Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): Modeling performance of 13 amino acid descriptor sets

Journal of Cheminformatics

Volumn 5, Issue 9, 2013, Pages

  Van Westen, Gerard J P ^a,d   Swier, Remco F ^a   Cortes Ciriano, Isidro ^b   Wegner, Jorg K ^c   Overington, John P ^d   Jzerman, Adriaan P I ^a   Van Vlijmen, Herman W T ^a,c   Bender, Andreas ^a,e  

^a LEIDEN UNIVERSITY   (Netherlands)

^b INSTITUT PASTEUR   (France)

^c TIBOTEC BVBA   (Belgium)

^d WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^e UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Amino acid index; GPCR; HIV; Peptides; Polypharmacology; Protein descriptors; QSAM

Indexed keywords

EID: 84885053019     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-5-42     Document Type: Article

References (62)

1. Lapinsh M, Prusis P, Gutcaits A, Lundstedt T, Wikberg JE: Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions. Biochim Biophys Acta 2001, 1525:180-190.
2. Wikberg JES, Mutulis F, Mutule I, Veiksina S, Lapinsh M, Petrovska R, Prusis P: Melanocortin receptors: ligands and proteochemometrics modeling. In Annals of the New York Academy of Sciences Volume 994. Edited by Braaten D. New York: Blackwell Publishing Ltd; 2003:21-26.
3. Kontijevskis A, Prusis P, Petrovska R, Yahorava S, Mutulis F, Mutule I, Komorowski J, Wikberg JE: A look inside HIV resistance through retroviral protease interaction maps. PLoS Comput Biol 2007, 3:e48.
4. Van Westen GJP, Wegner JK, Ijzerman AP, Van Vlijmen HWT, Bender A: Proteochemometric modeling as a tool for designing selective compounds and extrapolating to novel targets. Med Chem Commun 2011, 2:16-30.
5. Van Westen GJP, Van den Hoven OO, Van der Pijl R, Mulder-Krieger T, de Vries H, Wegner JK, Ijzerman AP, Van Vlijmen HWT, Bender A: Identifying novel adenosine receptor ligands by simultaneous proteochemometric modeling of Rat and human bioactivity data. J Med Chem2012, 55(16):7010-7020.
6. De Bruyn T, Van Westen GJP, IJzerman AP, Stieger B, de Witte P, Augustijns PF, Annaert PP: Structure-based Identification of OATP1B1/3 Inhibitors. Mol Pharmacol 2013, 83(6):1257-1267.
7. Van Westen GJP, Hendriks A, Wegner JK, IJzerman AP, Van Vlijmen HWT, Bender A: Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data. PLoS Comput Biol 2013, 9(2):e1002899.
8. Lapinsh M, Prusis P, Lundstedt T, Wikberg JES: Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands. Mol Pharmacol 2002, 61:1465-1475.
9. Bock JR, Gough DA: Virtual screen for ligands of orphan G protein-coupled receptors. J Chem Inf Model 2005, 45(5):1402-1414.
10. Prusis P, Uhln S, Petrovska R, Lapinsh M, Wikberg JES: Prediction of indirect interactions in proteins. BMC Bioinforma 2006, 7:167.
11. Hellberg S, Sjstrm M, Skagerberg B, Wold S: Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem 1987, 30:1126-1135.
12. Jonsson J, Norberg T, Carlsson L, Gustafsson C, Wold S: Quantitative sequence-activity models (QSAM)-tools for sequence design. Nucleic Acids Res 1993, 21:733-739.
13. Collantes ER, Dunn WJ: Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogs. J Med Chem 1995, 38(14):2705-2713.
14. Sandberg M, Eriksson L, Jonsson J, Sjstrm M, Wold S: New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem 1998, 41:2481-2491.
15. Zhou P, Chen X, Wu Y, Shang Z: Gaussian process: an alternative approach for QSAM modeling of peptides. Amino Acids 2010, 38(1):199-212.
16. Strombergsson H, Kryshtafovych A, Prusis P, Fidelis K, Wikberg JES, Komorowski J, Hvidsten TR: Generalized modeling of enzyme-ligand interactions using proteochemometrics and local protein substructures. Proteins: Struct, Funct, Bioinf 2006, 65:568-579.
