Inhibition of protein interactions: co-crystalized protein–protein interfaces are nearly as good as holo proteins in rigid-body ligand docking | Journal of Computer-Aided Molecular Design
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Inhibition of protein interactions: co-crystalized protein–protein interfaces are nearly as good as holo proteins in rigid-body ligand docking

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Abstract

Modulating protein interaction pathways may lead to the cure of many diseases. Known protein–protein inhibitors bind to large pockets on the protein–protein interface. Such large pockets are detected also in the protein–protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein–protein complex as a starting point for drug design.

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References

  1. https://www.fda.gov/Drugs/InformationOnDrugs/ApprovedDrugs/ucm495351.htm. Accessed 2016

  2. Kang MH, Reynolds CP (2009) Bcl-2 inhibitors: targeting mitochondrial apoptotic pathways in cancer therapy. Clin Cancer Res 15:1126–1132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Stumpf MP, Thorne T, de Silva E, Stewart R, An HJ, Lappe M, Wiuf C (2008) Estimating the size of the human interactome. Proc Natl Acad Sci USA 105:6959–6964

    Article  PubMed  PubMed Central  Google Scholar 

  4. Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, Maniatis T, Califano A, Honig B (2012) Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490:556–560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cierpicki T, Grembecka J (2015) Targeting protein–protein interactions in hematologic malignancies: still a challenge or a great opportunity for future therapies? Immunol Rev 263:279–301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Higueruelo AP, Schreyer A, Bickerton GRJ, Pitt WR, Groom CR, Blundell TL (2009) Atomic interactions and profile of small molecules disrupting protein–protein interfaces: the TIMBAL database. Chem Biol Drug Des 74:457–467

    Article  CAS  PubMed  Google Scholar 

  7. Basse MJ, Betzi S, Bourgeas R, Bouzidi S, Chetrit B, Hamon V, Morelli X, Roche P (2013) 2P2Idb: a structural database dedicated to orthosteric modulation of protein–protein interactions. Nucl Acid Res 41:D824–D827

    Google Scholar 

  8. Gorczynski MJ, Grembecka J, Zhou Y, Kong Y, Roudaia L, Douvas MG, Newman M, Bielnicka I, Baber G, Corpora T, Shi J, Sridharan M, Lilien R, Donald BR, Speck NA, Brown ML, Bushweller JH (2007) Allosteric inhibition of the protein-protein interaction between the leukemia-associated proteins Runx1 and CBFβ. Chem Biol 14:1186–1197

    Article  CAS  PubMed  Google Scholar 

  9. Smith MC, Gestwicki JE (2012) Features of protein-protein interactions that translate into potent inhibitors: topology, surface area and affinity. Expert Rev Mol Med 14:e16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Acuner Ozbabacan SE, Gursoy A, Keskin O, Nussinov R (2010) Conformational ensembles, signal transduction and residue hot spots: application to drug discovery. Curr Opin Drug Discov Dev 13:527–537

    Google Scholar 

  11. Kozakov D, Hall DR, Chuang GY, Cencic R, Brenke R, Grove LE, Beglov D, Pelletier J, Whitty A, Vajda S (2011) Structural conservation of druggable hot spots in protein–protein interfaces. Proc Natl Acad Sci USA 108:13528–13533

    Article  PubMed  PubMed Central  Google Scholar 

  12. Koes DR, Camacho CJ (2012) Small-molecule inhibitor starting points learned from protein–protein interaction inhibitor structure. Bioinformatics 28:784–791

    Article  CAS  PubMed  Google Scholar 

  13. London N, Raveh B, Movshovitz-Attias D, Schueler-Furman O (2010) Can self-inhibitory peptides be derived from the interfaces of globular protein–protein interactions? Proteins 78:3140–3149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zaidman D, Wolfson HJ (2016) PinaColada: peptide–inhibitor ant colony ad-hoc design algorithm. Bioinformatics 32:2289–2296

    Article  CAS  PubMed  Google Scholar 

  15. Fuller JC, Burgoyne NJ, Jackson RM (2009) Predicting druggable binding sites at the protein-protein interface. Drug Discov Today 14:155–161

    Article  CAS  PubMed  Google Scholar 

  16. Gowthaman R, Deeds EJ, Karanicolas J (2013) Structural properties of non-traditional drug targets present new challenges for virtual screening. J Chem Inf Model 53:2073–2081

    Article  CAS  PubMed  Google Scholar 

  17. Shoichet BK, Walters WP, Jiang H, Bajorath J (2016) Advances in computational medicinal chemistry: a reflection on the evolution of the field and perspective going forward. J Med Chem 59:4033–4034

