Generalized Scalable Multiple Copy Algorithms for Molecular Dynamics Simulations in NAMD

Contact-Map-Driven Exploration of Heterogeneous Protein-Folding Paths

We have recently shown how physically realizable protein-folding pathways can be generated using directed walks in the space of inter-residue contact-maps; combined with a back-transformation to move from protein contact-maps to Cartesian coordinates, we have demonstrated how this approach can generate protein-folding trajectory ensembles without recourse to molecular dynamics. In this article, we demonstrate that this framework can be used to study a challenging protein-folding problem that is known to exhibit two different folding paths which were previously identified through molecular dynamics simulation at several different temperatures. From the viewpoint of protein-folding mechanism prediction, this particular problem is extremely challenging to address, specifically involving folding to an identical nontrivial compact native structure along distinct pathways defined by heterogeneous secondary structural elements. Here, we show how our previously reported contact-map-based protein-folding strategy can be significantly enhanced to enable accurate and robust prediction of heterogeneous folding paths by (i) introducing a novel topologically informed metric for comparing two protein contact maps, (ii) reformulating our graph-represented folding path generation, and (iii) introducing a new and more reliable structural back-mapping algorithm. These changes improve the reliability of generating structurally sound folding intermediates and dramatically decrease the number of physically irrelevant folding intermediates generated by our previous simulation strategy. Most importantly, we demonstrate how our enhanced folding algorithm can successfully identify the alternative folding mechanisms of a multifolding-pathway protein, in line with direct molecular dynamics simulations.

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study

Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major pathological hallmarks of AD and has gained the interest of researchers as a potential drug target to treat AD. Lately, Artificial Intelligence (AI) has revolutionized the drug discovery process by speeding it up and reducing the overall cost. As a part of our continuous effort to identify potential tau aggregation inhibitors, and leveraging the power of AI, in this study, we used a fully automated AI-assisted ligand-based virtual screening tool, PyRMD to screen a library of 12 million compounds from the ZINC database to identify potential tau aggregation inhibitors. The preliminary hits from virtual screening were filtered for similar compounds and pan-assay interference compounds (the compounds containing reactive functional groups which can interfere with the assays) using RDKit. Further, the selected compounds were prioritized based on their molecular docking score with the binding pocket of tau where the binding pockets were identified using replica exchange molecular dynamics simulation. Thirty-three compounds showing good docking scores for all the tau clusters were selected and were further subjected to in silico pharmacokinetic prediction. Finally, top 10 compounds were selected for molecular dynamics simulation and MMPBSA binding free energy calculations resulting in the identification of UNK_175, UNK_1027, UNK_1172, UNK_1173, UNK_1237, UNK_1518, and UNK_2181 as potential tau aggregation inhibitors.

Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib

Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-μs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.

Structure-based lead optimization of peptide-based vinyl methyl ketones as SARS-CoV-2 main protease inhibitors

Despite several major achievements in the development of vaccines and antivirals, the fight against SARS-CoV-2 and the health problems accompanying COVID-19 are still ongoing. SARS-CoV-2 main protease (M^pro), an essential viral cysteine protease, is a crucial target for the development of antiviral agents. A virtual screening analysis of in-house cysteine protease inhibitors against SARS-CoV-2 M^pro allowed us to identify two hits (i.e., 1 and 2) bearing a methyl vinyl ketone warhead. Starting from these compounds, we herein report the development of Michael acceptors targeting SARS-CoV-2 M^pro, which differ from each other for the warhead and for the amino acids at the P2 site. The most promising vinyl methyl ketone-containing analogs showed sub-micromolar activity against the viral protease. SPR38, SPR39, and SPR41 were fully characterized, and additional inhibitory properties towards hCatL, which plays a key role in the virus entry into host cells, were observed. SPR39 and SPR41 exhibited single-digit micromolar EC₅₀ values in a SARS-CoV-2 infection model in cell culture.

Importance of molecular dynamics equilibrium protocol on protein-lipid interaction near channel pore

Multiscale molecular dynamics simulations using Martini coarse-grained (CG) and all-atom (AA) force fields are commonly used in membrane protein studies. In particular, reverse mapping an equilibrated CG model to an AA model offers an efficient way for preparing large membrane protein systems with complex protein shapes and lipid compositions. Here, we report that this hybrid CG-equilibrium-AA-production protocol may artificially increase lipid density and decrease hydration in ion channel pores walled with transmembrane gaps. To understand the origin of this conundrum, we conducted replicas of CG, AA, and CG reverse-mapped AA simulations of the pore domain of the mechanosensitive Piezo1 channel in a nonconducting conformation. Lipid/water density analysis and free energy calculations reveal that the lack of initial pore hydration allows excessive lipids to enter the upper pore lumen through gaps between pore helices during CG simulation. Due to the mismatch between CG and AA lipid kinetics, these pore lipids remain trapped in the subsequent AA simulations, despite unfavorable binding free energy. We tested several CG equilibrium protocols and found that a protocol restraining the whole lipid produces pore hydration consistent with AA results, thus eliminating this artifact for further studies of lipid gating and protein-lipid interactions.

Elucidating the enhanced binding affinity of a double mutant SP-D with trimannose on the influenza A virus using molecular dynamics

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The Asp325Ala mutation in SP-D promotes a trimannose conformational change to a more stable state.

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The Arg343Val mutation in SP-D reduces its interaction with Glu333 to increase the binding affinity with trimannose.

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The Arg343Val mutation contributes more to the increase of SP-D’s binding affinity with trimannose than Asp325Ala.

