TY - JOUR
T1 - Antimicrobial peptides
T2 - An update on classifications and databases
AU - Bin Hafeez, Ahmer
AU - Jiang, Xukai
AU - Bergen, Phillip J.
AU - Zhu, Yan
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an in-dispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimi-crobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.
AB - Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an in-dispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimi-crobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.
KW - Antimicrobial peptide
KW - BLAST
KW - Database
KW - HMM
KW - Machine learning
KW - Mode of action
KW - Structure
UR - http://www.scopus.com/inward/record.url?scp=85117927708&partnerID=8YFLogxK
U2 - 10.3390/ijms222111691
DO - 10.3390/ijms222111691
M3 - Review Article
C2 - 34769122
AN - SCOPUS:85117927708
VL - 22
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
SN - 1422-0067
IS - 21
M1 - 11691
ER -