Efficient nonbonded interactions for molecular dynamics on a graphics processing unit

Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy

Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model's integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field.

The vitamin D receptor as a potential target for the toxic effects of per- and polyfluoroalkyl substances (PFASs): An in-silico study

Due to their persistence and toxicity, perfluoroalkyl and polyfluoroalkyl substances (PFASs) constitute significant hazards to human health and the environment. Their effects include immune suppression, altered hormone levels, and osteoporosis. Recently, the most studied PFAS, perfluorooctanoic acid (PFOA), was shown to competitively binding to the Vitamin D receptor (VDR). VDR plays a crucial role in regulating genes involved in maintaining immune, endocrine, and calcium homeostasis, suggesting it may be a target for at least some of the health effects of PFAS. Hence, this study examined the potential binding of 5206 PFASs to VDR using molecular docking, molecular dynamics, and free energy binding calculations. We identified 14 PFAS that are predicted to interact strongly with VDR, similar to the natural ligands. We further investigated the interactions of VDR with 256 PFASs of established commercial importance. Eighty-three (32%) of these 256 commercially important PFAS were predicted to be stronger binders to VDR than PFOA. At least 16 PFASs of regulatory importance, because they have been identified in water supplies and human blood samples, were also more potent binders to VDR than PFOA. Further, PFASs are usually found together in contaminated drinking water and human blood samples, which raises the concern that multiple PFASs may act together as a mixture on VDR function, potentially producing harmful effects on the immune, endocrine, and bone homeostasis.

Assessing the effect of forcefield parameter sets on the accuracy of relative binding free energy calculations

Software for accurate prediction of protein-ligand binding affinity can be a key enabling tool for small molecule drug discovery. Free energy perturbation (FEP) is a computational technique that can be used to compute binding affinity differences between molecules in a congeneric series. It has shown promise in reliably generating accurate predictions and is now widely used in the pharmaceutical industry. However, the high computational cost and use of commercial software, together with the technical challenges to setup, run, and analyze the simulations, limits the usage of FEP. Here, we use an automated FEP workflow which uses the open-source OpenMM package. To enable effective application of FEP, we compared the performance of different water models, partial charge assignments, and AMBER protein forcefields in eight benchmark test cases previously assembled for FEP validation studies.

Comparing the Performance of Different AMBER Protein Forcefields, Partial Charge Assignments, and Water Models for Absolute Binding Free Energy Calculations

Identifying chemical starting points is a vital first step in small molecule drug discovery and can take significant time and money. For this reason, computational approaches to virtual screening are of great interest as they can lower the cost and shorten timeframes. However, simple approaches such as molecular docking and pharmacophore screening are of limited accuracy and provide a low probability of success. Alchemical binding free energies represent a promising approach for virtual screening as they naturally incorporate the key effects of water molecules, protein flexibility, and binding entropy. However, the calculations are technically very challenging, with performance depending on the specific forcefield used. For this reason, it is important that the community has access to benchmark test sets to assess prediction accuracy. In this paper, we present an approach to alchemical binding free energies using OpenMM. We identify effective simulation parameters using an existing BRD4(1) test set and present two new benchmark sets (cMET and PDE2A) that can be used in the community for validation purposes. Our findings also highlight the effectiveness of some AMBER forcefields, in particular, AMBER ff15ipq.

Ultrathin 2D Titanium Carbide MXene (Ti3 C2 Tx ) Nanoflakes Activate WNT/HIF-1α-Mediated Metabolism Reprogramming for Periodontal Regeneration

Periodontal defect regeneration in severe periodontitis relies on the differentiation and proliferation of periodontal ligament cells (PDLCs). Recently, an emerging 2D nanomaterial, MXene (Ti₃ C₂ T_x ), has gained more and more attention due to the extensive antibacterial and anticancer activity, while its potential biomedical application on tissue regeneration remains unclear. Through a combination of experimental and multiscale simulation schemes, Ti₃ C₂ T_x has exhibited satisfactory biocompatibility and induced distinguish osteogenic differentiation of human PDLCs (hPDLCs), with upregulated osteogenesis-related genes. Ti₃ C₂ T_x manages to activate the Wnt/β-catenin signaling pathway by enhancing the Wnt-Frizzled complex binding, thus stabilizing HIF-1α and altering metabolic reprogramming into glycolysis. In vivo, hPDLCs pretreated by Ti₃ C₂ T_x display excellent performance in new bone formation and osteoclast inhibition with enhanced RUNX2, HIF-1α, and β-catenin in an experimental rat model of periodontal fenestration defects, indicating that this material has high efficiency of periodontal regeneration promotion. It is demonstrated in this work that Ti₃ C₂ T_x has highly efficient therapeutic effects in osteogenic differentiation and periodontal defect repairment.