    Article  CAS  PubMed  Google Scholar 

  18. Janin J, Bahadur RP, Chakrabarti P (2008) Protein-protein interaction and quaternary structure. Quart Rev Biophys 41:133–180

    Article  CAS  Google Scholar 

  19. Gao M, Skolnick J (2012) The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation. Proc Natl Acad Sci USA 109:3784–3789

    Article  PubMed  PubMed Central  Google Scholar 

  20. Eyrisch S, Helms V (2007) Transient pockets on protein surfaces involved in protein-protein interaction. J Med Chem 50:3457–3464

    Article  CAS  PubMed  Google Scholar 

  21. Metz A, Pfleger C, Kopitz H, Pfeiffer-Marek S, Baringhaus KH, Gohlke H (2011) Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface. J Chem Inf Model 52:120–133

    Article  CAS  PubMed  Google Scholar 

  22. Johnson DK, Karanicolas J (2013) Druggable protein interaction sites are more predisposed to surface pocket formation than the rest of the protein surface. PLoS Comp Biol 9:e1002951

    Article  CAS  Google Scholar 

  23. Ulucan O, Eyrisch S, Helms V (2012) Druggability of dynamic protein-protein interfaces. Curr Pharm Design 18:4599–4606

    Article  CAS  Google Scholar 

  24. Kuenemann MA, Sperandio O, Labbe CM, Lagorce D, Miteva MA, Villoutreix BO (2015) In silico design of low molecular weight protein-protein interaction inhibitors: overall concept and recent advances. Prog Biophys Mol Biol 119:20–32

    Article  CAS  PubMed  Google Scholar 

  25. Vakser IA, Matar OG, Lam CF (1999) A systematic study of low-resolution recognition in protein-protein complexes. Proc Natl Acad Sci USA 96:8477–8482

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Brylinski M, Skolnick J (2008) Q-Dock: low-resolution flexible ligand docking with pocket-specific threading restraints. J Comput Chem 29:1574–1588

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhao J, Dundas J, Kachalo S, Ouyang Z, Liang J (2011) Accuracy of functional surfaces on comparatively modeled protein structures. J Struct Funct Genomics 12:97–107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Skolnick J, Zhou H, Gao M (2013) Are predicted protein structures of any value for binding site prediction and virtual ligand screening? Curr Opin Struct Biol 23:191–197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bohnuud T, Kozakov D, Vajda S (2014) Evidence of conformational selection driving the formation of ligand binding sites in protein-protein interfaces. PLoS Comp Biol 10:e1003872

    Article  CAS  Google Scholar 

  30. Zhang Y, Skolnick J (2005) TM-align: a protein structure alignment algorithm based on the TM-score. Nucl Acid Res 33:2302–2309

    Article  CAS  Google Scholar 

  31. Liu P, Agrafiotis DK, Theobald DL (2010) Fast determination of the optimal rotational matrix for macromolecular superpositions. J Comput Chem 31:1561–1563

    CAS  PubMed  Google Scholar 

  32. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL (2009) BLAST+: architecture and applications. BMC Bioinform 10:421

    Article  CAS  Google Scholar 

  33. Katchalski-Katzir E, Shariv I, Eisenstein M, Friesem AA, Aflalo C, Vakser IA (1992) Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci USA 89:2195–2199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Vakser IA (1995) Protein docking for low-resolution structures. Protein Eng 8:371–377

    Article  CAS  PubMed  Google Scholar 

  35. Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Mod 15:359–363

    Article  CAS  Google Scholar 

  36. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:66111

    Article  CAS  Google Scholar 

  37. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Anishchenko I, Kundrotas PJ, Tuzikov AV, Vakser IA (2015) Structural templates for comparative protein docking. Proteins 83:1563–1570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ruvinsky AM, Kirys T, Tuzikov AV, Vakser IA (2011) Side-chain conformational changes upon protein-protein association. J Mol Biol 408:356–365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ruvinsky AM, Kirys T, Tuzikov AV, Vakser IA (2013) Ensemble-based characterization of unbound and bound states on protein energy landscape. Protein Sci 22:734–744

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Camacho CJ, Vajda S (2002) Protein-protein association kinetics and protein docking. Curr Opin Struct Biol 12:36–40

    Article  CAS  PubMed  Google Scholar 

  42. Lensink MF, Wodak SJ (2013) Docking, scoring, and affinity prediction in CAPRI. Proteins 81:2082–2095

    Article  CAS  PubMed  Google Scholar 

  43. Tuszynska I, Bujnicki JM (2011) DARS-RNP and QUASI-RNP: new statistical potentials for protein-RNA docking. BMC Bioinformatics 12:348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bliznyuk AA, Gready JE (1999) Simple method for locating possible ligand binding sites on protein surfaces. J Comput Chem 20(9):983–988

