A new approach to multi-modal diffusions with applications to protein folding

TL;DR

This paper introduces a flexible method for modeling multi-modal diffusions by transforming simple diffusion models, enabling better analysis of protein folding dynamics with state-dependent diffusion coefficients.

Contribution

It presents a novel transformation-based approach to create multi-modal diffusion models, applicable to protein folding data, with proven parameter identifiability and improved fit over previous models.

Findings

Successfully modeled protein folding data with new diffusion models.

Estimated folding and unfolding rates from molecular dynamics data.

Demonstrated improved fit due to state-dependent diffusion coefficients.

Abstract

This article demonstrates that flexible and statistically tractable multi-modal diffusion models can be attained by transformation of simple well-known diffusion models such as the Ornstein-Uhlenbeck model, or more generally a Pearson diffusion. The transformed diffusion inherits many properties of the underlying simple diffusion including its mixing rates and distributions of first passage times. Likelihood inference and martingale estimating functions are considered in the case of a discretely observed bimodal diffusion. It is further demonstrated that model parameters can be identified and estimated when the diffusion is observed with additional measurement error. The new approach is applied to molecular dynamics data in form of a reaction coordinate of the small Trp-zipper protein, for which the folding and unfolding rates are estimated. The new models provide a better fit to this type of protein folding data than previous models because the diffusion coefficient is state-dependent.