CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Journal of Molecular Biology

Volumn 428, Issue 4, 2016, Pages 709-719

  Cimermancic, Peter ^a   Weinkam, Patrick ^a   Rettenmaier, T Justin ^a,b   Bichmann, Leon ^a   Keedy, Daniel A ^a   Woldeyes, Rahel A ^a   Schneidman Duhovny, Dina ^a   Demerdash, Omar N ^c   Mitchell, Julie C ^d   Wells, James A ^a,b   Fraser, James S ^a   Sali, Andrej ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

cryptic binding sites; machine learning; protein dynamics; undruggable proteins

Indexed keywords

PROTEIN TYROSINE PHOSPHATASE 1B; PROTEOME; PROTEIN;

ARTICLE; BINDING SITE; CONFORMATIONAL TRANSITION; CRYPTIC BINDING SITE; CRYSTAL STRUCTURE; GENETIC CONSERVATION; HYDROPHOBICITY; MOLECULAR DOCKING; MOLECULAR DYNAMICS; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; SIGNAL TRANSDUCTION; SURFACE PROPERTY; BIOLOGY; CHEMISTRY; HUMAN; MACHINE LEARNING; METABOLISM; PROCEDURES; PROTEIN CONFORMATION;

BINDING SITES; COMPUTATIONAL BIOLOGY; HUMANS; MACHINE LEARNING; PROTEIN CONFORMATION; PROTEINS; PROTEOME;

EID: 84959471868     PISSN: 00222836     EISSN: 10898638     Source Type: Journal    
DOI: 10.1016/j.jmb.2016.01.029     Document Type: Article

References (67)

