title: Identification of Penicillin-binding proteins employing support vector machines and random forest.
abstract: Penicillin-Binding Proteins are peptidases that play an important role in cell-wall biogenesis in bacteria and thus maintaining bacterial infections. A wide class of β-lactam drugs are known to act on these proteins and inhibit bacterial infections by disrupting the cell-wall biogenesis pathway. Penicillin-Binding proteins have recently gained importance with the increase in the number of multi-drug resistant bacteria. In this work, we have collected a dataset of over 700 Penicillin-Binding and non-Penicillin Binding Proteins and extracted various sequence-related features. We then created models to classify the proteins into Penicillin-Binding and non-binding using supervised machine learning algorithms such as Support Vector Machines and Random Forest. We obtain a good classification performance for both the models using both the methods.
authors.affiliation: Center for Development of Advanced Computing, Pune, India.
authors.forename: Vinay
authors.initials: V
authors.lastname: Nair
authors.forename: Monalisa
authors.initials: M
authors.lastname: Dutta
authors.forename: Sowmya S
authors.initials: SS
authors.lastname: Manian
authors.forename: Ramya Kumari
authors.initials: RK
authors.lastname: S
authors.forename: Valadi K
authors.initials: VK
authors.lastname: Jayaraman
keywords: Penicillin-Binding Proteins; Protein Classification; Random Forest; Support Vector Machines