Using a Coarse-Grained Modeling Framework to Identify Oligomeric Motifs with Tunable Secondary Structure

Coarse-grained modeling can be used to explore general theories that are independent of specific chemical detail. In this paper, we present cg_openmm, a Python-based simulation framework for modeling coarse-grained hetero-oligomers and screening them for structural and thermodynamic characteristics of cooperative secondary structures. cg_openmm facilitates the building of coarse-grained topology and random starting configurations, setup of GPU-accelerated replica exchange molecular dynamics simulations with the OpenMM software package, and features a suite of postprocessing thermodynamic and structural analysis tools. In particular, native contact analysis, heat capacity calculations, and free energy of folding calculations are used to identify and characterize cooperative folding transitions and stable secondary structures. In this work, we demonstrate the capabilities of cg_openmm on a simple 1-1 Lennard-Jones coarse-grained model, in which each residue contains 1 backbone and 1 side-chain bead. By scanning both nonbonded and bonded force-field parameter spaces at the coarse-grained level, we identify and characterize sets of parameters which result in the formation of stable helices through cooperative folding transitions. Moreover, we show that the geometries and stabilities of these helices can be tuned by manipulating the force-field parameters.

Defining the Energetic Basis for a Conformational Switch Mediating Ligand-Independent Activation of Mutant Estrogen Receptors in Breast Cancer

Although most primary estrogen receptor (ER)-positive breast cancers respond well to endocrine therapies, many relapse later as metastatic disease due to endocrine therapy resistance. Over one third of these are associated with mutations in the ligand-binding domain (LBD) that activate the receptor independent of ligand. We have used an array of advanced computational techniques rooted in molecular dynamics simulations, in concert with and validated by experiments, to characterize the molecular mechanisms by which specific acquired somatic point mutations give rise to ER constitutive activation. By comparing structural and energetic features of constitutively active mutants and ligand-bound forms of ER-LBD with unliganded wild-type (WT) ER, we characterize a spring force originating from strain in the Helix 11-12 loop of WT-ER, opposing folding of Helix 12 into the active conformation and keeping WT-ER off and disordered, with the ligand-binding pocket open for rapid ligand binding. We quantify ways in which this spring force is abrogated by activating mutations that latch (Y537S) or relax (D538G) the folded form of the loop, enabling formation of the active conformation without ligand binding. We also identify a new ligand-mediated hydrogen-bonding network that stabilizes the active, ligand-bound conformation of WT-ER LBD, and similarly stabilizes the active conformation of the ER mutants in the hormone-free state. IMPLICATIONS: Our investigations provide deep insight into the energetic basis for the structural mechanisms of receptor activation through mutation, exemplified here with ER in endocrine-resistant metastatic breast cancers, with potential application to other dysregulated receptor signaling due to driver mutations.

SAMPL7 Host-Guest Challenge Overview: assessing the reliability of polarizable and non-polarizable methods for binding free energy calculations

The SAMPL challenges focus on testing and driving progress of computational methods to help guide pharmaceutical drug discovery. However, assessment of methods for predicting binding affinities is often hampered by computational challenges such as conformational sampling, protonation state uncertainties, variation in test sets selected, and even lack of high quality experimental data. SAMPL blind challenges have thus frequently included a component focusing on host-guest binding, which removes some of these challenges while still focusing on molecular recognition. Here, we report on the results of the SAMPL7 blind prediction challenge for host-guest affinity prediction. In this study, we focused on three different host-guest categories-a familiar deep cavity cavitand series which has been featured in several prior challenges (where we examine binding of a series of guests to two hosts), a new series of cyclodextrin derivatives which are monofunctionalized around the rim to add amino acid-like functionality (where we examine binding of two guests to a series of hosts), and binding of a series of guests to a new acyclic TrimerTrip host which is related to previous cucurbituril hosts. Many predictions used methods based on molecular simulations, and overall success was mixed, though several methods stood out. As in SAMPL6, we find that one strategy for achieving reasonable accuracy here was to make empirical corrections to binding predictions based on previous data for host categories which have been studied well before, though this can be of limited value when new systems are included. Additionally, we found that alchemical free energy methods using the AMOEBA polarizable force field had considerable success for the two host categories in which they participated. The new TrimerTrip system was also found to introduce some sampling problems, because multiple conformations may be relevant to binding and interconvert only slowly. Overall, results in this challenge tentatively suggest that further investigation of polarizable force fields for these challenges may be warranted.

Molecular dynamics simulations of liquid-liquid interfaces in an electric field: The water-1,2-dichloroethane interface

The polarized interface between two immiscible liquids plays a central role in many technological processes. In particular, for electroanalytical and ion extraction applications, an external electric field is typically used to selectively induce the transfer of ionic species across the interfaces. Given that it is experimentally challenging to obtain an atomistic insight into the ion transfer process and the structure of liquid-liquid interfaces, atomistic simulations have often been used to fill this knowledge gap. However, due to the long-range nature of the electrostatic interactions and the use of 3D periodic boundary conditions, the use of external electric fields in molecular dynamics simulations requires special care. Here, we show how the simulation setup affects the dielectric response of the materials and demonstrate how by a careful design of the system it is possible to obtain the correct electric field on both sides of a liquid-liquid interface when using standard 3D Ewald summation methods. In order to prove the robustness of our approach, we ran extensive molecular dynamics simulations with a rigid-ion and polarizable force field of the water/1,2-dichloroethane interface in the presence of weak external electric fields.

Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS

The introduction of accelerator devices such as graphics processing units (GPUs) has had profound impact on molecular dynamics simulations and has enabled order-of-magnitude performance advances using commodity hardware. To fully reap these benefits, it has been necessary to reformulate some of the most fundamental algorithms, including the Verlet list, pair searching, and cutoffs. Here, we present the heterogeneous parallelization and acceleration design of molecular dynamics implemented in the GROMACS codebase over the last decade. The setup involves a general cluster-based approach to pair lists and non-bonded pair interactions that utilizes both GPU and central processing unit (CPU) single instruction, multiple data acceleration efficiently, including the ability to load-balance tasks between CPUs and GPUs. The algorithm work efficiency is tuned for each type of hardware, and to use accelerators more efficiently, we introduce dual pair lists with rolling pruning updates. Combined with new direct GPU-GPU communication and GPU integration, this enables excellent performance from single GPU simulations through strong scaling across multiple GPUs and efficient multi-node parallelization.

Nucleotide Loading Modes of Human RNA Polymerase II as Deciphered by Molecular Simulations

Mapping the route of nucleoside triphosphate (NTP) entry into the sequestered active site of RNA polymerase (RNAP) has major implications for elucidating the complete nucleotide addition cycle. Constituting a dichotomy that remains to be resolved, two alternatives, direct NTP delivery via the secondary channel (CH2) or selection to downstream sites in the main channel (CH1) prior to catalysis, have been proposed. In this study, accelerated molecular dynamics simulations of freely diffusing NTPs about RNAPII were applied to refine the CH2 model and uncover atomic details on the CH1 model that previously lacked a persuasive structural framework to illustrate its mechanism of action. Diffusion and binding of NTPs to downstream DNA, and the transfer of a preselected NTP to the active site, are simulated for the first time. All-atom simulations further support that CH1 loading is transcription factor IIF (TFIIF) dependent and impacts catalytic isomerization. Altogether, the alternative nucleotide loading systems may allow distinct transcriptional landscapes to be expressed.

Comparing the Binding Interactions in the Receptor Binding Domains of SARS-CoV-2 and SARS-CoV

SARS-CoV-2, since emerging in Wuhan, China, has been a major concern because of its high infection rate and has left more than six million infected people around the world. Many studies endeavored to reveal the structure of the SARS-CoV-2 compared to the SARS-CoV, in order to find solutions to suppress this high infection rate. Some of these studies showed that the mutations in the SARS-CoV spike (S) protein might be responsible for its higher affinity to the ACE2 human cell receptor. In this work, we used molecular dynamics simulations and Monte Carlo sampling to compare the binding affinities of the S proteins of SARS-CoV and SARS-CoV-2 to the ACE2. Our results show that the protein surface of the ACE2 at the receptor binding domain (RBD) exhibits negative electrostatic potential, while a positive potential is observed for the S proteins of SARS-CoV/SARS-CoV-2. In addition, the binding energies at the interface are slightly higher for SARS-CoV-2 because of enhanced electrostatic interactions. The major contributions to the electrostatic binding energies result from the salt bridges forming between R426 and ACE-2-E329 in the case of SARS-CoV and K417 and ACE2-D30 in the SARS-CoV-2. In addition, our results indicate that the enhancement in the binding energy is not due to a single mutant but rather because of the sophisticated structural changes induced by all these mutations together. This finding suggests that it is implausible for the SARS-CoV-2 to be a lab-engineered virus.

Ion Pairing and Multiple Ion Binding in Calcium Carbonate Solutions Based on a Polarizable AMOEBA Force Field and Ab Initio Molecular Dynamics

The speciation of calcium carbonate in water is important to the geochemistry of the world's oceans and has ignited significant debate regarding the mechanism by which nucleation occurs. Here, it is vital to be able to quantify the thermodynamics of ion pairing versus higher order association processes in order to distinguish between possible pathways. Given that it is experimentally challenging to quantify such species, here we determine the thermodynamics for ion pairing and multiple binding of calcium carbonate species using bias-enhanced molecular dynamics. In order to examine the uncertainties underlying these results, we derived a new polarizable force field for both calcium carbonate and bicarbonate in water based on the AMOEBA model to compare against our earlier rigid ion model, both of which are further benchmarked against ab initio molecular dynamics for the ion pair. Both force fields consistently indicate that the association of calcium carbonate ion pairs to form larger species is stable, though with an equilibrium constant that is lower than for ion pairing itself.

