Marinelli, F. | Search bioRxiv

bioRxiv2021

Weighted force analysis method for derivation of multidimensional free energy landscapes from adaptively-biased replica simulations

A methodology is proposed for the calculation of multidimensional free-energy landscapes of molecular systems, based on analysis of multiple Molecular Dynamics trajectories wherein adaptive biases have been applied to enhance the sampling of different collective variables. In this approach, which we refer to as Force Correction Analysis Method (FCAM), local averages of the total and biasing forces are evaluated post-hoc, and the latter are subtracted from the former to obtain unbiased estimates of the mean force across collective-variable space. Multidimensional free-energy surfaces and minimum free-energy pathways are then derived from integration of the mean force landscape through kinetic Monte Carlo algorithm. To evaluate the proposed method, a series of numerical tests and comparisons with existing approaches were carried out for small molecules, peptides, and proteins, based on all-atom trajectories generated with standard, concurrent and replica-exchange Metadynamics in collective-variable spaces ranging from one- to six-dimensional. The tests confirm the correctness of the FCAM formulation and demonstrate that calculated mean forces and free energies converge rapidly and accurately, outperforming other methods used to unbias this kind of simulation data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/431654v5_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1412289org.highwire.dtl.DTLVardef@1425d53org.highwire.dtl.DTLVardef@1fcf5d0org.highwire.dtl.DTLVardef@6e6941_HPS_FORMAT_FIGEXP M_FIG TOC/Abstract Graphic C_FIG

biophysics↗

bioRxiv2019

Structural Interpretation of Hydrogen-Deuterium Exchange with Maximum-Entropy Simulation Reweighting

Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules in native environments without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of measured HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post-hoc to the resulting ensemble, such that averaged peptide-deuteration levels, as predicted by an empirical model of a value called the protection factor, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments, and by the model of exchange, are sufficient to recover correctly-weighted structural ensembles from simulations, even when the relevant conformations are observed rarely. Degrading the information content of the target data, e.g., by reducing sequence coverage or by averaging exchange levels over longer peptide segments, reduces the quantitative structural accuracy of the reweighted ensemble but still allows for useful, molecular-level insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric with which candidate structural ensembles can be ranked based on their correspondence with target data, or revealed to be inadequate. Thus, not only does HDXer facilitate a rigorous mechanistic interpretation of HDX-MS measurements, but it may also inform experimental design and further the development of empirical models of the HDX reaction.\n\nStatement of significanceHDX-MS experiments are a powerful approach for probing the conformational dynamics and mechanisms of proteins. However, the mechanistic implications of HDX-MS observations are frequently difficult to interpret, due to the limited spatial resolution of the technique as well as the lack of quantitative tools to translate measured data into structural information. To overcome these problems, we have developed a computational approach to construct structural ensembles that are maximally diverse while reproducing target experimental HDX-MS data within a given level of uncertainty. Using artificial test data, we demonstrate that the approach can correctly discern distinct structural ensembles reflected in the target data, and thereby facilitate statistically robust evaluations of competing mechanistic interpretations of HDX-MS experiments.

biophysics↗