Surfactant protein D (SP-D) is an essential component of the human pulmonary surfactant system, which is crucial in the innate immune response against glycan-containing pathogens, including Influenza A viruses (IAV) and SARS-CoV-2. Previous studies have shown that wild-type (WT) SP-D can bind IAV but exhibits poor antiviral activities. However, a double mutant (DM) SP-D consisting of two point mutations (Asp325Ala and Arg343Val) inhibits IAV more potently. Presently, the structural mechanisms behind the point mutations’ effects on SP-D’s binding affinity with viral surface glycans are not fully understood. Here we use microsecond-scale, full-atomistic molecular dynamics (MD) simulations to understand the molecular mechanism of mutation-induced SP-D’s higher antiviral activity. We find that the Asp325Ala mutation promotes a trimannose conformational change to a more stable state. Arg343Val increases the binding with trimannose by increasing the hydrogen bonding interaction with Glu333. Free energy perturbation (FEP) binding free energy calculations indicate that the Arg343Val mutation contributes more to the increase of SP-D’s binding affinity with trimannose than Asp325Ala. This study provides a molecular-level exploration of how the two mutations increase SP-D binding affinity with trimannose, which is vital for further developing preventative strategies for related diseases.

Molecular Modeling and Simulation of the Peptidoglycan Layer of Gram-Positive Bacteria Staphylococcus aureus

The peptidoglycan (PG) layer is a vital component of the bacterial cell wall that protects the cell from rupturing due to internal pressure. Its ubiquity across the bacterial kingdom but not animals has made it the target of drug discovery efforts. The PG layer composed of cross-linked PG strands is porous enough to allow the diffusion of molecules through the PG mesh and into the cell. The lack of an accurate atomistic model of the PG mesh has limited the computational investigations of drug diffusion in Gram-positive bacteria, which lack the outer membrane but consist of a much thicker PG layer compared to Gram-negative bacteria. In this work, we built an atomistic model of the Staphylococcus aureus PG layer architecture with horizontally aligned PG strands and performed molecular dynamics simulations of the diffusion of curcumin molecules through the PG mesh. An accurate model of the Gram-positive bacterial cell wall may aid in developing novel antibiotics to tackle the threat posed by antibiotic resistance.

Structure of Human TMPRSS2 in Complex with SARS-CoV-2 Spike Glycoprotein and Implications for Potential Therapeutics

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 520 million people around the globe resulting in more than 6.2 million as of May 2022. Understanding the cell entry mechanism of SARS-CoV-2 and its entire repertoire is a high priority for developing improved therapeutics. The SARS-CoV-2 spike glycoprotein (S-protein) engages with host receptor ACE2 for adhesion and serine proteases furin and TMPRSS2 for proteolytic activation and subsequent entry. Recent studies have highlighted the molecular details of furin and S-protein interaction. However, the structural and molecular interplay between TMPRSS2 and S-protein remains enigmatic. Here, using biochemical, structural, computational, and molecular dynamics approaches, we investigated how TMPRSS2 recognizes and activates the S-protein to facilitate viral entry. First, we identified three potential TMPRSS2 cleavage sites in the S2 domain of S-protein (S2', T1, and T2) and reported the structure of TMPRSS2 with its individual catalytic triad. By employing computational modeling and structural analyses, we modeled the macromolecular structure of TMPRSS2 in complex with S-protein, which incited the mechanism of S-protein processing or cleavage for a new path of viral entry. On the basis of structure-guided drug screening, we also report the potential TMPRSS2 inhibitors and their structural interaction in blocking TMPRSS2 activity, which could impede the interaction with the spike protein. These findings reveal the role of TMPRSS2 in the activation of SARS-CoV-2 for its entry and insight into possible intervention strategies.

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case

Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under:Data Science > Computer Algorithms and ProgrammingData Science > Databases and Expert SystemsMolecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods.

NMR Molecular Replacement Provides New Insights into Binding Modes to Bromodomains of BRD4 and TRIM24

Structure-based drug discovery (SBDD) largely relies on structural information from X-ray crystallography because traditional NMR structure calculation methods are too time consuming to be aligned with typical drug discovery timelines. The recently developed NMR molecular replacement (NMR²) method dramatically reduces the time needed to generate ligand-protein complex structures using published structures (apo or holo) of the target protein and treating all observed NOEs as ambiguous restraints, bypassing the laborious process of obtaining sequence-specific resonance assignments for the protein target. We apply this method to two therapeutic targets, the bromodomain of TRIM24 and the second bromodomain of BRD4. We show that the NMR² methodology can guide SBDD by rationalizing the observed SAR. We also demonstrate that new types of restraints and selective methyl labeling have the potential to dramatically reduce "time to structure" and extend the method to targets beyond the reach of traditional NMR structure elucidation.

A distinct mechanism of C-type inactivation in the Kv-like KcsA mutant E71V

C-type inactivation is of great physiological importance in voltage-activated K⁺ channels (Kv), but its structural basis remains unresolved. Knowledge about C-type inactivation has been largely deduced from the bacterial K⁺ channel KcsA, whose selectivity filter constricts under inactivating conditions. However, the filter is highly sensitive to its molecular environment, which is different in Kv channels than in KcsA. In particular, a glutamic acid residue at position 71 along the pore helix in KcsA is substituted by a valine conserved in most Kv channels, suggesting that this side chain is a molecular determinant of function. Here, a combination of X-ray crystallography, solid-state NMR and MD simulations of the E71V KcsA mutant is undertaken to explore inactivation in this Kv-like construct. X-ray and ssNMR data show that the filter of the Kv-like mutant does not constrict under inactivating conditions. Rather, the filter adopts a conformation that is slightly narrowed and rigidified. On the other hand, MD simulations indicate that the constricted conformation can nonetheless be stably established in the mutant channel. Together, these findings suggest that the Kv-like KcsA mutant may be associated with different modes of C-type inactivation, showing that distinct filter environments entail distinct C-type inactivation mechanisms.

A Companion Guide to the String Method with Swarms of Trajectories: Characterization, Performance, and Pitfalls

The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand.

