Nanosecond Molecular Dynamics Research Articles

#2263 Computational drug repurposing screen targeting PLA2R antibody binding

Abstract Background and Aims Approximately 60% of patients with membranous nephropathy have antibodies against the phospholipase A2 receptor (PLA2R). The cysteine rich (CysR) epitope of PLA2R is a likely site of protein-antibody interaction. Current treatment regimens for membranous nephropathy involve maximal conservative management or systemic immunosuppression in severe cases. We have developed protocols and programs for an in silico high-throughput repurposing screen using structural modelling, docking, and molecular dynamics (MD). Uniquely, our approach uses a bespoke interface on Google Colab for graphic processing units to run the MD simulations. We describe their application to a repurposing screen aiming to identify larger licensed drug molecules capable of impeding antibody-protein interactions at the site of the CysR epitope. Method A complete model of PLA2R was obtained from I-TASSER using the canonical sequence on the UNIPROT database. This structure was equilibrated using a five nanosecond molecular dynamics (MD) simulation. The simulation was prepared using CHARMM-GUI and run on the GROMACS-ON-COLAB platform. The centroid of the MD system between three and five nanoseconds was used for docking experiments using PLANTS PLP with a study area that included the CysR epitope. A library of compounds listed in the British National Formulary was used for the docking studies. The results would be analysed by PLANTS energy and by key residue overlap score. Results Viable docking results were obtained for 891 of the 1510 compounds tested. 60 of these compounds were in contact with at least 10% of the residues in the CysR region. By restricting these results to compounds in the lowest 20% of the total sample for calculated binding energy, 22 compounds remained. 16 of these compounds have been assessed for their safety for oral or intravenous use. These sixteen compounds were then identified for further investigation by MD to assess the stability of the identified interactions. The sixteen compounds include Lymecycline, Trazodone, Mirabegron, Sulfasalazine, Methotrexate, Rosuvastatin and Montelukast. Conclusion Structural bioinformatics methods provide a relatively fast and economical option for identifying protein-drug interactions that could result in new treatment strategies. The compounds identified for investigation are used for an array of different health conditions. However, they all share the chemical properties of large molecular weight and have the potential to form multiple hydrogen bonds. If validated experimentally, these compounds could provide new treatment options for PLA2R antibody positive membranous nephropathy.

Computational design, docking, and molecular dynamics simulation study of RNA helicase inhibitors of dengue virus.

RNA viruses are complex pathogens in terms of their genetic makeup, mutation frequency, and transmission modes. They contain the RNA helicase enzyme, which plays a crucial role in the viral genome replication process. This work aims to develop and screen a potential molecule that could function as a dengue virus (DENV) RNA helicase inhibitor. The present study was performed by taking 26 potential derivatives of gedunin phytochemicals from the PubChem database as ligands. The binding study of the compounds were analyzed by in silico docking method considering DENV RNA helicase enzyme as the receptor. After a thorough analysis of the docking scores, toxicity, and physicochemical properties, compound tetrahydrogedunin was obtained as the best. Based on tetrahydrogedunin molecular structure, 100 drug-like molecules were designed using the Data Warrior tool. After the screening process for drug-likeness and ADMET properties, the derivative number 42 was considered as the promising. Further comparative docking of derivative 42 and a standard inhibitor molecule ST-610 with DENV RNA helicase enzyme showed binding affinity as -10.0 kcal/mol and -9.6 kcal/mol, respectively. The favorable interaction between DENV RNA helicase and derivative 42 was further validated by 50 nanoseconds molecular dynamics simulation and MM-GBSA analysis. Since the antiviral activity of derivative 42 has not been reported till date, the compound was predicted as a novel therapeutic molecule that can act against the dengue virus (DENV) RNA helicase enzyme.