Efficient Construction of Atomic-Resolution Models of Non-Sulfated Chondroitin Glycosaminoglycan Using Molecular Dynamics Data

Glycosaminoglycans (GAGs) are linear, structurally diverse, conformationally complex carbohydrate polymers that may contain up to 200 monosaccharides. These characteristics present a challenge for studying GAG conformational thermodynamics at atomic resolution using existing experimental methods. Molecular dynamics (MD) simulations can overcome this challenge but are only feasible for short GAG polymers. To address this problem, we developed an algorithm that applies all conformational parameters contributing to GAG backbone flexibility (i.e., bond lengths, bond angles, and dihedral angles) from unbiased all-atom explicit-solvent MD simulations of short GAG polymers to rapidly construct models of GAGs of arbitrary length. The algorithm was used to generate non-sulfated chondroitin 10- and 20-mer ensembles which were compared to MD-generated ensembles for internal validation. End-to-end distance distributions in constructed and MD-generated ensembles have minimal differences, suggesting that our algorithm produces conformational ensembles that mimic the backbone flexibility seen in simulation. Non-sulfated chondroitin 100- and 200-mer ensembles were constructed within a day, demonstrating the efficiency of the algorithm and reduction in time and computational cost compared to simulation.

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge

The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.

What Is the Value of Water Contact Angle on Silicon?

Silicon is a widely applied material and the wetting of silicon surface is an important phenomenon. However, contradictions in the literature appear considering the value of the water contact angle (WCA). The purpose of this study is to present a holistic experimental and theoretical approach to the WCA determination. To do this, we checked the chemical composition of the silicon (1,0,0) surface by using the X-ray photoelectron spectroscopy (XPS) method, and next this surface was purified using different cleaning methods. As it was proved that airborne hydrocarbons change a solid wetting properties the WCA values were measured in hydrocarbons atmosphere. Next, molecular dynamics (MD) simulations were performed to determine the mechanism of wetting in this atmosphere and to propose the force field parameters for silica wetting simulation. It is concluded that the best method of surface cleaning is the solvent-reinforced de Gennes method, and the WCA value of silicon covered by SiO₂ layer is equal to 20.7° (at room temperature). MD simulation results show that the mechanism of pure silicon wetting is similar to that reported for graphene, and the mechanism of silicon covered by SiO₂ layer wetting is similar to this observed recently for a MOF.

Principles of meiotic chromosome assembly revealed in S. cerevisiae

During meiotic prophase, chromosomes organise into a series of chromatin loops emanating from a proteinaceous axis, but the mechanisms of assembly remain unclear. Here we use Saccharomyces cerevisiae to explore how this elaborate three-dimensional chromosome organisation is linked to genomic sequence. As cells enter meiosis, we observe that strong cohesin-dependent grid-like Hi-C interaction patterns emerge, reminiscent of mammalian interphase organisation, but with distinct regulation. Meiotic patterns agree with simulations of loop extrusion with growth limited by barriers, in which a heterogeneous population of expanding loops develop along the chromosome. Importantly, CTCF, the factor that imposes similar features in mammalian interphase, is absent in S. cerevisiae, suggesting alternative mechanisms of barrier formation. While grid-like interactions emerge independently of meiotic chromosome synapsis, synapsis itself generates additional compaction that matures differentially according to telomere proximity and chromosome size. Collectively, our results elucidate fundamental principles of chromosome assembly and demonstrate the essential role of cohesin within this evolutionarily conserved process.

Generalized Born Implicit Solvent Models for Biomolecules

It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.

Infinite Dilution Activity Coefficients as Constraints for Force Field Parametrization and Method Development

Molecular simulations begin with an underlying energy model or force field and from this can predict diverse physical properties. However, force fields were often developed with relatively limited data sets, yet accuracy for diverse properties across a broad chemical space is desirable; therefore, tests of such accuracy are particularly important. Here, to this end, we calculated 237 infinite dilution activity coefficients (IDACs), comparing with experimental values from NIST's ThermoML database. We found that calculated IDAC values correlate strongly with experiment (Pearson R of 0.92 ± 0.01) and allow us to identify specific functional groups that appear to present challenges to the force field employed. One potentially valuable aspect of IDACs, as compared to solvation free energies, which have been frequently employed as force field tests, is that the same molecules serve both as solutes and solvents in different cases, allowing us to ensure that force fields are not overly tuned to one particular environment or solvent.

Unraveling electronic absorption spectra using nuclear quantum effects: Photoactive yellow protein and green fluorescent protein chromophores in water

Many physical phenomena must be accounted for to accurately model solution-phase optical spectral line shapes, from the sampling of chromophore-solvent configurations to the electronic-vibrational transitions leading to vibronic fine structure. Here we thoroughly explore the role of nuclear quantum effects, direct and indirect solvent effects, and vibronic effects in the computation of the optical spectrum of the aqueously solvated anionic chromophores of green fluorescent protein and photoactive yellow protein. By analyzing the chromophore and solvent configurations, the distributions of vertical excitation energies, the absorption spectra computed within the ensemble approach, and the absorption spectra computed within the ensemble plus zero-temperature Franck-Condon approach, we show how solvent, nuclear quantum effects, and vibronic transitions alter the optical absorption spectra. We find that including nuclear quantum effects in the sampling of chromophore-solvent configurations using ab initio path integral molecular dynamics simulations leads to improved spectral shapes through three mechanisms. The three mechanisms that lead to line shape broadening and a better description of the high-energy tail are softening of heavy atom bonds in the chromophore that couple to the optically bright state, widening the distribution of vertical excitation energies from more diverse solvation environments, and redistributing spectral weight from the 0-0 vibronic transition to higher energy vibronic transitions when computing the Franck-Condon spectrum in a frozen solvent pocket. The absorption spectra computed using the combined ensemble plus zero-temperature Franck-Condon approach yield significant improvements in spectral shape and width compared to the spectra computed with the ensemble approach. Using the combined approach with configurations sampled from path integral molecular dynamics trajectories presents a significant step forward in accurately modeling the absorption spectra of aqueously solvated chromophores.

