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Nayarisseri A, Abdalla M, Joshi I, Yadav M, Bhrdwaj A, Chopra I, Khan A, Saxena A, Sharma K, Panicker A, Panwar U, Mendonça Junior FJB, Singh SK. Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer. Sci Rep 2024; 14:13251. [PMID: 38858458 PMCID: PMC11164920 DOI: 10.1038/s41598-024-63762-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 05/31/2024] [Indexed: 06/12/2024] Open
Abstract
Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
| | - Mohnad Abdalla
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 Cultural West Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Isha Joshi
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Manasi Yadav
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | - Ishita Chopra
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- School of Medicine and Health Sciences, The George Washington University, Ross Hall, 2300 Eye Street, Washington, D.C., NW, 20037, USA
| | - Arshiya Khan
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | - Arshiya Saxena
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Khushboo Sharma
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | - Aravind Panicker
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | | | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India.
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2
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Nayarisseri A, Bandaru S, Khan A, Sharma K, Bhrdwaj A, Kaur M, Ghosh D, Chopra I, Panicker A, Kumar A, Saravanan P, Belapurkar P, Mendonça Junior FJB, Singh SK. Epigenetic dysregulation in cancers by isocitrate dehydrogenase 2 (IDH2). ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 141:223-253. [PMID: 38960475 DOI: 10.1016/bs.apcsb.2023.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Recent advances in genome-wide studies have revealed numerous epigenetic regulations brought about by genes involved in cellular metabolism. Isocitrate dehydrogenase (IDH), an essential enzyme, that converts isocitrate into -ketoglutarate (KG) predominantly in the tricarboxylic acid (TCA) cycle, has gained particular importance due to its cardinal role in the metabolic pathway in cells. IDH1, IDH2, and IDH3 are the three isomeric IDH enzymes that have been shown to regulate cellular metabolism. Of particular importance, IDH2 genes are associated with several cancers, including gliomas, oligodendroglioma, and astrocytomas. These mutations lead to the production of oncometabolite D-2-hydroxyglutarate (D-2-HG), which accumulates in cells promoting tumor growth. The enhanced levels of D-2-HG competitively inhibit α-KG dependent enzymes, inhibiting cell TCA cycle, upregulating the cell growth and survival relevant HIF-1α pathway, promoting DNA hypermethylation related epigenetic activity, all of which synergistically contribute to carcinogenesis. The present review discusses epigenetic mechanisms inIDH2 regulation in cells and further its clinical implications.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India; Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Indore, Madhya Pradesh, India.
| | - Srinivas Bandaru
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India; Department of Biotechnology, Koneru Lakshmaiah Educational Foundation (KLEF), Green Fields, Vaddeswaram, Andhra Pradesh, India
| | - Arshiya Khan
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India; Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Khushboo Sharma
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India; Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India; Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Manmeet Kaur
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Dipannita Ghosh
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Ishita Chopra
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India; School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Aravind Panicker
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Abhishek Kumar
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India; Department of Biosciences, Acropolis Institute, Indore, Madhya Pradesh, India
| | - Priyadevi Saravanan
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Pranoti Belapurkar
- Department of Biosciences, Acropolis Institute, Indore, Madhya Pradesh, India
| | | | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
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Higgins WT, Vibhute S, Bennett C, Lindert S. Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods. J Chem Inf Model 2024; 64:435-448. [PMID: 38175956 DOI: 10.1021/acs.jcim.3c01358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.
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Affiliation(s)
- William T Higgins
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Sandip Vibhute
- Medicinal Chemistry Shared Resource, Comprehensive Cancer Center, Ohio State University, Columbus, Ohio 43210, United States
| | - Chad Bennett
- Medicinal Chemistry Shared Resource, Comprehensive Cancer Center, Ohio State University, Columbus, Ohio 43210, United States
- Drug Development Institute, Ohio State University, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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Ouassaf M, Daoui O, Alam S, Elkhattabi S, Belaidi S, Chtita S. Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors. J Biomol Struct Dyn 2023; 41:7712-7724. [PMID: 36106982 DOI: 10.1080/07391102.2022.2123403] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 10/14/2022]
Abstract
FLT3 is considered a potential target of acute myeloid leukemia therapy. In this study, we applied a computer-aided methodology unifying molecular docking and pharmacophore screening to identify potent inhibitors against FLT3. To investigate the pharmacophore area and binding mechanism of FLT3, the reported co-crystallized Gilteritinib ligand was docked into the active site using Glide XP. Based on the docking results, we identified structure-based pharmacophore characteristics resistant to potent FLT3 inhibitors. The best hypothesis was corroborated using test and decoy sets, and the verified hypo was utilized to screen the chemical database. The hits from the pharmacophore-based screening were then screened again using a structure-based method that included molecular docking at various precisions; the selected molecules were further examined and refined using drug-like filters and ADMET analysis. Finally, two hits were picked out for molecular dynamic simulation. The results showed two hits were expected to have potent inhibitory activity and excellent ADMET characteristics, and they might be used as new leads in the development of FLT3 inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mebarka Ouassaf
- Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, Biskra, Algeria
| | - Ossama Daoui
- Laboratory of Engineering, Systems, and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
| | - Sarfaraz Alam
- Department of Chemical Engineering, University of Waterlo, Waterloo, Canada
| | - Souad Elkhattabi
- Laboratory of Engineering, Systems, and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
| | - Salah Belaidi
- Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, Biskra, Algeria
| | - Samir Chtita
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
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5
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Bhrdwaj A, Abdalla M, Pande A, Madhavi M, Chopra I, Soni L, Vijayakumar N, Panwar U, Khan MA, Prajapati L, Gujrati D, Belapurkar P, Albogami S, Hussain T, Selvaraj C, Nayarisseri A, Singh SK. Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma. Appl Biochem Biotechnol 2023; 195:5094-5119. [PMID: 36976507 DOI: 10.1007/s12010-023-04430-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2023] [Indexed: 03/29/2023]
Abstract
Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.
