High-throughput Molecular Dynamics Simulations Research Articles

StreaMD: the toolkit for high-throughput molecular dynamics simulations.

Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.

Optimized performance of membrane-based desalination by high-throughput molecular dynamic simulations and machine learning analysis

The influence of pore size and hydrophilicity on the permeance of reverse osmosis (RO) membranes has been mostly focused. However, their influence is hardly to be clearly identified as these two kinds of factor interfere with each other. In this work, high-throughput molecular dynamics (HTMD) simulations with CNTs are used to extensively produce the data of water permeance and NaCl rejection. These data are then analyzed by machine learning (ML) method to obtain the optimized desalination performance. The HTMD results indicate that the pressure drop has little effect on the water permeance. Moreover, rising pore size and degrading hydrophilicity will generally boost water permeance but will somehow sacrifice the NaCl rejection. The interference effect between pore size and hydrophilicity is also found in this work, the mechanism of which is then revealed from molecular level. Additionally, ML is applied to analyze the abundant data of water permeance and NaCl rejection. The optimal conditions are identified to achieve the highest water permeance with 100% NaCl rejection, which are also validated via additional MD simulations. This work suggests that the integration of HTMD and ML promises the future of designing new kind of RO membranes for better performance.

Chicoric acid acts as an ALOX15 inhibitor to prevent ferroptosis in asthma

BackgroundChicoric acid (CA) is a crucial immunologically active compound found in chicory and echinacea, possessing a range of biological activities. Ferroptosis, a type of iron-dependent cell death induced by lipid peroxidation, plays a key role in the development and advancement of asthma. Targeting ferroptosis could be a potential therapeutic strategy for treating asthma. PurposeThe purpose of this study was to explore the screening of ALOX15, a pivotal target of ferroptosis in asthma, and potential therapeutic agents, as well as to investigate the promising potential of CA as an ALOX15 inhibitor for modulating ferroptosis in asthma. MethodsThrough high-throughput data processing of bronchial epithelial RNA from asthma patients using bioinformatics and machine learning, the key target of ferroptosis in asthma, ALOX15, was identified. An inhibitor of ALOX15 was then obtained through high-throughput molecular docking and molecular dynamics simulation tests. In vitro experiments were conducted using a 16HBE cell model induced by house dust mite (HDM) and lipopolysaccharide (LPS), which were treated with the ALOX15 inhibitor (PD146176), CA treatment, or ALOX15 knockdown. In vivo experiments were also carried out using a mouse model induced by HDM and LPS. ResultsThe composite model of ALOX15 and CA in molecular dynamics simulations shows good stability and flexibility. Network pharmacological analysis reveals that CA regulates ferroptosis through ALOX15 in treating asthma. In vitro studies show that ALOX15 is highly expressed in HDM and LPS treatments, while CA inhibits HDM and LPS-induced ferroptosis in 16HBE cells by reducing ALOX15 expression. Knockdown of ALOX15 has the opposite effect. Metabolomics analysis identifies key compounds associated with ferroptosis, including L-Targinine, eicosapentaenoic acid, 16-hydroxy hexadecanoic acid, and succinic acid. In vivo experiments demonstrate that CA suppresses ALOX15 expression, inhibits ferroptosis, and improves asthma symptoms in mice. ConclusionOur research initially identified CA as a promising asthma treatment that effectively blocks ferroptosis by specifically targeting ALOX15. This study not only highlights CA as a potential therapeutic agent for asthma but also introduces novel targets and treatment options for this condition, along with innovative approaches for utilizing natural compounds to target diseases associated with ferroptosis.

