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Design and in vitro Testing of New Antimicrobial Peptides Based on QSAR Modelling
* 1 , 1 , 2 , 2 , * 1
1  I. Beritashvili Center of Experimental Biomedicine, Gotua str. 14, Tbilisi 0160, Gerogia
2  Agricultural University of Georgia, 240 David Aghmashenebeli Alley, Tbilisi 0159, Georgia

Published: 01 November 2016 by MDPI in 2nd International Electronic Conference on Medicinal Chemistry session ECMC-2

Antimicrobial peptides (AMPs) are anti-infective agents that may represent a novel and untapped class of biotherapeutics. In the lab of bioinformatics of IBCEB, the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv.2 - accessible at has been developed. DBAASP provides information and analytical resources to the scientific community in order to develop antimicrobial compounds with high therapeutic index.

Quantitative structure-activity relationship (QSAR) studies for the development of predictive model for AMPs are generally based on discriminative analysis and especially machine learning methods. These methods, as a positive training set, have used a full set of antimicrobial peptide sequences, without taking into account variation in mechanisms of action, structure, mode of interaction with membrane and other differences. Contrary to available approaches, we think that strategy of prediction should be based on the fact that there are at least four kinds of AMPs for which four independent algorithms of prediction have to be developed in order to get high efficacy. We can distinguish linear cationic antimicrobial peptides (LCAP), cationic peptides stabilizing structure by intra-chain covalent bonds, proline and arginine-rich peptides, and anionic antimicrobial peptides.

Simple predictive model which can discriminate AMPs from non-AMPs has been developed for LCAP. Sequences have been taken from DBAASP.  As descriptors the sequence-based physical-chemical characteristics responsible for capability of the peptide to interact with an anionic membrane were considered. On the basis of these characteristics, a new simple algorithm of prediction is developed and in silico evaluation of the efficacy of characteristics is done. The algorithm was based on the clusterization of AMPs by their physicochemical properties. The results show that descriptors relied mainly on hydrophobic and hydrophilic features allow us to predict AMPs with the high accuracy.

The developed predictive model was used to create new amino acid sequences. On the basis  of  designed sequences  the  peptides  have  been  synthesized. Antimicrobial potency of  the new  peptides  has  been  evaluated   by  in vitro testing of peptides activity against  different  pathogenic   bacteria  (including  drug  resistant strains). In vitro  estimation  shows  that  the  accuracy  of  th developed  predictive  model  is higher than 90%.