Metainference: A Bayesian Inference Method for Heterogeneous Systems

TL;DR

Metainference is a Bayesian inference method designed to accurately model complex, heterogeneous systems by accounting for experimental errors and averaging over multiple states, improving structural predictions.

Contribution

It introduces a novel Bayesian approach that models errors and state heterogeneity using a replica method, advancing the analysis of complex systems.

Findings

Successfully applied to a heterogeneous model system

Determined an ensemble of protein structures from thermal fluctuations

Handles errors in experimental data effectively

Abstract

Modelling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model, and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system populates simultaneously an ensemble of different states and experimental data are measured as averages over such states. To address this problem we present a Bayesian inference method, called metainference, that is able to deal with errors in experimental measurements as well as with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modelling based on the maximum entropy principle. To illustrate the method we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to model complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.