Stepping up enhanced rate calculations with EATR-flooding

TL;DR

EATR-flooding is a new method that improves rate constant calculations in biomolecular simulations by varying bias strength across multiple runs, enabling accurate estimates without time-dependent biases.

Contribution

The paper introduces EATR-flooding, a generalized approach that replaces time-dependent bias with stepped biasing, broadening applicability and simplifying rate calculations.

Findings

EATR-flooding achieves accurate rate estimates comparable to standard EATR.

The method performs well on both coarse-grained and atomistic models.

It includes an internal check for over-biasing and uses a single gamma parameter.

Abstract

Several recent methods have shown that it is possible to compute rate constants of very slow biomolecular processes using simulations where a time-dependent bias is added along one or several collective variables (CVs). We previously reported the exponential average time-dependent rate (EATR) method, which can improve upon these approaches by accounting for how efficiently the external biasing potential modifies the observed rate using a learned CV-quality factor $\gamma$. This results in more accurate rate estimates using the same data when biasing a suboptimal coordinate. However, as formulated EATR depended on the biasing potential varying over time to properly determine the biasing efficiency, which limits the method's applicability to quasi-static biasing schemes such as ``flooding'' or on-the-fly probability enhanced sampling (OPES). Here, we present the EATR-flooding approach, which generalizes our method by replacing the need for a time dependent bias by instead varying (stepping up) the strength of the biasing potential across multiple sets of simulations. We implement this approach as an open-source Python library, and demonstrate that this approach is accurate without substantial loss of efficiency compared to standard EATR for a coarse-grained protein system, and also show good performance on a fully atomistic cavity-ligand model. Two additional appealing features of EATR-flooding are an internal check for over-biasing and the fact that only a single $\gamma$ parameter is predicted for a given choice of CVs, as compared to our earlier results where $\gamma$ empirically depended on biasing rate. Finally, we believe EATR-flooding applies not only to OPES simulations but more generally to CV biasing enhanced sampling approaches, making it broadly useful.