Traditional Molecular Dynamics Simulations Research Articles

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics.

Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

Charging and discharging a supercapacitor in molecular simulations.

As the world moves more toward unpredictable renewable energy sources, better energy storage devices are required. Supercapacitors are a promising technology to meet the demand for short-term, high-power energy storage. Clearly, understanding their charging and discharging behaviors is essential to improving the technology. Molecular Dynamics (MD) simulations provide microscopic insights into the complex interplay between the dynamics of the ions in the electrolyte and the evolution of the charge distributions on the electrodes. Traditional MD simulations of (dis)charging supercapacitors impose a pre-determined evolving voltage difference between the electrodes, using the Constant Potential Method (CPM). Here, we present an alternative method that explicitly simulates the charge flow to and from the electrodes. For a disconnected capacitor, i.e., an open circuit, the charges are allowed to redistribute within each electrode while the sum charges on both electrodes remain constant. We demonstrate, for a model capacitor containing an aqueous salt solution, that this method recovers the charge-potential curve of CPM simulations. The equilibrium voltage fluctuations are related to the differential capacitance. We next simulate a closed circuit by introducing equations of motion for the sum charges, by explicitly accounting for the external circuit element(s). Charging and discharging of the model supercapacitor via a resistance proceed by double exponential processes, supplementing the usual time scale set by the electrolyte dynamics with a novel time scale set by the external circuit. Finally, we propose a simple equivalent circuit that reproduces the main characteristics of this supercapacitor.

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based, or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa for the prediction of elastic constants and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations. This breakthrough promises to expedite materials design processes and facilitate scale-bridging in materials science, offering a new perspective on addressing computational demands in atomistic simulations.

Machine Learning Generation of Dynamic Protein Conformational Ensembles.

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

Investigation of Polymer Aging Mechanisms Using Molecular Simulations: A Review.

Aging has a serious impact on the properties of functional polymers. Therefore, it is necessary to study the aging mechanism to prolong the service and storage life of polymer-based devices and materials. Due to the limitations of traditional experimental methods, more and more studies have adopted molecular simulations to analyze the intrinsic mechanisms of aging. In this paper, recent advances in molecular simulations of the aging of polymers and their composites are reviewed. The characteristics and applications of commonly used simulation methods in the study of the aging mechanisms (traditional molecular dynamics simulation, quantum mechanics, and reactive molecular dynamics simulation) are outlined. The current simulation research progress of physical aging, aging under mechanical stress, thermal aging, hydrothermal aging, thermo-oxidative aging, electric aging, aging under high-energy particle impact, and radiation aging is introduced in detail. Finally, the current research status of the aging simulations of polymers and their composites is summarized, and the future development trend has been prospected.

A Coarse-Grained Interaction Model for Sodium Dominant Montmorillonite.

Montmorillonite is the main crystalline mineral present in bentonite. It is an absorbent, swelling material; the physical chemistry underlying its ability to absorb water and swell occurs at the nanoscale, governed by electrical double-layer interactions. In turn, absorption and swelling lead to important changes in the macroscopic transport properties of the clay. Mesoscale models can help us establish a link between these nanoscale processes and macroscale properties, notably by providing a detailed description of its pore network. Models on the scale of hundreds to thousands of nanometers are required, which cannot realistically be handled using traditional all-atom molecular dynamics simulations. This work presents a coarse-grained (CG) mesoscale model of sodium montmorillonite. In our model, montmorillonite platelets are represented by two types of particles: central nonhydrogen-bonded particles and edge hydrogen-bonding particles. The particle interactions are described by two-body potentials, which were optimized based on all-atom molecular dynamics simulations. Specifically, several potential mean force calculations involving dry and hydrated montmorillonite were performed, using the ClayFF potential to calculate interatomic forces. The CG model was validated by testing the scalability of the model, testing its ability to reproduce potentials of mean force reported elsewhere in the literature, and by comparing the calculated elastic properties of a system containing 1000 Na montmorillonite platelets to experimentally measured elastic properties of bentonite. The simulated elastic properties obtained using our mesoscale model agree with these experimental values.

Real-time monitoring the staged interactions between cationic surfactants and a phospholipid bilayer membrane.

