The SARS-CoV-2 main protease (M pro ) is of major interest as an antiviral drug target. Structure-based virtual screening efforts, fueled by a growing list of apo and inhibitor-bound SARS-CoV/CoV-2 M pro crystal structures, are underway in many laboratories. However, little is known about the dynamic enzyme mechanism, which is needed to inform both assay development and structure-based inhibitor design. Here, we apply biodynamics theory to characterize the structural dynamics of substrate-induced M pro activation under nonequilibrium conditions . The catalytic cycle is governed by concerted dynamic structural rearrangements of domain 3 and the m-shaped loop (residues 132–147) on which Cys145 (comprising the thiolate nucleophile and half of the oxyanion hole) and Gly143 (comprising the second half of the oxyanion hole) reside. In particular, we observed the following: (1) Domain 3 undergoes dynamic rigid-body rotation about the domain 2–3 linker, alternately visiting two primary conformational states (denoted as M 1 pro ↔ M 2 pro ); (2) The Gly143-containing crest of the m-shaped loop undergoes up and down translations caused by conformational changes within the rising stem of the loop (Lys137–Asn142) in response to domain 3 rotation and dimerization (denoted as M 1/down pro ↔ 2·M 2/up pro ) (noting that the Cys145-containing crest is fixed in the up position). We propose that substrates associate to the M 1/down pro state, which promotes the M 2/down pro state, dimerization (denoted as 2·M 2/up pro –substrate), and catalysis. Here, we explore the state transitions of M pro under nonequilibrium conditions, the mechanisms by which they are powered, and the implications thereof for efficacious inhibition under in vivo conditions.

Ion-specific interfacial behaviors of monovalent halides impact processes such as protein denaturation, interfacial stability, surface tension modulation, and as such, their molecular and thermodynamic underpinnings garner much attention. We use molecular dynamics simulations of monovalent anions in water to explore effects on distant interfaces. We observe long-ranged ion-induced perturbations of the aqueous environment as suggested by experiment and theory. Surface stable ions, characterized as such by minima in potentials of mean force computed using umbrella sampling MD simulations, induce larger interfacial fluctuations compared to non-surface active species, conferring more entropy approaching the interface. Smaller anions and cations show no interfacial potential of mean force minima. The difference is traced to hydration shell properties of the anions, and the coupling of these shells with distant solvent. The effects correlate with the positions of the anions in the Hofmeister series (acknowledging variations in force field ability to recapitulate essential underlying physics), suggesting how differences in induced, non-local perturbations of interfaces may be related to different specific-ion effects in dilute biophysical and nanomaterial systems.

Hydrogen/deuterium exchange (HDX) is a powerful technique to investigate protein conformational dynamics at amino acid resolution. Because HDX provides a measurement of solvent exposure of backbone hydrogens, ensemble-averaged over potentially slow kinetic processes, it has been challenging to use HDX protection factors to refine structural ensembles obtained from molecular dynamics simulations. This entails two dual challenges: (1) identifying structural observables that best correlate with backbone amide protection from exchange, and (2) restraining these observables in molecular simulations to model ensembles consistent with experimental measurements. Here, we make significant progress on both fronts. First, we describe an improved predictor of HDX protection factors from structural observables in simulated ensembles, parameterized from ultra-long molecular dynamics simulation trajectory data, with a Bayesian inference approach used to retain the full posterior distribution of model parameters.

We present a maximum-caliber method for inferring transition rates of a Markov state model (MSM) with perturbed equilibrium populations given estimates of state populations and rates for an unperturbed MSM. It is similar in spirit to previous approaches, but given the inclusion of prior information, it is more robust and simple to implement. We examine its performance in simple biased diffusion models of kinetics and then apply the method to predicting changes in folding rates for several highly nontrivial protein folding systems for which non-native interactions play a significant role, including (1) tryptophan variants of the GB1 hairpin, (2) salt-bridge mutations of the Fs peptide helix, and (3) MSMs built from ultralong folding trajectories of FiP35 and GTT variants of the WW domain. In all cases, the method correctly predicts changes in folding rates, suggesting the wide applicability of maximum-caliber approaches to efficiently predict how mutations perturb protein conformational dynamics.

In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow timescales. A promising approach to enhanced sampling of MSMs is to use so-called adaptive methods, in which new MD trajectories are seeded preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape, and for mini-protein folding MSMs of WW domain and NTL9(1-39). Our results reveal the practical challenges of reseeding simulations, and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities.