Applied causality to infer protein dynamics and kinetics

TL;DR

This paper integrates AlphaFold2 generated protein structural ensembles with a causal Langevin model to estimate conformational timescales, revealing how MSA depth influences protein dynamics and providing a new approach to infer kinetics from static structures.

Contribution

It introduces a novel method combining AlphaFold2 ensembles with causal modeling to estimate protein conformational timescales, bridging static structure prediction and dynamic behavior.

Findings

MSA depth inversely correlates with conformational timescales.

AlphaFold2 ensembles can probe microsecond timescales.

The approach generalizes to other structural prediction methods.

Abstract

The use of generative machine learning models, trained on the experimentally resolved structures deposited in the protein data bank, is an attractive approach to sampling conformational ensembles of proteins. However, the ensembles generated by these models lack timescale or causal information. We use the structural ensembles generated from AlphaFold2 at a range of MSA depths to parameterize the potential of mean force of an overdamped, memory-free, coarse-grained Langevin equation. This approach couples the AlphaFold2 ensembles to a causal model, allowing us to estimate the timescales spanned by the ensembles generated at each MSA depth. Performing this analysis on six variants of HIV-1 protease, we confirm an inverse relationship between MSA depth and the timescale of an ensemble's conformational fluctuations. The MSA depth essentially serves as a conformational restraint, and AlphaFold2 is generally able to probe timescales at or below those seen in microsecond-long, unbiased molecular dynamics simulations. We conclude by generalizing this approach to other generative structural ensemble-prediction methods as well as co-folding models, in this case the biologically functional HIV-1 protease dimer.