Identification of potential drugs targeting L,L‐diaminopimelate aminotransferase of <i>Chlamydia trachomatis</i>: An integrative pharmacoinformatics approach

Sadhasivam, Anupriya; Vetrivel, Umashankar

doi:10.1002/jcb.27553

Cited by 16 publications

(6 citation statements)

References 75 publications

(145 reference statements)

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“…The produced trajectories of MD simulations were then used to generate the root mean square deviation (RMSD) and hydrogen bond graphs by various inbuilt scripts of GROMACS. MD simulations were used in several integrative studies to tackle various conditions such as cancers , identification of novel inhibitors Sadhasivam & Vetrivel, 2019;Sharma et al, 2020;Singh et al, 2019), role of various mutations (Nagarajan et al, 2020;Rajendran et al, 2018), and exploration of drug resistance mechanisms (Rajendran & Sethumadhavan, 2014).…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors

Bhardwaj

Singh

Sharma

et al. 2020

Journal of Biomolecular Structure and Dynamics

281

243

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The SARS-CoV-2 is the causative agent of COVID-19 pandemic that is causing a global health emergency. The lack of targeted therapeutics and limited treatment options have triggered the scientific community to develop new vaccines or small molecule therapeutics against various targets of SARS-CoV-2. The main protease (Mpro) is a well characterized and attractive drug target because of its crucial role in processing of the polyproteins which are required for viral replication. In order to provide potential lead molecules against the Mpro for clinical use, we docked a set of 65 bioactive molecules of Tea plant followed by exploration of the vast conformational space of protein-ligand complexes by long term molecular dynamics (MD) simulations (1.50 ms). Top three bioactive molecules (Oolonghomobisflavan-A, Theasinensin-D, and Theaflavin-3-O-gallate) were selected by comparing their docking scores with repurposed drugs (Atazanavir, Darunavir, and Lopinavir) against SARS-CoV-2. Oolonghomobisflavan-A molecule showed a good number of hydrogen bonds with Mpro and higher MM-PBSA binding energy when compared to all three repurposed drug molecules. during the time of simulation. This study showed Oolonghomobisflavan-A as a potential bioactive molecule to act as an inhibitor for the Mpro of SARS-CoV-2.

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Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors

Bhardwaj

Singh

Sharma

et al. 2020

Journal of Biomolecular Structure and Dynamics

281

243

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“…Computed aided drug design has become an indispensable part of Drug Discovery right from targeting microbes 9, 10 to disease conditions in humans. This poses a need for efficient open source based pipelines for efficient analysis.…”

Section: Resultsmentioning

confidence: 99%

PocketPipe: A computational pipeline for integrated Pocketome prediction and comparison

Ansar¹,

Sadhasivam²,

Vetrivel³

2019

Bioinformation

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Functional characterisation of proteins often depends on specific interactions with other molecules. In the drug discovery scenario, the ability of a protein to bind with drug-like molecule with a high affinity is referred as druggability. Deciphering such druggable binding pockets on proteins plays an important role in structure-based drug designing studies. Moreover, availability of plethora of structural data poses a need automated pipelines which can efficiently integrate robust algorithms towards large-scale pocket identification and comparison. These pipelines have direct applicability on off-target analysis, drug repurposing and structural prioritization of drug targets in pathogenic microbes. However, currently there is a paucity of such efficient pipelines. Hence, by this study a highly optimized shell script based pipeline (PocketPipe) has been developed with seamless integration of robust algorithms namely, P2Rank (predicts binding sites based on machine learning) and PocketMatch-v2.1 (compares binding pockets by residue-based method), for pocketome generation and comparison, respectively. The process of input workflow and various steps carried out by PocketPipe and the output results are well documented in the operating manual. On execution, the pipeline features seamless operability, high scalability, dynamic file handling and results parsing.

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“…The MD simulations are employed in various research areas such as identification and optimization of new inhibitors, 39 cancer research, exploring mechanisms of drug resistance, 46 and protein–protein interactions 27 . MD simulations were performed for 100 ns using the OPLS‐3e force field.…”

Section: Methodsmentioning

confidence: 99%

Identification of polypharmacological anticancerous molecules against Aurora kinase family of proteins

Gupta

Kumar

Singh

et al. 2022

J of Cellular Biochemistry

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The Human Aurora Kinase (AURK) protein family is the key player of cell cycle events including spindle assembly, kinetochore formation, chromosomal segregation, centrosome separation, microtubule dynamics, and cytokinesis. Their aberrant expression has been extensively linked with chromosomal instability in addition to derangement of multiple tumor suppressors and oncoprotein regulated pathways. Therefore, the AURK family of kinases is a promising target for the treatment of various types of cancer. Over the past few decades, several potential inhibitors of AURK proteins have been identified and have reached various phases of clinical trials. But very few molecules have currently crossed the safety criteria due to their various toxic side effects. In the present study, we have adopted a computational polypharmacological strategy and identified four novel molecules that can target all three AURKs. These molecules were further investigated for their binding stabilities at the ATP binding pocket using molecular dynamics based simulation studies. The molecules selected adopting a multipronged computational approach can be considered as potential AURKs inhibitors for cancer therapeutics.

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Identification of potential drugs targeting L,L‐diaminopimelate aminotransferase of Chlamydia trachomatis: An integrative pharmacoinformatics approach

Cited by 16 publications

References 75 publications

Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors

Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors

PocketPipe: A computational pipeline for integrated Pocketome prediction and comparison

Identification of polypharmacological anticancerous molecules against Aurora kinase family of proteins

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