β-LacFamPred: An online tool for prediction and classification of β-lactamase class, subclass, and family

beta-Lactams are a broad class of antimicrobial agents with a high safety profile, making them the most widely used class in clinical, agricultural, and veterinary setups. The widespread use of beta-lactams has induced the extensive spread of beta-lactamase hydrolyzing enzymes known as beta-lactamases (BLs). To neutralize the effect of beta-lactamases, newer generations of beta-lactams have been developed, which ultimately led to the evolution of a highly diverse family of BLs. Based on sequence homology, BLs are categorized into four classes: A-D in Ambler's classification system. Further, each class is subdivided into families. Class B is first divided into subclasses B1-B3, and then each subclass is divided into families. The class to which a BL belongs gives a lot of insight into its hydrolytic profile. Traditional methods of determining the hydrolytic profile of BLs and their classification are time-consuming and require resources. Hence we developed a machine-learning-based in silico method, named as beta-LacFamPred, for the prediction and annotation of Ambler's class, subclass, and 96 families of BLs. During leave-one-out cross-validation, except one all beta-LacFamPred model HMMs showed 100% accuracy. Benchmarking with other BL family prediction methods showed beta-LacFamPred to be the most accurate. Out of 60 penicillin-binding proteins (PBPs) and 57 glyoxalase II proteins, beta-LacFamPred correctly predicted 56 PBPs and none of the glyoxalase II sequences as non-BLs. Proteome-wide annotation of BLs by beta-LacFamPred showed a very less number of false-positive predictions in comparison to the recently developed BL class prediction tool DeepBL. beta-LacFamPred is available both as a web-server and standalone tool at and GitHub repository respectively.

Automated summary

Beta-lactams, a widely used class of antimicrobial agents, have led to the extensive spread of beta-lactamase enzymes. To counteract this effect, newer generations of beta-lactams have been developed, resulting in a highly diverse family of beta-lactamases. Traditional methods for determining the hydrolytic profile and classification of beta-lactamases are time-consuming and resource-intensive. Therefore, a machine-learning-based in silico method called beta-LacFamPred was developed for the prediction and annotation of beta-lactamases. This method showed high accuracy and outperformed other prediction tools in benchmarking tests.