Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure

Bioinformatics

Volumn 28, Issue 6, 2012, Pages 784-791

  Koes, David Ryan ^a   Camacho, Carlos J ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LIGAND; PROTEIN;

ARTICLE; CHEMISTRY; COMPUTER PROGRAM; DRUG ANTAGONISM; DRUG EFFECT; HUMAN; METABOLISM; MOLECULAR LIBRARY; PROTEIN DATABASE; PROTEIN PROTEIN INTERACTION; SUPPORT VECTOR MACHINE;

DATABASES, PROTEIN; HUMANS; LIGANDS; PROTEIN INTERACTION MAPS; PROTEINS; SMALL MOLECULE LIBRARIES; SOFTWARE; SUPPORT VECTOR MACHINES;

EID: 84859023900     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr717     Document Type: Article

References (50)

1. Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990). Basic local alignment search tool. J. Mol. Biol., 215(3), 403-410.
2. An, J., Totrov, M., and Abagyan, R. (2005). Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol. Cell. Proteomics, 4(6), 752.
3. Batuwita, R. and Palade, V. (2010). Efficient resampling methods for training support vector machines with imbalanced datasets. In Neural Networks (IJCNN), The 2010 International Joint Conference on IEEE Computer Society, pages 1-8.
4. Bordner, A. J. and Abagyan, R. (2005). Statistical analysis and prediction of protein-protein interfaces. Proteins: Struct, Funct, Bioinf, 60(3), 353-366.
5. Bourgeas, R., Basse, M.-J., Morelli, X., and Roche, P. (2010). Atomic analysis of protein-protein interfaces with known inhibitors: The 2p2i database. PLoS One, 5(3), e9598.
6. Brenke, R., Kozakov, D., Chuang, G. Y., Beglov, D., Hall, D., Landon, M. R., Mattos, C., and Vajda, S. (2009). Fragment-based identification of druggable hot spots of proteins using fourier domain correlation techniques. Bioinformatics, 25(5), 621.
7. Bromberg, Y. and Rost, B. (2008). Comprehensive in silico mutagenesis highlights functionally important residues in proteins. Bioinformatics, 24(16), i207.
8. Brooks, B. R., Bruccoleri, R. E., and Olafson, B. D. (1983). Charmm: A program for macromolecular energy, minimization, and dynamics calculations. Journal of computational chemistry, 4(2), 187-217.
9. Camacho, C. and Zhang, C. (2005). FastContact: rapid estimate of contact and binding free energies. Bioinformatics, 21(10), 2534.
10. Chan, D., Chutkowski, C., and Kim, P. (1998). Evidence that a prominent cavity in the coiled coil of HIV type 1 gp41 is an attractive drug target. PNAS, 95(26), 15613.
11. Cho, K., Kim, D., and Lee, D. (2009). A feature-based approach to modeling protein-protein interaction hot spots. Nucleic Acids Res, 37(8), 2672.
12. Christ, F., Voet, A., Marchand, A., Nicolet, S., Desimmie, B. A., Marchand, D., Bardiot, D., Van der Veken, N. J., Van Remoortel, B., and Strelkov, S. V. (2010). Rational design of small-molecule inhibitors of the ledgf/p75-integrase interaction and hiv replication. Nature Chemical Biology, 6(6), 442-448.
13. Clackson, T. andWells, J. A. (1995). Ahot spot of binding energy in a hormone-receptor interface. Science, 267(5196), 383-386. 10.1126/science.7529940.
14. Darnell, S., Page, D., and Mitchell, J. (2007). An automated decision-tree approach to predicting protein interaction hot spots. Proteins: Struct. Funct. Bioinf., 68(4), 813-823.
15. Davis, F. P. and Sali, A. (2010). The overlap of small molecule and protein binding sites within families of protein structures. PLoS Computational Biology, 6(2), e1000668.
16. Dömling, A. (2008). Small molecular weight protein-protein interaction antagonists-an insurmountable challenge? Curr. Opin. Chem. Biol., 12(3), 281-291.
17. Fuller, J., Burgoyne, N., and Jackson, R. (2009). Predicting druggable binding sites at the protein-protein interface. Drug discovery today, 14(3-4), 155-161.
18. Guney, E., Tuncbag, N., Keskin, O., and Gursoy, A. (2008). HotSprint: database of computational hot spots in protein interfaces. Nucleic Acids Res, 36(suppl 1), D662.
19. Hajduk, P. J., Huth, J. R., and Tse, C. (2005). Predicting protein druggability. Drug Discovery Today, 10, 1675-1682. doi:10.1016/S1359-6446(05)03624-X.
20. Henrich, S., Salo-Ahen, O., Huang, B., Rippmann, F., Cruciani, G., andWade, R. (2010). Computational approaches to identifying and characterizing protein binding sites for ligand design. Journal of Molecular Recognition, 23(2), 209-219.
21. Higueruelo, A. P., Schreyer, A., Bickerton, G. R. J., Pitt, W. R., Groom, C. R., and Blundell, T. L. (2009). Atomic interactions and profile of small molecules disrupting protein-protein interfaces: Timbal database. Chem. Biol. Drug Des., 74(5), 457-467.
22. Hu, L., Benson, M. L., Smith, R. D., Lerner, M. G., and Carlson, H. A. (2005). Binding moad (mother of all databases). Proteins: Struct. Funct. Bioinf., 60(3), 333-340.
23. Jochim, A. L. and Arora, P. S. (2010). Systematic analysis of helical protein interfaces reveals targets for synthetic inhibitors. ACS Chemical Biology, 5(10), 919-923.
24. Keskin, O., Ma, B., and Nussinov, R. (2005). Hot regions in protein-protein interactions: the organization and contribution of structurally conserved hot spot residues. J. Mol. Biol., 345(5), 1281-1294.
25. Kortemme, T., Kim, D., and Baker, D. (2004). Computational alanine scanning of protein-protein interfaces. Sci STKE, 2004(219), l2.
26. Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I. M., Wilm, A., and Lopez, R. (2007). Clustal w and clustal x version 2.0. Bioinformatics, 23(21), 2947.
27. Lichtarge, O., Bourne, H. R., and Cohen, F. E. (1996). An evolutionary trace method defines binding surfaces common to protein families. J. Mol. Biol., 257(2), 342-358.
28. Lin, J., Chen, J., Elenbaas, B., and Levine, A. J. (1994). Several hydrophobic amino acids in the p53 amino-terminal domain are required for transcriptional activation, binding to mdm-2 and the adenovirus 5 e1b 55-kd protein. Genes Dev, 8(10), 1235.
29. Lise, S., Archambeau, C., Pontil, M., and Jones, D. (2009). Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energybased methods. BMC bioinformatics, 10(1), 365.
30. Liu, S., Wu, S., and Jiang, S. (2007). HIV entry inhibitors targeting gp41: from polypeptides to small-molecule compounds. Curr. Pharm. Des., 13(2), 143-162.
31. Liu, T., Lin, Y., Wen, X., Jorissen, R. N., and Gilson, M. K. (2006). Bindingdb: a webaccessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res, 35, D198.
32. Macarron, R. (2006). Critical review of the role of hts in drug discovery. Drug Discovery Today, 11, 277-279.
33. Mayrose, I., Graur, D., Ben-Tal, N., and Pupko, T. (2004). Comparison of site-specific rate-inference methods for protein sequences: Empirical bayesian methods are superior. Molecular biology and evolution, 21(9), 1781.
34. Meireles, L., Dömling, A. S., and Camacho, C. J. (2010). Anchor: a web server and database for analysis of protein-protein interaction binding pockets for drug discovery. Nucleic Acids Res, 38, W407.
35. Moreira, I., Fernandes, P., and Ramos, M. (2007). Hot spots: A review of the protein-protein interface determinant amino-acid residues. Proteins: Struct. Funct. Bioinf., 68(4), 803-812.
36. Ofran, Y. and Rost, B. (2007). Protein-protein interaction hotspots carved into sequences. PLoS. Comput. Biol., 3(7), e119.
37. Paolini, G., Shapland, R., van Hoorn, W., Mason, J., and Hopkins, A. (2006). Global mapping of pharmacological space. Nature biotechnology, 24(7), 805-815.
38. Pérot, S., Sperandio, O., Miteva, M. A., Camproux, A. C., and Villoutreix, B. O. (2010). Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug discovery today, 15, 656-667.
39. Popowicz, G., Dömling, A., and Holak, T. (2011). The Structure-Based Design of Mdm2/Mdmx-p53 Inhibitors Gets Serious. Angew. Chem. Int. Ed., 50(12), 2680-2688.
40. Rajamani, D., Thiel, S., Vajda, S., and Camacho, C. J. (2004). Anchor residues in protein-protein interactions. PNAS, 101(31), 11287.
41. Rijnbeek, P. R. and Kors, J. A. (2010). Finding a short and accurate decision rule in disjunctive normal form by exhaustive search. Machine learning, 80(1), 33-62.
42. Stewart, K. D., Huth, J. R., Ng, T. I., McDaniel, K., Hutchinson, R. N., Stoll, V. S., Mendoza, R. R., Matayoshi, E. D., Carrick, R., and Mo, H. M. (2010). Non-peptide entry inhibitors of hiv-1 that target the gp41 coiled coil pocket. Bioorg. Med. Chem. Lett., 20(2), 612-617.
43. Tuncbag, N., Gursoy, A., and Keskin, O. (2009). Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics, 25(12), 1513.
44. Valdar, W. S. J. (2002). Scoring residue conservation. Proteins: Struct Funct Bioinf, 48(2), 227-241.
45. Wang, R., Fang, X., Lu, Y., Yang, C. Y., and Wang, S. (2005). The pdbbind database: methodologies and updates. Journal of medicinal chemistry, 48(12), 4111-4119.
46. Weisel, M., Proschak, E., and Schneider, G. (2007). PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem. Cent. J, 1(7), 1-17.
47. Wells, J. and McClendon, C. (2007). Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature, 450(7172), 1001-1009.
48. Wu, C. H., Apweiler, R., Bairoch, A., Natale, D. A., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., and Lopez, R. (2006). The universal protein resource (uniprot): an expanding universe of protein information. Nucleic Acids Res, 34(suppl 1), D187.
49. Wu, T. F., Lin, C. J., and Weng, R. C. (2004). Probability estimates for multi-class classification by pairwise coupling. JMLR, 5, 975-1005.
50. Zhu, X. and Mitchell, J. C. (2011). Kfc2:Aknowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features. Proteins: Struct. Funct. Bioinf., 79(9), 2671-2683.