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Molecules (Basel, Switzerland) May 2023Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein... (Review)
Review
Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.
Topics: Protein Conformation; Intrinsically Disordered Proteins; Molecular Dynamics Simulation; Machine Learning
PubMed: 37241789
DOI: 10.3390/molecules28104047 -
Computers in Biology and Medicine Nov 2022Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can...
Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.
Topics: Humans; SARS-CoV-2; Molecular Dynamics Simulation; COVID-19; Coronavirus 3C Proteases; Molecular Docking Simulation; Protease Inhibitors
PubMed: 36240593
DOI: 10.1016/j.compbiomed.2022.106125 -
Molecules (Basel, Switzerland) Mar 2024The SARS-CoV-2 virus and its mutations have affected human health globally and created significant danger for the health of people all around the world. To cure this...
The SARS-CoV-2 virus and its mutations have affected human health globally and created significant danger for the health of people all around the world. To cure this virus, the human Angiotensin Converting Enzyme-2 (ACE2) receptor, the SARS-CoV-2 main protease (Mpro), and spike proteins were found to be likely candidates for the synthesis of novel therapeutic drug. In the past, proteins were capable of engaging in interaction with a wide variety of ligands, including both manmade and plant-derived small molecules. L., , , , and were some of the plant species that were studied for their tendency to interact with SARS-CoV-2 main protease (Mpro) in this research project (6LU7). This scenario investigates the geometry, electronic, and thermodynamic properties computationally. Assessing the intermolecular forces of phytochemicals with the targets of the SARS-CoV-2 Mpro spike protein (SP) resulted in the recognition of a compound, kaempferol, as the most potent binding ligand, -7.7 kcal mol. Kaempferol interacted with ASP-187, CYS-145, SER-144, LEU 141, MET-165, and GLU-166 residues. Through additional molecular dynamic simulations, the stability of ligand-protein interactions was assessed for 100 ns. GLU-166 remained intact with 33% contact strength with phenolic OH group. We noted a change in torsional conformation, and the molecular dynamics simulation showed a potential variation in the range from 3.36 to 7.44 against a 45-50-degree angle rotation. SAR, pharmacokinetics, and drug-likeness characteristic investigations showed that kaempferol may be the suitable candidate to serve as a model for designing and developing new anti-COVID-19 medicines.
Topics: Humans; Animals; Cricetinae; Molecular Docking Simulation; COVID-19; Kaempferols; Ligands; Molecular Dynamics Simulation; SARS-CoV-2; Spike Glycoprotein, Coronavirus; Mesocricetus; Protease Inhibitors; Coronavirus 3C Proteases
PubMed: 38474656
DOI: 10.3390/molecules29051144 -
Journal of Molecular Modeling Aug 2022Main protease (M) plays a key role in replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study was designed for finding natural inhibitors...
Main protease (M) plays a key role in replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study was designed for finding natural inhibitors of SARS-CoV-2 M by in silico methods. To this end, the co-crystal structure of M with telaprevir was explored and receptor-ligand pharmacophore models were developed and validated using pharmit. The database of "ZINC Natural Products" was screened, and 288 compounds were filtered according to pharmacophore features. In the next step, Lipinski's rule of five was applied and absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the filtered compounds were calculated using in silico methods. The resulted 15 compounds were docked into the active site of M and those with the highest binding scores and better interaction including ZINC61991204, ZINC67910260, ZINC61991203, and ZINC08790293 were selected. Further analysis by molecular dynamic simulation studies showed that ZINC61991203 and ZINC08790293 dissociated from M active site, while ZINC426421106 and ZINC5481346 were stable. Root mean square deviation (RMSD), radius of gyration (Rg), number of hydrogen bonds between ligand and protein during the time of simulation, and root mean square fluctuations (RMSF) of protein and ligands were calculated, and components of binding free energy were calculated using the molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) method. The result of all the analysis indicated that ZINC61991204 and ZINC67910260 are drug-like and nontoxic and have a high potential for inhibiting M.
Topics: COVID-19; Coronavirus 3C Proteases; Humans; Ligands; Molecular Docking Simulation; Molecular Dynamics Simulation; Protease Inhibitors; SARS-CoV-2
PubMed: 36031629
DOI: 10.1007/s00894-022-05286-6 -
International Journal of Environmental... Mar 2022Physicochemical properties of poly-L-arginine (P-Arg) molecules in NaCl solutions were determined by molecular dynamics (MD) modeling and various experimental...
Physicochemical properties of poly-L-arginine (P-Arg) molecules in NaCl solutions were determined by molecular dynamics (MD) modeling and various experimental techniques. Primarily, the molecule conformations, the monomer length and the chain diameter were theoretically calculated. These results were used to interpret experimental data, which comprised the molecule secondary structure, the diffusion coefficient, the hydrodynamic diameter and the electrophoretic mobility determined at various ionic strengths and pHs. Using these data, the electrokinetic charge and the effective ionization degree of P-Arg molecules were determined. In addition, the dynamic viscosity measurements for dilute P-Arg solutions enabledto determine the molecule intrinsic viscosity, which was equal to 500 and 90 for ionic strength of 10 and 0.15 M, respectively. This confirmed that P-Arg molecules assumed extended conformations and approached the slender body limit at the low range of ionic strength. The experimental data were also used to determine the molecule length and the chain diameter, which agreed with theoretical predictions. Exploiting these results, a robust method for determining the molar mass of P-Arg samples, the hydrodynamic diameter, the radius of gyration and the sedimentation coefficient was proposed.
Topics: Arginine; Electrolytes; Hydrodynamics; Molecular Dynamics Simulation; Viscosity
PubMed: 35329277
DOI: 10.3390/ijerph19063588 -
Scientific Reports Nov 2022Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate,...
Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q ) and external (Q ) validation-were found i.e., MLR1 (Q = 0.893, Q = 0.897), RF1 (Q = 0.889, Q = 0.907), and IBK1 (Q = 0.891, Q = 0.907). An ensemble model was built by averaging the predicted pIC of the three models, obtaining a Q = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.
Topics: Molecular Docking Simulation; Molecular Dynamics Simulation; Quantitative Structure-Activity Relationship; Alcohol Oxidoreductases
PubMed: 36402831
DOI: 10.1038/s41598-022-24196-4 -
Computers in Biology and Medicine Jun 2022The ongoing COVID-19 pandemic has affected millions of people worldwide and caused substantial socio-economic losses. Few successful vaccine candidates have been...
Phytochemicals-based targeting RdRp and main protease of SARS-CoV-2 using docking and steered molecular dynamic simulation: A promising therapeutic approach for Tackling COVID-19.
The ongoing COVID-19 pandemic has affected millions of people worldwide and caused substantial socio-economic losses. Few successful vaccine candidates have been approved against SARS-CoV-2; however, their therapeutic efficacy against the mutated strains of the virus remains questionable. Furthermore, the limited supply of vaccines and promising antiviral drugs have created havoc in the present scenario. Plant-based phytochemicals (bioactive molecules) are promising because of their low side effects and high therapeutic value. In this study, we aimed to screen for suitable phytochemicals with higher therapeutic value using the two most crucial proteins of SARS-CoV-2, the RNA-dependent RNA polymerase (RdRp) and main protease (Mpro). We used computational tools such as molecular docking and steered molecular dynamics simulations to gain insights into the different types of interactions and estimated the relative binding forces between the phytochemicals and their respective targets. To the best of our knowledge, this is the first report that not only involves a search for a therapeutic bioactive molecule but also sheds light on the mechanisms underlying target inhibition in terms of calculations of force and work needed to extractthe ligand from the pocket of its target. The complexes showing higher binding forces were subjected to 200 ns molecular dynamic simulations to check the stability of the ligand inside the binding pocket. Our results suggested that isoskimmiwallin and terflavin A are potential inhibitors of RdRp, whereas isoquercitrin and isoorientin are the lead molecules against Mpro. Collectively, our findings could potentially aid in the development of novel therapeutics against COVID-19.
Topics: Humans; Ligands; Molecular Docking Simulation; Molecular Dynamics Simulation; Pandemics; Peptide Hydrolases; Phytochemicals; Protease Inhibitors; RNA-Dependent RNA Polymerase; SARS-CoV-2; COVID-19 Drug Treatment
PubMed: 35390745
DOI: 10.1016/j.compbiomed.2022.105468 -
Brazilian Journal of Biology = Revista... 2021In the current report, we studied the possible inhibitors of COVID-19 from bioactive constituents of Centaurea jacea using a threefold approach consisting of quantum...
A threefold approach including quantum chemical, molecular docking and molecular dynamic studies to explore the natural compounds from Centaurea jacea as the potential inhibitors for COVID-19.
In the current report, we studied the possible inhibitors of COVID-19 from bioactive constituents of Centaurea jacea using a threefold approach consisting of quantum chemical, molecular docking and molecular dynamic techniques. Centaurea jacea is a perennial herb often used in folk medicines of dermatological complaints and fever. Moreover, anticancer, antioxidant, antibacterial and antiviral properties of its bioactive compounds are also reported. The Mpro (Main proteases) was docked with different compounds of Centaurea jacea through molecular docking. All the studied compounds including apigenin, axillarin, Centaureidin, Cirsiliol, Eupatorin and Isokaempferide, show suitable binding affinities to the binding site of SARS-CoV-2 main protease with their binding energies -6.7 kcal/mol, -7.4 kcal/mol, -7.0 kcal/mol, -5.8 kcal/mol, -6.2 kcal/mol and -6.8 kcal/mol, respectively. Among all studied compounds, axillarin was found to have maximum inhibitor efficiency followed by Centaureidin, Isokaempferide, Apigenin, Eupatorin and Cirsiliol. Our results suggested that axillarin binds with the most crucial catalytic residues CYS145 and HIS41 of the Mpro, moreover axillarin shows 5 hydrogen bond interactions and 5 hydrophobic interactions with various residues of Mpro. Furthermore, the molecular dynamic calculations over 60 ns (6×106 femtosecond) time scale also shown significant insights into the binding effects of axillarin with Mpro of SARS-CoV-2 by imitating protein like aqueous environment. From molecular dynamic calculations, the RMSD and RMSF computations indicate the stability and dynamics of the best docked complex in aqueous environment. The ADME properties and toxicity prediction analysis of axillarin also recommended it as safe drug candidate. Further, in vivo and in vitro investigations are essential to ensure the anti SARS-CoV-2 activity of all bioactive compounds particularly axillarin to encourage preventive use of Centaurea jacea against COVID-19 infections.
Topics: COVID-19; Centaurea; Humans; Molecular Docking Simulation; Molecular Dynamics Simulation; Pharmaceutical Preparations; Protease Inhibitors; SARS-CoV-2
PubMed: 34495156
DOI: 10.1590/1519-6984.247604 -
BioMed Research International 2022In this study, we investigated the potential material basis of Yupingfeng powder in the prevention and treatment of 2019 novel coronavirus pneumonia (NCP) by applying...
Potential Material Basis of Yupingfeng Powder for the Prevention and Treatment of 2019 Novel Coronavirus Pneumonia: A Study Involving Molecular Docking and Molecular Dynamic Simulation Technology.
OBJECTIVE
In this study, we investigated the potential material basis of Yupingfeng powder in the prevention and treatment of 2019 novel coronavirus pneumonia (NCP) by applying molecular docking and molecular dynamic simulation technology.
DESIGN
The active ingredients and predictive targets of Yupingfeng powder were sourced using the TCMSP, ETCM, and TCMIP traditional Chinese medicine databases. NCP-related targets were then acquired from the DisGeNET and GeneCards databases, and common disease-drug targets were imported into the STRING database, and Cytoscape software was used to generate a protein-protein interaction network following the use of a network topology algorithm to identify key target genes. Gene Ontology (GO) and KEGG pathway enrichment analysis was then performed using the target genes and GOEAST and DAVID online tools. The mechanism of Yupingfeng powder in the prevention and treatment of NCP was analyzed with reference to the relevant literature. AutoDock software was used for molecular docking, the preliminary analysis of binding status, and to identify the best conformation. Desmond software was used to perform molecular dynamic simulations for protein and compound complexes, perform free energy calculations and hydrogen bond analysis, and to further verify the binding mode.
RESULTS
Overall, 38 main active components and 218 predictive targets of Yupingfeng powder were identified and 298 disease targets related to NCP were retrieved from disease databases. Yupingfeng powder was found to act predominantly on the TNF, Toll-like receptor, HIF-1, NOD-like receptor, cytokine-receptor interaction, MAPK, T cell receptor, and VEGF signaling pathways. Molecular docking of the three selected key active components with the 3CL-like protease (3CL-Pro) of SARS-CoV-2 showed that they each had a strong binding force and good affinity.
CONCLUSIONS
Yupingfeng powder primarily acts on multiple active ingredients and potential targets through multiple action channels and signal pathways. Molecular docking and molecular dynamic simulation technology were used to effectively predict and analyze the potential mechanism by which this Chinese medicine can combat NCP. These results provide a reference for developing new modern Chinese medicine preparations against NCP in the future.
Topics: COVID-19; Drugs, Chinese Herbal; Humans; Molecular Docking Simulation; Molecular Dynamics Simulation; Pneumonia; Powders; SARS-CoV-2; Technology
PubMed: 35782070
DOI: 10.1155/2022/7892397 -
PloS One 2022The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is one of the optimum targets for antiviral drug design and development. The hydroxyl groups of cytidine structures...
Potential SARS-CoV-2 RdRp inhibitors of cytidine derivatives: Molecular docking, molecular dynamic simulations, ADMET, and POM analyses for the identification of pharmacophore sites.
The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is one of the optimum targets for antiviral drug design and development. The hydroxyl groups of cytidine structures were modified with different aliphatic and aromatic groups to obtain 5´-O-acyl and 2´,3´-di-O-acyl derivatives, and then, these derivatives were employed in molecular modeling, antiviral prediction, molecular docking, molecular dynamics, pharmacological and POM studies. Density functional theory (DFT) at the B3LYP/6-31G++ level analyzed biochemical behavior and molecular electrostatic potential (MESP) of the modified cytidine derivatives. The antiviral parameters of the mutated derivatives revealed promising drug properties compared with those of standard antiviral drugs. Molecular docking has determined binding affinities and interactions between the cytidine derivatives and SARS-CoV-2 RdRp. The modified derivatives strongly interacted with prime Pro620 and Lys621 residues. The binding conformation and interactions stability were investigated by 200 ns of molecular dynamics simulations and predicted the compounds to firmly dock inside the RdRp binding pocket. Interestingly, the binding residues of the derivatives were revealed in high equilibrium showing an enhanced binding affinity for the molecules. Intermolecular interactions are dominated by both Van der Waals and electrostatic energies. Finally, the pharmacokinetic characterization of the optimized inhibitors confirmed the safety of derivatives due to their improved kinetic properties. The selected cytidine derivatives can be suggested as potential inhibitors against SARS-CoV-2. The POM Theory supports the hypothesis above by confirming the existence of an antiviral (Oδ--O'δ-) pharmacophore site of Hits.
Topics: Humans; Molecular Docking Simulation; Molecular Dynamics Simulation; SARS-CoV-2; Cytidine; Receptors, Drug; Antiviral Agents; RNA-Dependent RNA Polymerase; COVID-19 Drug Treatment
PubMed: 36441684
DOI: 10.1371/journal.pone.0273256