Algorithms for the selection of molecular descriptors applied to antimicrobial peptides identification

Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model’s performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. In this talk, we will also discuss other related problems we are dealing with in the Biocomputing Lab at CICESE.