Homology modeling and docking study of recent SHV type β-lactamses with traditional and novel inhibitors: an in silico approach to combat problem of multiple drug resistance in various infections

Abstract

Extended-spectrum β-lactamases (ESBLs) are enzymes produced by bacteria, which impart resistance against advanced generation-cephalosporins. Sulfhydryl reagent variable (SHV) enzymes are among the most prevalent ESBLs. The molecular interactions involved in the binding of recent SHV-variants like SHV-48, SHV-61, SHV-89, SHV-95 or SHV-105 to β-lactamase inhibitors have not been reported yet. The crystal structures of all SHV enzymes taken in this study are not available; homology modeling was used to generate three dimensional structure of these SHV enzymes, the models generated were subjected for model validation to confirm the stereo chemical quality of these models. Some of the most effective novel and traditional β-lactamase inhibitors were selected to target the active site amino acid residues of these modeled SHV enzymes for predicting comparative efficacies of these inhibitors against the said enzymes on the basis of interaction energies of docking. All SHV-variants were found to be interacted with clavulanic acid through A237, T235, K73, and S70 as the common residues in this study. Out of 20 docking interactions studied, only residues A237 and T235 were found crucial for the correct positioning of inhibitors within the binding site of SHV enzymes in 13 and 10 instances, respectively. On the basis of interaction energy and Ki (inhibitor constant) calculations, Ln-1255 appeared as the most efficient inhibitor against all the studied bacterial enzymes except for SHV-61, which was best inhibited by Penem2, and which was also found to be a promising inhibitor against SHV type betalactamases enzymes. Furthermore, this study provides new insights to understand crucial amino acids residues for ‘SHV–inhibitor’ interactions, which might be useful in designing new versatile SHV-inhibitors. Moreover, empirical treatment can be improved by knowing the efficacies of specific inhibitor against different variants.