Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations.

Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning

Carbon nanotubes (CNTs) are novel materials with extraordinary mechanical properties. To gain insight on the design of high-mechanical-performance CNT-reinforced composites, the optimal structure of CNTs with high nominal tensile strength was determined in this study, where the nominal values correspond to the cross-sectional area of the entire specimen, including the hollow core. By using machine learning-assisted high-throughput molecular dynamics (HTMD) simulation, the relationship among the following structural parameters/properties was investigated: diameter, number of walls, chirality, and crosslink density. A database, comprising the various tensile test simulation results, was analyzed using a self-organizing map (SOM). It was observed that the influence of crosslink density on the nominal tensile strength tends to gradually decrease from the outside to the inside; generally, the crosslink density between the outermost wall and its adjacent wall is highly significant. In particular, based on our calculation conditions, five-walled, armchair-type CNTs with an outer diameter of 43.39 Å and crosslink densities (between the inner wall and outer wall) of 1.38 ± 1.16%, 1.13 ± 0.69%, 1.54 ± 0.57%, and 1.36 ± 0.35% were believed to be the optimal structure, with the nominal tensile strength and nominal Young’s modulus reaching approximately 58–64 GPa and 677–698 GPa.

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium–silicate–hydrate (C–S–H) gel—the primary binding phase in concrete formed via the hydration of ordinary portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C–S–H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excellent predictions. To interpret the predicted results from NN, we employ SHapley Additive exPlanations (SHAP), which reveals that the influence of silicate network on all the elastic constants of C–S–H is significantly higher than that of water and CaO content. Additionally, the water content is found to have a more prominent influence on the shear components than the normal components along the direction of the interlayer spaces within C–S–H. This result suggests that the in-plane elastic response is controlled by water molecules whereas the transverse response is mainly governed by the silicate network. Overall, by seamlessly integrating MD simulations with ML, this paper can be used as a starting point toward accelerated optimization of C–S–H nanostructures to design efficient cementitious binders with targeted properties.

The 5-hydroxytryptamin 1A (5HT1A) receptor, a class A G-Protein Coupled Receptor (GPCR), is a proven therapeutic target for neuropsychiatric and neurodegenerative illnesses, including depression, anxiety, schizophrenia, and Alzheimer's disease. Finding structurally new ligands with enhanced binding stability and druglike qualities is still a major problem, even with the clinical availability of partial agonists. Using high-throughput virtual screening, molecular docking, ADMET profiling, and long-time-scale Molecular Dynamics (MD) simulations, the current study used an integrated structure-based drug design approach to find possible high affinity 5 HT1A receptor inhibitors. BIOVIA Discovery Studio was used to screen a well-selected library against the orthosteric binding pocket of the 5HTA receptor. Top-ranked hits showed conserved aminergic GPCR interactions, such as π-π stacking interactions with Phe361 (TM6), stabilising hydrogen bonds with Ser119 (TM5) and Tyr390 (TM7), and a crucial ionic salt bridge with Asp116 (TM3). To assess dynamic stability under physiologically relevant conditions, lead complexes underwent a 300 ns membrane-embedded MD simulation using the Desmond engine with the OPLS4 force field. The function of GPCRs in endocrine, Alzheimer's, and cancer-like conditions is another characteristic. The human body only produces serotonin, also known as 5-hydroxytryptamine (5-HT), for a variety of physiological and psychological processes. When it interacts with serotonin receptors, also known as 5-HT receptors, a subtype of GPCRs, it is regulated at the functional level. 5-HT receptors can alter serotonin secretion depending on where they are in the neural network; postsynaptically, they can enhance serotonin signalling, whereas presynaptically, they can inhibit serotonin release. Apart from pathways, proteins in the 5-HT receptor family are divided into seven subtypes, including 5HT1. All of these subfamilies, with the exception of 5-HT3, are proteins that bind guanine nucleotides. Complex C3 is one of the hits that were shown to have better conformation stability, long-lasting intermolecular interaction, and a preference for dynamic behaviour. When taken as a whole, the extensive structural and dynamic data highlights the scaffolds' resilience and validates their promise as a promising lead option for additional optimization and experimental validation that targets the 5-HT1A receptor.

