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101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aComputational approaches in discovery & design of antimicrobial peptides /$fedited by Agostinho Agu?ero-Chapin, Guillermin Agu?ero-Chapin, Yovani Marrero-Ponce
210  1$a[Place of publication not identified] :$cMultidisciplinary Digital Publishing Institute,$d2023.
215   $a1 online resource (258 pages)
311   $a3-0365-7960-5 
330   $aAntimicrobial resistance remains a major concern in medicine, especially during the COVID-19 pandemic, where microbial infections were frequent complications. To combat drug-resistant pathogens, there has been a renewed interest in the use of antimicrobial peptides (AMPs). This reprint focuses on the in-silico approaches used for the rational discovery and design of AMPs. Such computational methodologies range from classical homology-based and machine-learning prediction algorithms to complex similarity networks and evolutionary algorithms that use models of sequence evolution. Furthermore, the reprint explores the improvement of high-throughput screening techniques in the discovery of AMPs from biological samples, which has also led to the evolution of computational approaches that aid in this biodiscovery process. The reprint contains original research and review papers, which serve as valuable references for researchers dedicated to peptide drug development.
606   $aMicrobial peptides
615  0$aMicrobial peptides.
676   $a615.3
702   $aAgu?ero-Chapin$b Guillermin
702   $aMarrero-Ponce$b Yovani
702   $aAgu?ero-Chapin$b Agostinho
801  0$bNjHacI
801  1$bNjHacl
906   $aBOOK
912   $a9910734364603321
996   $aComputational Approaches in Discovery & Design of Antimicrobial Peptides$93399766
997   $aUNINA