title: Enhanced identification of β-lactamases and its classes using sequence, physicochemical and evolutionary information with sequence feature characterization of the classes.
abstract: β-lactamases provides one of the most successful means of evading the therapeutic effects of β lactam class of antibiotics by many gram positive and gram negative bacteria. On the basis of sequence identity, β-lactamases have been identified into four distinct classes- A, B, C and D. The classes A, C and D are the serine β-lactamases and class B is the metallo-lactamse. In the present study, we developed a two stage cascade classification system. The first-stage performs the classification of β-lactamases from non-β-lactamases and the second-stage performs the further classification of β-lactamases into four different β-lactamase classes. In the first-stage binary classification, we obtained an accuracy of 97.3% with a sensitivity of 89.1% and specificity of 98.0% and for the second stage multi-class classification, we obtained an accuracy of 87.3% for the class A, 91.0% for the class B, 96.3% for the class C and 96.4% for class D. A systematic statistical analysis is carried out on the sieved-out, correctly-predicted instances from the second stage classifier, which revealed some interesting patterns. We analyzed different classes of β-lactamases on the basis of sequence and physicochemical property differences between them. Among amino acid composition, H, W, Y and V showed significant differences between the different β-lactamases classes. Differences in average physicochemical properties are observed for isoelectric point, volume, flexibility, hydrophobicity, bulkiness and charge in one or more β-lactamase classes. The key differences in physicochemical property groups can be observed in small and aromatic groups. Among amino acid property group n-grams except charged n-grams, all other property group n-grams are significant in one or more classes. Statistically significant differences in dipeptide counts among different β-lactamase classes are also reported.
authors.affiliation: Department of Computer Science, Banaras Hindu University, Varanasi, 221005, India. Electronic address: abhigyannath01@gmail.com.
authors.forename: Abhigyan
authors.initials: A
authors.lastname: Nath
authors.affiliation: Department of Computer Science, Banaras Hindu University, Varanasi, 221005, India. Electronic address: karthinikita@gmail.com.
authors.forename: S
authors.initials: S
authors.lastname: Karthikeyan
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D055598:Chemical Phenomena; D030562:Databases, Protein; D019143:Evolution, Molecular; D000069550:Machine Learning; D001618:beta-Lactamases / Q000032:analysis* / Q000737:chemistry / Q000145:classification* / Q000235:genetics
chemical_list: D001618:beta-Lactamases
keywords: Average one dependence estimator; Dipeptide counts; PSSM; Physicochemical properties; β-Lactamases