Influence of Aqueous Arginine Solution on Regulating Conformational Stability and Hydration Properties of the Secondary Structural Segments of a Protein at Elevated Temperatures: A Molecular Dynamics Study

The effects of aqueous arginine solution on the conformational stability of the secondary structural segments of a globular protein, ubiquitin, and the structure and dynamics of the surrounding water and arginine were examined by performing atomistic molecular dynamics (MD) simulations. Attempts have been made to identify the osmolytic efficacy of arginine solution, and its influence in guiding the hydration properties of the protein at an elevated temperature of 450 K. The similar properties of the protein in pure water at elevated temperatures were computed and compared. Replica exchange MD simulation was performed to explore the arginine solution's sensitivity in stabilizing the protein conformations for a wide range of temperatures (300-450 K). It was observed that although all the helices and strands of the protein undergo unfolding at elevated temperature in pure water, they exhibited native-like conformational dynamics in the presence of arginine at both ambient and elevated temperatures. We find that the higher free energy barrier between the folded native and unfolded states of the protein primarily arises from the structural transformation of α-helix, relative to the strands. Our study revealed that the water structure around the secondary segments depends on the nature of amino acid compositions of the helices and strands. The reorientation of water dipoles around the helices and strands was found hindered due to the presence of arginine in the solution; such hindrance reduces the possibility of exchange of hydrogen bonds that formed between the secondary segments of protein and water (PW), and as a result, PW hydrogen bonds take longer time to relax than in pure water. On the other hand, the origin of slow relaxation of protein-arginine (PA) hydrogen bonds was identified to be due to the presence of different types of protein-bound arginine molecules, where arginine interacts with the secondary structural segments of the protein through multiple/bifurcated hydrogen bonds. These protein-bound arginine formed different kinds of bridged PA hydrogen bonds between amino acid residues of the same secondary segments or among multiple bonds and helped protein to conserve its native folded form firmly.

Lipid II Binding and Transmembrane Properties of Various Antimicrobial Lanthipeptides

There has been an alarming rise in antibacterial resistant infections in recent years due to the widespread use of antibiotics, and there is a dire need for the development of new antibiotics utilizing novel modes of action. Lantibiotics are promising candidates to engage in the fight against resistant strains of bacteria due to their unique modes of action, including interference with cell wall synthesis by binding to lipid II and creating pores in bacterial membranes. In this study, we use atomic-scale molecular dynamics computational studies to compare both the lipid II binding ability and the membrane interactions of five lanthipeptides that are commonly used in antimicrobial research: nisin, Mutacin 1140 (MU1140), gallidermin, NVB302, and NAI107. Among the five peptides investigated, nisin is found to be the most efficient at forming water channels through a membrane, whereas gallidermin and MU1140 are found to be better at binding the lipid II molecules. Nisin's effectiveness in facilitating water transport across the membrane is due to the creation of several different water trajectories along with no significant water delay points along the paths. The shorter peptide deoxyactagardine B (NVB302) was found to not form a water channel. These detailed observations provide insights into the dual mechanisms of the action of lantibiotic peptides and can facilitate the design and development of novel lanthipeptides by strategic placement of different residues.

Structure of native HIV-1 cores and their interactions with IP6 and CypA

The viral capsid plays essential roles in HIV replication and is a major platform engaging host factors. To overcome challenges in study native capsid structure, we used the perfringolysin O to perforate the membrane of HIV-1 particles, thus allowing host proteins and small molecules to access the native capsid while improving cryo–electron microscopy image quality. Using cryo–electron tomography and subtomogram averaging, we determined the structures of native capsomers in the presence and absence of inositol hexakisphosphate (IP6) and cyclophilin A and constructed an all-atom model of a complete HIV-1 capsid. Our structures reveal two IP6 binding sites and modes of cyclophilin A interactions. Free energy calculations substantiate the two binding sites at R18 and K25 and further show a prohibitive energy barrier for IP6 to pass through the pentamer. Our results demonstrate that perfringolysin O perforation is a valuable tool for structural analyses of enveloped virus capsids and interactions with host cell factors.

TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES

Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.

Molecular Dynamics Simulations Identify Tractable Lead-like Phenyl-Piperazine Scaffolds as eIF4A1 ATP-competitive Inhibitors

eIF4A1 is an ATP-dependent RNA helicase whose overexpression and activity have been tightly linked to oncogenesis in a number of malignancies. An understanding of the complex kinetics and conformational changes of this translational enzyme is necessary to map out all targetable binding sites and develop novel, chemically tractable inhibitors. We herein present a comprehensive quantitative analysis of eIF4A1 conformational changes using protein-ligand docking, homology modeling, and extended molecular dynamics simulations. Through this, we report the discovery of a novel, biochemically active phenyl-piperazine pharmacophore, which is predicted to target the ATP-binding site and may serve as the starting point for medicinal chemistry optimization efforts. This is the first such report of an ATP-competitive inhibitor for eiF4A1, which is predicted to bind in the nucleotide cleft. Our novel interdisciplinary pipeline serves as a framework for future drug discovery efforts for targeting eiF4A1 and other proteins with complex kinetics.

Lpp positions peptidoglycan at the AcrA-TolC interface in the AcrAB-TolC multidrug efflux pump

The multidrug efflux pumps of Gram-negative bacteria are a class of complexes that span the periplasm, coupling both the inner and outer membranes to expel toxic molecules. The best-characterized example of these tripartite pumps is the AcrAB-TolC complex of Escherichia coli. However, how the complex interacts with the peptidoglycan (PG) cell wall, which is anchored to the outer membrane (OM) by Braun's lipoprotein (Lpp), is still largely unknown. In this work, we present molecular dynamics simulations of a complete, atomistic model of the AcrAB-TolC complex with the inner membrane, OM, and PG layers all present. We find that the PG localizes to the junction of AcrA and TolC, in agreement with recent cryo-tomography data. Free-energy calculations reveal that the positioning of PG is determined by the length and conformation of multiple Lpp copies anchoring it to the OM. The distance between the PG and OM measured in cryo-electron microscopy images of wild-type E. coli also agrees with the simulation-derived spacing. Sequence analysis of AcrA suggests a conserved role for interactions with PG in the assembly and stabilization of efflux pumps, one that may extend to other trans-envelope complexes as well.

