Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences

Abstract

A majority of microbial infections are associated with biofilms. Targeting biofilms is considered an effective strategy to limit microbial virulence while minimizing the development of antibiotic resistance. Toward this need, antibiofilm peptides are an attractive arsenal since they are bestowed with properties orthogonal to small molecule drugs. In this work, we developed machine learning models to identify the distinguishing characteristics of known antibiofilm peptides, and to mine peptide databases from diverse habitats to classify new peptides with potential antibiofilm activities. Additionally, we used the reported minimum inhibitory/eradication concentration (MBIC/MBEC) of the antibiofilm peptides to create a regression model on top of the classification model to predict the effectiveness of new antibiofilm peptides. We used a positive dataset containing 242 antibiofilm peptides, and a negative dataset which, unlike previous datasets, contains peptides that are likely to promote biofilm formation. Our model achieved a classification accuracy greater than 98% and harmonic mean of precision-recall (F1) and Matthews correlation coefficient (MCC) scores greater than 0.90; the regression model achieved an MCC score greater than 0.81. We utilized our classification-regression pipeline to evaluate 135,015 peptides from diverse sources for potential antibiofilm activity, and we identified 185 candidates that are likely to be effective against preformed biofilms at micromolar concentrations. Structural analysis of the top 37 hits revealed a larger distribution of helices and coils than sheets, and common functional motifs. Sequence alignment of these hits with known antibiofilm peptides revealed that, while some of the hits showed relatively high sequence similarity with known peptides, some others did not indicate the presence of antibiofilm activity in novel sources or sequences. Further, some of the hits had previously recognized therapeutic properties or host defense traits suggestive of drug repurposing applications. Taken together, this work demonstrates a new in silico approach to predicting antibiofilm efficacy, and identifies promising new candidates for biofilm eradication.

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials’ Discovery

“…Thus, the most important or central peptides can be highlighted, and the edges weighted with high or low similarities. This work aimed at the outset to exploit the visual representation of complex networks representing ABFPs in order to analyze their structural space and associated metadata, both of which are relevant for the discovery and design of antibiofilm agents [ 31 ].…”

“…Our motif search approach assisted by complex networks produced results that are not so far from the few findings reported in the literature. Recently, Anastasiu et al found that the following motifs “RIRV,” “RIVQRIK,” and “IGKEFKR” appeared with more frequency in 242 ABFPs collected from APD and BaAMP databases with respect to a curated negative set [ 31 ], when using the “MERCI” software [ 72 ]. In this sense, we agree with them in the detection of the “RIRV” which was fully integrated in the RIRVR motif detected in cluster 14 by the MAFFT algorithm, and also enriched in the BaAMP dataset.…”

“…In a previous report, the authors also found that the dipeptides “IR/RI”, “WR/RW”, and “KK” were the most common among the selected ABFPs [ 31 ]. Certainly, these dipeptides are present in the motifs discovered with the intervention of complex networks, at a relatively high frequency.…”

“…Thus, their antibiofilm activity has gained attention in the AMPs world, being evaluated in vitro and in vivo models and registered either in general AMP databases such as UniProtKB [ 24 ], the Antimicrobial Peptide Database (APD) [ 25 ], the Data Repository of Antimicrobial Peptides (DRAMP) [ 26 ], or in others exclusively dedicated to antibiofilm peptides, such as the Biofilm-active AMPs (BaAMPS) database [ 27 ]. This last specific database has been useful for researchers to train machine learning (ML) models to predict antibiofilm activity [ 28 , 29 , 30 , 31 ].…”

Prospection and prediction of highly active antibiofilm peptides using machine learning-based methods

“…DPC analysis revealed the high frequency of charged-hydrophobicity theme in antibiofilm peptides with significant antibiofilm effects. Bose et al indicated that these combinations of dipeptides outline the amphipathic characteristics of ABFs [58]. A comparison of DPC between hiABFs and QSPs is shows in Fig.…”