Protocol for Protein Structure Modelling

Jabeen, Amara; Mohamedali, Abidali; Ranganathan, Shoba

doi:10.1016/b978-0-12-809633-8.20477-9

Cited by 16 publications

(12 citation statements)

References 74 publications

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“…We then calculated query coverage between the six shortlisted templates and OR1A2. Query coverage is an important factor to be considered for homology modelling ( Jabeen et al., 2019b ). Based on query coverage assessment (Supporting information: Table S4 ), we shortlisted four templates: bovine rhodopsin (PDBID: 1U19 ) ( Crasto, 2010 ), beta-2 adrenergic receptor (PDBID: 2RH1 ) ( Wacker et al., 2017 ), adenosine receptor A2a (PDBID: 5IU4 ) ( Wolf and Grünewald, 2015 ) and muscarinic acetylcholine receptor M2 (PDBID: 5ZKC ) ( Vaidehi and Bhattacharya, 2016 ).…”

Section: Resultsmentioning

confidence: 99%

“…In the second stage, we have used structure-based virtual screening (SBVS) using a homology model of OR1A2 to select the metabolites obtained through APF screening. Our earlier homology modelling approach for GPCRs ( Jabeen et al., 2019b ) has been extended to ORs in the current study, to select the bovine rhodopsin template, rigorously identified by a biophysical approach proposed here, based on multiple parameters including sequence identity, query coverage, resolution, hydrophobicity, ligand profile, and binding site comparison. The top five ligands from SBVS were subjected to molecular dynamics (MD) simulations, with four putative ligands identified, with greater binding affinity than the control ligand.…”

Section: Introductionmentioning

confidence: 99%

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A two-stage computational approach to predict novel ligands for a chemosensory receptor

Jabeen

Vijayram

Ranganathan

2020

Current Research in Structural Biology

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Olfactory receptor (OR) 1A2 is the member of largest superfamily of G protein-coupled receptors (GPCRs). OR1A2 is an ectopically expressed receptor with only 13 known ligands, implicated in reducing hepatocellular carcinoma progression, with enormous therapeutic potential. We have developed a two-stage screening approach to identify novel putative ligands of OR1A2. We first used a pharmacophore model based on atomic property field (APF) to virtually screen a library of 5942 human metabolites. We then carried out structure-based virtual screening (SBVS) for predicting the potential agonists, based on a 3D homology model of OR1A2. This model was developed using a biophysical approach for template selection, based on multiple parameters including hydrophobicity correspondence, applied to the complete set of available GPCR structures to pick the most appropriate template. Finally, the membrane-embedded 3D model was refined by molecular dynamics (MD) simulations in both the apo and holo forms. The refined model in the apo form was selected for SBVS. Four novel small molecules were identified as strong binders to this olfactory receptor on the basis of computed binding energies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A two-stage computational approach to predict novel ligands for a chemosensory receptor

Jabeen

Vijayram

Ranganathan

2020

Current Research in Structural Biology

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“…Molecular dynamics simulations are performed for energy minimizations of the wild-type and mutant drug targets obtaining the most stable protein structures; standalone programs such as GROMACS and Amber are used for performing the task ( Singh et al, 2017 ; Zhang et al, 2019 ). In computational chemistry, energy minimizations which may also be referred to as geometry optimization entail the exploration of the conformational space for a collection of atoms to find a proper molecular arrangement in space which is energy favorable and stable; it is also referred to as the global energy minimum ( Jabeen et al, 2019 ). The resultant structures are then subjected to molecular docking, where the position of the ligand when bound to a protein receptor is predicted for the drug’s wild type and mutated targets.…”

Section: Discussionmentioning

confidence: 99%

Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

Mugumbate

Nyathi

Zindoga

et al. 2021

Front. Mol. Biosci.

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The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) impedes the End TB Strategy by the World Health Organization aiming for zero deaths, disease, and suffering at the hands of tuberculosis (TB). Mutations within anti-TB drug targets play a major role in conferring drug resistance within Mtb; hence, computational methods and tools are being used to understand the mechanisms by which they facilitate drug resistance. In this article, computational techniques such as molecular docking and molecular dynamics are applied to explore point mutations and their roles in affecting binding affinities for anti-TB drugs, often times lowering the protein’s affinity for the drug. Advances and adoption of computational techniques, chemoinformatics, and bioinformatics in molecular biosciences and resources supporting machine learning techniques are in abundance, and this has seen a spike in its use to predict mutations in Mtb. This article highlights the importance of molecular modeling in deducing how point mutations in proteins confer resistance through destabilizing binding sites of drugs and effectively inhibiting the drug action.

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“…Bio-GATS provides a complete alignment that was used to build a 3-D structural model for SBVS using Modeller 9.18 ( Webb and Sali, 2017 ) by a previously established protocol for GPCR homology modeling ( Jabeen et al, 2019b ). The sequence alignment between the target and the template can be manually adjusted using MEGA7 ( Kumar et al, 2016 ) by tethering center residues, class A GPCR conserved motifs, and cysteine residues forming a disulphide bridge.…”

Section: Methodsmentioning

confidence: 99%

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

Jabeen

Vijayram

Ranganathan

2021

Front. Mol. Biosci.

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G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

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Protocol for Protein Structure Modelling

Cited by 16 publications

References 74 publications

A two-stage computational approach to predict novel ligands for a chemosensory receptor

A two-stage computational approach to predict novel ligands for a chemosensory receptor

Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

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