17. Strmbergsson H, Daniluk P, Kryshtafovych A, Fidelis K, Wikberg JES, Kleywegt GJ, Hvidsten TR: Interaction model based on local protein substructures generalizes to the entire structural enzyme-ligand space. J Chem Inf Model 2008, 48:2278-2288.
18. Weill N, Rognan D: Development and validation of a novel protein â' ligand fingerprint to mine chemogenomic space: application to G protein-coupled receptors and their ligands. J Chem Inf Model 2009, 49(4):1049-1062.
19. Lapins M, Wikberg J: Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques. BMC Bioinforma 2010, 11:339.
20. Meslamani J, Li J, Sutter J, Stevens A, Bertrand H-O, Rognan D: Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling. J ChemInf Model 2012, 52(4):943-955.
21. Van Westen GJP, Swier RF, Wegner JK, IJzerman AP, Van Vlijmen HWT, Bender A: Benchmarking of protein descriptors in proteochemometric modeling (Part 1): comparative study of 13 amino acid descriptors. J Cheminform 2013, 5:41.
22. Mei H, Liao ZH, Zhou Y, Li SZ: A new set of amino acid descriptors and its application in peptide QSARs. Biopolymers 2005, 80:775-786.
23. Tian F, Zhou P, Li Z: T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides. J Mol Struct 2007, 830:106-115.
24. Yang L, Shu M, Ma K, Mei H, Jiang Y, Li Z: ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues. Amino Acids 2010, 38:805-816.
25. Liang G, Li Z: Factor analysis scale of generalized amino acid information as the source of a New Set of descriptors for elucidating the structure and activity relationships of cationic antimicrobial peptides. QSAR Comb Sci 2007, 26:754-763.
26. Zaliani A, Gancia E: MS-WHIM scores for amino acids: a New 3D-description for peptide QSAR and QSPR studies. J Chem Inf Comput Sci 1999, 39:525-533.
27. Georgiev AG: Interpretable numerical descriptors of amino acid space. J Comput Biol 2009, 16:703-723.
28. Van Westen GJP, Wegner JK, Geluykens P, Kwanten L, Vereycken I, Peeters A, IJzerman AP, Van Vlijmen HWT, Bender A: Which compound to select in lead optimization? prospectively validated proteochemometric models guide preclinical development. PLoS ONE 2011, 6:e27518.
29. Hellberg S, Eriksson L, Jonsson J, Lindgren F, Sjostrom M, Skagerberg B, Wold S, Andrews P: Minimum analogue peptide sets (MAPS) for quantitative structure activity relationships. Int J Pept Protein Res 1991, 37(5):414-424.
30. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, et al: ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012, 40(D1):D1100-D1107.
31. Patterson DE, Cramer RD, Ferguson AM, Clark RD, Weinberger LE: Neighborhood behavior: a useful concept for validation of "molecular diversity" descriptors. J Med Chem 1996, 39:3049-3059.
32. Matthews BW: Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim Biophys Acta 1975, 405:442-451.
33. de Graaf C, Vischer HF, de Kloe GE, Kooistra AJ, Nijmeijer S, Kuijer M, Verheij MHP, England PJ, van Muijlwijk-Koezen JE, Leurs R, et al: Small and colorful stones make beautiful mosaics: fragment-based chemogenomics. Drug Discov Today 2013, 18(7-8):323-330.
34. Kooistra AJ, Kuhne S, de Esch IJP, Leurs R, de Graaf C: A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design. Br J Pharmacol 2013, 170(1):101-126.
35. Surgand J-S, Rodrigo J, Kellenberger E, Rognan D: A chemogenomic analysis of the transmembrane binding cavity of human G-protein-coupled receptors. Proteins 2006, 62:509-538.
36. Johnson VA, Calvez V, Gunthard HF, Paredes R, Pillay D, Shafer RW, Wensing AM, Richman DD: Update of the drug resistance mutations in HIV-1: March 2013. Top Antivir Med 2013, 21(1):6-14.
37. Wilcoxon F: Individual comparisons by ranking methods. Biometrics Bull 1945, 1(6):80-83.
38. Jacob L, Hoffmann B, Stoven V, Vert J-P: Virtual screening of GPCRs: an in silico chemogenomics approach. BMC Bioinforma 2008, 9(1):363.