To efficiently insert a protein into an equilibrated and fully hydrated membrane with minimal membrane perturbation we present a computational tool, called g_membed, which is part of the Gromacs suite of programs. The input consists of an equilibrated membrane system, either flat or curved, and a protein structure in the right position and orientation with respect to the lipid bilayer. g_membed first decreases the width of the protein in the xy-plane and removes all molecules (generally lipids and waters) that overlap with the narrowed protein. Then the protein is grown back to its full size in a short molecular dynamics simulation (typically 1000 steps), thereby pushing the lipids away to optimally accommodate the protein in the membrane. After embedding the protein in the membrane, both the lipid properties and the hydration layer are still close to equilibrium. Thus, only a short equilibration run (less then 1 ns in the cases tested) is required to re-equilibrate the membrane. Its simplicity makes g_membed very practical for use in scripting and high-throughput molecular dynamics simulations.

Backgrounds: Given the distinct pathogenic mechanisms of early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD), identifying disease-specific therapeutic targets for each subtype is particularly critical. Methods: We performed proteome-wide Mendelian randomization (MR), colocalization analysis, summary-data-based MR, and Heterogeneity in Dependent Instruments (HEIDI) tests to identify the causal roles of candidate proteins in EOAD and LOAD. Further analyses included protein-protein interaction network analysis, GO/KEGG enrichment analyses, and single-cell RNA sequencing annotation. Druggability evaluation of the target proteins, Phenome-wide association study was conducted to systematically evaluate the potential adverse effects associated with druggable proteins. High-throughput molecular docking and molecular dynamics simulations were conducted to target the top therapeutic targets. Results: Genetically predicted levels of three proteins (APOE, NECTIN2, and PVR) were associated with EOAD risk, nine proteins (APOE, NECTIN2, PVR, EPHB4, SEMA3F, RNASET2, BTN1A1, PSAPL1, and GRN) were associated with LOAD risk, three proteins (APOE, NECTIN2, and PVR) were colocalized with EOAD, and six proteins (APOE, NECTIN2, PVR, SEMA3F, BTN1A1, and EPHB4) were colocalized with LOAD. Three of the proteins (APOE, NECTIN2, and PVR) serve as common targets for both EOAD and LOAD. EPHB4 and BTN1A1 were prioritized for LOAD with the most convincing evidence. The small molecule cucurbit[8]uril exhibits excellent binding affinity with both EPHB4 and BTN1A1 target proteins for the treatment of Alzheimer’s disease. Conclusions: This study pinpointed APOE, NECTIN2, and PVR as shared therapeutic targets for EOAD and LOAD, and EPHB4 and BTN1A1 singled out as a priority target for LOAD.

Hematopoietic progenitor kinase 1 (HPK1) acts as a negative regulator of T-cell receptor signaling, making it a high-priority target for cancer immunotherapy. Addressing the scarcity of structurally novel HPK1 inhibitors, we constructed and validated a comprehensive high-throughput virtual screening (HTVS) workflow to screen the ChemDiv database. This hierarchical protocol, characterized by a hybrid docking strategy and MM/GBSA rescoring, identified 10 candidates for biological testing. Experimental evaluation confirmed compound 8 as a potent hit, exhibiting an IC50 of 1585 nM and 91.65% inhibition at 25 μM. Subsequent 500 ns molecular dynamics simulations validated the stability of the compound 8-HPK1 complex (ΔGbinding = -41.59 kcal/mol), driven by critical interactions with residues GLU92 and CYS94. These findings establish compound 8 as a promising scaffold for structural optimization and demonstrate the robustness of our computational framework for HPK1 inhibitor discovery.