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

Theoretical model for elastic modulus prediction on basis of atomic structure information: Beginning from amorphous carbon to covalent bonding materials

The mechanical property is one of the most important properties of a material and is determined by the atomic-scale structure. It is thus crucial to establish the theoretical model for the prediction of materials' mechanical properties, benefiting the material design. In our previous work, we proposed an atomic-scale structure-based model for predicting the Young's modulus of hydrogenated amorphous carbon; however, the application range of this model is too narrow to be used practically for the materials development. Therefore, in this work, an extended prediction model of Young's modulus of materials with wide applicability is successfully constructed, based on the two important inherent properties of materials: one is effective coordination number (CNeff) evaluating how densely atoms are structured and the another one is effective bond stiffness (Keff) that is first proposed here and indicates how bonding types contribute the elastic properties. Through the high-throughput molecular dynamics simulations, the predictive model of Young's modulus (E) is determined as E = 3.37Keff (CNeff - 2.0)1.5. Then we demonstrate that this model is valid for a large variety of materials including both amorphous and crystalline structures, and the accuracy is proven by comparing with other work. Overall, this fundamental work may benefit the preparation, development, and utilization of new materials.

Charge govern ion-selective mechanism of YX2 desalination pore using high-throughput computations and machine learning

Energy-efficient and low-cost desalination of water continues to be a problem that society is facing. With the advances in nanotechnology, a variety of nanopore membranes have emerged for desalination. In particular, YX2 nanopores (YW, Mo, etc. and X = S, Se, Te, etc.) have been considered as promising membranes for water purification. In this work, we used a combination of high-throughput molecular dynamics simulations and machine learning to reveal the nanopore mass transfer mechanism and rationally design the YX2 nanopore. The pore size and the charge of the pore mouth were found to be the main factors affecting water permeation and salt rejection, as discovered through the machine learning study. The physical-chemical analysis provides us with a deep understanding of the charge-governed mechanism on the desalination performance of YX2 nanopores. It revealed that YX2 nanopores with a conical shape and minimized pore charges could be ideal candidates for excellent desalination performance. Based on this, we designed the YX2 pore with a specific charge distribution, which could achieve high water permeation with high salt rejection. Therefore, the combination of machine learning and high-throughput computation methods could aid in designing materials with excellent performance for desalination.

Unlocking enhanced thermal conductivity in polymer blends through active learning

Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field for efficiently designing and developing polymeric materials. However, the focus of polymer informatics has predominantly centered on single-component polymers, leaving the vast chemical space of polymer blends relatively unexplored. This study employs a high-throughput molecular dynamics (MD) simulation combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, the TC of about 600 amorphous single-component polymers and 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is identified, which involves a weighted sum approach that extends existing polymer representation from single-component polymers to polymer blends. An AL framework, combining MD simulation and ML, is employed to explore the TC of approximately 550,000 unlabeled polymer blends. The AL framework proves highly effective in accelerating the discovery of high-performance polymer blends for thermal transport. Additionally, we delve into the relationship between TC, radius of gyration (Rg), and hydrogen bonding, highlighting the roles of inter- and intra-chain interactions in thermal transport in amorphous polymer blends. A significant positive association between TC and Rg improvement and an indirect contribution from H-bond interaction to TC enhancement are revealed through a log-linear model and an odds ratio calculation, emphasizing the impact of increasing Rg and H-bond interactions on enhancing polymer blend TC.

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning

Antifungal plant flavonoids identified in silico with potential to control rice blast disease caused by Magnaporthe oryzae.