39. Frimurer TM, Ulven T, Elling CE, Gerlach L-O, Kostenis E, Hgberg T: A physicogenetic method to assign ligand-binding relationships between 7TM receptors. Bioorg Med Chem Lett 2005, 15:3707-3712.
40. Ha C: Protein flexibility and drug design: how to hit a moving target. Curr Opin Chem Biol 2002, 6:447-452.
41. Van Westen GJP, Wegner JK, Bender A, IJzerman AP, Van Vlijmen HWT: Mining protein dynamics from sets of crystal structures using aconsensus structuresa. Protein Sci 2010, 19:742-752.
42. Li ZR, Lin HH, Han LY, Jiang L, Chen X, Chen YZ: PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 2006, 34(suppl 2):W32-W37.
43. Cao D-S, Liang Y-Z, Deng Z, Hu Q-N, He M, Xu Q-S, Zhou G-H, Zhang L-X, Deng Z-x, Liu S: Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach. PLoS ONE 2013, 8(4):e57680.
44. Cao D-S, Xu Q-S, Liang Y-Z: propy: a tool to generate various modes of Chouas PseAAC. Bioinformatics 2013, 29(7):960-962.
45. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK: Relating protein pharmacology by ligand chemistry. Nat Biotechnol 2007, 25(2):197-206.
46. Van Westen GJP, Overington JP: A ligandas-eye view of protein similarity. Nat Methods 2013, 10(2):116-117.
47. Mizuguchi K, Deane CM, Blundell TL, Johnson MS, Overington JP: JOY: protein sequence-structure representation and analysis. Bioinformatics1998, 14(7):617-623.
48. Vroling B, Sanders M, Baakman C, Borrmann A, Verhoeven S, Klomp J, Oliveira L, de Vlieg J, Vriend G: GPCRDB: information system for G proteincoupled receptors. Nucleic Acids Res 2011, 39(suppl 1):D309-D319.
49. Heikamp K, Bajorath J: Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening. J Chem Inf Model 2013, 53(7):1595-1601.
50. Accelrys Software Inc: Pipeline pilot. Version 8. 5. .
51. Korber B, Foley BT, Kuiken C, Pillai SK, Sodroski JG: Numbering positions in HIV relative to HXB2CG. AIDS Res Hum Retroviruses 1998, 3:102-111.
52. Lapins M, Wikberg JES: Proteochemometric modeling of drug resistance over the mutational space for multiple HIV protease variants and multiple protease inhibitors. J Chem Inf Model 2009, 49:1202-1210.
53. Kontijevskis A, Petrovska R, Yahorava S, Komorowski J, Wikberg JES: Proteochemometrics mapping of the interaction space for retroviral proteases and their substrates. Bioorg Med Chem 2009, 17:5229-5237.
54. Lapins M, Eklund M, Spjuth O, Prusis P, Wikberg JES: Proteochemometric modeling of HIV protease susceptibility. BMC Bioinformatics 2008, 9:181.
55. van der Horst E, Peironcely J, IJzerman AP, Beukers M, Lane J, van Vlijmen HWT, Emmerich M, Okuno Y, Bender A: A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization. BMC Bioinformatics 2010, 11:316.
56. van der Horst E, Peironcely EJ, van Westen JP G, van den Hoven O, Galloway RJD W, Spring RD, Wegner KJ, van Vlijmen WT H, IJzerman PA, Overington PJ: Chemogenomics approaches for receptor deorphanization and extensions of the chemogenomics concept to phenotypic space. Curr Top Med Chem 2011, 11(15):1964-1977.
57. Rogers D, Hahn M: Extended-connectivity fingerprints. J Chem Inf Model 2010, 50:742-754.
58. Bender A, Jenkins JL, Scheiber J, Sukuru SCK, Glick M, Davies JW: How similar are similarity searching methods? a principal component analysis of molecular descriptor space. J Chem Inf Model 2009, 49:108-119.
59. R Development Core Team: R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2009.
60. Liaw A, Wiener M: Classification and regression by randomForest. R News 2002, 2(3):18-22.
61. Tropsha A, Golbraikh A: Predictive Quantitative Structure-Activity Relationships Modeling. In Handbook of Chemoinformatics Algorithms. Edited by Faulon JL, Faulon JL, Bender A. London: Chapman & Hall / CRC; 2010:213-233.
62. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16(5):412-424.