Molecular Dynamics-Based Virtual Screening: Accelerating the Drug Discovery Process by High-Performance Computing

Hu, Ge; Wang, Yu; Li, Chanjuan; Chen, Nanhao; Xie, Yufang; Xu, Mengyan; He, Yanjie; Gu, Xinchun; Wu, Ruibo; Gu, Qiong; Zeng, Liang; Xu, Jing

doi:10.1021/ci400391s

Cited by 57 publications

(31 citation statements)

References 31 publications

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“…The applications of virtual screening are commonly used. Notably, given the exponential growth of high through put 34 , high-performance computing 35 , machine learning 36 and deep learning 37 techniques, the integrated method flourishes quickly. For example, Pereira et al 38 applied deep learning approach in virtual screening, which extracts relevant features from molecular docking data to create the distributed vector representations for protein-ligand complexes.…”

Section: Virtual Screening To Discover the Lead Compound And Hit Compmentioning

confidence: 99%

Molecular Docking Software’s Applications and Basic Challenges Faced: A Review

Sawhney¹,

Singh²

2020

Int Res J Pharm

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Molecular docking shows a very important role in the rational design of drugs. Method of molecular docking calculates the preferred orientation of one molecule with the second molecule when bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules. Docking is normally used to predict the binding orientation of small molecule drug candidates to their protein targets in order to predict the affinity and activity of the small molecule. Given the biological and pharmaceutical importance of molecular docking, considerable efforts have been directed towards improving the methods used to calculate docking. The docking process simulates the ligand-protein pairwise interaction and the energies are calculated. There are three types of Docking: Protein-Protein Docking, Protein-Ligand Docking and Protein-Protein and Protein-Ligand docking. Molecular docking software includes Auto dock, Flex-X, Glide, Gold, ZDOCK, RDOCK, LeDOCK, Dock, Auto dock Vina, Mdock etc. The present review accumulates the characteristics and applications of the different software used and the basic challenges faced in molecular docking studies.

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Section: Virtual Screening To Discover the Lead Compound And Hit Compmentioning

confidence: 99%

Molecular Docking Software’s Applications and Basic Challenges Faced: A Review

Sawhney¹,

Singh²

2020

Int Res J Pharm

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“…The virtual screening method as previously reported [26,27]. Briefly, standard precision of Autodock Vina was employed to screen the tetrapeptide library.…”

Section: Peptide Library and Virtual Screeningmentioning

confidence: 99%

EGAR, A Food Protein-Derived Tetrapeptide, Reduces Seizure Activity in Pentylenetetrazole-Induced Epilepsy Models Through α-Amino-3-Hydroxy-5-Methyl-4-Isoxazole Propionate Receptors

Cai

Ling

et al. 2017

Neurotherapeutics

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A primary pathogeny of epilepsy is excessive activation of α-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptors (AMPARs). To find potential molecules to inhibit AMPARs, high-throughput screening was performed in a library of tetrapeptides in silico. Computational results suggest that some tetrapeptides bind stably to the AMPAR. We aligned these sequences of tetrapeptide candidates with those from in vitro digestion of the trout skin protein. Among salmon-derived products, Glu-Gly-Ala-Arg (EGAR) showed a high biological affinity toward AMPAR when tested in silico. Accordingly, natural EGAR was hypothesized to have anticonvulsant activity, and in vitro experiments showed that EGAR selectively inhibited AMPAR-mediated synaptic transmission without affecting the electrophysiological properties of hippocampal pyramidal neurons. In addition, EGAR reduced neuronal spiking in an in vitro seizure model. Moreover, the ability of EGAR to reduce seizures was evaluated in a rodent epilepsy model. Briefer and less severe seizures versus controls were shown after mice were treated with EGAR. In conclusion, the promising experimental results suggest that EGAR inhibitor against AMPARs may be a target for antiepilepsy pharmaceuticals. Epilepsy is a common brain disorder characterized by the occurrence of recurring, unprovoked seizures. Twenty to 30 % of persons with epilepsy do not achieve adequate seizure control with any drug. Here we provide a possibility in which a natural and edible tetrapeptide, EGAR, can act as an antiepileptic agent. We have combined computation with in vitro experiments to show how EGAR modulates epilepsy. We also used an animal model of epilepsy to prove that EGAR can inhibit seizures in vivo. This study suggests EGAR as a potential pharmaceutical for the treatment of epilepsy.

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“…Compared with binding modes of ATP-competitive inhibitors based on recently solved crystal structures, these published pharmacophore models are not well consistent with the experimental results [2]. The ATP binding pocket of mTOR is flexible, which makes it difficult to screen new inhibitors based on traditional 3D methods [2], [22], [23]. The broad multi-specificity of mTOR and the lack of an extensive database of ATP-competitive mTOR inhibitors have proven to be almost insurmountable obstacles to establish accurate prediction models.…”

Section: Introductionmentioning

confidence: 96%

Predicting mTOR Inhibitors with a Classifier Using Recursive Partitioning and Naïve Bayesian Approaches

et al. 2014

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BackgroundMammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. Thus, there is a great deal of interest in developing clinical drugs based on mTOR. In this paper, in silico models based on multi-scaffolds were developed to predict mTOR inhibitors or non-inhibitors.MethodsFirst 1,264 diverse compounds were collected and categorized as mTOR inhibitors and non-inhibitors. Two methods, recursive partitioning (RP) and naïve Bayesian (NB), were used to build combinatorial classification models of mTOR inhibitors versus non-inhibitors using physicochemical descriptors, fingerprints, and atom center fragments (ACFs).ResultsA total of 253 models were constructed and the overall predictive accuracies of the best models were more than 90% for both the training set of 964 and the external test set of 300 diverse compounds. The scaffold hopping abilities of the best models were successfully evaluated through predicting 37 new recently published mTOR inhibitors. Compared with the best RP and Bayesian models, the classifier based on ACFs and Bayesian shows comparable or slightly better in performance and scaffold hopping abilities. A web server was developed based on the ACFs and Bayesian method (http://rcdd.sysu.edu.cn/mtor/). This web server can be used to predict whether a compound is an mTOR inhibitor or non-inhibitor online.Conclusion In silico models were constructed to predict mTOR inhibitors using recursive partitioning and naïve Bayesian methods, and a web server (mTOR Predictor) was also developed based on the best model results. Compound prediction or virtual screening can be carried out through our web server. Moreover, the favorable and unfavorable fragments for mTOR inhibitors obtained from Bayesian classifiers will be helpful for lead optimization or the design of new mTOR inhibitors.

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Molecular Dynamics-Based Virtual Screening: Accelerating the Drug Discovery Process by High-Performance Computing

Cited by 57 publications

References 31 publications

Molecular Docking Software’s Applications and Basic Challenges Faced: A Review

Molecular Docking Software’s Applications and Basic Challenges Faced: A Review

EGAR, A Food Protein-Derived Tetrapeptide, Reduces Seizure Activity in Pentylenetetrazole-Induced Epilepsy Models Through α-Amino-3-Hydroxy-5-Methyl-4-Isoxazole Propionate Receptors

Predicting mTOR Inhibitors with a Classifier Using Recursive Partitioning and Naïve Bayesian Approaches

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