    Article  CAS  Google Scholar 

  45. Porter KA, Xia B, Beglov D, Bohnuud T, Alam N, Schueler-Furman O, Kozakov D (2017) ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT. Bioinformatics 33:3299–3301

    Article  PubMed  PubMed Central  Google Scholar 

  46. Bamborough P, Cohen F (1996) Modeling protein-ligand complexes. Curr Opin Struct Biol 6:236–241

    Article  CAS  PubMed  Google Scholar 

  47. Blom NS, Sygusch J (1997) High resolution fast quantitative docking using fourier domain correlation techniques. Proteins 27:493–506

    Article  CAS  PubMed  Google Scholar 

  48. Kufareva I, Rueda M, Katritch V, participants GD, Stevens RC, Abagyan R (2011) Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure 19:1108–1126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Padhorny D, Hall DR, Mirzaei H, Mamonov AB, Moghadasi M, Alekseenko A, Beglov D, Kozakov D (2018) Protein-ligand docking using FFT based sampling: D3R case study. J Comput Aided Mol Des 32:225–230

    Article  CAS  PubMed  Google Scholar 

  50. Vakser IA (1996) Low-resolution docking: prediction of complexes for underdetermined structures. Biopolymers 39:455–464

    Article  CAS  PubMed  Google Scholar 

  51. Johnson DK, Karanicolas J (2015) Selectivity by small-molecule inhibitors of protein interactions can be driven by protein surface fluctuations. PLoS Comp Biol 11:e1004081

    Article  CAS  Google Scholar 

  52. Levitt M (2009) Nature of the protein universe. Proc Natl Acad Sci USA 106:11079–11084

    Article  PubMed  PubMed Central  Google Scholar 

  53. Kundrotas PJ, Zhu Z, Janin J, Vakser IA (2012) Templates are available to model nearly all complexes of structurally characterized proteins. Proc Natl Acad Sci USA 109:9438–9441

    Article  PubMed  PubMed Central  Google Scholar 

  54. Sinha R, Kundrotas PJ, Vakser IA (2010) Docking by structural similarity at protein-protein interfaces. Proteins 78:3235–3241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Sinha R, Kundrotas PJ, Vakser IA (2012) Protein docking by the interface structure similarity: how much structure is needed? PloS ONE 7:e31349

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kundrotas PJ, Vakser IA (2013) Global and local structural similarity in protein-protein complexes: implications for template-based docking. Proteins 81:2137–2142

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kundrotas PJ, Anishchenko I, Dauzhenka T, Vakser IA (2018) Modeling CAPRI targets 110–120 by template-based and free docking using contact potential and combined scoring function. Proteins 86:302–310

    Article  CAS  PubMed  Google Scholar 

  58. Vakser IA (2014) Protein-protein docking: from interaction to interactome. Biophys J 107:1785–1793

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Huang Z, Sutton SE, Wallenfang AJ, Orchard RC, Wu X, Feng Y, Chai J, Alto NM (2009) Structural insights into host GTPase isoform selection by a family of bacterial GEF mimics. Nat Struct Mol Biol 16:853–860

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ford B, Verger D, Dodson K, Volkan E, Kostakioti M, Elam J, Pinkner J, Waksman G, Hultgren S (2012) The structure of the PapD-PapGII pilin complex reveals an open and flexible P5 pocket. J Bacteriol 194:6390–6397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Shi H, Xu R (2003) Crystal structure of the Drosophila Mago nashi—Y14 complex. Genes Dev 17:971–976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Paaventhan P, Kong C, Joseph JS, Chung MCM, Kolatkar PR (2005) Structure of rhodocetin reveals noncovalently bound heterodimer interface. Protein Sci 14:169–175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by National Institutes of Health Grant R01GM074255 and National Science Foundation Grants DBI1262621, DBI1565107 and CNS1337899.

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Authors and Affiliations

  1. Center for Computational Biology, The University of Kansas, 2030 Becker Drive, Lawrence, KS, 66047, USA

    Saveliy Belkin, Petras J. Kundrotas & Ilya A. Vakser

  2. Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, 66045, USA

    Ilya A. Vakser

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  1. Saveliy Belkin
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  2. Petras J. Kundrotas
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  3. Ilya A. Vakser
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Correspondence to Petras J. Kundrotas or Ilya A. Vakser.

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Belkin, S., Kundrotas, P.J. & Vakser, I.A. Inhibition of protein interactions: co-crystalized protein–protein interfaces are nearly as good as holo proteins in rigid-body ligand docking. J Comput Aided Mol Des 32, 769–779 (2018). https://doi.org/10.1007/s10822-018-0124-z

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