Rice blast disease, caused by the fungus Magnaporthe oryzae, poses a severe threat to rice production, particularly in Asia where rice is a staple food. Concerns over fungicide resistance and environmental impact have sparked interest in exploring natural fungicides as potential alternatives. This study aimed to identify highly potent natural fungicides against M. oryzae to combat rice blast disease, using advanced molecular dynamics techniques. Four key proteins (CATALASE PEROXIDASES 2, HYBRID PKS-NRPS SYNTHETASE TAS1, MANGANESE LIPOXYGENASE, and PRE-MRNA-SPLICING FACTOR CEF1) involved in M. oryzae's infection process were identified. A list of 30 plant metabolites with documented antifungal properties was compiled for evaluation as potential fungicides. Molecular docking studies revealed that 2-Coumaroylquinic acid, Myricetin, Rosmarinic Acid, and Quercetin exhibited superior binding affinities compared to reference fungicides (Azoxystrobin and Tricyclazole). High throughput molecular dynamics simulations were performed, analyzing parameters like RMSD, RMSF, Rg, SASA, hydrogen bonds, contact analysis, Gibbs free energy, and cluster analysis. The results revealed stable interactions between the selected metabolites and the target proteins, involving important hydrogen bonds and contacts. The SwissADME server analysis indicated that the metabolites possess fungicide properties, making them effective and safe fungicides with low toxicity to the environment and living beings. Additionally, bioactivity assays confirmed their biological activity as nuclear receptor ligands and enzyme inhibitors. Overall, this study offers valuable insights into potential natural fungicides for combating rice blast disease, with 2-Coumaroylquinic acid, Myricetin, Rosmarinic Acid, and Quercetin standing out as promising and environmentally friendly alternatives to conventional fungicides. These findings have significant implications for developing crop protection strategies and enhancing global food security, particularly in rice-dependent regions.

Novel Inhibitors to MmpL3 Transporter of Mycobacterium tuberculosis by Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulations.

Tuberculosis (TB)-causing bacterium Mycobacterium tuberculosis (Mtb) utilizes mycolic acids for building the mycobacterial cell wall, which is critical in providing defense against external factors and resisting antibiotic action. MmpL3 is a secondary resistance nodulation division transporter that facilitates the coupled transport of mycolic acid precursor into the periplasm using the proton motive force, thus making it an attractive drug target for TB infection. In 2019, X-ray crystal structures of MmpL3 from M. smegmatis were solved with a promising inhibitor SQ109, which showed promise against drug-resistant TB in Phase II clinical trials. Still, there is a pressing need to discover more effective MmpL3 inhibitors to counteract rising antibiotic resistance. In this study, structure-based high-throughput virtual screening combined with molecular dynamics (MD) simulations identified potential novel MmpL3 inhibitors. Approximately 17 million compounds from the ZINC15 database were screened against the SQ109 binding site on the MmpL3 protein using drug property filters and glide XP docking scores. From this, the top nine compounds and the MmpL3-SQ109 crystal complex structure each underwent 2 × 200 ns MD simulations to probe the inhibitor binding energetics to MmpL3. Four of the nine compounds exhibited stable binding properties and favorable drug properties, suggesting these four compounds could be potential novel inhibitors of MmpL3 for M. tuberculosis.

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning.

Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.

Retracted: High-Throughput Screening and Molecular Dynamics Simulation of Natural Products for the Identification of Anticancer Agents against MCM7 Protein.

[This retracts the article DOI: 10.1155/2022/8308192.].

Exploration of comprehensive marine natural products database against dengue viral non-structural protein 1 using high-throughput computational studies

Dengue virus (DENV) non-structural protein 1 (NS1) is a versatile quasi-protein essential for the multiplication of the virus. This study applied high-throughput virtual screening (HTVS) and molecular dynamics (MD) simulation to detect the potential marine natural compounds against the NS1 of DENV. The structure of the NS1 protein was retrieved from Protein Data Bank with (PDB ID: 4O6B). Missing residues were added using modeler software. Molecular operating environment (MOE) programme was used to prepare the protein before docking. Virtual screening was performed on PyRx software to identify natural compounds retrieved from Comprehensive Marine Natural Products Database (CMNPD) against the NS1 protein, and best-docked compounds were examined by molecular docking and molecular dynamic (MD) simulation. Out of 31,561 marine compounds, the top 10 compounds showed docking scores lesser than −8.0 kcal/mol. One of the best hit compounds, CMNPD6802, was further analyzed using MD simulation study at 100 nanoseconds and Molecular Mechanics with Generalized Born and Surface Area Solvation (MM/GBSA). Based on its total binding energy, determined using the MM/GBSA approach, CMNPD6802 was ranked first. Its pharmacokinetic properties concerning the target protein NS1 were also evaluated. The results of the MD simulation showed that CMNPD6802 remained in close contact with the protein throughout the activation period, mapped using principal component analysis. These findings suggest that CMNPD6802 could serve as an NS1 inhibitor and may be a potential candidate for treating DENV infections. Communicated by Ramaswamy H. Sarma

Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility.

Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations.

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

Higher-order elasticity theories are able to model the mechanics at nanoscale. However, these theories have length-scale parameters that need to be evaluated either through experiments or molecular dynamics (MD) simulations. In the current literature, these length-scale parameters are assumed to be constant for a chosen material. However, this is not true, as shown in this paper through a set of high-throughput MD simulations on a variety of boundary value problems (BVP). For carbon and boron nitride nanotubes, the length-scale parameter in the modified strain gradient theory is found to vary with the absolute dimensions, type of the BVP, and the deformation level. This complex dependence makes prediction of the length-scale parameter l0 challenging. To address this issue, a supervised machine learning (ML) based framework is developed here. In this new framework, MD, continuum formulation, and ML are used in tandem to predict the length-scale parameter for a given material, dimension, and boundary condition. It is a three-step process. First, a number of MD simulations are performed for varying chirality, length, and boundary conditions. Then, the values of l0 are found using these MD output in the continuum formulation. Finally, an ML-based regression model is used to capture the variability of l0 and subsequently predict its value for any chirality, length, and boundary condition. Here three regression models – Gaussian process regression, support vector machine, and neural network – are used to make the predictions.This predictive tool eliminates need for expensive MD simulations for further continuum scale analysis. In a broader context, this novel three-step framework opens an accurate yet inexpensive door for applying non-classical continuum theories to nanoscale mechanics problems. The accuracy is imposed by the MD simulations, while the computational cost reduction is achieved by machine learning.

High-throughput virtual screening and molecular dynamics simulation reveals NPC170742 a novel chalconoid compound as a potential inhibitor of D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase in Helicobacter pylori.

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.

Multiplex Cas9-based excision of CLCuV betasatellite and DNA-A revealed reduction of viral load with asymptomatic cotton plants.

Multiplexed Cas9-based genome editing of cotton resulted in reduction of viral load with asymptomatic cotton plants. In depth imaging of proteomic dynamics of resulting CLCuV betasatellite and DNA-A protein was also performed. The notorious cotton leaf curl virus (CLCuV), which is transmitted by the sap-sucking insect whitefly, continuously damages cotton crops. Although the application of various toxins and RNAi has shown some promise, sustained control has not been achieved. Consequently, CRISPR_Cas9 was applied by designing multiplex targets against DNA-A (AC2 and AC3) and betasatellite (βC1) of CLCuV using CRISPR direct and ligating into the destination vector of the plant using gateway ligation method. The successful ligation of targets into the destination vector was confirmed by the amplification of 1049 bp using a primer created from the promoter and target, while restriction digestion using the AflII and Asc1 enzymes determined how compact the plasmid developed and the nucleotide specificity of the plasmid was achieved through Sanger sequencing. PCR confirmed the successful introduction of plasmid into CKC-1 cotton variety. Through Sanger sequencing and correlation with the mRNA expression of DNA-A and betasatellite in genome-edited cotton plants subjected to agroinfiltration of CLCuV infectious clone, the effectiveness of knockout was established. The genome-edited cotton plants demonstrated edited efficacy of 72% for AC2 and AC3 and 90% for the (βC1) through amplicon sequencing, Molecular dynamics (MD) simulations were used to further validate the results. Higher RMSD values for the edited βC1 and AC3 proteins indicated functional loss caused by denaturation. Thus, CRISPR_Cas9 constructs can be rationally designed using high-throughput MD simulation technique. The confidence in using this technology to control plant virus and its vector was determined by the knockout efficiency and the virus inoculation assay.

