Small-molecule ligand docking into comparative models with Rosetta

Nature Protocols

Volumn 8, Issue 7, 2013, Pages 1277-1298

  Combs, Steven A ^a,b   Deluca, Samuel L ^a   Deluca, Stephanie H ^a   Lemmon, Gordon H ^a   Nannemann, David P ^a,b   Nguyen, Elizabeth D ^a   Willis, Jordan R ^a   Sheehan, Jonathan H ^a   Meiler, Jens ^a,b,c  

^a VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b VANDERBILT UNIVERSITY   (United States)

^c VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ALPHA HELIX; AMINO ACID SEQUENCE; ARTICLE; COMPARATIVE STUDY; COMPUTER PROGRAM; GENETIC ALGORITHM; GEOMETRY; HIGH THROUGHPUT SCREENING; HYDROGEN BOND; LIGAND DOCKING; MOLECULAR DOCKING; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PREDICTION; PRIORITY JOURNAL; PROTEIN DATA BANK; PROTEIN STRUCTURE; PROTEIN TERTIARY STRUCTURE; ROSETTA; SCORING SYSTEM; SEQUENCE ALIGNMENT; STATIC ELECTRICITY; X RAY CRYSTALLOGRAPHY; CHEMICAL STRUCTURE; COMPUTER INTERFACE; DRUG DESIGN; PROCEDURES; PROTEIN CONFORMATION;

RUMEX;

LIGAND;

DRUG DESIGN; LIGANDS; MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; PROTEIN CONFORMATION; SEQUENCE ALIGNMENT; SOFTWARE; USER-COMPUTER INTERFACE;

EID: 84879371161     PISSN: 17542189     EISSN: 17502799     Source Type: Journal    
DOI: 10.1038/nprot.2013.074     Document Type: Article

References (92)

1. Rohl, C.A., Strauss, C.E.M., Misura, K.M.S. & Baker, D. Protein structure prediction using Rosetta. Methods Enzymol. 383, 66-93 (2004).
2. Siegel, J.B. et al. Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction. Science 329, 309-313 (2010).
3. Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364-1368 (2003).
4. Davis, I.W. & Baker, D. RosettaLigand docking with full ligand and receptor fexibility. J. Mol. Biol. 385, 381-392 (2009).
5. Misura, K.M.S., Chivian, D., Rohl, C.A., Kim, D.E. & Baker, D. Physically realistic homology models built with ROSETTA can be more accurate than their templates. Proc. Natl. Acad. Sci. USA 103, 5361-5366 (2006).
6. Davis, I.W., Raha, K., Head, M.S. & Baker, D. Blind docking of pharmaceutically relevant compounds using RosettaLigand. Protein Sci. 18, 1998-2002 (2009).
7. Das, R. & Baker, D. Macromolecular modeling with Rosetta. Annu. Rev. Biochem. 77, 363-382 (2008).
8. Kaufmann, K.W., Lemmon, G.H., Deluca, S.L., Sheehan, J.H. & Meiler, J. Practically useful: what the Rosetta protein modeling suite can do for you. Biochemistry 49, 2987-2998 (2010).
9. Rohl, C.A., Strauss, C.E.M., Chivian, D. & Baker, D. Modeling structurally variable regions in homologous proteins with Rosetta. Proteins 55, 656-677 (2004).
10. Meiler, J. & Baker, D. Coupled prediction of protein secondary and tertiary structure. Proc. Natl. Acad. Sci. USA 100, 12105-12110 (2003).
11. Yarov-Yarovoy, V., Schonbrun, J. & Baker, D. Multipass membrane protein structure prediction using Rosetta. Proteins 62, 1010-1025 (2006).
12. Bradley, P. et al. Free modeling with Rosetta in CASP6. Proteins 61 (suppl. 7), 128-134 (2005).
13. Bradley, P., Misura, K.M.S. & Baker, D. Toward high-resolution de novo structure prediction for small proteins. Science 309, 1868-1871 (2005).
14. Das, R. et al. Structure prediction for CASP7 targets using extensive all-atom refnement with Rosetta@home. Proteins 69 (suppl. 8), 118-128 (2007).
15. Rohl, C.A. Protein structure estimation from minimal restraints using Rosetta. Methods Enzymol. 394, 244-260 (2005).
16. Lange, O.F. et al. Determination of solution structures of proteins up to 40 kDa using CS-Rosetta with sparse NMR data from deuterated samples. Proc. Natl. Acad. Sci. USA 109, 10873-10878 (2012).
17. Lange, O.F. & Baker, D. Resolution-adapted recombination of structural features signifcantly improves sampling in restraint-guided structure calculation. Proteins 80, 884-895 (2012).
18. Kaufmann, K.W. et al. Structural determinants of species-selective substrate recognition in human and Drosophila serotonin transporters revealed through computational docking studies. Proteins 74, 630-642 (2009).
19. Lees-Miller, J.P. et al. Interactions of H562 in the S5 helix with T618 and S621 in the pore helix are important determinants of hERG1 potassium channel structure and function. Biophys. J. 96, 3600-3610 (2009).
20. Keeble, A.H. et al. Experimental and computational analyses of the energetic basis for dual recognition of immunity proteins by colicin endonucleases. J. Mol. Biol. 379, 745-759 (2008).
21. Fortenberry, C. et al. Exploring symmetry as an avenue to the computational design of large protein domains. J. Am. Chem. Soc. 133, 18026-18029 (2011).
22. Eswar, N., Eramian, D., Webb, B., Shen, M.-Y. & Sali, A. Protein structure modeling with MODELLER. Methods Mol. Biol. 426, 145-159 (2008).
23. Perola, E., Walters, W.P. & Charifson, P.S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins 56, 235-249 (2004).
24. Carlsson, J. et al. Ligand discovery from a dopamine D3 receptor homology model and crystal structure. Nat. Chem. Biol. 7, 769-778 (2011).
25. Ballester, P.J., Westwood, I., Laurieri, N., Sim, E. & Richards, W.G. Prospective virtual screening with Ultrafast Shape Recognition: the identifcation of novel inhibitors of arylamine N-acetyltransferases. J. R. Soc. Interface 7, 335-342 (2010).
26. Schneider, G. & Fechner, U. Computer-based de novo design of drug-like molecules. Nat. Rev. Drug Discov. 4, 649-663 (2005).
27. Schneider, G. et al. Voyages to the (un)known: adaptive design of bioactive compounds. Trends Biotechnol. 27, 18-26 (2009).
28. Meiler, J. & Baker, D. ROSETTALIGAND: protein-small molecule docking with full side-chain fexibility. Proteins 65, 538-548 (2006).
29. Lemmon, G. & Meiler, J. Rosetta Ligand docking with fexible XML protocols. Methods Mol. Biol. 819, 143-155 (2012).