A direct-compatible formulation of the coupled perturbed complete active space self-consistent field equations on graphical processing units

We recently developed an algorithm to compute response properties for the state-averaged complete active space self-consistent field method (SA-CASSCF) that capitalized on sparsity in the atomic orbital basis. Our original algorithm was limited to treating small to moderate sized active spaces, but the recent development of graphical processing unit (GPU) based direct-configuration interaction algorithms provides an opportunity to extend this to large active spaces. We present here a direct-compatible version of the coupled perturbed equations, enabling us to compute response properties for systems treated with arbitrary active spaces (subject to available memory and computation time). This work demonstrates that the computationally demanding portions of the SA-CASSCF method can be formulated in terms of seven fundamental operations, including Coulomb and exchange matrix builds and their derivatives, as well as, generalized one- and two-particle density matrix and σ vector constructions. As in our previous work, this algorithm exhibits low computational scaling and is accelerated by the use of GPUs, making possible optimizations and nonadiabatic dynamics on systems with O(1000) basis functions and O(100) atoms, respectively.

Molecular mechanism of GPCR-mediated arrestin activation

Despite intense interest in discovering drugs that cause G-protein-coupled receptors (GPCRs) to selectively stimulate or block arrestin signalling, the structural mechanism of receptor-mediated arrestin activation remains unclear1,2. Here we reveal this mechanism through extensive atomic-level simulations of arrestin. We find that the receptor's transmembrane core and cytoplasmic tail-which bind distinct surfaces on arrestin-can each independently stimulate arrestin activation. We confirm this unanticipated role of the receptor core, and the allosteric coupling between these distant surfaces of arrestin, using site-directed fluorescence spectroscopy. The effect of the receptor core on arrestin conformation is mediated primarily by interactions of the intracellular loops of the receptor with the arrestin body, rather than the marked finger-loop rearrangement that is observed upon receptor binding. In the absence of a receptor, arrestin frequently adopts active conformations when its own C-terminal tail is disengaged, which may explain why certain arrestins remain active long after receptor dissociation. Our results, which suggest that diverse receptor binding modes can activate arrestin, provide a structural foundation for the design of functionally selective ('biased') GPCR-targeted ligands with desired effects on arrestin signalling.

Nanoscale Water Contact Angle on Polytetrafluoroethylene Surfaces Characterized by Molecular Dynamics-Atomic Force Microscopy Imaging

The aim of this study is to link polytetrafluoroethylene (PTFE) surface characteristics with its wetting properties in the nanoscale. To do this using molecular dynamics (MD) simulation, three series of rough PTFE surfaces were generated by annealing and compressing and next characterized by the application of the MD version of the atomic force microscopy (AFM) method. The values of specific surface areas were additionally calculated. The TIP4P/2005 water model was used to study the wetting properties of obtained PTFE samples. The simulated water contact angle (WCA) value for the most flat (but slightly rough) sample having PTFE density is equal to 106.94°, and it is close to the value suggested for a perfect PTFE surface on the basis of experimental results. Also, the changes in the WCA with PTFE compression are in the same range as experimentally reported. The obtained MD simulation results make it possible to link, for the first time, the WCA values with the surface MD-AFM root-mean-square roughness and with the PTFE density. Finally, we show that for PTFE wetting in the nanoscale, the line tension is negligible and the Bormashenko's equation reduces to the Cassie-Baxter (CB) model. In fact, our simulation results are close to the CB mechanism.

A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method

There has been a resurgence of interest in free energy methods motivated by the performance enhancements offered by molecular dynamics (MD) software written for specialized hardware, such as graphics processing units (GPUs). In this work, we exploit the properties of a parameter-interpolated thermodynamic integration (PI-TI) method to connect states by their molecular mechanical (MM) parameter values. This pathway is shown to be better behaved for Mg2+ → Ca2+ transformations than traditional linear alchemical pathways (with and without soft-core potentials). The PI-TI method has the practical advantage that no modification of the MD code is required to propagate the dynamics, and unlike with linear alchemical mixing, only one electrostatic evaluation is needed (e.g., single call to particle-mesh Ewald) leading to better performance. In the case of AMBER, this enables all the performance benefits of GPU-acceleration to be realized, in addition to unlocking the full spectrum of features available within the MD software, such as Hamiltonian replica exchange (HREM). The TI derivative evaluation can be accomplished efficiently in a post-processing step by reanalyzing the statistically independent trajectory frames in parallel for high throughput. We also show how one can evaluate the particle mesh Ewald contribution to the TI derivative evaluation without needing to perform two reciprocal space calculations. We apply the PI-TI method with HREM on GPUs in AMBER to predict p Ka values in double stranded RNA molecules and make comparison with experiments. Convergence to under 0.25 units for these systems required 100 ns or more of sampling per window and coupling of windows with HREM. We find that MM charges derived from ab initio QM/MM fragment calculations improve the agreement between calculation and experimental results.