Design, synthesis, and bioevaluation of novel unsaturated cyanoacetamide derivatives: In vitro and in silico exploration

In this study, we synthesized novel α,β-unsaturated 2-cyanoacetamide derivatives (1–5) using microwave-assisted Knoevenagel condensation. Characterization of these compounds was carried out using FTIR and 1H NMR spectroscopy. We then evaluated their in vitro antibacterial activity against both gram-positive and gram-negative pathogenic bacteria. Additionally, we employed in silico methods, including ADMET prediction and density functional theory (DFT) calculations of molecular orbital properties, to investigate these cyanoacetamide derivatives (1–5). Molecular docking was used to assess the binding interactions of these derivatives (1–5) with seven target proteins (5MM8, 4NZZ, 7FEQ, 5NIJ, ITM2, 6SE1, and 5GVZ) and compared them to the reference standard tyrphostin AG99. Notably, derivative 5 exhibited the most favorable binding affinity, with a binding energy of -7.7 kcal mol−1 when interacting with the staphylococcus aureus (PDB:5MM8), while also meeting all drug-likeness criteria. Additionally, molecular dynamics simulations were carried out to evaluate the stability of the interaction between the protein and ligand, utilizing parameters such as Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), Radius of Gyration (Rg), and Principal Component Analysis (PCA). A 50 nanosecond molecular dynamics (MD) simulation was performed to investigate stability further, incorporating RMSD and RMSF analyses on compound 5 within the active binding site of the modeled protein across different temperatures (300, 305, 310, and 320 K). Among these temperatures, compound 5 exhibited an RMSD value ranging from approximately 0.2 to 0.3 nm at 310 K (body temperature) with the 5MM8 target, which differed from the other temperature conditions. The in silico results suggest that compound 5 maintained significant conformational stability throughout the 50 ns simulation period. It is consistent with its low docking energy and in vitro findings concerning α,β-unsaturated cyanoacetamides.Key insights from this study include:•The creation of innovative α,β-unsaturated 2-cyanoacetamide derivatives (1–5) employing cost-effective, licensed, versatile, and efficient software for both in silico and in vitro assessment of antibacterial activity.•Utilization of FTIR and NMR techniques for characterizing compounds 1–5.

In Silico Study on Natural Chemical Compounds from Citric Essential Oils as Potential Inhibitors of an Omicron (BA.1) SARS-CoV-2 Mutants' Spike Glycoprotein.

SARS-CoV-2's remarkable capacity for genetic mutation enables it to swiftly adapt to environmental changes, influencing critical attributes, such as antigenicity and transmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest. This study aimed to investigate natural compounds that could unitedly inhibit spike glycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones that would inhibit the viral entry disregard of occurred mutations. An extensive analysis of relevant literature was conducted to compile a library of chemical compounds sourced from citrus essential oils. Ten homology models representing mutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1), and the compound library was screened against them. Subsequently, employing comprehensive molecular docking and molecular dynamics simulations, we successfully identified promising compounds that exhibited sufficient binding efficacy towards the receptor binding domains (RBDs) of the mutant viral strains. The scoring of ligands was based on their average potency against all models generated herein, in addition to a reference Omicron RBD structure. Furthermore, the toxicity profile of the highest-scoring compounds was predicted. Out of ten built homology models, seven were successfully validated and showed to be reliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations in their secondary structure and needed further refinement. Notably, through a 100 nanosecond molecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the Omicron SARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors. The outcomes of this investigation hold significant potential for the utilization of a homology modeling approach for the prediction of RBD's secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunities for further advancing the drug discovery process, offering novel avenues for the development of multifunctional, non-toxic natural medications.

Crystal structure of 1-(E)- [(5‑bromo-2-hydroxybenzylidene amino) pyrrolidin-2-one]: Design, synthesis and computational evaluation of a novel racetam congener for epilepsy