Helicobacter pylori is a gram negative spiral shaped bacteria that causes peptic ulcer and gastric cancer. It Is the sixth most prevalent cancer in the world and the third leading cause of cancer death. The increase in reported cases of H. pylori resistance to the drugs and antibiotics shows the need for the development of new and efficient drugs against the pathogen. In the present study, D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase (GmhB), an enzyme involved in the biosynthesis of lipopolysaccharides that encourages bacterial adherence, self-aggregation and identifying the host cells was modelled and the active sites were predicted through POCASA which is an automated ligand binding site prediction server. Natural product activity and species source (NPASS) is a database of 96,481 natural compounds that were subjected to virtual screening workflow that includes Qikprop, Lipinski rule, filtering out reactive functional groups followed by high throughput virtual screening and the top 10 compounds were selected for further induced fit docking along with the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate. The compound NPC170742 (Alpha, Beta, 3,4,5,2′,4′,6′-Octahydroxy dihydrochalcone) showed higher affinity than the substrate, and both the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate and the compound NPC170742 were subjected to molecular dynamics simulation. The results exposed the compound NPC170742 could be a potential lead compound against the enzyme D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase of H. pylori. Communicated by Ramaswamy H. Sarma

Prediction and screening of glass properties based on high-throughput molecular dynamics simulations and machine learning

SHP2 is a non-receptor protein tyrosine phosphatase to remove tyrosine phosphorylation. Functionally, SHP2 is an essential bridge to connect numerous oncogenic cell-signaling cascades including RAS-ERK, PI3K-AKT, JAK-STAT, and PD-1/PD-L1 pathways. This study aims to discover novel and potent SHP2 inhibitors using a hierarchical structure-based virtual screening strategy that combines molecular docking and the fragment molecular orbital method (FMO) for calculating binding affinity (referred to as the Dock-FMO protocol). For the SHP2 target, the FMO method prediction has a high correlation between the binding affinity of the protein-ligand interaction and experimental values (R2 = 0.55), demonstrating a significant advantage over the MM/PBSA (R2 = 0.02) and MM/GBSA (R2 = 0.15) methods. Therefore, we employed Dock-FMO virtual screening of ChemDiv database of ∼2,990,000 compounds to identify a novel SHP2 allosteric inhibitor bearing hydroxyimino acetamide scaffold. Experimental validation demonstrated that the new compound (E)-2-(hydroxyimino)-2-phenyl-N-(piperidin-4-ylmethyl)acetamide (7188-0011) effectively inhibited SHP2 in a dose-dependent manner. Molecular dynamics (MD) simulation analysis revealed the binding stability of compound 7188-0011 and the SHP2 protein, along with the key interacting residues in the allosteric binding site. Overall, our work has identified a novel and promising allosteric inhibitor that targets SHP2, providing a new starting point for further optimization to develop more potent inhibitors. All the molecular docking studies were employed to identify potential leads with Maestro v10.1. The protein-ligand binding affinities of potential leads were further predicted by FMO calculations at MP2/6-31G* level using GAMESS v2020 system. MD simulations were carried out with AmberTools18 by applying the FF14SB force field. MD trajectories were analyzed using VMD v1.9.3. MM/GB(PB)SA binding free energy analysis was carried out with the mmpbsa.py tool of AmberTools18. The docking and MD simulation results were visualized through PyMOL v2.5.0.