The cationic surfactant-lipid interaction directs the development of novel types of nanodrugs or nanocarriers. The membrane action of cationic surfactants also has a wide range of applications. In this work, combining a photo-voltage transient method with the traditional dynamic giant unilamellar vesicle (GUV) leakage assay and molecular dynamics (MD) simulations, we monitored the molecular actions of a representative cationic surfactant, tetradecyl trimethyl ammonium bromide (TTAB), in a wide concentration range (i.e., 0.5 μM-10 mM), on a phospholipid bilayer membrane in real time. With low concentrations (e.g., ≤10 μM), TTAB performed a three-stage acting process, including the structural-disturbance-dominated, adsorption-dominated, and dynamic equilibrium stages. At higher concentrations (e.g., ≥100 μM), this process was accelerated to two stages. Furthermore, TTAB induced deformation and even rupture of the membrane, due to the asymmetric disturbance of surfactant molecules on the two leaflets of a bilayer. All these disturbances induced membrane permeabilization, and the times at which these transitions occurred are given. This work provides information on time and molecular mechanism during the membrane actions of cationic surfactants, and provides a simple and real-time method in studying the dynamic processes at the membrane interface.

The Role of Cyclodextrins against Interface-Induced Denaturation in Pharmaceutical Formulations: A Molecular Dynamics Approach

Protein-based pharmaceutical products are subject to a variety of environmental stressors, during both production and shelf-life. In order to preserve their structure, and, therefore, functionality, it is necessary to use excipients as stabilizing agents. Among the eligible stabilizers, cyclodextrins (CDs) have recently gained interest in the scientific community thanks to their properties. Here, a computational approach is proposed to clarify the role of β-cyclodextrin (βCD) and 2-hydroxypropyl-β-cyclodextrin (HPβCD) against granulocyte colony-stimulating (GCSF) factor denaturation at the air–water and ice–water interfaces, and also in bulk water at 300 or 260 K. Both traditional molecular dynamics (MD) simulations and enhanced sampling techniques (metadynamics, MetaD) are used to shed light on the underlying molecular mechanisms. Bulk simulations revealed that CDs were preferentially included within the surface hydration layer of GCSF, and even included some peptide residues in their hydrophobic cavity. HPβCD was able to stabilize the protein against surface-induced denaturation in proximity of the air–water interface, while βCD had a destabilizing effect. No remarkable conformational changes of GCSF, or noticeable effect of the CDs, were instead observed at the ice surface. GCSF seemed less stable at low temperature (260 K), which may be attributed to cold-denaturation effects. In this case, CDs did not significantly improve conformational stability. In general, the conformationally altered regions of GCSF seemed not to depend on the presence of excipients that only modulated the extent of destabilization with either a positive or a negative effect.

Covalent Docking and Molecular Dynamics Simulations Reveal the Resistance-Granting Mutations A237R/K in TEM Beta-Lactamase

The rate of modern drug discovery using experimental screening methods still lags far behind the rate at which pathogens mutate, highlighting the dire need for accurate and fast predictive simulations of macromolecular evolution. Particularly threatening, multidrug-resistant bacteria evade our defenses by expressing a series of resistance factors, the most famous of which is the 29 kilodaltons antibiotic cleaver, TEM beta-lactamase. Rising to this challenge, we applied biophysical simulations and predictions to identify two new resistance-conferring mutations in TEM and their respective modes of action. Using homology modeling and traditional all-atom molecular dynamics simulations, we predicted the structures of 50 TEM mutants, then deployed covalent docking to estimate their binding affinities against 85 beta-lactam-containing drugs to identify potential substrate specificity shifts. Finally, we used binding pose metadynamics and normal mode analyses to further scan the most promising results (validated against an empirical fitness dataset). This heuristic identified putative mechanisms for the previously-unreported, resistance-conferring mutations A237R and A237K. We also uncovered concerted structural rearrangements that could explain the specificity shifts observed in the clinically-entrenched mutations R164S/G/D. In addition to reaffirming the power of using simulations as molecular microscopes, our results could prove useful for guiding the rational design of next-generation beta-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.

Atomistic hybrid particle-field molecular dynamics combined with slip-springs: Restoring entangled dynamics to simulations of polymer melts.

In hybrid particle-field (hPF) simulations (J. Chem. Phys., 2009 130, 214106), the entangled dynamics of polymer melts is lost due to chain crossability. Chains cross, because the field-treatment of the nonbonded interactions makes them effectively soft-core. We introduce a multi-chain slip-spring model (J. Chem. Phys., 2013 138, 104907) into the hPF scheme to mimic the topological constraints of entanglements. The structure of the polymer chains is consistent with that of regular molecular dynamics simulations and is not affected by the introduction of slip-springs. Although slight deviations are seen at short times, dynamical properties such as mean-square displacements and reorientational relaxation times are in good agreement with traditional molecular dynamics simulations and theoretical predictions at long times.

Frontier Expansion Sampling: A Method to Accelerate Conformational Search by Identifying Novel Seed Structures for Restart.