30. Laskowski, R.A. SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J. Mol. Graph 13, 323-330, (1995).
31. Huang, B. & Schroeder, M. LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct. Biol. 6, 19 (2006).
32. Kalidas, Y. & Chandra, N. PocketDepth: a new depth-based algorithm for identifcation of ligand binding sites in proteins. J. Struct. Biol. 161, 31-42 (2008).
33. Dunbrack, R.L. & Karplus, M. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 230, 543-574 (1993).
34. Li, Z. & Scheraga, H. A. Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc. Natl. Acad. Sci. USA 84, 6611-6615 (1987).
35. Simons, K.T., Kooperberg, C., Huang, E. & Baker, D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268, 209-225 (1997).
36. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. & Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087 (1953).
37. Rarey, M., Kramer, B., Lengauer, T. & Klebe, G. A fast fexible docking method using an incremental construction algorithm. J. Mol. Biol. 261, 470-489 (1996).
38. Verdonk, M.L., Cole, J.C., Hartshorn, M.J., Murray, C.W. & Taylor, R.D. Improved protein-ligand docking using GOLD. Proteins 52, 609-623 (2003).
39. Friesner, R.A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739-1749 (2004).
40. Ewing, T.J., Makino, S., Skillman, A.G. & Kuntz, I.D. DOCK 4.0: search strategies for automated molecular docking of fexible molecule databases. J. Comput. Aided Mol. Des. 15, 411-428 (2001).
41. Gohlke, H., Hendlich, M. & Klebe, G. Knowledge-based scoring function to predict protein-ligand interactions. J. Mol. Biol. 295, 337-356 (2000).
42. Kaufmann, K.W. & Meiler, J. Using RosettaLigand for small molecule docking into comparative models. PLoS ONE 7, e50769 (2012).
43. Mobley, D.L. et al. Predicting absolute ligand binding free energies to a simple model site. J. Mol. Biol. 371, 1118-1134 (2007).
44. Mooers, B.H.M. & Matthews, B.W. Extension to 2268 atoms of direct methods in the ab initio determination of the unknown structure of bacteriophage P22 lysozyme. Acta Crystallogr. D Biol. Crystallogr. 62, 165-176 (2006).
45. Altschul, S.F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389-3402 (1997).
46. Zhang, Z. et al. Protein sequence similarity searches using patterns as seeds. Nucleic Acids Res. 26, 3986-3990 (1998).
47. Söding, J., Biegert, A. & Lupas, A.N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33, W244-W248 (2005).
48. Eddy, S.R. A new generation of homology search tools based on probabilistic inference. Genome Inform. 23, 205-211 (2009).
49. Eddy, S.R. Accelerated profle HMM searches. PloS Comput. Biol. 7, e1002195 (2011).
50. Kelley, L.A. & Sternberg, M.J.E. Protein structure prediction on the Web: a case study using the Phyre server. Nat. Protoc. 4, 363-371 (2009).
51. Canutescu, A.A. & Dunbrack, R.L. Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci. 12, 963-972 (2003).
52. Mandell, D.J., Coutsias, E.A. & Kortemme, T. Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat. Methods 6, 551-552 (2009).
53. Coutsias, E.A., Seok, C., Jacobson, M.P. & Dill, K.A. A kinematic view of loop closure. J. Comput. Chem. 25, 510-528 (2004).
54. Gront, D., Kulp, D.W., Vernon, R.M., Strauss, C.E.M. & Baker, D. Generalized fragment picking in Rosetta: design, protocols and applications. PLoS ONE 6, e23294 (2011).
55. Wang, G. & Dunbrack, R.L. PISCES: a protein sequence culling server. Bioinformatics 19, 1589-1591 (2003).
56. Leaver-Fay, A., Kuhlman, B. & Snoeyink, J. Rotamer-pair energy calculations using a trie data structure. Lect. Notes Comput. Sci. 3692, 389-400 (2005).
57. Jones, D., Taylor, W. & Thorton, J. A New approach to protein fold recognition. Nature 358, 86-89 (1992).
58. Raman, S. et al. Structure prediction for CASP8 with all-atom refnement using Rosetta. Proteins 77, 89-99 (2009).
59. Hirst, S.J., Alexander, N., McHaourab, H.S. & Meiler, J. RosettaEPR: an integrated tool for protein structure determination from sparse EPR data. J. Struct. Biol. 173, 506-514 (2011).
60. Meiler, J. & Baker, D. The fumarate sensor DcuS: progress in rapid protein fold elucidation by combining protein structure prediction methods with NMR spectroscopy. J. Magn. Reson. 173, 310-316 (2005).
61. Alexander, N., Bortolus, M., Al-Mestarihi, A., Mchaourab, H. & Meiler, J. De novo high-resolution protein structure determination from sparse spin-labeling EPR data. Structure 16, 181-195 (2008).
62. DiMaio, F. et al. Improved molecular replacement by density-and energy-guided protein structure optimization. Nature 473, 540-543 (2011).
63. Shen, Y. et al. De novo structure generation using chemical shifts for proteins with high-sequence identity but different folds. Protein Sci. 19, 349-356 (2010).
64. Herzog, F. et al. Structural probing of a protein phosphatase 2A network by chemical cross-linking and mass spectrometry. Science 337, 1348-1352 (2012).
65. Grishaev, A., Guo, L., Irving, T. & Bax, A. Improved ftting of solution X-ray scattering data to macromolecular structures and structural ensembles by explicit water modeling. J. Am. Chem. Soc. 132, 15484-15486 (2010).
66. Pandit, D. et al. Mapping of discontinuous conformational epitopes by amide hydrogen/deuterium exchange mass spectrometry and computational docking. J. Mol. Recognit. 25, 114-124 (2012).
67. Rathmann, D. et al. Ligand-mimicking receptor variant discloses binding and activation mode of prolactin-releasing peptide. J. Biol. Chem. 287, 32181-32194 (2012).
68. Combs, S., Kaufmann, K., Field, J.R., Bakely, R.D. & Meiler, J. Y95 and E444 interaction required for high-affnity S-citalopram binding in the human serotonin transporter. ACS Chem. Neurosci. 2, 75-81 (2011).
69. Nannemann, D.P., Kaufmann, K.W., Meiler, J. & Bachmann, B.O. Design and directed evolution of a dideoxy purine nucleoside phosphorylase. Protein Eng. Des. Sel. 23, 607-616 (2010).
70. Leach, A.R., Shoichet, B.K. & Peishoff, C.E. Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem. 49, 5851-5855 (2006).
71. Smith, J.A., Vanoye, C.G., George, A.L., Meiler, J. & Sanders, C.R. Structural models for the KCNQ1 voltage-gated potassium channel. Biochemistry 46, 14141-14152 (2007).
72. Larkin, M.A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947-2948 (2007).
73. Pettersen, E.F. et al. UCSF Chimera-a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605-1612 (2004).
74. Ward, J.J., McGuffn, L.J., Buxton, B.F. & Jones, D.T. Secondary structure prediction with support vector machines. Bioinformatics 19, 1650-1655 (2003).
75. Leman, J.K., Koehler, J., Mueller, R., Karakas, M., Woetzel, N. & Meiler, J. Simultaneous prediction of protein secondary structure and transmembrane spans. Proteins http://dx.doi.org/10.1002/prot.24258 (10 April 2013).
76. Wang, C., Bradley, P. & Baker, D. Protein-protein docking with backbone fexibility. J. Mol. Biol. 373, 503-519 (2007).
77. Qian, B. et al. High-resolution structure prediction and the crystallographic phase problem. Nature 450, 259-264 (2007).
78. Lemmon, G., Kaufmann, K. & Meiler, J. Prediction of HIV-1 protease/inhibitor affnity using RosettaLigand. Chem. Biol. Drug Des. 79, 888-896 (2012).
79. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph 14, 33-38 (1996).
80. Wass, M.N., Kelley, L.A. & Sternberg, M.J.E. 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res. 38, W469-W473 (2010).
81. Morris, G.M. et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor fexibility. J. Comput. Chem. 30, 2785-2791 (2009).
82. Case, D.A. et al. The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668-1688 (2005).
83. Alexander, N., Woetzel, N. & Meiler, J. Bcl:Cluster: A method for clustering biological molecules coupled with visualization in the Pymol molecular graphics system. Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on, 13-18 (2011).
84. Dunbrack, R.L. & Cohen, F.E. Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci. 6, 1661-1681 (1997).
85. Simons, K.T. et al. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins 34, 82-95 (1999).
86. Kuhlman, B. & Baker, D. Native protein sequences are close to optimal for their structures. Proc. Natl. Acad. Sci. USA 97, 10383-10388 (2000).
87. Neria, E., Fischer, S. & Karplus, M. Simulation of activation free energies in molecular systems. J. Chem. Phys. 105, 1902-1921 (1996).
88. Lazaridis, T. & Karplus, M. Effective energy function for proteins in solution. Proteins 35, 133-152 (1999).
89. Gordon, D.B., Marshall, S.A. & Mayo, S.L. Energy functions for protein design. Curr. Opin. Struct. Biol. 9, 509-513 (1999).
90. Wedemeyer, W.J. & Baker, D. Effcient minimization of angle-dependent potentials for polypeptides in internal coordinates. Proteins 53, 262-272 (2003).
91. Ramachandran, G.N., Ramakrishnan, C. & Sasisekharan, V. Stereochemistry of polypeptide chain confgurations. J. Mol. Biol. 7, 95-99 (1963).
92. Fleishman, S.J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS ONE 6, e20161 (2011).