The present investigation illustrates the conceptualization, synthesis, crystallographic analysis, and computational assessment of a new racetam derivative with a pyrrolidone ring as the pharmacophore. The compound demonstrates drug-like characteristics similar to LEV, an approved anti-epileptic drug, indicating its potential for developing novel epilepsy drugs. Molecular docking and molecular dynamic simulations were used to evaluate the binding affinity between the compound and SV2A, a protein-ligand complex. The protein-ligand complex attained structural equilibrium in the final 50 nanoseconds of the 200 nanosecond molecular dynamics simulation. The analysis reveals that regions with higher flexibility are primarily located in the extramembrane regions of the protein. Intermolecular contact analysis reveals hydrogen bonding and hydrophobic interactions as the primary types of interactions. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) calculation highlights the energetic aspects of ligand binding and the participation of important residues in the binding pocket. Unique interactions like those involving bifurcated hydrogen bonding, and novel pi-anion interaction makes the study significant. Quantum chemical calculations for the compound (LIG) done using DFT corroborates the protein-ligand interactions on the basis of Molecular Electrostatic Potential (MEP) maps. The study establishes the structure-activity relationship (SAR) of the newly developed pyrrolidone-based compound, identifying it as a promising lead molecule for epilepsy treatment.

Assessing the inhibitory potential of anti-dengue compounds against Japanese encephalitis virus RNA dependent RNA polymerase: an in silico study

Japanese encephalitis (JE), a neurological infection of severe nature, is caused by the Japanese encephalitis virus (JEV) and is transmitted by the mosquito vector. The polymerase domain of Non-structural 5 (NS5), which is also referred to as RdRp (RNA-dependent RNA polymerase), is considered a potential therapeutic target for JEV. The present study employed molecular dynamics modelling and high-throughput virtual screening to evaluate the possible antiviral activity of anti-dengue drugs against JEV RdRp. Furthermore, a ranking was performed utilising the MM/GBSA analysis to identify the three most promising compounds. Compound ID 57409246 exhibited the highest binding affinity with the protein, as evidenced by its minimum binding free energy of −72.96 kcal/mole. In contrast, the other two compounds had minimum binding free energies of −67.57 and −59.19 kcal/mole, respectively. Upon conducting a 100 nanosecond molecular dynamics simulation to confirm the binding of the chemical complexes, it was observed that the three hits, namely 57409246, 70683874, and 44577154, exhibited a consistent and stable RMSD. Subsequently, the binding strength of the trajectory was confirmed through MM/GBSA analysis. The compounds 70683874 and 57409246 exhibited the lowest binding free energies, which were −97.58 kcal/mol and −96.38 kcal/mol, respectively. The binding free energy (ΔG Bind) values for the native ligand ATP and molecule 44577154 were −65.64 kcal/mol and −69.44 kcal/mol, respectively. Overall, compared to the native ligand ATP, all three compounds exhibited higher binding affinity. The study proposes three anti-dengue molecules as a potential remedy for JE, which can be confirmed through in vitro and in vivo investigations. Communicated by Ramaswamy H. Sarma

Revelation of potential drug targets of luteolin in Plasmodium falciparum through multi-target molecular dynamics simulation studies

In-silico techniques offer a fast, accurate, reliable, and economical approach to studying the molecular interactions between compounds and proteins. In this study, our main aim is to use in-silico techniques as a rational approach for the prediction of the molecular drug targets for luteolin against Plasmodium falciparum. Multi-target molecular docking, 100 nanoseconds (ns) molecular dynamics (MD) simulations, and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) binding free energy calculations were carried out for luteolin against dihydrofolate reductase thymidylate synthase (PfDHFR-TS), dihydroorotate dehydrogenase (PfDHODH), and falcipain-2. The native ligands of each protein were used as a reference to evaluate the performance of luteolin. Luteolin outperformed the native ligands of all proteins at molecular docking and MD simulations studies. However, in the MM-GBSA calculations, luteolin outperformed the native ligand of only PfDHFR-TS but not PfDHODH and falcipain-2. Among the studied proteins, the in-silico approach predicted PfDHFR-TS as the most favorable drug target for luteolin. Communicated by Ramaswamy H. Sarma

In silico Identification of Potential Small Molecules Targeting Six Proteins in Nipah Virus using Molecular Docking, Pharmacophore and Molecular Dynamics Simulation

