Constant-pH Simulations with the Polarizable Atomic Multipole AMOEBA Force Field.
Andrew C ThielMatthew J SperanzaSanika JadhavLewis L StevensDaniel K UnruhPengyu RenJay W PonderJana ShenMichael J SchniedersPublished in: Journal of chemical theory and computation (2024)
Accurately predicting protein behavior across diverse pH environments remains a significant challenge in biomolecular simulations. Existing constant-pH molecular dynamics (CpHMD) algorithms are limited to fixed-charge force fields, hindering their application to biomolecular systems described by permanent atomic multipoles or induced dipoles. This work overcomes these limitations by introducing the first polarizable CpHMD algorithm in the context of the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field. Additionally, our implementation in the open-source Force Field X (FFX) software has the unique ability to handle titration state changes for crystalline systems including flexible support for all 230 space groups. The evaluation of constant-pH molecular dynamics (CpHMD) with the AMOEBA force field was performed on 11 crystalline peptide systems that span the titrating amino acids (Asp, Glu, His, Lys, and Cys). Titration states were correctly predicted for 15 out of the 16 amino acids present in the 11 systems, including for the coordination of Zn 2+ by cysteines. The lone exception was for a HIS-ALA peptide where CpHMD predicted both neutral histidine tautomers to be equally populated, whereas the experimental model did not consider multiple conformers and diffraction data are unavailable for rerefinement. This work demonstrates the promise polarizable CpHMD simulations for p K a predictions, the study of biochemical mechanisms such as the catalytic triad of proteases, and for improved protein-ligand binding affinity accuracy in the context of pharmaceutical lead optimization.
Keyphrases
- molecular dynamics
- amino acid
- single molecule
- density functional theory
- machine learning
- molecular dynamics simulations
- deep learning
- healthcare
- big data
- room temperature
- protein protein
- heavy metals
- binding protein
- electronic health record
- electron microscopy
- high glucose
- artificial intelligence
- small molecule
- quality improvement
- risk assessment
- endothelial cells
- crystal structure
- drug induced