 (2015) Schrödinger, Inc., Portland, ORGoogle Scholar
  12. 12.
    Kozakov D, Brenke R, Comeau SR, Vajda S (2006) PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65(2):392–406. doi: 10.1002/prot.21117 CrossRefPubMedGoogle Scholar
  13. 13.
    MacroModel v11.0 (2015) Schrödinger, Inc., Portland, ORGoogle Scholar
  14. 14.
    Kozakov D, Hall DR, Beglov D, Brenke R, Comeau SR, Shen Y, Li K, Zheng J, Vakili P, Paschalidis I, Vajda S (2010) Achieving reliability and high accuracy in automated protein docking: ClusPro, PIPER, SDU, and stability analysis in CAPRI rounds 13–19. Proteins 78(15):3124–3130. doi: 10.1002/prot.22835 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Shen Y, Brenke R, Kozakov D, Comeau SR, Beglov D, Vajda S (2007) Docking with PIPER and refinement with SDU in rounds 6–11 of CAPRI. Proteins 69(4):734–742. doi: 10.1002/prot.21754 CrossRefPubMedGoogle Scholar
  16. 16.
    Miller EB, Murrett CS, Zhu K, Zhao S, Goldfeld DA, Bylund JH, Friesner RA (2013) Prediction of long loops with embedded secondary structure using the protein local optimization program. J Chem Theory Comput 9(3):1846–4864. doi: 10.1021/ct301083q CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Zhao S, Zhu K, Li J, Friesner RA (2011) Progress in super long loop prediction. Proteins 79(10):2920–2935. doi: 10.1002/prot.23129 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Zhu K, Pincus DL, Zhao S, Friesner RA (2006) Long loop prediction using the protein local optimization program. Proteins 65:438–452CrossRefPubMedGoogle Scholar
  19. 19.
    Nourry C, Grant SG, Borg JP (2003) PDZ domain proteins: plug and play! Sci STKE 2003(179):RE7. doi: 10.1126/stke.2003.179.re7 PubMedGoogle Scholar
  20. 20.
    Bell JA, Ho KL, Farid R (2012) Significant reduction in errors associated with nonbonded contacts in protein crystal structures: automated all-atom refinement with PrimeX. Acta Crystallogr D Biol Crystallogr 68(Pt 8):935–952. doi: 10.1107/S0907444912017453 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Greenidge PA, Kramer C, Mozziconacci JC, Sherman W (2014) Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA. J Chem Inf Model 54(10):2697–2717. doi: 10.1021/ci5003735 CrossRefPubMedGoogle Scholar
  22. 22.
    Guimaraes CR, Cardozo M (2008) MM-GB/SA rescoring of docking poses in structure-based lead optimization. J Chem Inf Model 48(5):958–970. doi: 10.1021/ci800004w CrossRefPubMedGoogle Scholar
  23. 23.
    Zhu K, Shirts MR, Friesner RA (2007) Improved methods for side chain and loop predictions via the protein local optimization program: variable dielectric model for implicitly improving the treatment of polarization effects. J Chem Theory Comput 3(6):2108–2119. doi: 10.1021/ct700166f CrossRefPubMedGoogle Scholar
  24. 24.
    Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA (2011) The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins 79(10):2794–2812. doi: 10.1002/prot.23106 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein ligand binding. Proc Natl Acad Sci U S A 104:808–813CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Ylilauri M, Pentikainen OT (2013) MMGBSA as a tool to understand the binding affinities of filamin-peptide interactions. J Chem Inf Model 53(10):2626–2633. doi: 10.1021/ci4002475 CrossRefPubMedGoogle Scholar
  27. 27.
    Beard H, Cholleti A, Pearlman D, Sherman W, Loving KA (2013) Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes. PLoS One 8(12):e82849. doi: 10.1371/journal.pone.0082849 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Steinbrecher TB, Dahlgren M, Cappel D, Lin T, Wang L, Krilov G, Abel R, Friesner R, Sherman W (2015) Accurate binding free energy predictions in fragment optimization. J Chem Inf Model 55(11):2411–2420. doi: 10.1021/acs.jcim.5b00538 CrossRefPubMedGoogle Scholar
  29. 29.
    Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J, Romero DL, Masse C, Knight JL, Steinbrecher T, Beuming T, Damm W, Harder E, Sherman W, Brewer M, Wester R, Murcko M, Frye L, Farid R, Lin T, Mobley DL, Jorgensen WL, Berne BJ, Friesner RA, Abel R (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137(7):2695–2703. doi: 10.1021/ja512751q CrossRefPubMedGoogle Scholar
  30. 30.
    Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130(9):2817–2831CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Li Z, Lazaridis T (2006) Thermodynamics of buried water clusters at a protein-ligand binding interface. J Phys Chem B 110(3):1464–1475. doi: 10.1021/jp056020a CrossRefPubMedGoogle Scholar
  32. 32.