Introduction: Nipah virus (NiV) is a highly pathogenic zoonotic virus of the genus Henipavirus, which causes severe respiratory illness and deadly encephalitis with a fatality rate of 50%-70 % in humans. A total of 16 NiV proteins are available in the Protein Data Bank (PDB) of which six proteins belong to the structural class. Method: In this study, a cluster of six proteins of classes Viral attachment glycoproteins (2VWD, 2VSM), Fusion glycoprotein (5EVM, 6PD4), Matrix protein (6BK6), and Phosphoprotein (4HEO) were considered as potential therapeutic targets. Here, 25 small molecule inhibitors were chosen which include 23 natural compounds with antiviral properties and 2 antiviral drug molecules as control. The potential inhibitors among the selected compounds were identified based on docking score, significant intermolecular interactions, ADME (absorption, distribution, metabolism, and excretion) properties, pharmacophore and toxicity studies. Moreover, 100 nanoseconds molecular dynamics simulation was carried out for the best selected compound with all protein targets to understand the stability and binding strength. Result and Discussion: In this study, we propose that the baicalin was found to be the most potential lead molecule with higher binding affinity, strong bonded interactions, favorable pharmacophore features and higher complex stability. Conclusion: Hence, the compound identified shall prove effective against the Nipah virus targeting the viral attachment glycoprotein.

Designing Bcl-2 protein binding aptamers using screened Coulomb interaction method & regulate apoptosis.

Cancer cells develop different physiological abnormalities. One of them is overexpression of Bcl-2 protein, an anti-apoptotic biomolecule helping cancer cells to sustain. We planned to regulate the apoptosis by inhibiting the Bcl-2 protein's functions in cancer cells. We used computer aided drug design techniques to make a nucleic acid-based aptamer drug that targets Bcl-2 proteins. We applied a theoretical biophysics technique ‘screened Coulomb interactions approach (SCIA)’ for designing aptamers. This technique was patented (US10916330B1) and found suitable for designing novel aptamers to target biomoleculesin physiological conditions. Physicochemical analysis of Bcl-2 protein helps detect drug binding site(s) and charge distribution inamino acid structures. We made a judicious choice to select amino acid arginine as our target molecule in Bcl-2 structure. We constructed a universal drug design platform by applying SCIA, developed our algorithms and applied in Mathematica 13 to perform numerical computations (NCs) addressing the drug-target binding energetics. The NC results show that nucleotide guanine has the lowest binding energy over other nucleotides while interacting with arginine. Selection of other subsequent nucleotides as aptamer building blocks at various screening order is being made using their preferred SCI binding energetics with arginine. Thus, the discovery of a few aptamers to target arginine (specifically) and Bcl-2 protein (in general) has been made. Based on target binding energetic preferences we rank the aptamers. We are now working on the following two things: (i) perform 100 nanosecond molecular dynamics (MD) simulations to address drug-target binding kinetics and (ii) perform in vitro protein binding assays using top selected aptamers. Thus, we shall be able to finalize a few candidate drugs as optimal therapeutic agents capable of binding with Bcl-2 proteins and thus inhibit the protein functions. This research will lead us to discovering a few aptamer candidates for future chemotherapy applications.

Novel Small Molecule and Peptide Inhibitors of CREB in Leukemia Cells

Background: Acute Lymphoblastic Leukemia (ALL) and Acute Myelogenous Leukemia (AML) account for nearly 45% of all childhood cancer. Current therapy for acute leukemia results in significant morbidity and late complications in children. Therefore, more effective and less toxic therapies to treat leukemia are needed. Previous work in our laboratory has identified CREB (cAMP Response Element Binding Protein), a transcription factor that promotes cell proliferation and survival in acute leukemia, is overexpressed in primary ALL and AML cells and is a potential target for therapy. We have investigated small molecules and peptides to inhibit the critical interaction between the phosphorylated KID (Kinase-Inducible) domain of CREB and the KIX (KID-interacting) domain of the coactivator CBP (CREB Binding Protein). Methods: We assessed a library of previously untested alpha-mimetic compounds from ChemDiv designed to mimic interactions between α-helices and protein surfaces for predicted interaction with a shallow hydrophobic groove on the interaction surface of KIX. We interrogated the 24,737 compounds in the library using rigid-receptor ligand docking, followed by induced-fit ligand docking of the top compounds using two different software, MOE and Schrodinger's Bioluminate. The top 50 binding poses from each software's rankings were combined to yield 87 candidate alpha-mimetic molecule inhibitors. In addition to our screen of small molecules, we have designed peptide inhibitors that closely mimic the αB helix of pKID. We investigated two methods to enhance the stability and cell-penetrating ability of our peptides: hydrocarbon stapling and the utilization of a rigid scaffold from the scorpion toxin peptide BmBKTtx1. Based on the calculated binding scores and locations, the most promising chimeric peptide designs were further investigated for binding stability using 500 nanosecond molecular dynamics (MD) simulations modeling their interaction with KIX. Results: The binding poses of the final set of small molecules were visually inspected for occupation of key hotspots on the pKID:KIX interaction surface, including the location of the phosphorylation sight of KID. Of the 87 molecules and their binding poses, only 21 of them (24%) obstructed fewer than two of the three hotspots chosen for the analysis. Moreover, 80 of the molecules (92%) extended into the hydrophobic pocket, the most critical hotspot. The multi-residue construction of this pocket likely contributed to the high similarity in interaction fingerprint between these molecules and pKID; occupation of this pocket can serve as a good indicator of strong ligand binding to KIX in future assays. The most-promising peptide designs using the scorpion toxin scaffold were assessed with MD simulations and compared to a control peptide comprised primarily of the αB helix of pKID. The chimeric peptides showed increased binding stability over the control peptide with an average RMSD of 6.8Å vs. 12.5Å, illustrating the benefits of the stability afforded by the scorpion scaffold design. Several of the hydrocarbon stapled peptides were tested in vitro using fluorescence polarization (FP) assay developed by our lab (Chae et al. Bioorganic & Medicinal Chemistry Letters, 2019) and in AML HL-60 and KG-1 cells and were shown to have growth inhibitory effects by Cell-Titer Glo Assays. Discussion: These small molecules and scorpion toxin-derived peptides represent novel candidate inhibitors of the pKID:KIX interaction implicated in leukemia. Based on promising results from our in silico analyses of these inhibitors, we have obtained ten structurally diverse alpha-mimetic small molecules and three chimeric peptides and aim to test their efficacy in vitro using the FP assay. Furthermore, we will obtain high resolution cryo-EM structures of successful compounds and peptides bound to the KIX domain using a double-shell protein scaffold (Zhang et al. ACS Central Science, 2022), and use these structures for further refinement of the inhibitors. The development of new inhibitors for the pKID:KIX interaction promises to reveal insights into the structural relationship between the two domains and the basis for effective, targeted leukemia therapeutics.

Seafood Paramyosins as Sources of Anti-Angiotensin-Converting-Enzyme and Anti-Dipeptidyl-Peptidase Peptides after Gastrointestinal Digestion: A Cheminformatic Investigation

Paramyosins, muscle proteins occurring exclusively in invertebrates, are abundant in seafoods. The potential of seafood paramyosins (SP) as sources of anti-angiotensin-converting-enzyme (ACE) and anti-dipeptidyl-peptidase (DPP-IV) peptides is underexplored. This in silico study investigated the release of anti-ACE and anti-DPP-IV peptides from SP after gastrointestinal (GI) digestion. We focused on SP of the common octopus, Humboldt squid, Japanese abalone, Japanese scallop, Mediterranean mussel, Pacific oyster, sea cucumber, and Whiteleg shrimp. SP protein sequences were digested on BIOPEP-UWM, followed by identification of known anti-ACE and anti-DPP-IV peptides liberated. Upon screening for high-GI-absorption, non-allergenicity, and non-toxicity, shortlisted peptides were analyzed via molecular docking and dynamic to elucidate mechanisms of interactions with ACE and DPP-IV. Potential novel anti-ACE and anti-DPP-IV peptides were predicted by SwissTargetPrediction. Physicochemical and pharmacokinetics of peptides were predicted with SwissADME. GI digestion liberated 2853 fragments from SP. This comprised 26 known anti-ACE and 53 anti-DPP-IV peptides exhibiting high-GI-absorption, non-allergenicity, and non-toxicity. SwissTargetPrediction predicted three putative anti-ACE (GIL, DL, AK) and one putative anti-DPP-IV (IAL) peptides. Molecular docking found most of the anti-ACE peptides may be non-competitive inhibitors, whereas all anti-DPP-IV peptides likely competitive inhibitors. Twenty-five nanoseconds molecular dynamics simulation suggests the stability of these screened peptides, including the three predicted anti-ACE and one predicted anti-DPP-IV peptides. Seven dipeptides resembling approved oral-bioavailable peptide drugs in physicochemical and pharmacokinetic properties were revealed: AY, CF, EF, TF, TY, VF, and VY. In conclusion, our study presented in silico evidence for SP being a promising source of bioavailable and safe anti-ACE and anti-DPP-IV peptides following GI digestions.

