Mining of Gram-Negative Surface-Active Enzybiotic Candidates by Sequence-Based Calculation of Physicochemical Properties

Abstract

Phage (endo)lysins are nowadays one of the most promising ways out of the current antibiotic resistance crisis. Either as sole therapeutics or as a complement to common antibiotic chemotherapy, lysins are already entering late clinical phases to get regulatory agencies’ authorization. Even the old paradigm of the inability of lysins to attack Gram-negative bacteria from without has already been overcome in a variety of ways: either by engineering approaches or investigating the natural mechanisms by which some wild-type lysins are able to interact with the bacterial surface. Such inherent ability of some lysins has been linked to antimicrobial peptide (AMP)-like regions, which are, on their own, a significant source for novel antimicrobials. Currently, though, many of the efforts for searching novel lysin-based antimicrobial candidates rely on experimental screenings. In this work, we have bioinformatically analyzed the C-terminal end of a collection of lysins from phages infecting the Gram-negative genus Pseudomonas. Through the computation of physicochemical properties, the probability of such regions to be an AMP was estimated by means of a predictive k-nearest neighbors (kNN) model. This way, a subset of putatively membrane-interacting lysins was obtained from the original database. Two of such candidates (named Pae87 and Ppl65) were prospectively tested in terms of muralytic, bacteriolytic, and bactericidal activity. Both of them were found to possess an activity against Pseudomonas aeruginosa and other Gram-negative bacterial pathogens, implying that the prediction of AMP-like regions could be a useful approach toward the mining of phage lysins to design and develop antimicrobials or antimicrobial parts for further engineering.