Modeling synergistic antimicrobial activities in peptide cocktails: strategies for production and delivery | AMP2012 Antimicrobial Peptide Symposium, Lille 2012

Pablo Carbonell (Selected oral communication) – Modeling Synergistic Antimicrobial Activities in Peptide Cocktails: Strategies for Production and Delivery

June 14th 2012, Lille, France

Antimicrobial peptides (AMPs) are a novel source of therapeutics to treat infectious diseases that can overcome the global rise of antibiotic resistance. Promising developments in synthetic biology have demonstrated the effectiveness of using synthetic circuits in AMP-producing bacteria in order to control the delivery of the killing agent. In this study, we present strategies for in situ production and delivery [1-2] of synergistic combinations of antimicrobial peptides. Even though synergy between AMPs has been demonstrated experimentally, multi-drug cocktails principles, however, have not been fully exploited in the case of AMPs, and its promiscuous side effects are still to be quantified. Quantitative structure???activity relationship (QSAR) tools have been developed for toxicology studies on antimicrobial peptides, but, to the best of our knowledge, accurately modelling the activity of AMPs cocktails is yet to be addressed. Based on structure-activity information from several databases of antimicrobial peptides (APD, CAMP, AMSDb, BAGEL, PhytAMP, RAPD, Peptaibol, PenBase), our group has built a computational model [3-5] to compile antibacterial peptide cocktails with predicted increased efficacy and specificity for target bacteria


[1] Planson AG, Carbonell P, Grigoras I & Faulon JL (2011) Engineering antibiotic production and overcoming bacterial resistance. Biotechnol J 6: 812-825.
[2] Carbonell P, Planson AG, Fichera D & Faulon JL (2011) A retrosynthetic biology approach to metabolic pathway design for therapeutic production. BMC Syst Biol  5: 122+.
[3] Churchwell C J, Rintoul MD, Martin S, Visco DP, Kotu A, Larson RS, Sillerud LO, Brown DC & Faulon JL (2004) The signature molecular descriptor. 3. inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides. J Mol Graph Model 22: 263-273.
[4] Carbonell P, & Faulon JL (2010) Molecular signatures-based prediction of enzyme promiscuity. Bioinformatics 26: 2012-2019.
[5] Planson AG, Carbonell P, Paillard E, Pollet N & Faulon JL (2012). Compound toxicity screening and structure-activity relationship modeling in escherichia coli. Biotechnol Bioeng  109: 846-850.