The Structural Basis of Mycobacterium tuberculosis RpoB Drug-Resistant Clinical Mutations on Rifampicin Drug Binding.

Tuberculosis (TB), caused by the Mycobacterium tuberculosis infection, continues to be a leading cause of morbidity and mortality in developing countries. Resistance to the first-line anti-TB drugs, isoniazid (INH) and rifampicin (RIF), is a major drawback to effective TB treatment. Genetic mutations in the β-subunit of the DNA-directed RNA polymerase (rpoB) are reported to be a major reason of RIF resistance. However, the structural basis and mechanisms of these resistant mutations are insufficiently understood. In the present study, thirty drug-resistant mutants of rpoB were initially modeled and screened against RIF via a comparative molecular docking analysis with the wild-type (WT) model. These analyses prioritized six mutants (Asp441Val, Ser456Trp, Ser456Gln, Arg454Gln, His451Gly, and His451Pro) that showed adverse binding affinities, molecular interactions, and RIF binding hinderance properties, with respect to the WT. These mutant models were subsequently analyzed by molecular dynamics (MD) simulations. One-hundred nanosecond all-atom MD simulations, binding free energy calculations, and a dynamic residue network analysis (DRN) were employed to exhaustively assess the impact of mutations on RIF binding dynamics. Considering the global structural motions and protein–ligand binding affinities, the Asp441Val, Ser456Gln, and His454Pro mutations generally yielded detrimental effects on RIF binding. Locally, we found that the electrostatic contributions to binding, particularly by Arg454 and Glu487, might be adjusted to counteract resistance. The DRN analysis revealed that all mutations mostly distorted the communication values of the critical hubs and may, therefore, confer conformational changes in rpoB to perturb RIF binding. In principle, the approach combined fundamental molecular modeling tools for robust “global” and “local” level analyses of structural dynamics, making it well suited for investigating other similar drug resistance cases.

Identification and Structure Prediction of Human Septin-4 as a Biomarker for Diagnosis of Asthenozoospermic Infertile Patients-Critical Finding Toward Personalized Medicine.

Semen parameters are been found as a key factor to evaluate the count and morphology in the given semen sample. The deep knowledge of male infertility will unravel with semen parameters correlated with molecular and biochemical parameters. The current research study is to identify the motility associated protein and its structure through the in-silico approach. Semen samples were collected and initial analysis including semen parameters was analyzed by using the World Health Organization protocol. Semen biochemical parameters, namely, seminal plasma protein concentration, fructose content, and glucosidase content were calculated and evaluated for correlation. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) were carried out for identification of Septin-4 presence in the semen sample. Mascot search was done for protein conformation and in-silico characterization of Septin-4 by structural modeling in Iterative Threading Assembly Refinement (I-TASSER). Twenty-five nanoseconds molecular dynamics (MD) simulations results showed the stable nature of Septin-4 in the dynamic system. Overall, our results showed the presence of motility-associated protein in normospermia and control samples and not in the case of asthenospermia and oligoasthenospermia. Molecular techniques characterized the presence of Septin-4 and as a novel biomarker for infertility diagnosis.

Molecular Mechanism of Food-Derived Polyphenols on PD-L1 Dimerization: A Molecular Dynamics Simulation Study.

