Stepwise identification of potent antimicrobial peptides from human genome

BioSystems

Volumn 113, Issue 1, 2013, Pages 1-8

  Yan, Li ^a   Yan, Yuxian ^a   Liu, Hongqi ^a   Lv, Qi ^a  

^a MEDICAL COLLEGE OF CHINESE PEOPLE'S ARMED POLICE FORCES   (China)

Author keywords

Antibacterial agent; Antibiotics; Bioinformatics; Infection; Rational drug design

Indexed keywords

POLYPEPTIDE ANTIBIOTIC AGENT;

ANTIBIOTICS; ANTIMICROBIAL ACTIVITY; BACTERIUM; BIOINFORMATICS; FUNGUS; GENOME; INFECTIVITY; INHIBITOR; MORBIDITY; MORTALITY; PATHOGEN; PEPTIDE; PROTEIN; PROTOZOAN; TOXICITY;

ANTIBACTERIAL ACTIVITY; ARTICLE; BACTERIAL STRAIN; CHEMICAL STRUCTURE; COMPUTER MODEL; CYTOTOXICITY; DRUG DESIGN; ESCHERICHIA COLI; GENETIC ASSOCIATION; GRAM NEGATIVE BACTERIUM; GRAM POSITIVE BACTERIUM; HIGH THROUGHPUT SCREENING; HUMAN GENOME; IN VITRO STUDY; KLEBSIELLA PNEUMONIAE; LISTERIA MONOCYTOGENES; MINIMUM INHIBITORY CONCENTRATION; MOLECULAR DOCKING; MYOBLAST; NONHUMAN; PSEUDOMONAS AERUGINOSA; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STAPHYLOCOCCUS AUREUS;

EID: 84878221453     PISSN: 03032647     EISSN: 18728324     Source Type: Journal    
DOI: 10.1016/j.biosystems.2013.03.021     Document Type: Article

References (44)

