Dissecting Allosteric Mutations for Antibiotic Resistance by Time-Dependent Linear Response Theory.

Area of Science:

• Biophysics
• Computational Biology
• Enzymology

Background:

• Understanding protein allostery is crucial for drug discovery.
• TEM-1 beta-lactamase is a key enzyme conferring antibiotic resistance.
• Predicting the impact of mutations on protein function remains challenging.

Purpose of the Study:

• To develop and validate a novel computational approach for analyzing protein residue dynamics.
• To investigate allosteric communication pathways in TEM-1 beta-lactamase.
• To identify residues critical for antibiotic resistance through computational modeling.

Main Methods:

• Combining molecular dynamics (MD) simulations with time-dependent linear response (TDLR) theory.
• Analyzing residue fluctuation responses to targeted force perturbations.
• Applying Fourier transformations to convert time-domain data to frequency-domain representations.
• Developing classification models based on perturbation response profiles.

Main Results:

• Distinct time-resolved perturbation response profiles were observed for allosteric versus non-allosteric sites in TEM-1 beta-lactamase.
• Frequency-space representations of perturbation responses correlated with mutational behavior observed in deep sequencing data.
• Computational models accurately identified distal residues regulating antibiotic resistance.
• The approach successfully predicted allosteric sites without extensive simulations.

Conclusions:

• The novel MD-TDLR approach provides a powerful tool for characterizing protein allostery and dynamics.
• Perturbation response profiles offer insights into residue contributions to antibiotic resistance.
• This method can accelerate the identification of allosteric sites and potential drug targets.