Desirability-based multi-criteria virtual screening of selective antimicrobial cyclic β-hairpin Cationic Peptidomimetics

Current Pharmaceutical Design

Volumn 19, Issue 12, 2013, Pages 2148-2163

  Cruz Monteagudo, Maykel ^a,b   Romero, Yansy ^c   Cordeiro, M Natália D S ^c   Borges, Fernanda ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b CENTRAL UNIVERSITY OF LAS VILLAS   (Cuba)

^c UNIVERSIDAD DE CIENCIAS MÉDICAS DE VILLA CLARA   (Cuba)

Author keywords

Antimicrobial peptides; Chemoinformatics; Desirability theory; Desirability credibility similarity score (DCS score); Multicriteria QSAR; Virtual screening

Indexed keywords

ANTIMICROBIAL CYCLIC BETA HAIRPIN CATIONIC PEPTIDOMIMETIC; PEPTIDOMIMETIC AGENT; POLYPEPTIDE ANTIBIOTIC AGENT; UNCLASSIFIED DRUG;

ACCURACY; ANTIMICROBIAL ACTIVITY; ARTICLE; CHEMICAL STRUCTURE; CHEMOMETRICS; CODING; DESIRABILITY CREDIBILITY SIMILARITY SCORE; DRUG IDENTIFICATION; DRUG SCREENING; ERYTHROCYTE; HEMOLYSIS; HUMAN; MINIMUM INHIBITORY CONCENTRATION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; STAPHYLOCOCCUS AUREUS;

ANTI-BACTERIAL AGENTS; ANTI-INFECTIVE AGENTS; ANTIMICROBIAL CATIONIC PEPTIDES; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATA MINING; DATABASES, CHEMICAL; DRUG DESIGN; DRUG EVALUATION, PRECLINICAL; DRUG RESISTANCE, MICROBIAL; DRUG RESISTANCE, MULTIPLE; EXPERT SYSTEMS; HEMOLYSIS; HUMANS; MICROBIAL SENSITIVITY TESTS; MODELS, MOLECULAR; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; ROC CURVE; STAPHYLOCOCCUS AUREUS;

EID: 84877802372     PISSN: 13816128     EISSN: 18734286     Source Type: Journal    
DOI: 10.2174/1381612811319120003     Document Type: Article

References (53)

