Water regulates the residence time of Benzamidine in Trypsin

Abstract

The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time.

Rare Event Kinetics from Adaptive Bias Enhanced Sampling

“…Thus, with the correct choice of a handful of parameters, the user has direct control over the accuracy of the kinetic results obtained from OPES f simulations. Besides the results presented here mostly for deductive purpose, the interested reader can find a more real-life application of OPES f for calculating millisecond timescale dissociation kinetics of a protein–ligand complex in ref . Considering the additional advantages over traditional enhanced sampling methods and the growing need for better methods for predicting kinetics in molecular systems, we expect that this OPES flooding technique will find a wide range of applications in molecular biophysics, chemistry, and material science.…”

Predicting Biomolecular Binding Kinetics: A Review

“…41 In another study, Filizola et al 38 the predicted k of f for the morphine and bruprenorphine were 0.057 ± 0.005 s −1 and 0.021 ± 0.003 s −1 , respectively, being within 1 order of magnitude difference from experimental values of 0.0023 ± 0.001 s −1 and 0.0018 ± 0.03 s −1 . Very recently, Narjes et al 87 combined ML and a novel Metadynamics approach, On-the-fly Probability Enhanced Sampling (OPES) flooding, to investigate the binding of benzamidine to trypsin. Based on a total of ∼2.74 μs OPES simulations, they captured 55 benzamidine unbinding events and predicted the k off value of 1560 s −1 , being highly consistent with the experimental data.…”

Characterizing Metastable States with the Help of Machine Learning

“…More elaborate descriptors can also be employed. For example, if one wishes to illuminate the role of solvent in the different states, one could use as descriptors hydration spots as recently proposed in ref , or again, one could use experimentally measurable quantities such as structure factor peaks or NMR chemical shifts . Nevertheless, while machine learning can give precious hints, ultimately the physical interpretation is in the hand of the investigator.…”