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Constant-pH Simulations with the Polarizable Atomic Multipole AMOEBA Force Field.

Andrew C Thiel1, Matthew J Speranza1, Sanika Jadhav2

  • 1Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa 52242, United States.

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|March 20, 2024
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Summary
This summary is machine-generated.

This study introduces the first polarizable constant-pH molecular dynamics (CpHMD) algorithm with the AMOEBA force field, accurately predicting protein titration states in crystalline systems. This advance enhances biomolecular simulations for drug discovery and biochemical mechanism studies.

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Area of Science:

  • Biomolecular simulations
  • Computational chemistry
  • Structural biology

Background:

  • Predicting protein behavior across pH is crucial but challenging for simulations.
  • Existing constant-pH molecular dynamics (CpHMD) methods are restricted to fixed-charge force fields.
  • This limits their application to systems with polarizable force fields like AMOEBA.

Purpose of the Study:

  • To develop the first polarizable CpHMD algorithm using the AMOEBA force field.
  • To implement this algorithm within the open-source Force Field X (FFX) software.
  • To enable accurate titration state predictions for crystalline biomolecular systems.

Main Methods:

  • Developed a novel polarizable CpHMD algorithm integrated with the AMOEBA force field.
  • Implemented the algorithm in the Force Field X (FFX) software, supporting all 230 space groups.
  • Evaluated the method on 11 crystalline peptide systems containing titratable amino acids (Asp, Glu, His, Lys, Cys).

Main Results:

  • Successfully predicted titration states for 15 out of 16 amino acids across 11 systems.
  • Accurately modeled cysteine coordination with Zn2+ ions.
  • Observed a minor discrepancy for a histidine residue in a peptide system, where simulations predicted equally populated tautomers.

Conclusions:

  • Polarizable CpHMD with AMOEBA shows significant promise for pKa prediction and studying biochemical mechanisms.
  • The developed method enhances accuracy for protein-ligand binding affinity in pharmaceutical lead optimization.
  • This work expands the capabilities of biomolecular simulations for complex biological systems.