Stability-Driven Pose Prediction and Ligand Design via Contact Persistence Analysis from Molecular Dynamics...

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Molecular dynamics (MD) simulations offer detailed insights into protein-ligand interactions but are computationally expensive for early drug design.
• Accelerating the assessment of binding poses is crucial for efficient structure-based drug design.

Purpose of the Study:

• To develop and validate a rapid computational workflow for assessing stable protein-ligand binding poses.
• To evaluate the correlation between contact persistence and experimental binding affinity.
• To demonstrate the workflow's utility in guiding ligand modification strategies.

Main Methods:

• Integration of classical docking, high-temperature (400 K) MD simulations, and residue-level contact persistence analysis using the R-value metric.
• Retrospective evaluation on 137 protein-ligand complexes across 10 targets (1.64 ms total simulation time).
• Analysis of functional group-level contact persistence and case study of KRASG12D inhibitors (TH-Z835, MRTX1133).

Main Results:

• The workflow successfully identified stable binding poses with 83.9% agreement (RMSD ≤3.0 Å) to experimental structures.
• The R-value metric showed a significant correlation (0.40–0.94) with experimental binding affinities (pKd) across 31 systems.
• In silico derivatives of KRASG12D inhibitors demonstrated improved predicted interaction persistence compared to parent compounds.

Conclusions:

• The developed computational workflow enables rapid and accurate assessment of protein-ligand binding poses.
• Contact persistence, quantified by the R-value, serves as a reliable indicator of binding affinity.
• The workflow can effectively guide hypothesis-driven ligand modifications in structure-based drug design.