Objectives: Helicobacter pylori is the major cause of duodenal ulcers and gastric cancer. Menaquinone is essential for the respiration and survival of H. pylori. Identification of compounds that have good binding affinity at the active site of MqnB will be the promising approach against H. pylori. This study aims to identify the natural lead compounds against MqnB enzymes in H. pylori through in silico methods. Methods: As the three-dimensional structure of H. pylori MqnB is not available, we have modeled the three-dimensional structure followed by identification of the active site. The binding of the compounds from the COlleCtion of Open NatUral prodUcTs database was screened against MqnB, and dynamics simulation studies were carried out for the top three lead compounds. Results: The predicted model of MqnB was subjected to a molecular dynamics (MD) simulation study to obtain the dynamic behavior of MqnB. The best representative model was validated and further used for structure-based virtual screening. Based on the docking results, three lead compounds were chosen among the top hits and subjected to 200 ns MD simulation. The results highlight the dynamic nature of MqnB in complex with the lead compounds and favorable binding interactions are comparable with the substrate. All the compounds showed promising interactions with MqnB during MD simulations. Conclusion: Identified lead compounds show good binding potential and also maintain interactions with amino acid residues at the active site of MqnB enzyme. These compounds could be further explored in the drug discovery process against H. pylori.

Monoamine oxidase B (MAO-B) serves as a critical target in the management of neurodegenerative diseases (NDDs) such as Alzheimer’s and Parkinson’s due to its role in regulating oxidative stress and dopamine metabolism. In this context, phytochemicals from Oxalis species, known for their neuroprotective properties, were explored for their potential MAO-B inhibitory activity using computational approach. Plant-derived compounds, offering a better safety profile than synthetic drugs and greater cost-effectiveness, present a promising avenue for developing alternative therapeutic strategies. Molecular docking (MD), molecular dynamics simulations (MDS), and binding free energy calculations were employed to evaluate the inhibitory potential of Oxalis phytochemicals against MAO-B (PDB ID: 4A79). Stable ligand-protein complexes with optimal docking scores were selected, and key parameters from molecular dynamics trajectories, including binding stability and interactions, were analyzed to identify high potential inhibitors of MAO-B for therapeutic development. Results showed beta-sitosterol (−11.92 kcal/mol), squalene (−11.89 kcal/mol), etretinate (−11.46 kcal/mol), rhoifolin (−11.44 kcal/mol), and swertisin (−11.13 kcal/mol) demonstrated superior binding affinities compared to the native ligand (−11.12 kcal/mol). Three additional compounds; phloridzin (−11.10 kcal/mol), rhapontin (−11.02 kcal/mol), and diosmetin 7-O-beta-D-glucopyranoside (−10.96 kcal/mol) exhibited better binding than reference drugs. The predominant interactions between protein and ligand were hydrophobic, with hydrogen bonds and Pi-stacking enhancing the complexes’ stability. The evaluation based on geometrical and thermodynamic metrics derived from 200 ns MDS, identified rhoifolin, beta-sitosterol, and swertisin as promising MAO-B inhibitors. Minimal translational and rotational movements of these ligands within the catalytic site of MAO-B under quasi-physiological conditions suggested effective inhibition. Preserved thermodynamic feasibility reinforced these findings. ADMET analysis identified squalene and beta-sitosterol as CNS active candidates with favorable pharmacokinetics, while etretinate, rhoifolin, and swertisin may act as peripheral modulators, requiring optimization for improved CNS delivery. Further experimental validation of efficacy, pharmacokinetics, and safety is recommended to advance the therapeutic potential of these hit candidates.

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

We propose a linear clustering approach for large datasets of molecular geometries produced by high-throughput molecular dynamics simulations (e.g., protein folding and protein-ligand docking simulations). To this scope, we transform each three-dimensional (3D) molecular conformation into a single point in the 3D space reducing the space complexity while still encoding the molecular similarities and geometries. We assign an identifier to each single 3D point mapping a docked ligand, generate a tree from the whole space, and apply a tree-based clustering on the reduced conformation space that identifies most dense hyperspaces. We adapt our method for MapReduce and implement it in Hadoop. The load-balancing, fault-tolerance, and scalability in MapReduce allows screening of very large conformation datasets not approachable with traditional clustering methods. We analyze results for datasets with different concentrations of optimal solutions, and draw conclusions about the limitations and usability of our method. The advantages of this approach make it attractive for complex applications in real-world high-throughput molecular simulations.

