Refinement of Highly Flexible Protein Structures using Simulation‐Guided Spectroscopy

Abstract

Highly flexible proteins present a special challenge for structure determination because they are multi-structured yet not disordered, so their conformational ensembles are essential for understanding function. Because spectroscopic measurements of multiple conformational populations often provide sparse data, experiment selection is a limiting factor in conformational refinement. A molecular simulations- and information-theory based approach to select which experiments best refine conformational ensembles has been developed. This approach was tested on three flexible proteins. For proteins where a clear mechanistic hypothesis exists, experiments that test this hypothesis were systematically identified. When available data did not yield such mechanistic hypotheses, experiments that significantly outperform structure-guided approaches in conformational refinement were identified. This approach offers a particular advantage when refining challenging, underdetermined protein conformational ensembles.

gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support

“…Additional examples of low-level operations include a "ReduceAll" to collect data across an in-flight ensemble. This operation can be used for adaptive updates across an ensemble of simulations, such as modifying biasing forces in restrained-ensemble simulations [26,27] or to update estimators and determine when a simulation ensemble should be terminated.…”

Inference of Joint Conformational Distributions from Separately Acquired Experimental Measurements

“…4B) while maintaining some secondary structure (see Supplement for further discussion). The presence or absence of such a structure can be tested via systematically-designed DEER experiments [1] or other methods such as cross-linking mass spectrometry [36]. Thus, EESM produces testable hypotheses about the syntaxin conformational ensemble.…”

“…If each random variable O i can take on values {o j (i) } with probability p(o j (i) ), then the probability of observing a particular conformation given a specific set of distances {o j=k (1) , ..., o j=m (N) } is trivially…”

“…Specifically, we would not expect a low-energy conformation to have an unstructured backbone region that maintains contacts with the structured domain; instead, we expect complete dissociation of the end region (rendered in green in Figure 4B) while maintaining some secondary structure (see the Supporting Information for further discussion). The presence or absence of such a structure can be tested via systematically designed DEER experiments 1 or other methods such as cross-linking mass spectrometry. 53 Thus, EESM produces testable hypotheses about the syntaxin conformational ensemble.…”

gmxapi: a GROMACS-native Python interface for molecular dynamics with ensemble and plugin support

“…Additional examples of low-level operations include a “ReduceAll” callable that collects data across an in-flight ensemble. This operation can be used for adaptive updates such as modifying biasing forces in restrained-ensemble simulations[26,27] or to update estimators and determine when a simulation ensemble should be terminated.…”