String Method with Swarms-of-Trajectories, Mean Drifts, Lag Time, and Committor

The kinetics of a dynamical system comprising two metastable states is formulated in terms of a finite-time propagator in phase space (position and velocity) adapted to the underdamped Langevin equation. Dimensionality reduction to a subspace of collective variables yields familiar expressions for the propagator, committor, and steady-state flux. A quadratic expression for the steady-state flux between the two metastable states can serve as a robust variational principle to determine an optimal approximate committor expressed in terms of a set of collective variables. The theoretical formulation is exploited to clarify the foundation of the string method with swarms-of-trajectories, which relies on the mean drift of short trajectories to determine the optimal transition pathway. It is argued that the conditions for Markovity within a subspace of collective variables may not be satisfied with an arbitrary short time-step and that proper kinetic behaviors appear only when considering the effective propagator for longer lag times. The effective propagator with finite lag time is amenable to an eigenvalue-eigenvector spectral analysis, as elaborated previously in the context of position-based Markov models. The time-correlation functions calculated by swarms-of-trajectories along the string pathway constitutes a natural extension of these developments. The present formulation provides a powerful theoretical framework to characterize the optimal pathway between two metastable states of a system.

PyAutoFEP: An Automated Free Energy Perturbation Workflow for GROMACS Integrating Enhanced Sampling Methods

Free energy perturbation (FEP) calculations are now routinely used in drug discovery to estimate the relative FEB (RFEB) of small molecules to a biomolecular target of interest. Using enhanced sampling can improve the correlation between predictions and experimental data, especially in systems with conformational changes. Due to the large number of perturbations required in drug discovery campaigns, the manual setup of FEP calculations is no longer viable. Here, we introduce PyAutoFEP, a flexible and open-source tool to aid the setup of RFEB FEP. PyAutoFEP is written in Python3, and automates the generation of perturbation maps, dual topologies, system building and molecular dynamics (MD), and analysis. PyAutoFEP supports multiple force fields, incorporates replica exchange with solute tempering (REST) and replica exchange with solute scaling (REST2) enhanced sampling methods, and allows flexible λ values along perturbation windows. To validate PyAutoFEP, it was applied to a set of 14 Farnesoid X receptor ligands, a system included in the drug design data resource grand challenge 2. An 88% mean correct sign prediction was achieved, and 75% of the predictions had an error below 1.5 kcal/mol. Results using Amber03/GAFF, CHARMM36m/CGenFF, and OPLS-AA/M/LigParGen had Pearson's r values of 0.71 ± 0.13, 0.30 ± 0.27, and 0.66 ± 0.20, respectively. The Amber03/GAFF and OPLS-AA/M/LigParGen results were on par with the top grand challenge 2 submissions. Applying REST2 improved the results using CHARMM36m/CGenFF (Pearson's r = 0.43 ± 0.21) but had little impact on the other force fields. CHARMM36-YF and CHARMM36-WYF modifications did not yield improved predictions compared to CHARMM36m. Finally, we estimated the probability of finding a molecule 1 pK_i better than a lead when using PyAutoFEP to screen 10 or 100 analogues. The probabilities, when compared to random sampling, increased up to sevenfold when 100 molecules were to be screened, suggesting that PyAutoFEP would likely be useful for lead optimization. PyAutoFEP is available on GitHub at https://github.com/lmmpf/PyAutoFEP.

Novel Pharmaceutical Strategy for Selective Abrogation of TSP1-Induced Vascular Dysfunction by Decoy Recombinant CD47 Soluble Receptor in Prophylaxis and Treatment Models

Elevated thrombospondin 1 (TSP1) is a prevalent factor, via cognate receptor CD47, in the pathogenesis of cardiovascular conditions, including ischemia-reperfusion injury (IRI) and pulmonary arterial hypertension (PAH). Moreover, TSP1/CD47 interaction has been found to be associated with platelet hyperaggregability and impaired nitric oxide response, exacerbating progression in IRI and PAH. Pathological TSP1 in circulation arises as a target of our novel therapeutic approach. Our “proof-of-concept” pharmacological strategy relies on recombinant human CD47 peptide (rh-CD47p) as a decoy receptor protein (DRP) to specifically bind TSP1 and neutralize TSP1-impaired vasorelaxation, strongly implicated in IRI and PAH. The binding of rh-CD47p and TSP1 was first verified as the primary mechanism via Western blotting and further quantified with modified ELISA, which also revealed a linear molar dose-dependent interaction. Ex vivo, pretreatment protocol with rh-CD47p (rh-CD47p added prior to TSP1 incubation) demonstrated a prophylactic effect against TSP1-impairment of endothelium-dependent vasodilation. Post-treatment set-up (TSP1 incubation prior to rh-CD47p addition), mimicking pre-existing excessive TSP1 in PAH, reversed TSP1-inhibited vasodilation back to control level. Dose titration identified an effective molar dose range (approx. ≥1:3 of tTSP1:rh-CD47p) for prevention of/recovery from TSP1-induced vascular dysfunction. Our results indicate the great potential for proposed novel decoy rh-CD47p-therapy to abrogate TSP1-associated cardiovascular complications, such as PAH.

Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors-S/Ace2, Tmprss2, Cathepsins L and K, and Mpro-to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

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.

Molecular Dynamics-Based Allosteric Prediction Method to Design Key Residues in Threonine Dehydrogenase for Amino-Acid Production

Allosteric proteins are considered as one of the most critical targets to design cell factories via synthetic biology approaches. Here, we proposed a molecular dynamics-based allosteric prediction method (MBAP) to screen indirect-binding sites and potential mutations for protein re-engineering. Using this MBAP method, we have predicted new sites to relieve the allosteric regulation of threonine dehydrogenase (TD) by isoleucine. An obtained mutation P441L has been verified with the ability to significantly reduce the allosteric regulation of TD in vitro assays and with the fermentation application in vivo for amino-acid production. These findings have proved the MBAP method as an effective and efficient predicting tool to find new positions of the allosteric enzymes, thus opening a new path to constructing cell factories in synthetic biology.