1. Rossolini GM, Mantengoli E. Antimicrobial resistance in Europe and its potential impact on empirical therapy. Clin Microbiol Infect 2008; 14: 2-8.
2. Goossens H, Grabein B. Prevalence and antimicrobial susceptibility data for extended-spectrum betalactamase and AmpC-producing Enterobacteriaceae from the MYSTIC Program in Europe and the United States (1997-2004). Diagn Microbiol Infect Dis 2005; 53: 257-64.
3. Pitout JD, Nordmann P, Laupland KB, Poirel L. Emergence of Enterobacteriaceae producing extended-spectrum beta-lactamases (ESBLs) in the community. J Antimicrob Chemother 2005; 56: 52-9.
4. Hancock RE, Sahl HG. Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nat Biotechnol 2006; 24: 1551-7.
5. Toke O. Antimicrobial peptides: new candidates in the fight against bacterial infections. Biopolymers 2005; 80: 717-35.
6. Robinson JA, Shankaramma SC, Jetter P, et al. Properties and structure-activity studies of cyclic beta-hairpin peptidomimetics based on the cationic antimicrobial peptide protegrin I. Bioorg Med Chem 2005; 13: 2055-64.
7. Marr AK, Gooderham WJ, Hancock RE. Antibacterial peptides for therapeutic use: obstacles and realistic outlook. Curr Opin Pharmacol 2006; 6: 468-72.
8. Kondejewski LH, Jelokhani-Niaraki M, Farmer SW, et al. Dissociation of antimicrobial and hemolytic activities in cyclic peptide diastereomers by systematic alterations in amphipathicity. J Biol Chem 1999; 274: 13181-92.
9. Fjell CD, Hancock REW, Jenssen H. Computer-aided design of antimicrobial peptides. Curr Pharm Anal 2010; 6: 66-75.
10. Frecer V. QSAR analysis of antimicrobial and haemolytic effects of cyclic cationic antimicrobial peptides derived from protegrin-1. Bioorg Med Chem 2006; 14: 6065-74.
11. Jenssen H. Descriptors for antimicrobial peptides. Expert Opin Drug Discov 2011; 6: 171-84.
12. Cruz-Monteagudo M, Borges F, Cordeiro MNDS, et al. Desirability-Based Methods of Multiobjective Optimization and Ranking for Global QSAR Studies. Filtering Safe and Potent Drug Candidates from Combinatorial Libraries. J Comb Chem 2008; 10: 897-913.
13. Nicolotti O, Giangreco I, Miscioscia TF, Carotti A. Improving quantitative structure-activity relationships through multiobjective optimization. J Chem Inf Model 2009; 49: 2290-302.
14. Harrington EC. The Desirability Function. Ind Quality Control 1965; 21: 494-8.
15. Derringer G, Suich R. Simultaneous Optimization of Several Response Variables. J Quality Technol 1980; 12: 214-9.
16. Segall MD. Multi-parameter optimization: identifying high quality compounds with a balance of properties. Curr Pharm Des 2012; 18: 1292-310.
17. ChemAxon. MarvinSketch 5.6.0.4. http://www.chemaxon.com (accessed November 1, 2011).
18. ChemAxon. MarvinView 5.6.0.4. http://www.chemaxon.com (accessed November 1, 2011).
19. Findlay B, Zhanel GG, Schweizer F. Cationic amphiphiles, a new generation of antimicrobials inspired by the natural antimicrobial peptide scaffold. Antimicrob Agents Chemother 2010; 54: 4049-58.
20. Nair CM, Vijayan M, Venkatachalapathi YV, Balaram P. X-ray crystal structure of pivaloyl-D-Pro-L-Pro-L-Ala-N-methylamide; Observation of a consecutive β-turn conformation. J Chem Soc, Chem Commun 1979: 1183-4.
21. Bean JW, Kopple KD, Peishoff CE. Conformational analysis of cyclic hexapeptides containing the D-Pro-L-Pro sequence to fix β-turn positions. J Am Chem Soc 1992; 114: 5328-34.
22. Chalmers DK, Marshall GR. Pro-D-NMe-amino acid and D-Pro-NMe-amino acid: Simple, efficient reverse turn constraints. J Am Chem Soc 1995; 117: 5927-37.
23. Tropsha A. Integrated Chemo-and Bioinformatics Approaches to Virtual Screening. In: Varnek A, Tropsha A, editors. Chemoinformatics Approaches to Virtual Screening. Cambridge: Royal Society of Chemistry; 2008. p. 295-325.
24. DRAGON 6.0. http://www.talete.mi.it/
25. Todeschini R, Consonni V. Molecular descriptors for chemoinformatics. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co; 2009.
26. Burden FR, Ford MG, Whitley DC, Winkler DA. Use of automatic relevance determination in QSAR studies using Bayesian neural networks. J Chem Inf Comput Sci 2000; 40: 1423-30.
27. STATISTICA 8.0. www.statsoft.com
28. Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol Inf [Review] 2010; 29: 476-88.
29. Kubinyi H, editor. Virtual Screening-The Road to Success. XIX International Symposium on Medicinal Chemistry; 2006; Istanbul, Turkey.
30. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Monterey, CA: Wadsworth; 1984.
31. Ivanciuc O. Drug Design with Machine Learning. In: Meyers RA, editor. Encyclopedia of Complexity and System Science. New York: Springer-Verlag; 2009.
32. Witten IH, Frank E. Chapter 5: Credibility: Evaluating what's been learned. In: Gray J, editor. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. San Francisco: Morgan Kaufman; 2005. p. 143-86.
33. Cannon EO, Amini A, Bender A, et al. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput-Aided Mol Des 2007; 21: 269-80.
34. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 1975; 405: 442-51.
35. Truchon JF, Bayly CI. Evaluating virtual screening methods: good and bad metrics for the "early recognition" problem. J Chem Inf Model 2007; 47: 488-508.
36. Kirchmair J, Markt P, Distinto S, Wolber G, Langer T. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes? J Comput-Aided Mol Des 2008; 22: 213-28.
37. Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. Gray J, editor. San Francisco: Morgan Kaufmann; 2005.
38. Drews J. Innovation deficit revisited: Reflections on the productivity of pharmaceutical R&D. Drug Discov Today 1998; 3: 491-4.
39. Bruce CL, Melville JL, Pickett SD, Hirst JD. Contemporary QSAR classifiers compared. J Chem Inf Model 2007; 47: 219-27.
40. Huang HW. Action of antimicrobial peptides: two-state model. Biochemistry 2000; 39: 8347-52.
41. Tam JP, Wu C, Yang JL. Membranolytic selectivity of cystinestabilized cyclic protegrins. Eur J Biochem 2000; 267: 3289-300.
42. Strom MB, Haug BE, Skar ML, Stensen W, Stiberg T, Svendsen JS. The pharmacophore of short cationic antibacterial peptides. J Med Chem 2003; 46: 1567-70.
43. Lejon T, Stiberg T, Strom MB, Svendsen JS. Prediction of antibiotic activity and synthesis of new pentadecapeptides based on lactoferricins. J Pept Sci 2004; 10: 329-35.
44. Frecer V, Ho B, Ding JL. De novo design of potent antimicrobial peptides. Antimicrob Agents Chemother 2004; 48: 3349-57.
45. Ostberg N, Kaznessis Y. Protegrin structure-activity relationships: using homology models of synthetic sequences to determine structural characteristics important for activity. Peptides 2005; 26: 197-206.
46. Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics. Mannhold R, Kubinyi H, Folkers G, editors. Weinheim: Wiley-VCH; 2009.
47. Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem 2006; 49: 6789-801.
48. Gasteiger J, Engel T. Chemoinformatics: a Textbook. Gasteiger J, Engel T, editors: Wiley-VCH; 2003.
49. Hooper G. A calculation of the credibility of human testimony. Phil Trans Royal Soc 1699; 21: 359-65.
50. Shafer G. The combination of evidence. Int J Intell Syst 1986; 1: 155-79.
51. Muchmore SW, Debe DA, Metz JT, Brown SP, Martin YC, Hajduk PJ. Application of belief theory to similarity data fusion for use in analog searching and lead hopping. J Chem Inf Model 2008; 48: 941-8.
52. Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 2006; 11: 1046-53.
53. Fjell CD, Hiss JA, Hancock RE, Schneider G. Designing antimicrobial peptides: form follows function. Nat Rev Drug Discov 2012; 11: 37-51.