In cancer immunotherapy, an emerging approach is to block the interactions of programmed cell death-1 (PD-1) and programmed cell death-ligand 1 (PD-L1) using small-molecule inhibitors. The food-derived polyphenols curcumin (CC), resveratrol (RSV) and epigallocatechin gallate (EGCG) have anticancer immunologic functions, which, recently, have been proposed to act via the downregulation of PD-L1 expression. However, it remains unclear whether they can directly target PD-L1 dimerization and, thus, interrupt the PD-1/PD-L1 pathway. To elucidate the molecular mechanism of such compounds on PD-L1 dimerization, molecular docking and nanosecond molecular dynamics simulations were performed. Binding free energy calculations show that the affinities of CC, RSV and EGCG to the PD-L1 dimer follow a trend of CC > RSV > EGCG. Hence, CC is the most effective inhibitor of the PD-1/PD-L1 pathway. Analysis on contact numbers, nonbonded interactions and residue energy decomposition indicate that such compounds mainly interact with the C-, F- and G-sheet fragments of the PD-L1 dimer, which are involved in interactions with PD-1. More importantly, nonpolar interactions between these compounds and the key residues Ile54, Tyr56, Met115, Ala121 and Tyr123 play a dominant role in binding. Free energy landscape and secondary structure analyses further demonstrate that such compounds can stably interact with the binding domain of the PD-L1 dimer. The results provide evidence that CC, RSV and EGCG can inhibit PD-1/PD-L1 interactions by directly targeting PD-L1 dimerization. This provides a novel approach to discovering food-derived small-molecule inhibitors of the PD-1/PD-L1 pathway with potential applications in cancer immunotherapy.

Identification of novel small molecule inhibitors for endoplasmic reticulum oxidoreductase 1α (ERO1α) enzyme: structure-based molecular docking and molecular dynamic simulation studies

The endoplasmic reticulum (ER) is a cellular organelle responsible for the folding of proteins. When protein folding demand exceeds the folding capacity, cells trigger ER stress. ER-oxidoreductase 1α (ERO1α) is an ER stress component that controls oxidative folding protein. Upregulation of ERO1α was reported in distinct types of cancer including breast cancer and colon cancer. It was reported that deletion of ERO1 gene compromised cancer progression and cell proliferation in colon cancer. Thereby, ERO1α inhibition might be a clinically promising anti-cancer therapeutic target. In the present study, we conducted a virtual screening of 6,000 natural-product molecules obtained from Zinc database using a multistep docking approach with a crystal structure of human ERO1α. Our analyses from high throughput virtual screening revealed the top-ranked scores of 3000 molecules with glide scores of less than −4.0 kcal/mol. These molecules were further advanced to standard precision (SP) docking. The top 300 molecules of SP docking with glide scores ≤ −7.5 kcal/mol were chosen to undergo extra precision (XP) docking. Around 40 molecules that have conserved interactions with the binding site of ERO1α were ranked by the XP docking. Based on visual inspection, seven-candidate molecules that have high binding affinity scores and more molecular interactions were shortlisted. The dynamic stability of binding between the candidate molecules and ERO1α was characterized using 100 nanoseconds molecular dynamics simulation method. Two candidates exhibited strong and stable binding complexes with ERO1α. Collectively, these findings suggest that the identified molecules may serve as potential anti-cancer lead molecules subjected to further experimental validation. Communicated by Ramaswamy H. Sarma

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations.

Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17β-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.

Molecular Dynamics Investigation of Phenolic Oxidative Coupling Protein Hyp-1 Derived from Hypericum perforatum

Molecular dynamics (MD) simulations provide a physics-based approach to understanding protein structure and dynamics. Here, we used this intriguing tool to validate the experimental structural model of Hyp-1, a pathogenesis-related class 10 (PR-10) protein from the medicinal herb Hypericum perforatum, with potential application in various pharmaceutical therapies. A nanosecond MD simulation using the all-atom optimized potentials for liquid simulations (OPLS–AA) force field was performed to reveal that experimental atomic displacement parameters (ADPs) underestimate their values calculated from the simulation. The average structure factors obtained from the simulation confirmed to some extent the relatively high compliance of experimental and simulated Hyp-1 models. We found, however, many outliers between the experimental and simulated side-chain conformations within the Hyp-1 model, which prompted us to propose more reasonable energetically preferred rotameric forms. Therefore, we confirmed that MD simulation may be applicable for the verification of refined, experimental models and the explanation of their structural intricacies.