1. B. Nisius, F. Sha, and H. Gohlke Structure-based computational analysis of protein binding sites for function and druggability prediction J. Biotechnol. 159 3 2012 123 134
2. S.J. Campbell, N.D. Gold, R.M. Jackson, and D.R. Westhead Ligand binding: Functional site location, similarity and docking Curr. Opin. Struct. Biol. 13 3 2003 389 395
3. A.T. Laurie, and R.M. Jackson Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites Bioinformatics 21 9 2005 1908 1916
4. J.C. Hermann, and et al. Structure-based activity prediction for an enzyme of unknown function Nature 448 7155 2007 775 779
5. R.A. Laskowski, N.M. Luscombe, M.B. Swindells, and J.M. Thornton Protein clefts in molecular recognition and function Protein Sci. Publ. Protein Soc. 5 12 1996 2438 2452
6. G.R. Bowman, and P.L. Geissler Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites Proc. Natl. Acad. Sci. U. S. A. 109 29 2012 11681 11686
7. R. Diskin, D. Engelberg, and O. Livnah A novel lipid binding site formed by the MAP kinase insert in p38 alpha J. Mol. Biol. 375 1 2008 70 79
8. J.D. Durrant, and J.A. McCammon Molecular dynamics simulations and drug discovery BMC Biol. 9 2011 71
9. J.R. Horn, and B.K. Shoichet Allosteric inhibition through core disruption J. Mol. Biol. 336 5 2004 1283 1291
10. K.W. Lexa, and H.A. Carlson Full protein flexibility is essential for proper hot-spot mapping J. Am. Chem. Soc. 133 2 2011 200 202
11. S. Mitternacht, and I.N. Berezovsky Binding leverage as a molecular basis for allosteric regulation PLoS Comput. Biol. 7 9 2011 e1002148
12. S. Henrich, and et al. Computational approaches to identifying and characterizing protein binding sites for ligand design J. Mol. Recognit.: JMR 23 2 2010 209 219
13. J.A. Capra, R.A. Laskowski, J.M. Thornton, M. Singh, and T.A. Funkhouser Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure PLoS Comput. Biol. 5 12 2009 e1000585
14. V. Le Guilloux, P. Schmidtke, and P. Tuffery Fpocket: An open source platform for ligand pocket detection BMC bioinformatics 10 2009 168
15. A. Rossi, M.A. Marti-Renom, and A. Sali Localization of binding sites in protein structures by optimization of a composite scoring function Protein Sci. Publ. Protein Soc. 15 10 2006 2366 2380
16. A.L. Hopkins, and C.R. Groom The druggable genome Nat. Rev. Drug Discov. 1 9 2002 727 730
17. R.P. Sheridan, V.N. Maiorov, M.K. Holloway, W.D. Cornell, and Y.D. Gao Drug-like density: A method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank J. Chem. Inf. Model. 50 11 2010 2029 2040
18. M.R. Arkin, and J.A. Wells Small-molecule inhibitors of protein-protein interactions: Progressing towards the dream Nature reviews. Drug discovery 3 4 2004 301 317
19. J.A. Wells, and C.L. McClendon Reaching for high-hanging fruit in drug discovery at protein-protein interfaces Nature 450 7172 2007 1001 1009
20. J.A. Hardy, and J.A. Wells Searching for new allosteric sites in enzymes Curr. Opin. Struct. Biol. 14 6 2004 706 715
21. J.M. Ostrem, U. Peters, M.L. Sos, J.A. Wells, and K.M. Shokat K-ras(G12C) inhibitors allosterically control GTP affinity and effector interactions Nature 503 7477 2013 548 551
22. J.D. Sadowsky, and et al. Turning a protein kinase on or off from a single allosteric site via disulfide trapping Proc. Natl. Acad. Sci. U. S. A. 108 15 2011 6056 6061
23. D.K. Johnson, and J. Karanicolas Druggable protein interaction sites are more predisposed to surface pocket formation than the rest of the protein surface PLoS Comput. Biol. 9 3 2013 e1002951
24. G.R. Bowman, E.R. Bolin, K.M. Hart, B.C. Maguire, and S. Marqusee Discovery of multiple hidden allosteric sites by combining Markov state models and experiments Proc. Natl. Acad. Sci. U. S. A. 112 9 2015 2734 2739
25. K.A. Loving, A. Lin, and A.C. Cheng Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility PLoS Comput. Biol. 10 7 2014 e1003741
26. A. Bakan, N. Nevins, A.S. Lakdawala, and I. Bahar Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules J. Chem. Theory Comput. 8 7 2012 2435 2447
27. R. Brenke, and et al. Fragment-based identification of druggable "hot spots" of proteins using Fourier domain correlation techniques Bioinformatics 25 5 2009 621 627
28. L.E. Grove, D.R. Hall, D. Beglov, S. Vajda, and D. Kozakov FTFlex: Accounting for binding site flexibility to improve fragment-based identification of druggable hot spots Bioinformatics 29 9 2013 1218 1219
29. D. Kozakov, and et al. The FTMap family of Web servers for determining and characterizing ligand-binding hot spots of proteins Nat. Protoc. 10 5 2015 733 755
30. C.H. Ngan, and et al. FTMAP: Extended protein mapping with user-selected probe molecules Nucleic Acids Res. 40 2012 W271 W275 (Web Server issue)
31. F.C. Bernstein, and et al. The Protein Data Bank: A computer-based archival file for macromolecular structures J. Mol. Biol. 112 3 1977 535 542
32. M.L. Benson, and et al. Binding MOAD, a high-quality protein-ligand database Nucleic Acids Res. 36 Database issue 2008 D674 D678
33. O.N. Demerdash, M.D. Daily, and J.C. Mitchell Structure-based predictive models for allosteric hot spots PLoS Comput. Biol. 5 10 2009 e1000531