Traditional molecular dynamics (MD) simulations have difficulties in tracking the slow molecular motions, at least partially due to the waste of sampling in already sampled regions. Here, we proposed a new enhanced sampling method, frontier expansion sampling (FEXS), to improve the sampling efficiency of molecular simulations by iteratively selecting seed structures diversely distributed at the "frontier" of an already sampled region to initiate new simulations. Different from other enhanced sampling methods, FEXS identifies the "frontier" seeds by integrating the Gaussian mixture model and the convex hull algorithm, which effectively improves the structural variation among the selected seeds and thus the descendant simulations. Validation in three protein systems, including the folding of chignolin, open-to-closed transition of maltodextrin binding protein, and internal conformational change of bovine pancreatic trypsin inhibitor, confirmed the effectiveness of this novel method in enhancing the sampling of conventional MD simulations to observe the large-scale protein conformational changes. When compared with other enhanced sampling methods like the structural dissimilarity sampling (SDS), FEXS reached at least the same level of sampling efficiency but was capable of providing complementary information in the three tested protein systems.

Development of New Methods for Enhanced Conformational Sampling of GPCRs

G protein coupled receptors (GPCRs) are integral membrane proteins that enable a cell to respond to extracellular stimuli like light, small molecules, peptides, and proteins. These receptors have evolved as highly flexible proteins that possess multiple functionally important conformational states, which enable these receptors to activate pleiotropic signaling events inside the cells. Recent progress in membrane protein structural biology techniques and cryo‐electron microscopy is providing structural evidence of two or more distinct receptor conformations for these receptors. Computational structural modeling approaches can complement and supplement these studies by predicting all functionally important states of a receptor, however, they run into the classical protein conformational sampling problem. We are developing a computational biophysical method called Enhanced Conformational Markov‐state Sampling in Membrane BiLayer Environment (EnCoMSeMBLE) to solve this sampling problem for GPCRs. We are utilizing Markov‐State‐Models to combine the Boltzmann sampling of receptor conformations from molecular dynamics (MD) with the brute‐force sampling of receptor conformations from our previously developed ActiveGEnSeMBLE method. The method was applied to the A2A receptor starting from the active state by only using traditional MD simulations. This is not surprising because the pre‐activce states is a downhill direction on the energy landscape. However, applying both MD and ActivateGEnSeMBLE methods was able to predict pre‐active states from the inactive states which, classical MD cannot achieve. Our results indicated that our methods provide an unbiased method to sample the conformational landscape. This can protentional sample unexplored regions of the conformational landscape and find novel conformations that can elucidate the mechanistic properties that GPCRs possess.Support or Funding InformationThis project was funded through a grant from the National Institutes of Health (NIH) Building Infrastructure Leading to Diversity (BUILD) #5TL4GM118977 or # 5RL5GM118975.

Coarse–grained model based on rigid grains interaction for single layer molybdenum disulfide

Single–layer molybdenum disulfide (SLMoS2) is a promising two–dimensional material with a wide range of possible applications in NEMS. Traditional molecular dynamics (MD) simulations of SLMoS2 are very time–consuming and cannot be applied to the real microscopic–level systems. We develop a coarse–grained model combining the atoms of crystal lattice into rigid ‘grains’. The interaction between the grains is based on Stillinger–Weber potential with parameters recalculated to fulfill the elastic properties of the original lattice. The model is applied to calculate the phonon spectrum and for the nanoindentation problem. It is shown that in the case of small strains the model is as accurate as regular MD simulations, but uses much less interatomic interactions; hence, it is much more time–efficient.

Temporally Coarse-Grained All-Atom Molecular Dynamics Achieved via Stochastic Padé Approximants.

A Padé approximant scheme for realizing the discrete-time evolution of the state of a many-atom system is introduced. This temporal coarse-graining scheme accounts for the underlying Newtonian physics and avoids the need for construction of spatially coarse-grained variables. Newtonian physics is incorporated through short molecular dynamics simulations at the beginning of each of the large coarse-grained timesteps. The balance between stochastic and coherent dynamics expressed by many-atom systems is captured via incorporation of the Ito formula into a Padé approximant for the time dependence of individual atom positions over large timesteps. Since the time for a many-atom system to express a characteristic ensemble of atomic velocity fluctuations is typically short relative to the characteristic time of large-scale atomic displacements, a computationally efficient and accurate temporal coarse-graining of the atom-resolved Newtonian dynamics is formulated, denoted all-atom Padé-Ito molecular dynamics (APIMD). Evolution of the system over a time step much longer than that required for standard molecular dynamics (MD) is achieved via incorporation of information from the short MD simulations into a Padé approximant extrapolation in time. The extrapolated atomic configuration is subjected to energy minimization and, when needed, thermal equilibration so as to avoid occasional unphysical close encounters deriving from the Padé approximant extrapolation and to represent configurations appropriate for the temperature of interest. APIMD is implemented and tested via comparison with traditional MD simulations of five phenomena: (1) pertussis toxin subunit deformation, (2) structural transition in a T = 1 capsid-like structure of HPV16 L1 protein, (3) coalescence of argon nanodroplets, and structural transitions in dialanine in (4) vacuum, and (5) water. Accuracy of APIMD is demonstrated using semimicroscopic descriptors (rmsd, radius of gyration, residue-residue contact maps, and densities) and the free energy. Significant computational acceleration relative to traditional molecular dynamics is illustrated.

