Structure-Activity Predictions From Computational Mining of Protein Databases to Assist Modular Design of Antimicrobial Peptides.

ABSTRACT: Antimicrobial peptides (AMPs) are naturally produced by pro- and eukaryotes and are promising alternatives to antibiotics to fight multidrug-resistant microorganisms. However, despite thousands of AMP entries in respective databases, predictions about their structure-activity relationships are still limited. Similarly, common or dissimilar properties of AMPs that have evolved in different taxonomic groups are nearly unknown. We leveraged data entries for 10,987 peptides currently listed in the three antimicrobial peptide databases APD, DRAMP and DBAASP to aid structure-activity predictions. However, this number reduced to 3,828 AMPs that we could use for computational analyses, due to our stringent quality control criteria. The analysis uncovered a strong bias towards AMPs isolated from am

SUBMITTER: Feurstein C 

PROVIDER: S-EPMC9075106 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature