Hierarchy of RNA functional dynamics.

RNA dynamics play a fundamental role in many cellular functions. However, there is no general framework to describe these complex processes, which typically consist of many structural maneuvers that occur over timescales ranging from picoseconds to seconds. Here, we classify RNA dynamics into distinct modes representing transitions between basins on a hierarchical free-energy landscape. These transitions include large-scale secondary-structural transitions at >0.1-s timescales, base-pair/tertiary dynamics at microsecond-to-millisecond timescales, stacking dynamics at timescales ranging from nanoseconds to microseconds, and other "jittering" motions at timescales ranging from picoseconds to nanoseconds. We review various modes within these three different tiers, the different mechanisms by which they are used to regulate function, and how they can be coupled together to achieve greater functional complexity.

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design.

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Ligand Modulates Cross-Coupling between Riboswitch Folding and Transcriptional Pausing.

Numerous classes of riboswitches have been found to regulate bacterial gene expression in response to physiological cues, offering new paths to antibacterial drugs. As common studies of isolated riboswitches lack the functional context of the transcription machinery, we here combine single-molecule, biochemical, and simulation approaches to investigate the coupling between co-transcriptional folding of the pseudoknot-structured preQ1 riboswitch and RNA polymerase (RNAP) pausing. We show that pausing at a site immediately downstream of the riboswitch requires a ligand-free pseudoknot in the nascent RNA, a precisely spaced sequence resembling the pause consensus, and electrostatic and steric interactions with the RNAP exit channel. While interactions with RNAP stabilize the native fold of the riboswitch, binding of the ligand signals RNAP release from the pause. Our results demonstrate that the nascent riboswitch and its ligand actively modulate the function of RNAP and vice versa, a paradigm likely to apply to other cellular RNA transcripts.

Deciphering the evolution of flavin-dependent monooxygenase stereoselectivity using ancestral sequence reconstruction.

Controlling the selectivity of a reaction is critical for target-oriented synthesis. Accessing complementary selectivity profiles enables divergent synthetic strategies, but is challenging to achieve in biocatalytic reactions given enzymes' innate preferences of a single selectivity. Thus, it is critical to understand the structural features that control selectivity in biocatalytic reactions to achieve tunable selectivity. Here, we investigate the structural features that control the stereoselectivity in an oxidative dearomatization reaction that is key to making azaphilone natural products. Crystal structures of enantiocomplementary biocatalysts guided the development of multiple hypotheses centered on the structural features that control the stereochemical outcome of the reaction; however, in many cases, direct substitutions of active site residues in natural proteins led to inactive enzymes. Ancestral sequence reconstruction (ASR) and resurrection were employed as an alternative strategy to probe the impact of each residue on the stereochemical outcome of the dearomatization reaction. These studies suggest that two mechanisms are active in controlling the stereochemical outcome of the oxidative dearomatization reaction: one involving multiple active site residues in AzaH and the other dominated by a single Phe to Tyr switch in TropB and AfoD. Moreover, this study suggests that the flavin-dependent monooxygenases (FDMOs) adopt simple and flexible strategies to control stereoselectivity, which has led to stereocomplementary azaphilone natural products produced by fungi. This paradigm of combining ASR and resurrection with mutational and computational studies showcases sets of tools for understanding enzyme mechanisms and provides a solid foundation for future protein engineering efforts.

A fine balance of hydrophobic-electrostatic communication pathways in a pH-switching protein.

Allostery is the phenomenon of coupling between distal binding sites in a protein. Such coupling is at the crux of protein function and regulation in a myriad of scenarios, yet determining the molecular mechanisms of coupling networks in proteins remains a major challenge. Here, we report mechanisms governing pH-dependent myristoyl switching in monomeric hisactophilin, whereby the myristoyl moves between a sequestered state, i.e., buried within the core of the protein, to an accessible state, in which the myristoyl has increased accessibility for membrane binding. Measurements of the pH and temperature dependence of amide chemical shifts reveal protein local structural stability and conformational heterogeneity that accompany switching. An analysis of these measurements using a thermodynamic cycle framework shows that myristoyl-proton coupling at the single-residue level exists in a fine balance and extends throughout the protein. Strikingly, small changes in the stereochemistry or size of core and surface hydrophobic residues by point mutations readily break, restore, or tune myristoyl switch energetics. Synthesizing the experimental results with those of molecular dynamics simulations illuminates atomistic details of coupling throughout the protein, featuring a large network of hydrophobic interactions that work in concert with key electrostatic interactions. The simulations were critical for discerning which of the many ionizable residues in hisactophilin are important for switching and identifying the contributions of nonnative interactions in switching. The strategy of using temperature-dependent NMR presented here offers a powerful, widely applicable way to elucidate the molecular mechanisms of allostery in proteins at high resolution.

Ancestral Sequence Reconstruction to Enable Biocatalytic Synthesis of Azaphilones.

Biocatalysis can be powerful in organic synthesis but is often limited by enzymes' substrate scope and selectivity. Developing a biocatalytic step involves identifying an initial enzyme for the target reaction followed by optimization through rational design, directed evolution, or both. These steps are time consuming, resource-intensive, and require expertise beyond typical organic chemistry. Thus, an effective strategy for streamlining the process from enzyme identification to implementation is essential to expanding biocatalysis. Here, we present a strategy combining bioinformatics-guided enzyme mining and ancestral sequence reconstruction (ASR) to resurrect enzymes for biocatalytic synthesis. Specifically, we achieve an enantioselective synthesis of azaphilone natural products using two ancestral enzymes: a flavin-dependent monooxygenase (FDMO) for stereodivergent oxidative dearomatization and a substrate-selective acyltransferase (AT) for the acylation of the enzymatically installed hydroxyl group. This cascade, stereocomplementary to established chemoenzymatic routes, expands access to enantiomeric linear tricyclic azaphilones. By leveraging the co-occurrence and coevolution of FDMO and AT in azaphilone biosynthetic pathways, we identified an AT candidate, CazE, and addressed its low solubility and stability through ASR, obtaining a more soluble, stable, promiscuous, and reactive ancestral AT (AncAT). Sequence analysis revealed AncAT as a chimeric composition of its descendants with enhanced reactivity likely due to ancestral promiscuity. Flexible receptor docking and molecular dynamics simulations showed that the most reactive AncAT promotes a reactive geometry between substrates. We anticipate that our bioinformatics-guided, ASR-based approach can be broadly applied in target-oriented synthesis, reducing the time required to develop biocatalytic steps and efficiently access superior biocatalysts.

Biocatalytic Stereoselective Oxidation of 2-Arylindoles.

3-Hydroxyindolenines can be used to access several structural motifs that are featured in natural products and pharmaceutical compounds, yet the chemical synthesis of 3-hydroxyindolenines is complicated by overoxidation, rearrangements, and complex product mixtures. The selectivity possible in enzymatic reactions can overcome these challenges and deliver enantioenriched products. Herein, we present the development of an asymmetric biocatalytic oxidation of 2-arylindole substrates aided by a curated library of flavin-dependent monooxygenases (FDMOs) sampled from an ancestral sequence space, a sequence similarity network, and a deep-learning-based latent space model. From this library of FDMOs, a previously uncharacterized enzyme, Champase, from the Valley fever fungus, Coccidioides immitis strain RS, was found to stereoselectively catalyze the oxidation of a variety of substituted indole substrates. The promiscuity of this enzyme is showcased by the oxidation of a wide variety of substituted 2-arylindoles to afford the respective 3-hydroxyindolenine products in moderate to excellent yields and up to 95:5 er.

Exploring the Limits of the Generalized CHARMM and AMBER Force Fields through Predictions of Hydration Free Energy of Small Molecules.

Accurate force field parameters, potential energy functions, and receptor-ligand models are essential for modeling the solvation and binding of drug-like molecules to a receptor. A large and ever-growing chemical space of medicinally relevant scaffolds has also required these factors, especially force field parameters, to be highly transferable. Generalized force fields such as the CHARMM General Force Field (CGenFF) and the generalized AMBER force field (GAFF) have accomplished this feat along with other contemporaneous ones like OPLS. Here, we analyze the limits in the parametrization of drug-like small molecules by CGenFF and GAFF in terms of the various functional groups represented within them. Specifically, we link the presence of specific functional groups to the error in the absolute hydration free energy of over 600 small molecules, predicted by alchemical free energy methods implemented in the CHARMM program. Our investigation reveals that molecules with (i) a nitro group in CGenFF and GAFF are, respectively, over- or undersolubilized in aqueous medium, (ii) amine groups are undersolubilized more so in CGenFF than in GAFF, and (iii) carboxyl groups are more oversolubilized in GAFF than in CGenFF. We present our analyses of the potential factors underlying these trends. We also showcase the use of a machine-learning-based approach combined with the SHapley Additive exPlanations framework to attribute these trends to specific functional groups, which can be easily adopted to explore the limits of other general force fields.

Investigating Polypharmacology through Targeting Known Human Neutrophil Elastase Inhibitors to Proteinase 3.

Using a combination of multisite λ-dynamics (MSλD) together with in vitro IC50 assays, we evaluated the polypharmacological potential of a scaffold currently in clinical trials for inhibition of human neutrophil elastase (HNE), targeting cardiopulmonary disease, for efficacious inhibition of Proteinase 3 (PR3), a related neutrophil serine proteinase. The affinities we observe suggest that the dihydropyrimidinone scaffold can serve as a suitable starting point for the establishment of polypharmacologically targeting both enzymes and enhancing the potential for treatments addressing diseases like chronic obstructive pulmonary disease.

Allostery in the dynamic coactivator domain KIX occurs through minor conformational micro-states.

The coactivator KIX of CBP uses two binding surfaces to recognize multiple activators and exhibits allostery in ternary complex formation. Activator•coactivator interactions are central to transcriptional regulation, yet the microscopic origins of allostery in dynamic proteins like KIX are largely unknown. Here, we investigate the molecular recognition and allosteric manifestations involved in two KIX ternary systems c-Myb•KIX•MLL and pKID•KIX•MLL. Exploring the hypothesis that binary complex formation prepays an entropic cost for positive cooperativity, we utilize molecular dynamics simulations, side chain methyl order parameters, and differential scanning fluorimetry (DSF) to explore conformational entropy changes in KIX. The protein's configurational micro-states from structural clustering highlight the utility of protein plasticity in molecular recognition and allostery. We find that apo KIX occupies a wide distribution of lowly-populated configurational states. Each binding partner has its own suite of KIX states that it selects, building a model of molecular recognition fingerprints. Allostery is maximized with MLL pre-binding, which corresponds to the observation of a significant reduction in KIX micro-states observed when MLL binds. With all binding partners, the changes in KIX conformational entropy arise predominantly from changes in the most flexible loop. Likewise, we find that a small molecule and mutations allosterically inhibit/enhance activator binding by tuning loop dynamics, suggesting that loop-targeting chemical probes could be developed to alter KIX•activator interactions. Experimentally capturing KIX stabilization is challenging, particularly because of the disordered nature of particular activators. However, DSF melting curves allow for inference of relative entropic changes that occur across complexes, which we compare to our computed entropy changes using simulation methyl order parameters.

FASTDock: A Pipeline for Allosteric Drug Discovery.

Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.

M2 amphipathic helices facilitate pH-dependent conformational transition in influenza A virus.

The matrix-2 (M2) protein from influenza A virus is a tetrameric, integral transmembrane (TM) protein that plays a vital role in viral replication by proton flux into the virus. The His37 tetrad is a pH sensor in the center of the M2 TM helix that activates the channel in response to the low endosomal pH. M2 consists of different regions that are believed to be involved in membrane targeting, packaging, nucleocapsid binding, and proton transport. Although M2 has been the target of many experimental and theoretical studies that have led to significant insights into its structure and function under differing conditions, the main mechanism of proton transport, its conformational dynamics, and the role of the amphipathic helices (AHs) on proton conductance remain elusive. To this end, we have applied explicit solvent constant pH molecular dynamics using the multisite λ-dynamics approach (CpHMDMSλD) to investigate the buried ionizable residues comprehensively and to elucidate their effect on the conformational transition. Our model recapitulates the pH-dependent conformational transition of M2 from closed to open state when the AH domain is included in the M2 construct, revealing the role of the amphipathic helices on this transition and shedding light on the proton-transport mechanism. This work demonstrates the importance of including the amphipathic helices in future experimental and theoretical studies of ion channels. Finally, our work shows that explicit solvent CpHMDMSλD provides a realistic pH-dependent model for membrane proteins.

Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization.

With the ability to sample combinations of alchemical perturbations at multiple sites off a small molecule core, multisite λ-dynamics (MSλD) has become an attractive alternative to conventional alchemical free energy methods for exploring large combinatorial chemical spaces. However, current software implementations dictate that combinatorial sampling with MSλD must be performed with a multiple topology model (MTM), which is nontrivial to create by hand, especially for a series of ligand analogues which may have diverse functional groups attached. This work introduces an automated workflow, referred to as msld_py_prep, to assist in the creation of a MTM for use with MSλD. One approach for partitioning partial atomic charges between ligands to create a MTM, called charge renormalization, is also presented and rigorously evaluated. We find that msld_py_prep greatly accelerates the preparation of MSλD ready-to-use files and that charge renormalization can provide a successful approach for MTM generation, as long as bookending calculations are applied to correct small differences introduced by charge renormalization. Charge renormalization also facilitates the use of many different force field parameters with MSλD, broadening the applicability of MSλD for computer-aided drug design.

Alchemical Free Energy Methods Applied to Complexes of the First Bromodomain of BRD4.

Accurate and rapid predictions of the binding affinity of a compound to a target are one of the ultimate goals of computer aided drug design. Alchemical approaches to free energy estimations follow the path from an initial state of the system to the final state through alchemical changes of the energy function during a molecular dynamics simulation. Herein, we explore the accuracy and efficiency of two such techniques: relative free energy perturbation (FEP) and multisite lambda dynamics (MSλD). These are applied to a series of inhibitors for the bromodomain-containing protein 4 (BRD4). We demonstrate a procedure for obtaining accurate relative binding free energies using MSλD when dealing with a change in the net charge of the ligand. This resulted in an impressive comparison with experiment, with an average difference of 0.4 ± 0.4 kcal mol-1. In a benchmarking study for the relative FEP calculations, we found that using 20 lambda windows with 0.5 ns of equilibration and 1 ns of data collection for each window gave the optimal compromise between accuracy and speed. Overall, relative FEP and MSλD predicted binding free energies with comparable accuracy, an average of 0.6 kcal mol-1 for each method. However, MSλD makes predictions for a larger molecular space over a much shorter time scale than relative FEP, with MSλD requiring a factor of 18 times less simulation time for the entire molecule space.

Frustration and folding of a TIM barrel protein.

Triosephosphate isomerase (TIM) barrel proteins have not only a conserved architecture that supports a myriad of enzymatic functions, but also a conserved folding mechanism that involves on- and off-pathway intermediates. Although experiments have proven to be invaluable in defining the folding free-energy surface, they provide only a limited understanding of the structures of the partially folded states that appear during folding. Coarse-grained simulations employing native centric models are capable of sampling the entire energy landscape of TIM barrels and offer the possibility of a molecular-level understanding of the readout from sequence to structure. We have combined sequence-sensitive native centric simulations with small-angle X-ray scattering and time-resolved Förster resonance energy transfer to monitor the formation of structure in an intermediate in the Sulfolobus solfataricus indole-3-glycerol phosphate synthase TIM barrel that appears within 50 µs and must at least partially unfold to achieve productive folding. Simulations reveal the presence of a major and 2 minor folding channels not detected in experiments. Frustration in folding, i.e., backtracking in native contacts, is observed in the major channel at the initial stage of folding, as well as late in folding in a minor channel before the appearance of the native conformation. Similarities in global and pairwise dimensions of the early intermediate, the formation of structure in the central region that spreads progressively toward each terminus, and a similar rate-limiting step in the closing of the ß-barrel underscore the value of combining simulation and experiment to unravel complex folding mechanisms at the molecular level.

Norstictic Acid Is a Selective Allosteric Transcriptional Regulator.

Inhibitors of transcriptional protein-protein interactions (PPIs) have high value both as tools and for therapeutic applications. The PPI network mediated by the transcriptional coactivator Med25, for example, regulates stress-response and motility pathways, and dysregulation of the PPI networks contributes to oncogenesis and metastasis. The canonical transcription factor binding sites within Med25 are large (∼900 Å2) and have little topology, and thus, they do not present an array of attractive small-molecule binding sites for inhibitor discovery. Here we demonstrate that the depsidone natural product norstictic acid functions through an alternative binding site to block Med25-transcriptional activator PPIs in vitro and in cell culture. Norstictic acid targets a binding site comprising a highly dynamic loop flanking one canonical binding surface, and in doing so, it both orthosterically and allosterically alters Med25-driven transcription in a patient-derived model of triple-negative breast cancer. These results highlight the potential of Med25 as a therapeutic target as well as the inhibitor discovery opportunities presented by structurally dynamic loops within otherwise challenging proteins.

A strategy for proline and glycine mutations to proteins with alchemical free energy calculations.

Computation of the thermodynamic consequences of protein mutations holds great promise in protein biophysics and design. Alchemical free energy methods can give improved estimates of mutational free energies, and are already widely used in calculations of relative and absolute binding free energies in small molecule design problems. In principle, alchemical methods can address any amino acid mutation with an appropriate alchemical pathway, but identifying a strategy that produces such a path for proline and glycine mutations is an ongoing challenge. Most current strategies perturb only side chain atoms, while proline and glycine mutations also alter the backbone parameters and backbone ring topology. Some strategies also perturb backbone parameters and enable glycine mutations. This work presents a strategy that enables both proline and glycine mutations and comprises two key elements: a dual backbone with restraints and scaling of bonded terms, facilitating backbone parameter changes, and a soft bond in the proline ring, enabling ring topology changes in proline mutations. These elements also have utility for core hopping and macrocycle studies in computer-aided drug design. This new strategy shows slight improvements over an alternative side chain perturbation strategy for a set T4 lysozyme mutations lacking proline and glycine, and yields good agreement with experiment for a set of T4 lysozyme proline and glycine mutations not previously studied. To our knowledge this is the first report comparing alchemical predictions of proline mutations with experiment. With this strategy in hand, alchemical methods now have access to the full palette of amino acid mutations.

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy.

The binding of small-molecule ligands to protein or nucleic acid targets is important to numerous biological processes. Accurate prediction of the binding modes between a ligand and a macromolecule is of fundamental importance in structure-based structure-function exploration. When multiple ligands with different sizes are docked to a target receptor, it is reasonable to assume that the residues in the binding pocket may adopt alternative conformations upon interacting with the different ligands. In addition, it has been suggested that the entropic contribution to binding can be important. However, only a few attempts to include the side chain conformational entropy upon binding within the application of flexible receptor docking methodology exist. Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. We demonstrate improved accuracy in flexible cross-docking experiments compared with rigid cross-docking. We test our developments by considering five protein targets, thrombin, dihydrofolate reductase(DHFR), T4 L99A, T4 L99A/M102Q, and PDE10A, which belong to different enzyme classes with different binding pocket environments, as a representative set of diverse ligands and receptors. Each target contains dozens of different ligands bound to the same binding pocket. We also demonstrate that this flexible docking algorithm may be applicable to RNA docking with a representative riboswitch example. Our findings show significant improvements in top ranking accuracy across this set, with the largest improvement relative to rigid, 23.64%, occurring for ligands binding to DHFR. We then evaluate the ability to identify lead compounds among a large chemical space for the proposed flexible receptor docking algorithm using a subset of the DUD-E containing receptor targets MCR, GCR, and ANDR. We demonstrate that our new algorithms show improved performance in modeling flexible binding site residues compared to DOCK. Finally, we select the T4 L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and experimentally validated nonbinders, to test our approach in distinguishing binders from nonbinders. We illustrate that our new algorithms for searching and scoring have superior performance to rigid receptor CDOCKER as well as AutoDock Vina. Finally, we suggest that flexible CDOCKER is sufficiently fast to be utilized in high-throughput docking screens in the context of hierarchical approaches.

Conservation of coactivator engagement mechanism enables small-molecule allosteric modulators.

Transcriptional coactivators are a molecular recognition marvel because a single domain within these proteins, the activator binding domain or ABD, interacts with multiple compositionally diverse transcriptional activators. Also remarkable is the structural diversity among ABDs, which range from conformationally dynamic helical motifs to those with a stable core such as a ß-barrel. A significant objective is to define conserved properties of ABDs that allow them to interact with disparate activator sequences. The ABD of the coactivator Med25 (activator interaction domain or AcID) is unique in that it contains secondary structural elements that are on both ends of the spectrum: helices and loops that display significant conformational mobility and a seven-stranded ß-barrel core that is structurally rigid. Using biophysical approaches, we build a mechanistic model of how AcID forms binary and ternary complexes with three distinct activators; despite its static core, Med25 forms short-lived, conformationally mobile, and structurally distinct complexes with each of the cognate partners. Further, ternary complex formation is facilitated by allosteric communication between binding surfaces on opposing faces of the ß-barrel. The model emerging suggests that the conformational shifts and cooperative binding is mediated by a flexible substructure comprised of two dynamic helices and flanking loops, indicating a conserved mechanistic model of activator engagement across ABDs. Targeting a region of this substructure with a small-molecule covalent cochaperone modulates ternary complex formation. Our data support a general strategy for the identification of allosteric small-molecule modulators of ABDs, which are key targets for mechanistic studies as well as therapeutic applications.

Accelerating the Generalized Born with Molecular Volume and Solvent Accessible Surface Area Implicit Solvent Model Using Graphics Processing Units.

The generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) implicit solvent model provides an accurate description of molecular volume and has the potential to accurately describe the conformational equilibria of structured and disordered proteins. However, its broader application has been limited by the computational cost and poor scaling in parallel computing. Here, we report an efficient implementation of both the electrostatic and nonpolar components of GBMV2/SA on graphics processing unit (GPU) within the CHARMM/OpenMM module. The GPU-GBMV2/SA is numerically equivalent to the original CPU-GBMV2/SA. The GPU acceleration offers ~60- to 70-fold speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins of various sizes. The current implementation can be further optimized to achieve even greater acceleration with minimal reduction on the numerical accuracy. The successful development of GPU-GBMV2/SA greatly facilitates its application to biomolecular simulations and paves the way for further development of the implicit solvent methodology. © 2019 Wiley Periodicals, Inc.