Mechanism of Action of Non-Synonymous Single Nucleotide Variations Associated with α-Carbonic Anhydrase II Deficiency.

Human carbonic anhydrase II (CA-II) is a Zinc (Zn) metalloenzyme responsible for maintenance of acid-base balance within the body through the reversible hydration of CO to produce protons (H) and bicarbonate (BCT). Due to its importance, alterations to the amino acid sequence of the protein as a result of single nucleotide variations (nsSNVs) have detrimental effects on homeostasis. Six pathogenic CA-II nsSNVs, K18E, K18Q, H107Y, P236H, P236R and N252D were identified, and variant protein models calculated using homology modeling. The effect of each nsSNV was analyzed using motif analysis, molecular dynamics (MD) simulations, principal component (PCA) and dynamic residue network (DRN) analysis. Motif analysis identified 11 functionally important motifs in CA-II. RMSD data indicated subtle SNV effects, while PCA analysis revealed that the presence of BCT results in greater conformational sampling and free energy in proteins. DRN analysis showed variant allosteric effects, and the average betweenness centrality (BC) calculations identified Glu117 as the most important residue for communication in CA-II. The presence of BCT was associated with a reduction to Glu117 usage in all variants, suggesting implications for Zn dissociation from the CA-II active site. In addition, reductions to Glu117 usage are associated with increases in the usage of the primary and secondary Zn ligands; His94, His96, His119 and Asn243 highlighting potential compensatory mechanisms to maintain Zn within the active site. Compared to traditional MD simulation investigation, DRN analysis provided greater insights into SNV mechanism of action, indicating its importance for the study of missense mutation effects in proteins and, in broader terms, precision medicine related research.

Accelerated Molecular Dynamics Simulation for Helical Proteins Folding in Explicit Water.

In this study, we examined the folding processes of eight helical proteins (2I9M, TC5B, 1WN8, 1V4Z, 1HO2, 1HLL, 2KFE, and 1YYB) at room temperature using the explicit solvent model under the AMBER14SB force field with the accelerated molecular dynamics (AMD) and traditional molecular dynamics (MD), respectively. We analyzed and compared the simulation results obtained by these two methods based on several aspects, such as root mean square deviation (RMSD), native contacts, cluster analysis, folding snapshots, free energy landscape, and the evolution of the radius of gyration, which showed that these eight proteins were successfully and consistently folded into the corresponding native structures by AMD simulations carried out at room temperature. In addition, the folding occurred in the range of 40~180 ns after starting from the linear structures of the eight proteins at 300 K. By contrast, these stable folding structures were not found when the traditional molecular dynamics (MD) simulation was used. At the same time, the influence of high temperatures (350, 400, and 450 K) is also further investigated. Study found that the simulation efficiency of AMD is higher than that of MD simulations, regardless of the temperature. Of these temperatures, 300 K is the most suitable temperature for protein folding for all systems. To further investigate the efficiency of AMD, another trajectory was simulated for eight proteins with the same linear structure but different random seeds at 300 K. Both AMD trajectories reached the correct folded structures. Our result clearly shows that AMD simulation are a highly efficient and reliable method for the study of protein folding.

Aspherical particle models for molecular dynamics simulation

In traditional molecular dynamics (MD) simulations, atoms and coarse-grained particles are modeled as point masses interacting via isotropic potentials. For studies where particle shape plays a vital role, more complex models are required. In this paper we describe a spectrum of approaches for modeling aspherical particles, all of which are now available (some recently) as options within the LAMMPS MD package. Broadly these include two classes of models. In the first, individual particles are aspherical, either via a pairwise anisotropic potential which implicitly assigns a simple geometric shape to each particle, or in a more general way where particles store internal state which can explicitly define a complex geometric shape. In the second class of models, individual particles are simple points or spheres, but rigid body constraints are used to create composite aspherical particles in a variety of complex shapes. We discuss parallel algorithms and associated data structures for both kinds of models, which enable dynamics simulations of aspherical particle systems across a wide range of length and time scales. We also highlight parallel performance and scalability and give a few illustrative examples of aspherical models in different contexts.