Elusive Intermediate State Key in the Conversion of ATP Hydrolysis into Useful Work Driving the Ca2+ Pump SERCA

A key event in the ATP-driven transport cycle of the calcium pump sarco/endoplasmic reticulum Ca²⁺-ATPase (SERCA) occurs when autophosphorylation of the pump with two bound ions Ca²⁺ triggers a large conformational change that opens a gate on the luminal side of the membrane allowing the release of the ions. It is believed that this conformational transition proceeds through a two-step mechanism, with an initial rearrangement of the three cytoplasmic domains of the pump responsible for ATP binding and hydrolysis followed by the opening of the gate toward the luminal side in the transmembrane region. Here, molecular dynamics computation of the free energy landscapes associated with this transition show how, in response to phosphorylation, the cytoplasmic domains are partially reconfigured into an intermediate state on the path toward the E2 state with a closed luminal gate. It is suggested that the free energy associated with this conformational reorganization must subsequently be used to drive the opening of the gate on the luminal side.

Permeability of the HIV-1 capsid to metabolites modulates viral DNA synthesis

Reverse transcription, an essential event in the HIV-1 life cycle, requires deoxynucleotide triphosphates (dNTPs) to fuel DNA synthesis, thus requiring penetration of dNTPs into the viral capsid. The central cavity of the capsid protein (CA) hexamer reveals itself as a plausible channel that allows the passage of dNTPs into assembled capsids. Nevertheless, the molecular mechanism of nucleotide import into the capsid remains unknown. Employing all-atom molecular dynamics (MD) simulations, we established that cooperative binding between nucleotides inside a CA hexamer cavity results in energetically favorable conditions for passive translocation of dNTPs into the HIV-1 capsid. Furthermore, binding of the host cell metabolite inositol hexakisphosphate (IP₆) enhances dNTP import, while binding of synthesized molecules like benzenehexacarboxylic acid (BHC) inhibits it. The enhancing effect on reverse transcription by IP₆ and the consequences of interactions between CA and nucleotides were corroborated using atomic force microscopy, transmission electron microscopy, and virological assays. Collectively, our results provide an atomistic description of the permeability of the HIV-1 capsid to small molecules and reveal a novel mechanism for the involvement of metabolites in HIV-1 capsid stabilization, nucleotide import, and reverse transcription.

This study shows that the HIV-1 capsid protein, in addition to its structural role, regulates reverse transcription, an essential metabolic process of the virus, by mediating the import of nucleotides. In addition, host cell metabolites such as inositol phosphates are recruited by the capsid to regulate viral DNA synthesis.

High-Fidelity Capture, Threading, and Infinite-Depth Sequencing of Single DNA Molecules with a Double-Nanopore System

Nanopore sequencing of nucleic acids has an illustrious history of innovations that eventually made commercial nanopore sequencing possible. Nevertheless, the present nanopore sequencing technology leaves much room for improvement, especially with respect to accuracy of raw reads and detection of nucleotide modifications. Double-nanopore sequencing-an approach where a DNA molecule is pulled back and forth by a tug-of-war of two nanopores-could potentially improve single-molecule read accuracy and modification detection by offering multiple reads of the same DNA fragment. One principle difficulty in realizing such a technology is threading single-stranded DNA through both nanopores. Here, we describe and demonstrate through simulations a nanofluidic system for loading and threading DNA strands through a double-nanopore setup with nearly 100% fidelity. The high-efficiency loading is realized by using hourglass-shaped side channels that not only deliver the molecules to the nanopore but also retain molecules that missed the nanopore at the first passage to attempt the nanopore capture again. The second nanopore capture is facilitated by an orthogonal microfluidic flow that unravels the molecule captured by the first nanopore and delivers it to the capture volume of the second nanopore. We demonstrate the potential utility of our double-nanopore system for DNA sequencing by simulating repeat back-and-forth motion-flossing-of a DNA strand through the double-nanopore system. We show that repeat exposure of the same DNA fragments to the nanopore sensing volume considerably increases accuracy of the nucleotide sequence determination and that correlated displacement of ssDNA through the two nanopores may facilitate recognition of homopolymer fragments.

CHARMM-GUI Free Energy Calculator for Absolute and Relative Ligand Solvation and Binding Free Energy Simulations

Alchemical free energy simulations have long been utilized to predict free energy changes for binding affinity and solubility of small molecules. However, while the theoretical foundation of these methods is well established, seamlessly handling many of the practical aspects regarding the preparation of the different thermodynamic end states of complex molecular systems and the numerous processing scripts often remains a burden for successful applications. In this work, we present CHARMM-GUI Free Energy Calculator (http://www.charmm-gui.org/input/fec) that provides various alchemical free energy perturbation molecular dynamics (FEP/MD) systems with input and post-processing scripts for NAMD and GENESIS. Four submodules are available: Absolute Ligand Binder (for absolute ligand binding FEP/MD), Relative Ligand Binder (for relative ligand binding FEP/MD), Absolute Ligand Solvator (for absolute ligand solvation FEP/MD), and Relative Ligand Solvator (for relative ligand solvation FEP/MD). Each module is designed to build multiple systems of a set of selected ligands at once for high-throughput FEP/MD simulations. The capability of Free Energy Calculator is illustrated by absolute and relative solvation FEP/MD of a set of ligands and absolute and relative binding FEP/MD of a set of ligands for T4-lysozyme in solution and the adenosine A_2A receptor in a membrane. The calculated free energy values are overall consistent with the experimental and published free energy results (within ∼1 kcal/mol). We hope that Free Energy Calculator is useful to carry out high-throughput FEP/MD simulations in the field of biomolecular sciences and drug discovery.

