Abstract
The different antimicrobial peptides (AMPs) isolated from various sources vary in their amino acid numbers and net charge, are recognized by nucleic acid sequencing. Since the discovery of new drugs and other alternative treatments for various diseases are limited, there is a growing concern among the scientific community for a new clinical treatment strategy. AMPs have been identified as a promising class of antimicrobials because they are less susceptible to bacterial resistance. The mode of action and the pharmacodynamic characteristics, of AMPs, differ significantly from conventional antibiotics. The mechanism of resistance involves broad-range resistance or specific resistance to AMPs. These resistance mechanisms of bacteria can be a critical contributor to bacterial virulence and enable them to survive within the host. Understanding the mechanisms of AMP resistance in microorganisms especially in gram-positive and gram-negative bacteria can provide an insight into the role of resistance in pathogenesis, and also provide guidelines in developing new AMPs and can be applied as a therapeutic agent in clinical treatment. Computational biology and data sciences are the recent approaches to studying the structural and functional properties of AMPs. The present chapter comprehensively illustrates the various AMP resistance mechanisms exhibited by both gram-positive and gram-negative bacterial pathogens. The chapter also emphasizes the scope of computational biology in AMP research and various computational biology tools and databases employed for AMP research and the study of their resistance mechanisms. Finally, the chapter prioritizes the scope of various machine learning techniques for the detection of AMPs and their resistance mechanisms. Thus the chapter is an eye-opener for understanding the basic mechanism of AMP resistance mechanism and the modern strategies employed in the AMP research.
Published Version
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