In-silico Screening of Novel Diterpene Derivatives for their Inhibitory Potentials against MurA Enzyme of Uropathogenic Escherichia coli

background: Enzyme UDP-NAG enolpyruvyl transferase (MurA), which aids in the formation of cell walls and is identified as the primary target of the antibiotic Fosfomycin, is a significant pharmacological target in the case of uropathogenic Escherichia coli (UPEC). Finding a potent drug with a distinct mode of action is required since UPEC treatment is challenging due to resistance and resurrection. methods: Here we have used virtual high-throughput screening and molecular dynamics simulation approach to explore the effectiveness of selected novel Diterpene derivatives against the therapeutic target MurA (both wild type and Cys115Asp mutant, which is responsible for lower effectiveness of Fosfomycin). results: The investigation showed improved binding efficacy of the ligands around the primary active site residue Cys115 as well as enhanced activity against Cys115Asp mutant. Against wildtype receptors, the docking score of best-docked diterpene derivative, DIT1 (-26.18) was found to be much better than that of the known drug, Fosfomycin (-23.72). There were 7 nos of hydrogen bonds formed by DIT1 and 5 nos by Fosfomycin, indicating a stronger interaction of DIT1 with MurA. The mutant form also showed similar findings, suggesting that DIT1 is much more effective than Fosfomycin. conclusion: The above result justifies the potential of Diterpene derivatives in blocking the MurA active site that can prevent peptidoglycan biosynthesis. Effectiveness of DIT1 against wild type as well as Cys115Asp mutant justifies that the selected ligands can be used as suitable drug candidates against MurA to add new molecules in the treatment pipeline. This establishes innovative frameworks for the creation of strong drugs that target MurA after suitable in-vitro and in-vivo trials.

Expanding the applicability of multiphoton fluorescence recovery after photobleaching by incorporating shear stress in laminar flow.

Multi-photon fluorescence recovery after photobleaching (MPFRAP) is a nonlinear microscopy technique used to measure the diffusion coefficient of fluorescently tagged molecules in solution. Previous MPFRAP fitting models calculate the diffusion coefficient in systems with diffusion or diffusion in laminar flow. We propose an MPFRAP fitting model that accounts for shear stress in laminar flow, making it a more applicable technique for in vitro and in vivo studies involving diffusion. Fluorescence recovery curves are generated using high-throughput molecular dynamics simulations and then fit to all three models (diffusion, diffusion and flow, and diffusion and shear flow) to define the limits within which accurate diffusion coefficients are produced. Diffusion is simulated as a random walk with a variable horizontal bias to account for shear flow. Contour maps of the accuracy of the fitted diffusion coefficient as a function of scaled velocity and scaled shear rate show the parameter space within which each model produces accurate diffusion coefficients; the shear-flow model covers a larger area than the previous models. The shear-flow model allows MPFRAP to be a viable optical tool for studying more biophysical systems than previous models.

CHARMM-GUI-Based Induced Fit Docking Workflow to Generate Reliable Protein-Ligand Binding Modes.

Molecular docking is a preferred method to predict ligand binding modes and their binding energy to target protein receptors, which is critical in early phase structure-based drug discovery. However, there is a persistent challenge in docking that can be attributed to the induced fit effect, as receptor binding sites undergo induced fit conformational changes upon ligand binding to achieve better binding modes. In this work, based on CHARMM-GUI LBS Finder& Refiner and High-Throughput Simulator, we present a straightforward CHARMM-GUI induced fit docking (CGUI-IFD) workflow to generate reliable protein-ligand binding modes. The CGUI-IFD workflow generates an ensemble of receptor binding site conformations through ligand-binding site (LBS) refinement, runs rigid receptor docking, and performs high-throughput molecular dynamics (MD) simulations of protein-ligand complex structures in explicit solvents. The results are evaluated based on the ligand root-mean-square deviation (RMSD)-based binding stability and the molecular mechanics generalized Born surface area binding energy. For a benchmark test, we used 258 cross-docking protein-ligand pairs across 41 target proteins from the Schrodinger IFD-MD data set. The application of CGUI-IFD on this data set shows 80% success rate (within 2.5 Å RMSD from the experimental structures). We expect that the CGUI-IFD workflow can be useful to generate reliable ligand binding modes for cross-docking cases.