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that for a water system of 12,582,912 atoms, the GPU version can be 7 times faster than the CPU version under the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113,246,208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions. Program summaryProgram Title: DeePMD-kitCPC Library link to program files:https://doi.org/10.17632/phyn4kgsfx.1Developer’s repository link:https://doi.org/10.5281/zenodo.3961106Licensing provisions: LGPLProgramming language: C++/Python/CUDAJournal reference of previous version: Comput. Phys. Commun. 228 (2018), 178–184.Does the new version supersede the previous version?: Yes.Reasons for the new version: Parallelize and optimize the DeePMD-kit for modern high performance computers.Summary of revisions: The optimized DeePMD-kit is capable of computing 100 million atoms molecular dynamics with ab initio accuracy, achieving 86 PFLOPS in double precision.Nature of problem: Modeling the many-body atomic interactions by deep neural network models. Running molecular dynamics simulations with the models.Solution method: The Deep Potential for Molecular Dynamics (DeePMD) method is implemented based on the deep learning framework TensorFlow. Standard and customized TensorFlow operators are optimized for GPU. Massively parallel molecular dynamics simulations with DeePMD models on high performance computers are supported in the new version.

Modeling and simulation study to identify threonine synthase as possible drug target in Leishmania major.

Leishmaniasis is one of the most neglected tropical diseases that demand immediate attention to the identification of new drug targets and effective drug candidates. The present study demonstrates the possibility of using threonine synthase (TS) as a putative drug target in leishmaniasis disease management. We report the construction of an effective homology model of the enzyme that appears to be structurally as well as functionally well conserved. The 200 nanosecond molecular dynamics data on TS with and without pyridoxal phosphate (PLP) shed light on mechanistic details of PLP-induced conformational changes. Moreover, we address some important structural and dynamic interactions in the PLP binding region of TS that are in good agreement with previously speculated crystallographic estimations. Additionally, after screening more than 44,000 compounds, we propose 10 putative inhibitor candidates for TS based on virtual screening data and refined Molecular Mechanics Generalized Born Surface Area calculations. We expect that structural and functional dynamics data disclosed in this study will help initiate experimental endeavors toward establishing TS as an effective antileishmanial drug target.

Raw nuclear magnetic resonance data of human linker histone H1x, lacking the C-terminal domain (NGH1x), and trajectory data of nanosecond molecular dynamics simulations of GH1x- and NGH1x-chromatosomes.

Linker histone H1 plays a vital role in the packaging of DNA. H1 has a tripartite structure: a conserved central globular domain that adopts a winged-helix fold, flanked by highly variable and intrinsically unstructured N- and C-terminal domains. The datasets presented in this article include raw 2D and 3D BEST-TROSY NMR data [1H-15 N HSQC; 15 N and 13C HNCO, HN(CO)CACB, HNCACB, HN(CA)CO] recorded for NGH1x, a truncated version of H1x containing the N-terminal and globular domains, but lacking the C-terminal domain. Experiments were conducted on double-labelled (15 N and 13C) NGH1x in 'low' and 'high salt,' to investigate the secondary structure content of the N-terminal domain of H1x under these conditions. We provide modelled structures of NGH1x (in low and high salt) based on the assigned chemical shifts in PDB format. The high salt structure of NGH1x (globular domain of H1x [GH1x; PDB: 2LSO] with the H1x NTD) was docked to the nucleosome to generate NGH1x- and GH1x-chromatosomes. The GH1x-chromatosome was generated for comparative purposes to elucidate the role of the N-terminal domain. We present raw data trajectories of molecular dynamics simulations of these chromatosomes in this article. The MD dataset provides nanosecond resolution data on the dynamics of GH1x- vs NGH1x-chromatosomes, which is useful to elucidate the DNA binding properties of the N-terminal domain of H1x in chromatin, as well as the dynamic behaviour of linker DNA in these chromatosomes.