    Li Z, Lazaridis T (2012) Computing the thermodynamic contributions of interfacial water. Methods Mol Biol 819:393–404. doi: 10.1007/978-1-61779-465-0_24 CrossRefPubMedGoogle Scholar
  33. 33.
    Beuming T, Farid R, Sherman W (2009) High-energy water sites determine peptide binding affinity and specificity of PDZ domains. Protein Sci 18(8):1609–1619. doi: 10.1002/pro.177 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Skelton NJ, Koehler MF, Zobel K, Wong WL, Yeh S, Pisabarro MT, Yin JP, Lasky LA, Sidhu SS (2003) Origins of PDZ domain ligand specificity. Structure determination and mutagenesis of the Erbin PDZ domain. J Biol Chem 278(9):7645–7654. doi: 10.1074/jbc.M209751200 CrossRefPubMedGoogle Scholar
  35. 35.
    Kolossváry I, Guida WC (1999) Low-mode conformational search elucidated. Application to C39H80 and flexible docking of 9-deazaguanine inhibitors to PNP. J Comput Chem 20:1671–1684CrossRefGoogle Scholar
  36. 36.
    Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL, Kaus JW, Cerutti DS, Krilov G, Jorgensen WL, Abel R, Friesner RA (2015) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput. doi: 10.1021/acs.jctc.5b00864 PubMedCentralGoogle Scholar
  37. 37.
    Chen IJ, Foloppe N (2010) Drug-like bioactive structures and conformational coverage with the LigPrep/ConfGen suite: comparison to programs MOE and catalyst. J Chem Inf Model 50(5):822–839. doi: 10.1021/ci100026x CrossRefPubMedGoogle Scholar
  38. 38.
    Watts KS, Dalal P, Murphy RB, Sherman W, Friesner RA, Shelley JC (2010) ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 50(4):534–546. doi: 10.1021/ci100015j CrossRefPubMedGoogle Scholar
  39. 39.
    Ahlbach CL, Lexa KW, Bockus AT, Chen V, Crews P, Jacobson MP, Lokey RS (2015) Beyond cyclosporine A: conformation-dependent passive membrane permeabilities of cyclic peptide natural products. Future Med Chem 7(16):2121–2130. doi: 10.4155/fmc.15.78 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Desmond v4.4 (2015) Schrödinger, Inc., Portland, ORGoogle Scholar
  41. 41.
    Guo Z, Mohanty U, Noehre J, Sawyer TK, Sherman W, Krilov G (2010) Probing the alpha-helical structural stability of stapled p53 peptides: molecular dynamics simulations and analysis. Chem Biol Drug Des 75(4):348–359. doi: 10.1111/j.1747-0285.2010.00951.x CrossRefPubMedGoogle Scholar
  42. 42.
    Guo Z, Streu K, Krilov G, Mohanty U (2014) Probing the origin of structural stability of single and double stapled p53 peptide analogs bound to MDM2. Chem Biol Drug Des 83(6):631–642. doi: 10.1111/cbdd.12284 CrossRefPubMedGoogle Scholar
  43. 43.
    Zhou R (2007) Replica exchange molecular dynamics method for protein folding simulation. Methods Mol Biol 350:205–223PubMedGoogle Scholar
  44. 44.
    Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9(9):646–652. doi: 10.1038/nsb0902-646 CrossRefPubMedGoogle Scholar
  45. 45.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174. doi: 10.1002/jcc.20035 CrossRefPubMedGoogle Scholar
  46. 46.
    Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD Jr (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31(4):671–690. doi: 10.1002/jcc.21367 PubMedPubMedCentralGoogle Scholar
  47. 47.
    Hellberg S, Sjostrom M, Skagerberg B, Wold S (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem 30(7):1126–1135CrossRefPubMedGoogle Scholar
  48. 48.
    Sandberg M, Eriksson L, Jonsson J, Sjostrom M, Wold S (1998) New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem 41(14):2481–2491. doi: 10.1021/jm9700575 CrossRefPubMedGoogle Scholar
  49. 49.
    Tian F, Lv F, Zhou P, Yang Q, Jalbout AF (2008) Toward prediction of binding affinities between the MHC protein and its peptide ligands using quantitative structure-affinity relationship approach. Protein Pept Lett 15(10):1033–1043CrossRefPubMedGoogle Scholar
  50. 50.
    He R, Ma H, Zhao W, Qu W, Zhao J, Luo L, Zhu W (2012) Modeling the QSAR of ACE-inhibitory peptides with ANN and its applied illustration. Int J Pept 2012:620609. doi: 10.1155/2012/620609 CrossRefPubMedGoogle Scholar
  51. 51.
    Canvas v2.5 (2015) Schrödinger, Inc., Portland, ORGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Schrödinger, Inc.New YorkUSA

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