The term molecular mechanics (MM) refers to the use of simple potential-energy functions (e.g., harmonic oscillator or Coulombic potentials) to model molecular systems. Molecular mechanics approaches are widely applied in molecular structure refinement, molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, and ligand-docking simulations. Typically, molecular mechanics models consist of spherical atoms connected by springs which represent bonds. Internal forces experienced in the model structure are described using simple mathematical functions. For example, Hooke’s law is commonly used to describe bonded interactions, and the nonbonded atoms might be treated as inelastic hard spheres or may interact according to a Lennard-Jones potential. Using these simple models, a molecular dynamics simulation numerically solves Newton’s equations of motion, thus allowing structural fluctuations to be observed with respect to time. Dynamic simulation methods are widely used to obtain information on the time evolution of conformations of proteins and other biological macromolecules1–4 and also kinetic and thermodynamic information. Simulations can provide fine detail concerning the motions of individual particles as a function of time. They can be utilized to quantify the properties of a system at a precision and on a time scale that is otherwise inaccessible, and simulation is, therefore, a valuable tool in extending our understanding of model systems. Theoretical consideration of a system additionally allows one to investigate the specific contributions to a property through “computational alchemy”,5 that is, modifying the simulation in a way that is nonphysical but nonetheless allows a model’s characteristics to be probed. One particular example is the artificial conversion of the energy function from that representing one system to that of another during a simulation. This is an important technique in free-energy calculations.6 Thus, molecular dynamics simulations, along with a range of complementary computational approaches, have become valuable tools for investigating the basis of protein structure and function. This review offers an outline of the origin of molecular dynamics simulation for protein systems and how it has developed into a robust and trusted tool. This review then covers more recent advances in theory and an illustrative selection of practical studies in which it played a central role. The range of studies in which MD has played a considerable or pivotal role is immense, and this review cannot do justice to them; MD simulations of biomedical importance were recently reviewed.4 Particular emphasis will be placed on the study of dynamic aspects of protein recognition, an area where molecular dynamics has scope to provide broad and far-ranging insights. This review concludes with a brief discussion of the future potential offered to advancement of the biological and biochemical sciences and the remaining issues that must be overcome to allow the full extent of this potential to be realized. 1.1. Historical Background MD methods were originally conceived within the theoretical physics community during the 1950s. In 1957, Alder and Wainwright7 performed the earliest MD simulation using the so-called hard-sphere model, in which the atoms interacted only through perfect collisions. Rahman8 subsequently applied a smooth, continuous potential to mimic real atomic interactions. During the 1970s, as computers became more widespread, MD simulations were developed for more complex systems, culminating in 1976 with the first simulation of a protein9,10 using an empirical energy function constructed using physics-based first-principles assumptions. MD simulations are now widely and routinely applied and especially popular in the fields of materials science11,12 and biophysics. As will be discussed later in this review, a variety of experimental conditions may be simulated with modern theories and algorithms. The initial simulations only considered single molecules in vacuo. Over time, more realistic or at least biologically relevant simulations could be performed. This trend is continuing today. The initial protein MD simulation, of the small bovine pancreatic trypsin inhibitor (BPTI), covered only 9.2 ps of simulation time. Modern simulations routinely have so-called equilibration periods much longer than that, and production simulations of tens of nanoseconds are routine, with the first microsecond MD simulation being reported in 1998.13 In addition, the original BPTI simulation included only about 500 atoms rather than the 104-106 atoms that are common today. While much of this advancement results from an immense increase in availability of computing power, major theoretical and methodological developments also contribute significantly. The number of publications regarding MD theory and application of MD to biological systems is growing at an extraordinary pace. A single review cannot do justice to the recent applications of MD. Using data from ISI Web of Science, the authors estimate that during 2005 at least 800 articles will be published that discuss molecular dynamics and proteins. The historical counts are shown in Figure 1. Open in a separate window Figure 1 Articles matching ISI Web of Science query “TS=(protein) AND TS=(molecular dynamics)”.