Scalable molecular dynamics on CPU and GPU architectures with NAMD

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

Polyvinylidene Fluoride-Added Ceramic Powder Composite Near-Field Electrospinned Piezoelectric Fiber-Based Low-Frequency Dynamic Sensors

In this study, near-field electrospinning (NFES) is used to fabricate Ba _x Sr1_-x TiO₃ (BST)/poly(vinylidene fluoride) (PVDF) piezoelectric fiber composites with excellent mechanical properties and chemical properties. BST ceramic powder is blended with PVDF solution uniformly to prepare a solution of appropriate conductance. The parameter for BST/PVDF fiber processing is based on PVDF fibers. Scanning electron microscopy, differential scanning calorimetry, microtensile testing, Fourier transform infrared spectroscopy, and electricity test of the blends of BST/PVDF fibers are incorporated. Mechanical properties of the fibers are then measured by microtensile testing. Effects of distinct ratios of Ba/Sr and the content of Ba_0.7Sr_0.3TiO₃ ceramic powder on BST/PVDF piezoelectric fibers are discussed. Finally, BST/PVDF piezoelectric fiber composites are patterned on a poly(ethylene terephthalate) (PET)-based structure with an interdigital electrode as a BST/PVDF flexible energy harvester to capture ambient energy. The results show that the BST ceramic powder is ∼58-93 nm, and the diameters of piezoelectric fiber composites are ∼6.8-13.7 μm. The tensile strength of piezoelectric fiber composites is ∼74.92 MPa, and the Young's coefficient tensile strength is ∼3.74 GPa. Mechanical properties are 2-3 times higher than those of pure PVDF piezoelectric fibers. The maximum open-circuit voltage and closed-loop current of BST/PVDF fibers reached ∼1025 mV and ∼391 nA, respectively. The electromechanical energy conversion efficiency of the BST/PVDF energy harvester is found to be 1-2 times higher than that of the PVDF energy harvester. It is confirmed and validated that the addition of BST ceramic powder could effectively increase the piezoelectric constant of PVDF piezoelectric fibers.

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials

Despite significant advances in resolution, the potential for cryo-electron microscopy (EM) to be used in determining the structures of protein-drug complexes remains unrealized. Determination of accurate structures and coordination of bound ligands necessitates simultaneous fitting of the models into the density envelopes, exhaustive sampling of the ligand geometries, and, most importantly, concomitant rearrangements in the side chains to optimize the binding energy changes. In this article, we present a flexible-fitting pipeline where molecular dynamics flexible fitting (MDFF) is used to refine structures of protein-ligand complexes from 3 to 5 Å electron density data. Enhanced sampling is employed to explore the binding pocket rearrangements. To provide a model that can accurately describe the conformational dynamics of the chemically diverse set of small-molecule drugs inside MDFF, we use QM/MM and neural-network potential (NNP)/MM models of protein-ligand complexes, where the ligand is represented using the QM or NNP model, and the protein is represented using established molecular mechanical force fields (e.g., CHARMM). This pipeline offers structures commensurate to or better than recently submitted high-resolution cryo-EM or X-ray models, even when given medium to low-resolution data as input. The use of the NNPs makes the algorithm more robust to the choice of search models, offering a radius of convergence of 6.5 Å for ligand structure determination. The quality of the predicted structures was also judged by density functional theory calculations of ligand strain energy. This strain potential energy is found to systematically decrease with better fitting to density and improved ligand coordination, indicating correct binding interactions. A computationally inexpensive protocol for computing strain energy is reported as part of the model analysis protocol that monitors both the ligand fit as well as model quality.

Membrane Anchoring of Hck Kinase via the Intrinsically Disordered SH4-U and Length Scale Associated with Subcellular Localization

Src family kinases (SFKs) are a group of nonreceptor tyrosine kinases that are characterized by their involvement in critical signal transduction pathways. SFKs are often found attached to membranes, but little is known about the conformation of the protein in this environment. Here, solution nuclear magnetic resonance (NMR), neutron reflectometry (NR), and molecular dynamics (MD) simulations were employed to study the membrane interactions of the intrinsically disordered SH4 and Unique domains of the Src family kinase Hck. Through development of a procedure to combine the information from the different techniques, we were able produce a first-of-its-kind atomically detailed structural ensemble of a membrane-bound intrinsically disordered protein. Evaluation of the model demonstrated its consistency with previous work and provided insight into how SFK Unique domains act to differentiate the family members from one another. Fortuitously, the position of the ensemble on the membrane allowed the model to be combined with configurations of the multidomain Hck kinase previously determined from small-angle solution X-ray scattering to produce full-length models of membrane-anchored Hck. The resulting models allowed us to estimate that the kinase active site is positioned about 65 ± 35 Å away from the membrane surface, offering the first estimations of the length scale associated with the concept of SFK subcellular localization.

Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM, pharmacophore and docking model

Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLIF) pharmacophore and docking was utilised for screening the CDK2 inhibitors. The evaluation of the validation set indicated that this method can be used to screen large chemical databases because it has a high hit-rate and enrichment factor (80.1% and 332.83 respectively). Six compounds were screened out from NCI, Enamine and Pubchem database. After molecular dynamics and binding free energy calculation, two compounds had great potential as novel CDK2 inhibitors and they also showed selective inhibition against CDK2 in the kinase activity assay.

Modulation of the helical properties of DNA: next-to-nearest neighbour effects and beyond

We used extensive molecular dynamics simulations to study the structural and dynamic properties of the central d(TpA) step in the highly polymorphic d(CpTpApG) tetranucleotide. Contrary to the assumption of the dinucleotide-model and its nearest neighbours (tetranucleotide-model), the properties of the central d(TpA) step change quite significantly dependent on the next-to-nearest (hexanucleotide) sequence context and in a few cases are modulated by even remote neighbours (beyond next-to-nearest from the central TpA). Our results highlight the existence of previously undescribed dynamical mechanisms for the transmission of structural information into the DNA and demonstrate the existence of certain sequences with special physical properties that can impact on the global DNA structure and dynamics.

