Computational Approaches in Discovery & Design of Antimicrobial Peptides

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: abscess model; allosteric inhibition; AMP discovery; amphipathic peptides; AMPs; AMPs database; anti-inflammatory; antibacterial peptide; anticancer; antimicrobial; antimicrobial peptide; antimicrobial peptides; antimicrobial resistance; antiviral; Apicomplexan; artificial intelligence; bioactive peptides; biofilms; biosynthetic gene clusters; cancer; cathelicidins; cell-penetrating; centrality measure; chemical space network; Chemistry; complex network; complex networks; Cryptococcus neoformans; cryptosporidiosis; design; ergosterol; evolutionary algorithms; evolutionary and PPI algorithms; FOF1-ATP synthase; genome; genome mining; Gram staining-based target; group fusion; high-performance liquid chromatography (HPLC); human peptidome; in silico drug discovery; LL-37; machine learning; malaria; mass spectrometry; Mathematics and Science; molecular descriptors; motif discovery; multifunctional; n/a; non-toxic; penetratins; peptide design; peptide library screening; proteogenomics; redox system; Reference, Information and Interdisciplinary subjects; reptiles; Research and information: general; ROS metabolism; secondary metabolites; similarity searching; skin model; StarPep; starPep toolbox software; Streptomyces; structure-based drug design; synthetic antimicrobial peptides; toxoplasmosis; tumor-homing peptide
• ISBN: 3036579605; 9783036579610; 3036579613; 9783036579603
• DOI: 10.3390/books978-3-0365-7960-3

Antimicrobial 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.