How to Determine Accurate Conformational Ensembles by Metadynamics Metainference: A Chignolin Study Case

Abstract

The reliability and usefulness of molecular dynamics simulations of equilibrium processes rests on their statistical precision and their capability to generate conformational ensembles in agreement with available experimental knowledge. Metadynamics Metainference (M&M), coupling molecular dynamics with the enhanced sampling ability of Metadynamics and with the ability to integrate experimental information of Metainference, can in principle achieve both goals. Here we show that three different Metadynamics setups provide converged estimate of the populations of the three-states populated by a model peptide. Errors are estimated correctly by block averaging, but higher precision is obtained by performing independent replicates. One effect of Metadynamics is that of dramatically decreasing the number of effective frames resulting from the simulations and this is relevant for M&M where the number of replicas should be large enough to capture the conformational heterogeneity behind the experimental data. Our simulations allow also us to propose that monitoring the relative error associated with conformational averaging can help to determine the minimum number of replicas to be simulated in the context of M&M simulations. Altogether our data provides useful indication on how to generate sound conformational ensemble in agreement with experimental data.

OneOPES, a Combined Enhanced Sampling Method to Rule Them All

“…We focus on such a relatively simple case and use OneOPES to study the folding of CLN025, a double mutant (G1Y, G10Y) of the Chignolin miniprotein, , a system largely employed in the last decade as a benchmark to study fast-folding proteins through both long unbiased molecular dynamics simulations and more recently enhanced sampling simulations. ,,,,,− We will employ a rather simple CV that is based on the linear combination of six interprotein contacts whose weights are obtained through harmonic linear discriminant analysis (HLDA) , (see SI for additional details). Following the scheme used in the previous examples, we perform 5 PT-WTE-MetaD, OneOPES and OneOPES MultiCV simulations, and compare their resulting free energies with the reference one from ref .…”

The Role of Force Fields and Water Models in Protein Folding and Unfolding Dynamics

“…Chignolin and CLN025 are among the most investigated fast-folding proteins concerning force field differences and testing various setups in computer simulations. ,,,, Due to their small size and distinct native conformation, they present fast-folding kinetics and hence are ideal candidates for examining protein folding. Characterizing the folding dynamics of Chignolin/CLN025 can facilitate the understanding of larger, biologically relevant systems and can consequently inform the design and development of new biotherapeutics.…”

OneOPES, a combined enhanced sampling method to rule them all

“…We focus on such a case and use OneOPES to study the folding of CLN025, a double mutant (G1Y, G10Y) of the Chignolin miniprotein, a system largely employed in the last decade as a benchmark to study fast-folding proteins through both long un- biased molecular dynamics simulations 75 and more recently enhanced sampling simulations 23,54,62,[77][78][79][80] . We will employ a relatively simple CV that is based on the linear combination of six interprotein contacts whose weights are obtained through harmonic linear discriminant analysis (HLDA) 18,62 (see SI for additional details).…”