String Method for Protein-Protein Binding Free-Energy Calculations

A powerful computational strategy to determine the equilibrium association constant of two macromolecules with explicit-solvent molecular dynamics (MD) simulations is the "geometric route", which considers the reversible physical separation of the bound complex in solution. Nonetheless, multiple challenges remain to render this type of methodology reliable and computationally efficient in practice. In particular, in one, formulation of the geometric route relies on the potential of mean force (PMF) for physically separating the two binding partners restrained along a straight axis, which must be selected prior to the calculation. However, practical applications indicate that the calculation of the separation PMF along the predefined rectilinear pathway may be suboptimal and slowly convergent. Recognizing that a rectilinear straight separation pathway is generally not representative of how the protein complex physically separates in solution, we put forth a novel theoretical framework for binding free-energy calculations, leaning on the optimal curvilinear minimum free-energy path (MFEP) determined from the string method. The proposed formalism is validated by comparing the results obtained using both rectilinear and curvilinear pathways for a prototypical host-guest complex formed by cucurbit[7]uril (CB[7]) binding benzene, and for the barnase-barstar protein complex. On the basis of multi-microsecond MD calculations, we find that the calculations following the traditional rectilinear pathway and the string-based curvilinear pathway agree quantitatively, but convergence is faster with the latter.

Computing Relative Binding Affinity of Ligands to Receptor: An Effective Hybrid Single-Dual-Topology Free-Energy Perturbation Approach in NAMD

An effective hybrid single-dual-topology protocol is designed for the calculation of relative binding affinities of small ligands to a receptor. The protocol was developed as an extension of the NAMD molecular dynamics program, which exclusively supports a dual-topology framework for relative alchemical free-energy perturbation (FEP) calculations. In this protocol, the alchemical end states are represented as two separate molecules sharing a common substructure identified through maximum structural mapping. Within the substructure, an atom-to-atom correspondence is established, and each pair of corresponding atoms is holonomically constrained to share identical coordinates at all time throughout the simulation. The forces are projected and combined at each step for propagation. Following this formulation, a set of illustrative calculations of reliable experiment/simulation data, including relative solvation free energies of small molecules and relative binding affinities of drug compounds to proteins, are presented. To enhance sampling of the dual-topology region, the FEP calculations were carried out within a replica-exchange MD scheme supported by the multiple-copy algorithm module of NAMD, with periodically attempted swapping of the thermodynamic coupling parameter λ between neighboring states. The results are consistent with experiments and benchmarks reported in the literature, lending support to the validity of the current protocol. In summary, this hybrid single-dual-topology approach combines the conceptual simplicity of the dual-topology paradigm with the advantageous sampling efficiency of the single-topology approach, making it an ideal strategy for high-throughput in silico drug design.

A Mutation in Histone H2B Represents a New Class of Oncogenic Driver

By examination of the cancer genomics database, we identified a new set of mutations in core histones that frequently recur in cancer patient samples and are predicted to disrupt nucleosome stability. In support of this idea, we characterized a glutamate to lysine mutation of histone H2B at amino acid 76 (H2B-E76K), found particularly in bladder and head and neck cancers, that disrupts the interaction between H2B and H4. Although H2B-E76K forms dimers with H2A, it does not form stable histone octamers with H3 and H4 in vitro, and when reconstituted with DNA forms unstable nucleosomes with increased sensitivity to nuclease. Expression of the equivalent H2B mutant in yeast restricted growth at high temperature and led to defective nucleosome-mediated gene repression. Significantly, H2B-E76K expression in the normal mammary epithelial cell line MCF10A increased cellular proliferation, cooperated with mutant PIK3CA to promote colony formation, and caused a significant drift in gene expression and fundamental changes in chromatin accessibility, particularly at gene regulatory elements. Taken together, these data demonstrate that mutations in the globular domains of core histones may give rise to an oncogenic program due to nucleosome dysfunction and deregulation of gene expression. SIGNIFICANCE: Mutations in the core histones frequently occur in cancer and represent a new mechanism of epigenetic dysfunction that involves destabilization of the nucleosome, deregulation of chromatin accessibility, and alteration of gene expression to drive cellular transformation.See related commentary by Sarthy and Henikoff, p. 1346.This article is highlighted in the In This Issue feature, p. 1325.

Ranking Reversible Covalent Drugs: From Free Energy Perturbation to Fragment Docking

Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. We have previously used a simplistic two-state model for predicting the relative binding selectivity of reversible covalent inhibitors ( J. Am. Chem. Soc. 2017, 139 , 17945 ). Here we go beyond binding selectivity and demonstrate that it is possible to use free energy perturbation (FEP) molecular dynamics (MD) to calculate the overall reversible covalent binding using a specially designed thermodynamic cycle. We show that FEP can predict the varying binding free energies of the analogs sharing a common warhead. More importantly, our results revealed that the chemical modification away from warhead alters the binding affinity at both noncovalent and covalent binding states, and the computational prediction can be improved by considering the binding free energy of both states. Furthermore, we explored the possibility of using a more rapid computational method, site-identification by ligand competitive saturation (SILCS), to rank the same set of reversible covalent inhibitors. We found that the fragment docking to a set of precomputed fragment maps produces a reasonable ranking. In conclusion, two independent approaches provided consistent results that the covalent binding state is suitable for the initial ranking of the reversible covalent drug candidates. For lead-optimization, the FEP approach designed here can provide more rigorous and detailed information regarding how much the covalent and noncovalent binding states are contributing to the overall binding affinity, thus offering a new avenue for fine-tuning the noncovalent interactions for optimizing reversible covalent drugs.

