We use cookies to improve your experience. By continuing to browse this site, you accept our cookie policy.×

Recent work in the development and application of protein–peptide docking

    Joseph Audie

    * Author for correspondence

    CMDBioscience, LLC, 5 Science Park, New Haven, CT 06511, USA.

    Sacred Heart University, Department of Chemistry, 5151 Park Avenue, Fairfield, CT 06825, USA

    &
    Jon Swanson

    ChemModeling LLC, Suite 101, 500 Huber Park Ct, Weldon Spring, MO 63304, USA

    Published Online:https://doi.org/10.4155/fmc.12.99

    Interest in the development of novel peptide-based drugs is growing. There is, thus, a pressing need for the development of effective methods to enable novel peptide-based drug discovery. A cogent case can be made for the development and application of computational or in silico methods to assist with peptide discovery. In particular, there is a need for the development of effective protein–peptide docking methods. Here, recent work in the area of protein–peptide docking method development is reviewed and several drug-discovery projects that benefited from protein–peptide docking are discussed. In the present review, special attention is given to the search and scoring problems, the use of peptide docking to enable hit identification, and the use of peptide docking to help rationalize experimental results, and generate and test structure-based hypotheses. Finally, some recommendations are made for improving the future development and application of protein–peptide docking.

    References

    • Vlieghe P, Lisowski V, Martinez J, Khrestchatisky M. Synthetic therapeutic peptides: science and market. Drug Discov. Today15(1–2),40–56 (2010).Crossref, Medline, CASGoogle Scholar
    • Briand JP, Muller S. Emerging peptide therapeutics for inflammatory autoimmune diseases. Curr. Pharm. Des.16(9),1136–1142 (2010).Crossref, Medline, CASGoogle Scholar
    • Bellmann-Sickert K, Beck-Sickinger AG. Peptide drugs to target G protein-coupled receptors. Trends Pharmacol. Sci.31(9),434–441 (2010).Crossref, Medline, CASGoogle Scholar
    • Vicari D, Foy KC, Liotta EM, Kaumaya PT. Engineered conformation-dependent VEGF peptide mimics are effective in inhibiting VEGF signaling pathways. J. Biol. Chem.286(15),13612–13625 (2011).Crossref, Medline, CASGoogle Scholar
    • Ullman CG, Frigotto L, Cooley RN. In vitro methods for peptide display and their applications. Brief Funct. Genomics10(3),125–134 (2011).Crossref, Medline, CASGoogle Scholar
    • Devy L, Huang L, Naa L et al. Selective inhibition of matrix metalloproteinase-14 blocks tumor growth, invasion, and angiogenesis. Cancer Res.69(4),1517–1526 (2009).Crossref, Medline, CASGoogle Scholar
    • Shiheido H, Takashima H, Doi N, Yanagawa H. mRNA display selection of an optimized MDM2-binding peptide that potently inhibits MDM2-p53 interaction. PLoS ONE6(3),E17898 (2011).Crossref, Medline, CASGoogle Scholar
    • Liu T, Qian Z, Xiao Q, Pei D. High-throughput screening of one-bead-one-compound libraries: identification of cyclic peptidyl inhibitors against calcineurin/NFAT interaction. ACS Comb. Sci.13(5),537–546 (2011).Crossref, Medline, CASGoogle Scholar
    • Audie J, Boyd C. The synergistic use of computation, chemistry and biology to discover novel peptide-based drugs: the time is right. Curr. Pharm. Des.16(5),567–582 (2011).CrossrefGoogle Scholar
    • 10  Ewing TJ, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des.15(5),411–428 (2011).CrossrefGoogle Scholar
    • 11  Zsoldos Z, Reid D, Simon A, Sadjad BS, Johnson AP. eHiTS: an innovative approach to the docking and scoring function problems. Curr. Protein Pept. Sci.7(5),421–435 (2006).Crossref, Medline, CASGoogle Scholar
    • 12  Janin J. Protein–protein docking tested in blind predictions: the CAPRI experiment. Mol. Biosyst.6(12),2351–2362 (2011).CrossrefGoogle Scholar
    • 13  Plewczynski D, Łaźniewski M, Augustyniak R, Ginalski K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J. Comput. Chem.32(4),742–755 (2011).Crossref, Medline, CASGoogle Scholar
    • 14  Feng JA, Marshall GR. SKATE: a docking program that decouples systematic sampling from scoring. J. Comput. Chem.31(14),2540–2554 (2011).CrossrefGoogle Scholar
    • 15  Zhou Y, Zhou H, Zhang C, Liu S. What is a desirable statistical energy function for proteins and how can it be obtained? Cell. Biochem. Biophys.46(2),165–174 (2006).Crossref, Medline, CASGoogle Scholar
    • 16  Zhang C, Liu S, Zhu Q, Zhou Y. A knowledge-based energy function for protein–ligand, protein–protein, and protein-DNA complexes. J. Med. Chem.48(7),2325–2335 (2005).Crossref, Medline, CASGoogle Scholar
    • 17  Audie J, Scarlata S. A novel empirical free energy function that explains and predicts protein–protein binding affinities. Biophys. Chem.129(2–3),198–211 (2007).Crossref, Medline, CASGoogle Scholar
    • 18  Ponder JW, Case DA. Force fields for protein simulations. Adv. Protein Chem.66,27–85 (2003).Crossref, Medline, CASGoogle Scholar
    • 19  Morris GM, Goodsell DS, Robert S et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comp. Chem.19(14),1639–1662 (1998).Crossref, CASGoogle Scholar
