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.
Highlights
Many microbes in their natural habitats are found not as free-floating organisms, but as three dimensional aggregates encased in a polymeric matrix called biofilms (Costerton et al, 1987)
The objectives of this work are 3-fold: first, we seek to improve the classification algorithm for Antibiofilm peptides (ABP) by using a more realistic, curated negative dataset with mostly biofilm-favoring peptides which is 10-fold larger than the positive dataset; our model identifies the most useful amino-acid composition features and short-repeating patterns (“motifs”) indicative of antibiofilm activity; second, we seek to develop a regression model using the minimum biofilm inhibitory concentration (MBIC) and minimum biofilm eradication concentration (MBEC) of ABPs to predict the effectiveness of the novel peptides classified as antibiofilm candidates; third, we seek to understand the putative mechanisms of action of the peptide hits using their previously known properties, secondary structure, and similarity with known antibiofilm peptides
Our pipeline to predict peptides active against biofilms may be grouped into four key steps: identification of positive and negative datasets; development of a robust machine learning algorithm for classification of ABPs; collection of candidate potential ABPs from diverse habitats; and prediction of the efficacy of the novel peptides using our antibiofilm peptide classification model and a regression model based on known MBEC data
Summary
Many microbes in their natural habitats are found not as free-floating (planktonic) organisms, but as three dimensional aggregates encased in a polymeric matrix called biofilms (Costerton et al, 1987). Biofilms are responsible for 65–80% of recalcitrant infections in humans. Biofilms have the potential to initiate or prolong infections by providing a safe sanctuary from which organisms can invade local tissue, seed new infection sites and resist eradication efforts. Both bacteria and fungi form biofilms on abiotic (e.g., catheters and implants) or biotic (e.g., skin, wounds) surfaces (Torres et al, 2018; Ramasubramanian and Lopez-Ribot, 2019). There is an urgent need to develop agents that are effective against biofilm infections (de la Fuente-Núñez et al, 2013; Pierce et al, 2015)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