A pathway for mitotic chromosome formation

Mitotic chromosomes fold as compact arrays of chromatin loops. To identify the pathway of mitotic chromosome formation, we combined imaging and Hi-C analysis of synchronous DT40 cell cultures with polymer simulations. Here we show that in prophase, the interphase organization is rapidly lost in a condensin-dependent manner, and arrays of consecutive 60-kilobase (kb) loops are formed. During prometaphase, ~80-kb inner loops are nested within ~400-kb outer loops. The loop array acquires a helical arrangement with consecutive loops emanating from a central "spiral staircase" condensin scaffold. The size of helical turns progressively increases to ~12 megabases during prometaphase. Acute depletion of condensin I or II shows that nested loops form by differential action of the two condensins, whereas condensin II is required for helical winding.

Two independent modes of chromatin organization revealed by cohesin removal

Imaging and chromosome conformation capture studies have revealed several layers of chromosome organization, including segregation into megabase-sized active and inactive compartments, and partitioning into sub-megabase domains (TADs). It remains unclear, however, how these layers of organization form, interact with one another and influence genome function. Here we show that deletion of the cohesin-loading factor Nipbl in mouse liver leads to a marked reorganization of chromosomal folding. TADs and associated Hi-C peaks vanish globally, even in the absence of transcriptional changes. By contrast, compartmental segregation is preserved and even reinforced. Strikingly, the disappearance of TADs unmasks a finer compartment structure that accurately reflects the underlying epigenetic landscape. These observations demonstrate that the three-dimensional organization of the genome results from the interplay of two independent mechanisms: cohesin-independent segregation of the genome into fine-scale compartments, defined by chromatin state; and cohesin-dependent formation of TADs, possibly by loop extrusion, which helps to guide distant enhancers to their target genes.

SMC complexes differentially compact mitotic chromosomes according to genomic context

Structural maintenance of chromosomes (SMC) protein complexes are key determinants of chromosome conformation. Using Hi-C and polymer modelling, we study how cohesin and condensin, two deeply conserved SMC complexes, organize chromosomes in the budding yeast Saccharomyces cerevisiae. The canonical role of cohesin is to co-align sister chromatids, while condensin generally compacts mitotic chromosomes. We find strikingly different roles for the two complexes in budding yeast mitosis. First, cohesin is responsible for compacting mitotic chromosome arms, independently of sister chromatid cohesion. Polymer simulations demonstrate that this role can be fully accounted for through cis-looping of chromatin. Second, condensin is generally dispensable for compaction along chromosome arms. Instead, it plays a targeted role compacting the rDNA proximal regions and promoting resolution of peri-centromeric regions. Our results argue that the conserved mechanism of SMC complexes is to form chromatin loops and that distinct SMC-dependent looping activities are selectively deployed to appropriately compact chromosomes.

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.

Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units

We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.

Challenges in structural approaches to cell modeling

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.

On the use of mass scaling for stable and efficient simulated tempering with molecular dynamics

Simulated tempering (ST) is a generalized-ensemble algorithm that employs trajectories exploring a range of temperatures to effectively sample rugged energy landscapes. When implemented using the molecular dynamics method, ST can require the use of short time steps for ensuring the stability of trajectories at high temperatures. To address this shortcoming, a mass-scaling ST (MSST) method is presented in which the particle mass is scaled in proportion to the temperature. Mass scaling in the MSST method leads to velocity distributions that are independent of temperature and eliminates the need for velocity scaling after the accepted temperature updates that are required in conventional ST simulations. The homogeneity in time scales with changing temperature improves the stability of simulations and allows for the use of longer time steps at high temperatures. As a result, the MSST is found to be more efficient than the standard ST method, particularly for cases in which a large temperature range is employed. © 2016 Wiley Periodicals, Inc.

Native states of fast-folding proteins are kinetic traps

It has been suggested that the native state of a protein acts as a kinetic hub that can facilitate transitions between nonnative states. Using recently developed tools to quantify mediation probabilities ("hub scores"), we quantify hub-like behavior in atomic resolution trajectories for the first time. We use a data set of trajectory ensembles for 12 fast-folding proteins previously published by D. E. Shaw Research (Lindorff-Larsen, K.; et al. How Fast-Folding Proteins Fold. Science2011, 334, 517) with an aggregate simulation time of over 8.2 ms. We visualize the free-energy landscape of each molecule using configuration space networks, and show that dynamic quantities can be qualitatively understood from visual inspection of the networks. Modularity optimization is used to provide a parameter-free means of tessellating the network into a group of communities. Using hub scores, we find that the percentage of trajectories that are mediated by the native state is 31% when averaged over all molecules, and reaches a maximum of 52% for the homeodomain and chignolin. Furthermore, for these mediated transitions, we use Markov models to determine whether the native state acts as a facilitator for the transition, or as a trap (i.e., an off-pathway detour). Although instances of facilitation are found in 4 of the 12 molecules, we conclude that the native state acts primarily as a trap, which is consistent with the idea of a funnel-like landscape.

