Prediction of Novel Antibiofilm Peptides from Diverse Habitats using Machine Learning

Multidrug resistant bacteria often lead to biofilm formation. Biofilm is a colonizedform of pathogens (fungi, bacteria) attached to surfaces like animal or plant tissues, medical devices like catheters, and artificial heart valves. Biofilm formation prolongs the survival of microorganisms in an adaptive environment, leading to the spread of infection in different organs and causing a high morbidity rate. Given the rise of chronic infection and antibiotic resistance due to biofilm, it is essential to find an alternative solution to control biofilm infections. Antibiofilm peptides can interact with these biofilm-creating pathogens to inhibit growth, virulence, and biofilm formation. We hypothesized that mining the existing peptide databases from diverse habitats could provide potential antibiofilm activities for our work. We developed a computational model to predict the antibiofilm properties by applying machine learning algorithms like support vector machine, random forest, extreme gradient boosting, and multilayer perceptron classifier. We evaluated more than 240 antibiofilm peptides and more than 570 different compositions and motif-based features to build our prediction model. We also created a regression model on top of our classifier to predict the effectiveness of peptides by curating minimum inhibitory concentration against biofilm. Our classifiers achieved greater than 98% accuracy while the harmonic mean of precision-recall (F1) and Matthews correlation coefficient (MCC) scores obtained are greater than 0.91. Using this two-tier model approach, we assessed more extensive databases of antimicrobial, anticancer, antiviral, and dairy peptides for potential antibiofilm functionality and came up with the top ten potential candidates of antibiofilm peptides

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

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