Computational Approaches in Discovery & Design of Antimicrobial Peptides

Dear Colleagues,

Bacterial resistance to antibiotics is still a serious concern worldwide, especially nowadays when dealing with bacterial infections associated with COVID-19. Because of the potentialities of the naturally occurring antimicrobial peptides (AMPs) to face the multi-resistant problems, many computational approaches have been developed to assist in the search and design of antibiotic peptides within the AMPs chemical space. Currently, from classical alignment-based (AB) and alignment-free (AF) prediction algorithms to non-conventional approaches such as complex similarity networks are being applied for AMPs detection. On the other hand, the design and optimization of AMPs are also computationally assisted by the in silico generation of both random and rationally oriented peptide libraries. Artificial-intelligence-inspired evolutionary algorithms and models of sequence evolution have supported the optimization of peptide scaffolds and the rational generation of diversity-oriented libraries, respectively.

Last but not least, with the improvement of High-Throughput Screening (HTS) techniques applied to the discovery of AMPs in biological samples, the associated computational approaches have also evolved to assist this biodiscovery process. In this sense, proteogenomic analyses considering both transcriptomic and proteomic data have been successfully applied in the detection of AMPs.

This Special Issue of Antibiotics invites authors to publish original research including in silico approaches used for the rational search/discovery and design of AMPs. Review papers on this topic are welcome, too.

Prof. Dr. Agostinho Antunes
Dr. Guillermin Agüero-Chapin
Dr. Yovani Marrero-Ponce
Guest Editors

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