34. X. Zhu, and J.C. Mitchell KFC2: A knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features Proteins 79 9 2011 2671 2683
35. P. Weinkam, Y.C. Chen, J. Pons, and A. Sali Impact of mutations on the allosteric conformational equilibrium J. Mol. Biol. 425 3 2013 647 661
36. F. Pedregosa, and et al. Scikit-learn: Machine learning in python J. Mach. Learn. Res. 12 2011 2825 2830
37. T. Schaul, and et al. PyBrain J. Mach. Learn. Res. 11 2010 743 746
38. A.P. Combs Recent advances in the discovery of competitive protein tyrosine phosphatase 1B inhibitors for the treatment of diabetes, obesity, and cancer J. Med. Chem. 53 6 2010 2333 2344
39. N.M. O'Boyle, and et al. Open Babel: An open chemical toolbox J. Cheminformatics 3 2011 33
40. K. Gunasekaran, B. Ma, and R. Nussinov Is allostery an intrinsic property of all dynamic proteins? Proteins 57 3 2004 433 443
41. Z. Huang, and et al. ASD: A comprehensive database of allosteric proteins and modulators Nucleic Acids Res. 39 Database issue 2011 D663 D669
42. A.E. Jenkins, J.A. Hockenberry, T. Nguyen, and D.A. Bernlohr Testing of the portal hypothesis: Analysis of a V32G, F57G, K58G mutant of the fatty acid binding protein of the murine adipocyte Biochemistry 41 6 2002 2022 2027
43. A. Schlessinger, and B. Rost Protein flexibility and rigidity predicted from sequence Proteins 61 1 2005 115 126
44. C.H. Shih, C.M. Chang, Y.S. Lin, W.C. Lo, and J.K. Hwang Evolutionary information hidden in a single protein structure Proteins 80 6 2012 1647 1657
45. L.S. Swapna, R.M. Bhaskara, J. Sharma, and N. Srinivasan Roles of residues in the interface of transient protein-protein complexes before complexation Sci. Rep. 2 2012 334
46. I. Bartova, J. Koca, and M. Otyepka Functional flexibility of human cyclin-dependent kinase-2 and its evolutionary conservation Protein Sci. Publ. Protein Soc. 17 1 2008 22 33
47. D. Martens, J. Huysmans, R. Setiono, J. Vanthienen, and B. Baesens Rule extraction from Support Vector Machines: An overview of issues and application in credit scoring Stud. Comput. Intell. 80 2008 33 63
48. S.P. Brown, and P.J. Hajduk Effects of conformational dynamics on predicted protein druggability ChemMedChem 1 1 2006 70 72
49. D. Kozakov, and et al. Structural conservation of druggable hot spots in protein-protein interfaces Proc. Natl. Acad. Sci. U. S. A. 108 33 2011 13528 13533
50. P. Weinkam, and et al. (TBD) Mapping protein allosteric mechanisms with small angle X-ray scattering profiles 2016 submitted for publication
51. K.S. Molnar, and et al. Cys-scanning disulfide crosslinking and Bayesian modeling probe the transmembrane signaling mechanism of the histine kinase, PhoQ 2014 submitted for publication
52. M. Liao, E. Cao, D. Julius, and Y. Cheng Structure of the TRPV1 ion channel determined by electron cryo-microscopy Nature 504 7478 2013 107 112
53. J.P. Overington, B. Al-Lazikani, and A.L. Hopkins How many drug targets are there? Nat. Rev. Drug Discov. 5 12 2006 993 996
54. A.P. Russ, and S. Lampel The druggable genome: An update Drug Discov. Today 10 23-24 2005 1607 1610
55. P. Schmidtke, and X. Barril Understanding and predicting druggability. A high-throughput method for detection of drug binding sites J. Med. Chem. 53 15 2010 5858 5867
56. J. Hert, J.J. Irwin, C. Laggner, M.J. Keiser, and B.K. Shoichet Quantifying biogenic bias in screening libraries Nat. Chem. Biol. 5 7 2009 479 483
57. D.L. Mobley, and K.A. Dill Binding of small-molecule ligands to proteins: "What you see" is not always "what you get" Structure 17 4 2009 489 498
58. L.N. Makley, and J.E. Gestwicki Expanding the number of "druggable" targets: Non-enzymes and protein-protein interactions Chem. Biol. Drug Des. 81 1 2013 22 32
59. C. Wiesmann, and et al. Allosteric inhibition of protein tyrosine phosphatase 1B Nat. Struct. Mol. Biol. 11 8 2004 730 737
60. N. Krishnan, and et al. Targeting the disordered C terminus of PTP1B with an allosteric inhibitor Nat. Chem. Biol. 10 7 2014 558 566
61. S.K. Hansen, and et al. Allosteric inhibition of PTP1B activity by selective modification of a non-active site cysteine residue Biochemistry 44 21 2005 7704 7712
62. S. Meier, and et al. Backbone resonance assignment of the 298 amino acid catalytic domain of protein tyrosine phosphatase 1B (PTP1B) J. Biomol. NMR 24 2 2002 165 166
63. J.S. Fraser, and et al. Accessing protein conformational ensembles using room-temperature X-ray crystallography Proc. Natl. Acad. Sci. U. S. A. 108 39 2011 16247 16252
64. M. Fischer, B.K. Shoichet, and J.S. Fraser One crystal, two temperatures: Cryocooling penalties alter ligand binding to transient protein sites Chembiochem Euro. J. Chem. Biol. 2015
65. H.M. Berman, and et al. The Protein Data Bank Acta Crystallogr. D Biol. Crystallogr. 58 Pt 6 No 1 2002 899 907
66. H.M. Berman, and et al. The Protein Data Bank Nucleic Acids Res. 28 1 2000 235 242
67. E.F. Pettersen, and et al. UCSF Chimera - A visualization system for exploratory research and analysis J. Comput. Chem. 25 13 2004 1605 1612