    • 20  Böhm HJ. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J. Comput. Aided Mol. Des.12(4),309–323 (1998).Crossref, Medline, CASGoogle Scholar
    • 21  Audie J. Development and validation of an empirical free energy function for calculating protein–protein binding free energy surfaces. Biophys. Chem.139(2–3),84–91 (2009).Crossref, Medline, CASGoogle Scholar
    • 22  Audie J. Continued development of an empirical function for predicting and rationalizing protein–protein binding affinities. Biophys. Chem.143(3),139–144 (2009).Crossref, Medline, CASGoogle Scholar
    • 23  Sippl MJ. Knowledge-based potentials for proteins. Curr. Opin. Struct. Biol.5(2),229–235 (1995).Crossref, Medline, CASGoogle Scholar
    • 24  Liu S, Zhang C, Zhou H, Zhou Y. A physical reference state unifies the structure-derived potential of mean force for protein folding and binding. Proteins56(1),93–101 (2004).Crossref, Medline, CASGoogle Scholar
    • 25  Xue M, Zheng M, Xiong B, Li Y, Jiang H, Shen J. Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions. J. Chem. Inf. Model.50(8),1378–1386 (2010).Crossref, Medline, CASGoogle Scholar
    • 26  Feliu E, Aloy P, Oliva B. On the analysis of protein–protein interactions via knowledge-based potentials for the prediction of protein–protein docking. Protein Sci.20(3),529–541 (2011).Crossref, Medline, CASGoogle Scholar
    • 27  Aitaa T, Nishigakib K, Husimic Y. Toward the fast blind docking of a peptide to a target protein by using a four-body statistical pseudo-potential. Comput. Biol. Chem.34(1),53–62 (2010).Crossref, MedlineGoogle Scholar
    • 28  Yu L, Yu PS, Yee Yen Mui E et al. Phage display screening against a set of targets to establish peptide-based sugar mimetics and molecular docking to predict binding site. Bioorg. Med. Chem.17(13),4825–4832 (2009).Crossref, Medline, CASGoogle Scholar
    • 29  Hussain A, Shaw PE, Hirst JD. Molecular dynamics simulations and in silico peptide ligand screening of the Elk-1 ETS domain. J. Cheminform.3(1),49 (2011).Crossref, Medline, CASGoogle Scholar
    • 30  Liu Z, Dominy BN, Shakhnovich EI. Structural mining: self-consistent design on flexible protein–peptide docking and transferable binding affinity potential. J. Am. Chem. Soc.126(27),8515–8528 (2004).Crossref, Medline, CASGoogle Scholar
    • 31  Arun Prasad P, Gautham N. A new peptide docking strategy using a mean field technique with mutually orthogonal Latin square sampling. J. Comput. Aided Mol. Des.22(11),815–829 (2008).Crossref, Medline, CASGoogle Scholar
    • 32  Staneva I, Wallin S. All-atom Monte Carlo approach to protein–peptide binding. J. Mol. Biol.393(5),1118–1128 (2009).Crossref, Medline, CASGoogle Scholar
    • 33  Antes I. DynaDock: a new molecular dynamics-based algorithm for protein–peptide docking including receptor flexibility. Proteins78(5),1084–1104 (2009).CrossrefGoogle Scholar
    • 34  Huang Z, Chung F, Wong CF. Docking flexible peptide to flexible protein by molecular dynamics using two implicit-solvent models: an evaluation in protein kinase and phosphatase systems. J. Phys. Chem. B113(43),14343–14354 (2009).Crossref, Medline, CASGoogle Scholar
    • 35  Raveh B, London N, Zimmerman L, Schueler-Furman O. Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS ONE6(4),E18934 (2011).Crossref, Medline, CASGoogle Scholar
    • 36  Dagliyan O, Proctor EA, D’Auria KM, Ding F, Dokholyan NV. Structural and dynamic determinants of protein–peptide recognition. Structure19,1837–1845 (2011).Crossref, Medline, CASGoogle Scholar
    • 37  Hou T, Li Y, Wang W. Prediction of peptides binding to the PKA RIIα subunit using a hierarchical strategy. Bioinformatics27(13),1814–1821 (2011).Crossref, Medline, CASGoogle Scholar
    • 38  Fu X, Apgar JR, Keating AE. Modeling backbone flexibility to achieve sequence diversity: the design of novel α-helical ligands for Bcl-xL. J. Mol. Biol.371(4),1099–1117 (2007).Crossref, Medline, CASGoogle Scholar
    • 39  Abe K, Kobayashi N, Sode K, Ikebukuro K. Peptide ligand screening of α-synuclein aggregation modulators by in silico panning. BMC Bioinformat.8,451 (2007).Crossref, MedlineGoogle Scholar
    • 40  Song S, Liu D, Peng J et al. Novel peptide ligand directs liposomes toward EGF-R high-expressing cancer cells in vitro and in vivo. FASEB J.23(5),1396–1404 (2009).Crossref, Medline, CASGoogle Scholar
    • 41  Chaudhury S, Jeffrey J. Gray JJ. Identification of structural mechanisms of HIV-1 protease specificity using computational peptide docking: implications for drug resistance. Structure17(12),1636–1648 (2009).Crossref, Medline, CASGoogle Scholar
    • 42  Joshi A, Kate S, Poon V et al. Structure-based design of a heptavalent anthrax toxin inhibitor. Biomacromolecules12(3),791–796 (2011).Crossref, Medline, CASGoogle Scholar
    • 43  De Wachter R, de Graaf C, Keresztes A et al. Synthesis, biological evaluation, and automated docking of constrained analogues of the opioid peptide H-Dmt-D-Ala-Phe-Gly-NH2 using the 4- or 5-methyl substituted 4-amino-1,2,4,5-tetrahydro-2-benzazepin-3-one scaffold. J. Med. Chem.54(19),6538–6547 (2011).Crossref, Medline, CASGoogle Scholar