A practical guide to the simultaneous determination of protein structure and dynamics using metainference

TL;DR

This paper provides a practical guide to using metainference combined with metadynamics for determining accurate protein structures and dynamics, integrating experimental data with computational modeling.

Contribution

It introduces a step-by-step methodology for applying metainference with metadynamics using PLUMED, including practical tips for modeling protein ensembles from experimental data.

Findings

Successful modeling of a disordered peptide's structural ensemble.

Integration of NMR data with molecular mechanics force fields.

Guidelines to avoid common pitfalls in metainference simulations.

Abstract

Accurate protein structural ensembles can be determined with metainference, a Bayesian inference method that integrates experimental information with prior knowledge of the system and deals with all sources of uncertainty and errors as well as with system heterogeneity. Furthermore, metainference can be implemented using the metadynamics approach, which enables the computational study of complex biological systems requiring extensive conformational sampling. In this chapter, we provide a step-by-step guide to perform and analyse metadynamic metainference simulations using the ISDB module of the open-source PLUMED library, as well as a series of practical tips to avoid common mistakes. Specifically, we will guide the reader in the process of learning how to model the structural ensemble of a small disordered peptide by combining state-of-the-art molecular mechanics force fields with nuclear magnetic resonance data, including chemical shifts, scalar couplings and residual dipolar couplings.