1. Alberola J., Rodríguez A., Francino O., Roura X., Rivas L., Andreu D. Safety and efficacy of antimicrobial peptides against naturally acquired Leishmaniasis. Antimicrob. Agents Chemother. 2004, 48:641-643.
2. Amaral A.C., Silva O.N., Mundim N.C.C.R., de Carvalho M.J.A., Migliolo L., Leite J.R.S.A., Prates M.V., Bocca A.L., Franco O.L., Felipe M.S.S. Predicting antimicrobial peptides from eukaryotic genomes: in silico strategies to develop antibiotics. Peptides 2012, 37:301-308.
3. Boulesteix A.L., Strimmer K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief. Bioinform. 2007, 8:32-44.
4. Cherkasov A., Jankovic B. Application of 'inductive' QSAR descriptors for quantification of antibacterial activity of cationic polypeptides. Molecules 2004, 9:1034-1052.
5. Cherkasov A., Hilpert K., Jenssen H., Fjell C.D., Waldbrook M., Mullaly S.C., Volkmer R., Hancock R.E.W. Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS Chem. Biol. 2008, 4:65-74.
6. Cornell W.D., Cieplak P., Bayly C.I., Gould I.R., Merz K.M., Ferguson D.M., Spellmeyer D.C., Fox T., Caldwell J.W., Kollman P.A. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 1995, 117:5179-5197.
7. Cui W., Wei Z., Chen Q., Cheng Y., Geng L., Zhang J., Chen J., Hou T., Ji M. Structure-based design of peptides against G3BP with cytotoxicity on tumor cells. J. Chem. Inf. Model. 2010, 50:380-387.
8. Edlund C., Hedberg M., Engström A., Flock J.I., Wade D. Anti-anaerobic activity of a cecropin-melittin peptide. Clin. Microbiol. Infect. 1998, 4:181-185.
9. Fjell C.D., Jenssen H., Hilpert K., Cheung W.A., Pante N., Hancock R.E.W., Cherkasov A. Identification of novel antibacterial peptides by chemoinformatics and machine learning. J. Med. Chem. 2009, 52:2006-2015.
10. Fjell C.D., Hiss J.A., Hancock R.E.W., Schneider G. Designing antimicrobial peptides: form follows function. Nat. Rev. Drug Discov. 2011, 11:37-51.
11. Frecer V., Ho B., Ding J.L. De novo design of potent antimicrobial peptides. Antimicrob. Agents Chemother. 2004, 48:3349-3357.
12. Fruci D., Rovero P., Falasca G., Chersi A., Sorrentino R., Butler R., Tanigaki N., Tosi R. Anchor residue motifs of HLA class-I-binding peptides analyzed by the direct binding of synthetic peptides to HLA class I alpha chains. Hum. Immunol. 1993, 38:187-192.
13. Giangaspero A., Sandri L., Tossi A. Amphipathic α-helical antimicrobial peptides. Eur. J. Biochem. 2001, 268:5589-5600.
14. Hilpert K., Volkmer-Engert R., Walter T., Hancock R.E.W. High-throughput generation of small antibacterial peptides with improved activity. Nat. Biotechnol. 2005, 23:1008-1012.
15. Hilpert K., Elliott M.R., Volkmer-Engert R., Henklein P., Donini O., Zhou Q., Winkler D.F.H., Hancock R.E.W. Sequence requirements and an optimization strategy for short antimicrobial peptides. Chem. Biol. 2006, 13:1101-1107.
16. Jenssen H., Hamill P., Hancock R.E.W. Peptide antimicrobial agents. Clin. Microbiol. Rev. 2006, 19:491-511.
17. Kacprzyk L., Rydengård V., Mörgelin M., Davoudi M., Pasupuleti M., Malmsten M., Schmidtchen A. Antimicrobial activity of histidine-rich peptides is dependent on acidic conditions. Biochim. Biophys. Acta 2007, 1768:2667-2680.
18. Katritzky A.R., Lobanov V.S., Karelson M. QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem. Soc. Rev. 1995, 24:279-287.
19. Kovalchuk L.V., Gankovskaya L.V., Gankovskaya O.A., Lavrov V.F. Herpes simplex virus: treatment with antimicrobial peptides. Adv. Exp. Med. Biol. 2007, 601:369-376.
20. Levy O. Antimicrobial proteins and peptides of blood: templates for novel antimicrobial agents. Blood 2000, 96:2664-2672.
21. Loose C., Jensen K., Rigoutsos I., Stephanopoulos G. A linguistic model for the rational design of antimicrobial peptides. Nature 2006, 443:867-869.
22. Marr A.K., Gooderham W.J., Hancock R.E.W. Antibacterial peptides for therapeutic use: obstacles and realistic outlook. Curr. Opin. Pharmacol. 2006, 6:468-472.
23. Martín-Madrazo C., Cañada-Dorado A., Salinero-Fort M.A., Abanades-Herranz J.C., Arnal-Selfa R., García-Ferradal I., Espejo-Matorral F., Santa-Pau E.C., Soto-Diaz S. Effectiveness of a training programme to improve hand hygiene compliance in primary healthcare. BMC Public Health 2009, 9:469.
24. Maupetit J., Derreumaux P., Tuffery P. PEP-FOLD: an online resource for de novo peptide structure prediction. Nucleic Acids Res. 2009, 37:W498-W503.
25. Mee R.P., Auton T.R., Morgan P.J. Design of active analogs of a 15-residue peptide using D-optimal design, QSAR and a combinatorial search algorithm. J. Pept. Res. 1997, 49:89-102.
26. Mergaert P., Kondorosi E. Controlling symbiotic microbes with antimicrobial peptides. ACS Symp. Ser. 2012, 1095:215-233.
27. Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S., Olson A.J. Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 2009, 16:2785-2791.
28. Oh H., Hedberg M., Wade D., Edlund C. Activities of synthetic hybrid peptides against bacteria: aspects of methodology and stability. Antimicrob. Agents Chemother. 2000, 44:68-72.
29. Oren Z., Shai Y. Mode of action of linear amphipathic α-helical antimicrobial peptides. Biopolymers 1999, 47:451-463.
30. Pacor S., Giangaspero A., Bacac M., Sava G., Tossi A. Analysis of the cytotoxicity of synthetic antimicrobial peptides on mouse leucocytes: implications for systemic use. J. Antimicrob. Chemother. 2002, 50:339-348.
31. Pasupuleti M., Davoudi M., Malmsten M., Schmidtchen A. Antimicrobial activity of a C-terminal peptide from human extracellular superoxide dismutase. BMC Res. Notes 2009, 2:136.
32. Peters B.M., Shirtliff M.E., Jabra-Rizk M.A. Antimicrobial peptides: primeval molecules or future drugs?. PLoS Pathog. 2010, 6:e1001067.
33. Qui D., Shenkin P.S., Hollinger F.P., Still W.C. The GB/SA continuum model for solvation. A fast analytical method for the calculation of approximate born radii. J. Phys. Chem. A 1997, 101:3005-3014.
34. Ren P., Ponder J.W. Polarizable atomic multipole water model for molecular mechanics simulation. J. Phys. Chem. B 2003, 107:5933-5947.
35. Sato A.K., Viswanathan M., Kent R.B., Wood C.R. Therapeutic peptides: technological advances driving peptides into development. Curr. Opin. Biotechnol. 2006, 17:638-642.
36. Schweizer F. Cationic amphiphilic peptides with cancer-selective toxicity. Eur. J. Pharmacol. 2009, 625:190-194.
37. Shu M., Jiang Y., Yang L., Wu Y., Mei H., Li Z. Application of 'HESH' descriptors for the structure-activity relationships of antimicrobial peptides. Protein Pept. Lett. 2009, 16:143-149.
38. Tian F., Yang L., Lv F., Yang Q., Zhou P. In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure-activity relationship approach. Amino Acids 2009, 36:535-554.
39. Trent M.S. Biosynthesis, transport, and modification of lipid A. Biochem. Cell. Biol. 2004, 82:71-86.
40. Uniprot C. Ongoing and future developments at the universal protein resource. Nucleic Acids Res. 2010, 39:D214-D219.
41. Wang G., Li X., Wang Z. APD2: the updated antimicrobial peptide database and its application in peptide design. Nucleic Acids Res. 2009, 37:D933-D937.
42. Wu M., Hancock R.E.W. Improved derivatives of bactenecin, a cyclic dodecameric antimicrobial cationic peptide. Antimicrob. Agents Chemother. 1999, 43:1274-1276.
43. Yang Z.R. Biological applications of support vector machines. Brief. Bioinform. 2004, 5:328-338.
44. Zhou P., Tian F., Wu Y., Li Z., Shang Z. Quantitative sequence-activity model (QSAM): applying QSAR strategy to model and predict bioactivity and function of peptides, proteins and nucleic acids. Curr. Comput. Aid. Drug Des. 2008, 4:311-321.