Enabling QM-accurate simulation of dislocation motion in γ−Ni and α−Fe using a hybrid multiscale approach

We present an extension of the `learn on the fly' method to the study of the motion of dislocations in metallic systems, developed with the aim of producing information-efficient force models that can be systematically validated against reference QM calculations. Nye tensor analysis is used to dynamically track the quantum region centered at the core of a dislocation, thus enabling quantum mechanics/molecular mechanics simulations. The technique is used to study the motion of screw dislocations in Ni-Al systems, relevant to plastic deformation in Ni-based alloys, at a variety of temperature/strain conditions. These simulations reveal only a moderate spacing ($\ensuremath{\sim}5\phantom{\rule{0.16em}{0ex}}\AA{}$) between Shockley partial dislocations, at variance with the predictions of traditional molecular dynamics (MD) simulation using interatomic potentials, which yields a much larger spacing in the high stress regime. The discrepancy can be rationalized in terms of the elastic properties of an hcp crystal, which influence the behavior of the stacking fault region between Shockley partial dislocations. The transferability of this technique to more challenging systems is addressed, focusing on the expected accuracy of such calculations. The bcc $\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{Fe}$ phase is a prime example, as its magnetic properties at the open surfaces make it particularly challenging for embedding-based QM/MM techniques. Our tests reveal that high accuracy can still be obtained at the core of a dislocation, albeit at a significant computational cost for fully converged results. However, we find this cost can be reduced by using a machine learning approach to progressively reduce the rate of expensive QM calculations required during the dynamical simulations, as the size of the QM database increases.

Simulating Water Exchange to Buried Binding Sites.

Traditional molecular dynamics (MD) simulations of proteins, which relies on integration of Newton's equations of motion, cannot efficiently equilibrate water occupancy for buried cavities in proteins. This leads to slow convergence of thermodynamic averages for such systems. We have addressed this challenge by efficiently integrating standard Metropolis Monte Carlo (MC) translational water moves with MD in the AMBER simulation package. The translational moves allow water to easily enter or exit buried sites in a thermodynamically correct way during a simulation. To maximize efficiency, the algorithm avoids moves that only interchange waters within the bulk around the protein instead focusing on moves that can transfer water between bulk and the protein interior. In addition, a steric grid allows avoidance of moves that would lead to obvious steric clashes, and a fast grid-based energy evaluation is used to reduce the number of expensive full energy calculations. The potential energy distribution produced using MC/MD was found to be statistically indistinguishable from that of control simulations using only MD, and the algorithm effectively equilibrated water across steric barriers and into binding pockets that are not accessible with pure MD. The MC/MD method introduced here should be of increasing utility for applications spanning protein folding, the elucidation of protein mechanisms, and free energy calculations for computer-aided drug design. It is available in version 18 release of the widely disseminated AMBER simulation package.

Enhancing Side Chain Rotamer Sampling Using Nonequilibrium Candidate Monte Carlo.

Molecular simulations are a valuable tool for studying biomolecular motions and thermodynamics. However, such motions can be slow compared to simulation time scales, yet critical. Specifically, adequate sampling of side chain motions in protein binding pockets is crucial for obtaining accurate estimates of ligand binding free energies from molecular simulations. The time scale of side chain rotamer flips can range from a few ps to several hundred ns or longer, particularly in crowded environments like the interior of proteins. Here, we apply a mixed nonequilibrium candidate Monte Carlo (NCMC)/molecular dynamics (MD) method to enhance sampling of side chain rotamers. The NCMC portion of our method applies a switching protocol wherein the steric and electrostatic interactions between target side chain atoms and the surrounding environment are cycled off and then back on during the course of a move proposal. Between NCMC move proposals, simulation of the system continues via traditional molecular dynamics. Here, we first validate this approach on a simple, solvated valine-alanine dipeptide system and then apply it to a well-studied model ligand binding site in T4 lysozyme L99A. We compute the rate of rotamer transitions for a valine side chain using our approach and compare it to that of traditional molecular dynamics simulations. Here, we show that our NCMC/MD method substantially enhances side chain sampling, especially in systems where the torsional barrier to rotation is high (≥10 kcal/mol). These barriers can be intrinsic torsional barriers or steric barriers imposed by the environment. Overall, this may provide a promising strategy to selectively improve side chain sampling in molecular simulations.