Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling.

ABSTRACT: Understanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomic-level understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long timescales. Recent advances in rare event sampling have allowed us to reach these timescales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitudes of timescales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an appro

SUBMITTER: Lee S 

PROVIDER: S-EPMC11042289 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature