Relevant bibliographies by topics / Molecuar dynamics and docking simulation

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Academic literature on the topic 'Molecuar dynamics and docking simulation'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Molecuar dynamics and docking simulation"

1

Naqvi, Ahmad Abu Turab, Taj Mohammad, Gulam Mustafa Hasan, and Md Imtaiyaz Hassan. "Advancements in Docking and Molecular Dynamics Simulations Towards Ligand-receptor Interactions and Structure-function Relationships." Current Topics in Medicinal Chemistry 18, no. 20 (2018): 1755–68. http://dx.doi.org/10.2174/1568026618666181025114157.

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Protein-ligand interaction is an imperative subject in structure-based drug design and protein function prediction process. Molecular docking is a computational method which predicts the binding of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations are widely used in combination to predict the binding modes, binding affinities and stability of different protein-ligand systems. With advancements in algorithms and computational power, molecular dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic level. These methods in association with experimental support have been of great value in modern drug discovery and development. Nowadays, it has become an increasingly significant method in drug discovery process. In this review, we focus on protein-ligand interactions using molecular docking, virtual screening and molecular dynamics simulations. Here, we cover an overview of the available methods for molecular docking and molecular dynamics simulations, and their advancement and applications in the area of modern drug discovery. The available docking software and their advancement including application examples of different approaches for drug discovery are also discussed. We have also introduced the physicochemical foundations of molecular docking and simulations, mainly from the perception of bio-molecular interactions.
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李, 博. "Progress in Molecular Docking and Molecular Dynamics Simulation." Journal of Comparative Chemistry 03, no. 01 (2019): 1–10. http://dx.doi.org/10.12677/cc.2019.31001.

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Miyagawa, Hiroh, and Kunihiro Kitamura. "1P565 Molecular dynamics simulations of association and docking between an inhibitor and an enzyme.(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S288. http://dx.doi.org/10.2142/biophys.46.s288_1.

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4

Meng, Fancui. "Molecular Dynamics Simulation of VEGFR2 with Sorafenib and Other Urea-Substituted Aryloxy Compounds." Journal of Theoretical Chemistry 2013 (December 4, 2013): 1–7. http://dx.doi.org/10.1155/2013/739574.

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The binding mode of sorafenib with VEGFR2 was studied using molecular docking and molecular dynamics method. The docking results show that sorafenib forms hydrogen bonds with Asp1046, Cys919, and Glu885 of VEGFR2 receptor. Molecular dynamics simulation suggests that the hydrogen bond involving Asp1046 is the most stable one, and it is almost preserved during all the MD simulation time. The hydrogen bond formed with Cys919 occurs frequently after 6 ns, while the bifurcated hydrogen bonds involving Glu885 occurs occasionally. Meantime, molecular dynamics simulations of VEGFR2 with 11 other urea-substituted aryloxy compounds have also been performed, and the results indicate that a potent VEGFR2 inhibitor should have lower interaction energy with VEGFR2 and create at least 2 hydrogen bonds with VEGFR2.
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Bathelt, Christine, Rolf Schmid, and Jürgen Pleiss. "Regioselectivity of CYP2B6: homology modeling, molecular dynamics simulation, docking." Journal of Molecular Modeling 8, no. 11 (2002): 327–35. http://dx.doi.org/10.1007/s00894-002-0104-y.

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Kurniawan, Isman, Muhammad Salman Fareza, and Ponco Iswanto. "CoMFA, Molecular Docking and Molecular Dynamics Studies on Cycloguanil Analogues as Potent Antimalarial Agents." Indonesian Journal of Chemistry 21, no. 1 (2020): 66. http://dx.doi.org/10.22146/ijc.52388.

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Malaria is a disease that commonly infects humans in many tropical areas. This disease becomes a serious problem because of the high resistance of Plasmodium parasite against the well-established antimalarial agents, such as Artemisinin. Hence, new potent compounds are urgently needed to resolve this resistance problem. In the present study, we investigated cycloguanil analogues as a potent antimalarial agent by utilizing several studies, i.e., comparative of molecular field analysis (CoMFA), molecular docking and molecular dynamics (MD) simulation. A CoMFA model with five partial least square regressions (PLSR) was developed to predict the pIC50 value of the compound by utilizing a data set of 42 cycloguanil analogues. From statistical analysis, we obtained the r2 values of the training and test sets that were 0.85 and 0.70, respectively, while q2 of the leave-one-out cross-validation was 0.77. The contour maps of the CoMFA model were also interpreted to analyze the structural requirement regarding electrostatic and steric factors. The most active compound (c33) and least active compound (c8) were picked for molecular docking and MD analysis. From the docking analysis, we found that the attached substituent on the backbone structure of cycloguanil gives a significant contribution to antimalarial activity. The results of the MD simulation confirm the stability of the binding pose obtained from the docking simulations.
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Khare, Noopur, Sanjiv Kumar Maheshwari, Syed Mohd Danish Rizvi, et al. "Homology Modelling, Molecular Docking and Molecular Dynamics Simulation Studies of CALMH1 against Secondary Metabolites of Bauhinia variegata to Treat Alzheimer’s Disease." Brain Sciences 12, no. 6 (2022): 770. http://dx.doi.org/10.3390/brainsci12060770.

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Calcium homeostasis modulator 1 (CALHM1) is a protein responsible for causing Alzheimer’s disease. In the absence of an experimentally designed protein molecule, homology modelling was performed. Through homology modelling, different CALHM1 models were generated and validated through Rampage. To carry out further in silico studies, through molecular docking and molecular dynamics simulation experiments, various flavonoids and alkaloids from Bauhinia variegata were utilised as inhibitors to target the protein (CALHM1). The sequence of CALHM1 was retrieved from UniProt and the secondary structure prediction of CALHM1 was done through CFSSP, GOR4, and SOPMA methods. The structure was identified through LOMETS, MUSTER, and MODELLER and finally, the structures were validated through Rampage. Bauhinia variegata plant was used to check the interaction of alkaloids and flavonoids against CALHM1. The protein and protein–ligand complex were also validated through molecular dynamics simulations studies. The model generated through MODELLER software with 6VAM A was used because this model predicted the best results in the Ramachandran plot. Further molecular docking was performed, quercetin was found to be the most appropriate candidate for the protein molecule with the minimum binding energy of −12.45 kcal/mol and their ADME properties were analysed through Molsoft and Molinspiration. Molecular dynamics simulations showed that CALHM1 and CALHM1–quercetin complex became stable at 2500 ps. It may be seen through the study that quercetin may act as a good inhibitor for treatment. With the help of an in silico study, it was easier to analyse the 3D structure of the protein, which may be scrutinized for the best-predicted model. Quercetin may work as a good inhibitor for treating Alzheimer’s disease, according to in silico research using molecular docking and molecular dynamics simulations, and future in vitro and in vivo analysis may confirm its effectiveness.
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Zaki, Magdi E. A., Sami A. Al-Hussain, Vijay H. Masand, et al. "Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis." Pharmaceuticals 14, no. 4 (2021): 357. http://dx.doi.org/10.3390/ph14040357.

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Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.
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De Paris, Renata, Christian V. Quevedo, Duncan D. Ruiz, Osmar Norberto de Souza, and Rodrigo C. Barros. "Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/916240.

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Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for thek-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
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Luo, Lianxiang, Ai Zhong, Qu Wang, and Tongyu Zheng. "Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, ADMET, and Molecular Dynamics (MD) Simulation of Potential Inhibitors of PD-L1 from the Library of Marine Natural Products." Marine Drugs 20, no. 1 (2021): 29. http://dx.doi.org/10.3390/md20010029.

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Background: In the past decade, several antibodies directed against the PD-1/PD-L1 interaction have been approved. However, therapeutic antibodies also exhibit some shortcomings. Using small molecules to regulate the PD-1/PD-L1 pathway may be another way to mobilize the immune system to fight cancer. Method: 52,765 marine natural products were screened against PD-L1(PDBID: 6R3K). To identify natural compounds, a structure-based pharmacophore model was generated, following by virtual screening and molecular docking. Then, the absorption, distribution, metabolism, and excretion (ADME) test was carried out to select the most suitable compounds. Finally, molecular dynamics simulation was also performed to validate the binding property of the top compound. Results: Initially, 12 small marine molecules were screened based on the pharmacophore model. Then, two compounds were selected for further evaluation based on the molecular docking scores. After ADME and toxicity studies, molecule 51320 was selected for further verification. By molecular dynamics analysis, molecule 51320 maintains a stable conformation with the target protein, so it has the chance to become an inhibitor of PD-L1. Conclusions: Through structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET approaches, and molecular dynamics (MD) simulation, the marine natural compound 51320 can be used as a small molecule inhibitor of PD-L1.
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