The native GCN4 leucine-zipper domain does not uniquely specify a dimeric oligomerization state

The dimerization domain of the yeast transcription factor GCN4, one of the first coiled-coil proteins to be structurally characterized at high resolution, has served as the basis for numerous fundamental studies on α-helical folding. Mutations in the GCN4 leucine zipper are known to change its preferred oligomerization state from dimeric to trimeric or tetrameric; however, the wild-type sequence has been assumed to encode a two-chain assembly exclusively. Here we demonstrate that the GCN4 coiled-coil domain can populate either a dimer or trimer fold, depending on environment. We report high-resolution crystal structures of the wild-type sequence in dimeric and trimeric assemblies. Biophysical measurements suggest populations of both oligomerization states under certain experimental conditions in solution. We use parallel tempering molecular dynamics simulations on the microsecond time scale to compare the stability of the dimer and trimer folded states in isolation. In total, our results suggest that the folding behavior of the well-studied GCN4 leucine-zipper domain is more complex than was previously appreciated. Our results have implications in ongoing efforts to establish predictive algorithms for coiled-coil folds and the selection of coiled-coil model systems for design and mutational studies where oligomerization state specificity is an important consideration.

Revisiting Molecular Dynamics on a CPU/GPU system: Water Kernel and SHAKE Parallelization

We report Graphics Processing Unit (GPU) and Open-MP parallel implementations of water-specific force calculations and of bond constraints for use in Molecular Dynamics simulations. We focus on a typical laboratory computing-environment in which a CPU with a few cores is attached to a GPU. We discuss in detail the design of the code and we illustrate performance comparable to highly optimized codes such as GROMACS. Beside speed our code shows excellent energy conservation. Utilization of water-specific lists allows the efficient calculations of non-bonded interactions that include water molecules and results in a speed-up factor of more than 40 on the GPU compared to code optimized on a single CPU core for systems larger than 20,000 atoms. This is up four-fold from a factor of 10 reported in our initial GPU implementation that did not include a water-specific code. Another optimization is the implementation of constrained dynamics entirely on the GPU. The routine, which enforces constraints of all bonds, runs in parallel on multiple Open-MP cores or entirely on the GPU. It is based on Conjugate Gradient solution of the Lagrange multipliers (CG SHAKE). The GPU implementation is partially in double precision and requires no communication with the CPU during the execution of the SHAKE algorithm. The (parallel) implementation of SHAKE allows an increase of the time step to 2.0fs while maintaining excellent energy conservation. Interestingly, CG SHAKE is faster than the usual bond relaxation algorithm even on a single core if high accuracy is expected. The significant speedup of the optimized components transfers the computational bottleneck of the MD calculation to the reciprocal part of Particle Mesh Ewald (PME).

GPU/CPU Algorithm for Generalized Born/Solvent-Accessible Surface Area Implicit Solvent Calculations

Molecular dynamics methodologies comprise a vital research tool for structural biology. Molecular dynamics has benefited from technological advances in computing, such as multi-core CPUs and graphics processing units (GPUs), but harnessing the full power of hybrid GPU/CPU computers remains difficult. The generalized Born/solvent-accessible surface area implicit solvent model (GB/SA) stands to benefit from hybrid GPU/CPU computers, employing the GPU for the GB calculation and the CPU for the SA calculation. Here, we explore the computational challenges facing GB/SA calculations on hybrid GPU/CPU computers and demonstrate how NAMD, a parallel molecular dynamics program, is able to efficiently utilize GPUs and CPUs simultaneously for fast GB/SA simulations. The hybrid computation principles demonstrated here are generally applicable to parallel applications employing hybrid GPU/CPU calculations.

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born

We present an implementation of generalized Born implicit solvent all-atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA enabled NVIDIA graphics processing units (GPUs). We discuss the algorithms that are used to exploit the processing power of the GPUs and show the performance that can be achieved in comparison to simulations on conventional CPU clusters. The implementation supports three different precision models in which the contributions to the forces are calculated in single precision floating point arithmetic but accumulated in double precision (SPDP), or everything is computed in single precision (SPSP) or double precision (DPDP). In addition to performance, we have focused on understanding the implications of the different precision models on the outcome of implicit solvent MD simulations. We show results for a range of tests including the accuracy of single point force evaluations and energy conservation as well as structural properties pertainining to protein dynamics. The numerical noise due to rounding errors within the SPSP precision model is sufficiently large to lead to an accumulation of errors which can result in unphysical trajectories for long time scale simulations. We recommend the use of the mixed-precision SPDP model since the numerical results obtained are comparable with those of the full double precision DPDP model and the reference double precision CPU implementation but at significantly reduced computational cost. Our implementation provides performance for GB simulations on a single desktop that is on par with, and in some cases exceeds, that of traditional supercomputers.

Simulations of the alternating access mechanism of the sodium symporter Mhp1

Sodium coupled cotransporters of the five-helix inverted repeat (5HIR) superfamily use an alternating access mechanism to transport a myriad of small molecules across the cell membrane. One of the primary steps in this mechanism is the conformational transition from a state poised to bind extracellular substrates to a state that is competent to deliver substrate to the cytoplasm. Here, we construct a coarse-grained model of the 5HIR benzylhydantoin transporter Mhp1 that incorporates experimental structures of the outward- and inward-open states to investigate the mechanism of this conformational change. Using the weighted ensemble path-sampling method, we rigorously sample the outward- to inward-facing transition path ensemble. The transition path ensemble reveals a heterogeneous set of pathways connecting the two states and identifies two modes of transport: one consistent with a strict alternating access mechanism and another where decoupling of the inner and outer gates causes the transient formation of a continuous permeation pathway through the transporter. We also show that the conformational switch between the outward- and inward-open states results from rigid body motions of the hash motif relative to the substrate bundle, supporting the rocking bundle hypothesis. Finally, our methodology provides the groundwork for more chemically detailed investigations of the alternating mechanism.

Simbios: an NIH national center for physics-based simulation of biological structures

Physics-based simulation provides a powerful framework for understanding biological form and function. Simulations can be used by biologists to study macromolecular assemblies and by clinicians to design treatments for diseases. Simulations help biomedical researchers understand the physical constraints on biological systems as they engineer novel drugs, synthetic tissues, medical devices, and surgical interventions. Although individual biomedical investigators make outstanding contributions to physics-based simulation, the field has been fragmented. Applications are typically limited to a single physical scale, and individual investigators usually must create their own software. These conditions created a major barrier to advancing simulation capabilities. In 2004, we established a National Center for Physics-Based Simulation of Biological Structures (Simbios) to help integrate the field and accelerate biomedical research. In 6 years, Simbios has become a vibrant national center, with collaborators in 16 states and eight countries. Simbios focuses on problems at both the molecular scale and the organismal level, with a long-term goal of uniting these in accurate multiscale simulations.

Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation

Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also rapidly sample uncorrelated configurations. Here, we introduce a new class of moves based on nonequilibrium dynamics: Candidate configurations are generated through a finite-time process in which a system is actively driven out of equilibrium, and accepted with criteria that preserve the equilibrium distribution. The acceptance rule is similar to the Metropolis acceptance probability, but related to the nonequilibrium work rather than the instantaneous energy difference. Our method is applicable to sampling from both a single thermodynamic state or a mixture of thermodynamic states, and allows both coordinates and thermodynamic parameters to be driven in nonequilibrium proposals. Whereas generating finite-time switching trajectories incurs an additional cost, driving some degrees of freedom while allowing others to evolve naturally can lead to large enhancements in acceptance probabilities, greatly reducing structural correlation times. Using nonequilibrium driven processes vastly expands the repertoire of useful Monte Carlo proposals in simulations of dense solvated systems.

MOIL-opt: Energy-Conserving Molecular Dynamics on a GPU/CPU system

We report an optimized version of the molecular dynamics program MOIL that runs on a shared memory system with OpenMP and exploits the power of a Graphics Processing Unit (GPU). The model is of heterogeneous computing system on a single node with several cores sharing the same memory and a GPU. This is a typical laboratory tool, which provides excellent performance at minimal cost. Besides performance, emphasis is made on accuracy and stability of the algorithm probed by energy conservation for explicit-solvent atomically-detailed-models. Especially for long simulations energy conservation is critical due to the phenomenon known as "energy drift" in which energy errors accumulate linearly as a function of simulation time. To achieve long time dynamics with acceptable accuracy the drift must be particularly small. We identify several means of controlling long-time numerical accuracy while maintaining excellent speedup. To maintain a high level of energy conservation SHAKE and the Ewald reciprocal summation are run in double precision. Double precision summation of real-space non-bonded interactions improves energy conservation. In our best option, the energy drift using 1fs for a time step while constraining the distances of all bonds, is undetectable in 10ns simulation of solvated DHFR (Dihydrofolate reductase). Faster options, shaking only bonds with hydrogen atoms, are also very well behaved and have drifts of less than 1kcal/mol per nanosecond of the same system. CPU/GPU implementations require changes in programming models. We consider the use of a list of neighbors and quadratic versus linear interpolation in lookup tables of different sizes. Quadratic interpolation with a smaller number of grid points is faster than linear lookup tables (with finer representation) without loss of accuracy. Atomic neighbor lists were found most efficient. Typical speedups are about a factor of 10 compared to a single-core single-precision code.