User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation

Shaker, Bilal; Yu, Myung-Sang; Lee, Jingyu; Lee, Yongmin; Jung, Chanjin; Na, Dokyun

doi:10.1007/s12275-020-9563-z

Cited by 32 publications

(18 citation statements)

References 54 publications

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“…The docking positions between the selected compounds and ACE2 proteins were predicted by PyRx software [54]. PyRx is an open-source software utilize Auto Dock 4 (AD4) and Auto Dock Vina (ADV) tools for molecular docking simulation [55]. In this study, the PyRx tools Autodock vina (version 1.1.2) a commonly docking program for molecular docking simulation, has been used to predict the protein-ligand interaction [54].…”

Section: Molecular Docking (Md) Simulationmentioning

confidence: 99%

“…The molecular docking approach can be used to predict the predominant binding mode(s) of a ligand with a protein at the atomic level [4]. The goal of ligand-protein docking is to perform virtual screening, rank the results according to their binding energy, understanding the protein-ligand mechanism of action and propose a structural hypothesis of how the ligands inhibit the target [55]. To understand the binding activity of our targeted ACE2 protein with the 23 ligands generated from the SBPM, the PyRx tools Autodock vina (version 1.1.2) molecular docking program has been used in this study [52], [54].…”

Section: Molecular Docking Simulationmentioning

confidence: 99%

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Spike Protein Recognizer Receptor ACE2 Targeted Identification of Potential Natural Antiviral Drug Candidates Against SARS-CoV-2

Bouback

Samad

Nur

et al. 2021

Preprint

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Angiotensin-converting enzyme 2 (ACE2), also known as peptidyl-dipeptidase A, belongs to the dipeptidyl carboxydipeptidases family has emerged as a potential antiviral drug target against SARS-CoV-2. Most of the ACE2 inhibitors discovered until now are chemical synthesis; suffer from many limitations related to stability and adverse side effects. However, natural, and selective ACE2 inhibitors that possess strong stability and low side effects can be replaced instead of those chemicals’ inhibitors. To envisage structurally diverse natural entities as an ACE2 inhibitor with better efficacy, a structure-based-pharmacophore model (SBPM) was developed and validated by 20 known selective inhibitors with their correspondence 1166 decoy compounds. The validated SBPM has excellent goodness of hit score and good predictive ability, which has been appointed as a query model for further screening of 11,295 natural compounds. The resultant 23 hits compounds with pharmacophore fit score 75.31 to 78.81 were optimized using in-silico ADMET and molecular docking analysis. Four potential natural inhibitory molecules namely D-DOPA (Amb17613565), L-Saccharopine (Amb6600091), D-Phenylalanine (Amb3940754), and L-Mimosine (Amb21855906) have been selected based onbinding affinity (−7.5, −7.1, −7.1, and −7.0 kcal/mol), respectively. Moreover, 250 ns molecular dynamics (MD) simulations confirmed the structural stability of the ligands within the protein. Additionally, MM/GBSA approach also used to support the stability of molecules to the binding site of the protein that also confirm the stability of the selected four natural compounds. The virtual screening strategy used in this study demonstrated four natural compounds that can be utilized for designing a future class of potential natural ACE2 inhibitor that will block the spike (S) protein dependent entry of SARS-CoV-2 into the host cell.

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Section: Molecular Docking (Md) Simulationmentioning

confidence: 99%

Section: Molecular Docking Simulationmentioning

confidence: 99%

Spike Protein Recognizer Receptor ACE2 Targeted Identification of Potential Natural Antiviral Drug Candidates Against SARS-CoV-2

Bouback

Samad

Nur

et al. 2021

Preprint

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“…In addition, computational models of protein-drug interactions and machinelearning models for absorption, distribution, metabolism, excretion, and toxicity (ADME-Tox) have been developed using deep learning algorithms. Shaker et al (2020) introduce a user guide for in silico drug discovery that includes protein structure prediction, active site prediction, protein-drug interaction (docking), and ADME-Tox prediction, a virtual strategy for drug discovery. This might encourage biologists, even those who are not familiar with computer science, to carry out research on drug discovery.…”

Section: Drug Discoverymentioning

confidence: 99%

User guides for biologists to learn computational methods

2020

J Microbiol.

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System-wide studies of a given molecular type are referred to as "omics." These include genomics, proteomics, and metabolomics, among others. Recent biotechnological advances allow for high-throughput measurement of cellular components, and thus it becomes possible to take a snapshot of all molecules inside cells, a form of omics study. Advances in computational modeling methods also make it possible to predict cellular mechanisms from the snapshots. These technologies have opened an era of computation-based biology. Component snapshots allow the discovery of gene-phenotype relationships in diseases, microorganisms in the human body, etc. Computational models allow us to predict new outcomes, which are useful in strain design in metabolic engineering and drug discovery from protein-ligand interactions. However, as the quantity of data increases or the model becomes complicated, the process becomes less accessible to biologists. In this special issue, six protocol articles are presented as user guides in the field of computational biology. EDITORIAL User guides for biologists to learn computational methods

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“…To date, two conventional computational approaches are utilised for drug discovery/repurposing projects which are either, ligand-based [14] or structure-based [15] , [16] , [17] . The former primarily focusses on data mining of chemical structures and associated biological activity, while the latter is concerned with the interactions of potential drugs with targets of biological interest.…”

Section: Introductionmentioning

confidence: 99%

“…The application of machine-learning on several of these approaches will also be covered, alongside the increased need to perform experimental validation on computational predictions. However, before structure-based approaches can be undertaken, the selection of a target of interest and a chemical compound library to screen is essential [16] , [17] , hence, these will be briefly covered.…”

Section: Introductionmentioning

confidence: 99%

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Kingdon

Alderwick

2021

Computational and Structural Biotechnology Journal

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User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation

Cited by 32 publications

References 54 publications

Spike Protein Recognizer Receptor ACE2 Targeted Identification of Potential Natural Antiviral Drug Candidates Against SARS-CoV-2

Spike Protein Recognizer Receptor ACE2 Targeted Identification of Potential Natural Antiviral Drug Candidates Against SARS-CoV-2

User guides for biologists to learn computational methods

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

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