Accelerating Convergence of Free Energy Computations with Hamiltonian Simulated Annealing of Solvent (HSAS)

Coupling between binding of a ligand to a receptor and the displacement of a number of bound water molecules is a common event in molecular recognition processes. When the binding site is deeply buried and the exchange of water molecules with the bulk region is difficult to sample, the convergence and accuracy in free energy calculations can be severely compromised. Traditionally, Grand Canonical Monte Carlo (GCMC) based methods have been used to accelerate equilibration of water-at the expense, however, of lengthy trials before a molecular dynamics (MD) simulation. In this paper, a user-friendly and cost-efficient method, Hamiltonian simulated annealing of solvent in combination with λ-exchange of free energy perturbation (FEP) is proposed to accelerate the sampling of water molecules in free energy calculations. As an illustrative example with reliable data from previous GCMC simulations, absolute binding affinity of camphor to cytochrome P450 was calculated. The simulated hydration state change in the buried binding pocket quantitatively agrees with GCMC simulations. It is shown that the new protocol significantly accelerates sampling of water in a buried binding pocket and the convergence of free energy, with negligible setup and computing costs compared to GCMC methods.

In Silico Approaches for TRP Channel Modulation

The implication of several TRP ion channels (e.g., TRPV1) in diverse physiological and pathological processes has signaled them as pivotal drug targets. Consequently, the identification of selective and potent ligands for these channels is of great interest in pharmacology and biomedicine. However, a major challenge in the design of modulators is ensuring the specificity for their intended targets. In recent years, the emergence of high-resolution structures of ion channels facilitates the computer-assisted drug design at molecular levels. Here we describe some computational methods and general protocols to discover channel modulators, including homology modelling, docking and virtual screening, and structure-based peptide design.

Molecular mechanisms of pore formation and membrane disruption by the antimicrobial lantibiotic peptide Mutacin 1140

The emergence of antibiotic-resistance is a major concern to global human health and identification of novel antibiotics is critical to mitigate the threat.

PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches

A schematic diagram of HCC & TACE; injections of HepaSphere with DOX are made into the femoral artery, abdominal aorta, and hepatic artery to make the tumor shrink to a resectable size due to a shortage of nutrients and drug treatment.

Advances in distributed computing with modern drug discovery

INTRODUCTION

Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.

Constructing atomic structural models into cryo-EM densities using molecular dynamics - Pros and cons

Accurate structure determination from electron density maps at 3-5 Å resolution necessitates a balance between extensive global and local sampling of atomistic models, yet with the stereochemical correctness of backbone and sidechain geometries. Molecular Dynamics Flexible Fitting (MDFF), particularly through a resolution-exchange scheme, ReMDFF, provides a robust way of achieving this balance for hybrid structure determination. Employing two high-resolution density maps, namely that of β-galactosidase at 3.2 Å and TRPV1 at 3.4 Å, we showcase the quality of ReMDFF-generated models, comparing them against ones submitted by independent research groups for the 2015-2016 Cryo-EM Model Challenge. This comparison offers a clear evaluation of ReMDFF's strengths and shortcomings, and those of data-guided real-space refinements in general. ReMDFF results scored highly on the various metric for judging the quality-of-fit and quality-of-model. However, some systematic discrepancies are also noted employing a Molprobity analysis, that are reproducible across multiple competition entries. A space of key refinement parameters is explored within ReMDFF to observe their impact within the final model. Choice of force field parameters and initial model seem to have the most significant impact on ReMDFF model-quality. To this end, very recently developed CHARMM36m force field parameters provide now more refined ReMDFF models than the ones originally submitted to the Cryo-EM challenge. Finally, a set of good-practices is prescribed for the community to benefit from the MDFF developments.

Scalable Molecular Dynamics with NAMD on the Summit System

NAMD (NAnoscale Molecular Dynamics) is a parallel molecular dynamics application that has been used to make breakthroughs in understanding the structure and dynamics of large biomolecular complexes, such as viruses like HIV and various types of influenza. State-of-the-art biomolecular simulations often require integration of billions of timesteps, computing all interatomic forces for each femtosecond timestep. Molecular dynamics simulation of large biomolecular systems and long-timescale biological phenomena requires tremendous computing power. NAMD harnesses the power of thousands of heterogeneous processors to meet this demand. In this paper, we present algorithm improvements and performance optimizations that enable NAMD to achieve high performance on the IBM Newell platform (with POWER9 processors and NVIDIA Volta V100 GPUs) which underpins the Oak Ridge National Laboratory's Summit and Lawrence Livermore National Laboratory's Sierra supercomputers. The Top-500 supercomputers June 2018 list shows Summit at the number one spot with 187 Petaflop/s peak performance and Sierra third with 119 Petaflop/s. Optimizations for NAMD on Summit include: data layout changes for GPU acceleration and CPU vectorization, improving GPU offload efficiency, increasing performance with PAMI support in Charm++, improving efficiency of FFT calculations, improving load balancing, enabling better CPU vectorization and cache performance, and providing an alternative thermostat through stochastic velocity rescaling. We also present performance scaling results on early Newell systems.

Exploring Configuration Space and Path Space of Biomolecules Using Enhanced Sampling Techniques-Searching for Mechanism and Kinetics of Biomolecular Functions

To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics need to be characterized, but its atomistic details are hard to obtain both experimentally and computationally. Here, we review our recent computational studies using novel enhanced sampling techniques for conformational sampling of biomolecules and calculations of their kinetics. For efficiently characterizing the free energy landscape of a biomolecule, we introduce the multiscale enhanced sampling method, which uses a combined system of atomistic and coarse-grained models. Based on the idea of Hamiltonian replica exchange, we can recover the statistical properties of the atomistic model without any biases. We next introduce the string method as a path search method to calculate the minimum free energy pathways along a multidimensional curve in high dimensional space. Finally we introduce novel methods to calculate kinetics of biomolecules based on the ideas of path sampling: one is the Onsager⁻Machlup action method, and the other is the weighted ensemble method. Some applications of the above methods to biomolecular systems are also discussed and illustrated.