Induced Mutation Proves a Potential Target for TB Therapy: A Molecular Dynamics Study on LprG

Abstract

Molecular dynamics (MD) simulations of wild-type and V91W mutant Mycobacterium tuberculosis-LprG (Mtb-LprG) were performed with the goal to provide a comprehensive understanding of the Mtb-LprG as a potential antimycobacterial target. A long-range MD simulations and post-MD analyzes led us to various results that plainly explained the impact of V91W mutation on Mtb-LprG. Herein, the results revealed that the wild-type is less stable compared to V91W mutant. This was further supported by root mean square fluctuation, where the V91W mutant showed a higher degree of flexibility compared to the wild-type. Dynamic cross-correlation analysis revealed that induced mutation leads to higher residual flexibility in the mutant structure as compared to the wild-type structure thus resulting in the existence of negatively correlated motions. The difference in principal component analysis scatter plot across the first two normal modes suggests a greater mobility of the V91W mutant conformation compared to the wild-type. Thermodynamic calculations revealed that the van der Waals (E) forces contribute the most towards binding free energy in a case of the V91W mutant as compared to the wild-type LprG complex. In addition, the residue interaction networks revealed more of E interaction existence among residues in case of the V91W mutant. This study supports the Mtb-LprG as a potential antimycobacterial target and also serves as a cornerstone to identifying new potential targets that have no inhibitors.

Exploring the effect of inhibitor AKB‐9778 on VE‐PTP by molecular docking and molecular dynamics simulation

“…To explore the change in correlative motions of the key residues in the active site of VE‐PTP protein after binding with AKB‐9778, the 11 to 100 ns trajectories were extracted to constructed DCCM of two systems. By quantifying their relative motions as the value from −1.0 to 1.0, the cross‐correlation plots showed the communicative pattern of various regions of a protein . The positively correlative motion with the value from 0 to 1 meant residues moving in the same direction and negatively correlative motion with the value from −1 to 0 represented residues moving in the opposite direction.…”

“…By quantifying their relative motions as the value from −1.0 to 1.0, the cross-correlation plots showed the communicative pattern of various regions of a protein. 41,70 The positively correlative motion with the value from 0 to 1 meant residues moving in the same direction and negatively correlative motion with the value from −1 F I G U R E 5 Evaluation of the stability between the VE-PTP system and VE-PTP AKB-9778 system by the RMSD of all backbone atoms. The red curve indicates the result of the VE-PTP system, and the black curve indicates the result of the VE-PTP AKB-9778 system.…”

Design, Synthesis, Antiviral Evaluation, and Molecular Dynamics Simulation Studies of New Spirocyclic Thiopyrimidinones as Anti HCoV‐229E

“…Assessing the structural flexibility of protein during ligand binding is crucial for analyzing residue behavior and their connection to the ligand. [30] The inhibitor binding effect on the individual targets was examined using the Root-Mean-Square Fluctuation (RMSF) technique across 50 ns simulations. For the apo-protein and 9-bound to protein systems, the estimated average RMSF values were 1.18 � 1.02 Å and 1.09 � 0.50 Å, respectively.…”

Phytochemical Investigation of Cordia africana Lam. Stem Bark: Molecular Simulation Approach

“…The assessment of protein structure flexibility upon ligand binding is essential in probing residue behavior and their association with the ligand during MD simulation [35]. MRSA peptidoglycan transpeptidases and H. pylori urease residual fluctuations were evaluated using the Root-Mean-Square Fluctuation (RMSF) algorithm to assess the effect…”

“…In general, the RMSD profile of all four complexes' systems reveals favorable interaction of MR with the targeted receptors, which are thereby stabilized during dynamics. The assessment of protein structure flexibility upon ligand binding is essential in probing residue behavior and their association with the ligand during MD simulation [35]. MRSA peptidoglycan transpeptidases and H. pylori urease residual fluctuations were evaluated using the Root-Mean-Square Fluctuation (RMSF) algorithm to assess the effect of inhibitor binding towards the respective targets over 18 ns simulations.…”