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Affiliation(s)
- Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Mohnad Abdalla
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 Cultural West Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Aditi Pande
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Osmania University, Hyderabad, 500007, Telangana State, India
| | - Ishita Chopra
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Lovely Soni
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Natchimuthu Vijayakumar
- Department of Physics, M.Kumarasamy College of Engineering, Karur, 639113, Tamil Nadu, India
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Mohd Aqueel Khan
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Leena Prajapati
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Deepika Gujrati
- Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Begumpet, Hyderabad, 500016, India
| | - Pranoti Belapurkar
- Department of Biosciences, Acropolis Institute, Indore, 453771, Madhya Pradesh, India
| | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Tajamul Hussain
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Chandrabose Selvaraj
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha College of Dental and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 600077, Tamil Nadu, India
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India.
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India.
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia.
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India.
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India.
- Department of Data Sciences, Centre of Biomedical Research, SGPGIMS Campus, Raebareli Rd, Lucknow, 226014, Uttar Pradesh, India.
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Egbuna C, Patrick-Iwuanyanwu KC, Onyeike EN, Khan J, Alshehri B. FMS-like tyrosine kinase-3 (FLT3) inhibitors with better binding affinity and ADMET properties than sorafenib and gilteritinib against acute myeloid leukemia: in silico studies. J Biomol Struct Dyn 2022; 40:12248-12259. [PMID: 34486940 DOI: 10.1080/07391102.2021.1969286] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Over 30-35% of patients down with AML are caused by mutations of FLT3-ITD and FLT3-TKD which keeps the protein activated while it activates other signaling proteins downstream that are involved in cell proliferation, differentiation, and survival. As drug targets, many inhibitors are already in clinical practice. Unfortunately, the average overall survival rate for patients on medication suffering from AML is 5 years despite the huge efforts in this field. To perform docking simulation and ADMET studies on selected phytochemicals against FLT3 protein receptor for drug discovery against FLT3 induced AML, molecular docking simulation was performed using human FLT3 protein target (PDB ID: 6JQR) and 313 phytochemicals with standard anticancer drugs (Sorafenib and Gilteritinib in addition to other anticancer drugs). The crystal structure of the protein was downloaded from the protein data bank and prepared using Biovia Discovery Studio. The chemical structures of the phytochemicals were downloaded from the NCBI PubChem database and prepared using Open Babel and VConf softwares. Molecular docking was performed using PyRx on Autodock Vina. The ADMET properties of the best performing compounds were calculated using SwissADME and pkCMS web servers. The results obtained showed that glabridin, ellipticine and derivatives (elliptinium and 9-methoxyellipticine), mezerein, ursolic acid, formononetin, cycloartocarpesin, hypericin, silymarin, and indirubin are the best performing compounds better than sorafenib and gilteritinib based on their binding affinities. The top-performing compounds which had better binding and ADMET properties than sorafenib and gilteritinib could serve as scaffolds or leads for new drug discovery against FLT3 induced AML.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Chukwuebuka Egbuna
- Africa Centre of Excellence in Public Health and Toxicological Research (ACE-PUTOR), University of Port-Harcourt, Port Harcourt, Rivers State, Nigeria.,Department of Biochemistry, Faculty of Science, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria.,Department of Biochemistry, Faculty of Natural Sciences, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria
| | - Kingsley C Patrick-Iwuanyanwu
- Africa Centre of Excellence in Public Health and Toxicological Research (ACE-PUTOR), University of Port-Harcourt, Port Harcourt, Rivers State, Nigeria.,Department of Biochemistry, Faculty of Science, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria
| | - Eugene N Onyeike
- Africa Centre of Excellence in Public Health and Toxicological Research (ACE-PUTOR), University of Port-Harcourt, Port Harcourt, Rivers State, Nigeria.,Department of Biochemistry, Faculty of Science, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria
| | - Johra Khan
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah, Saudia Arabia.,Health and Basic Sciences Research Center, Majmaah University, Majmaah, Saudi Arabia
| | - Bader Alshehri
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah, Saudia Arabia.,Health and Basic Sciences Research Center, Majmaah University, Majmaah, Saudi Arabia
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7
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Apoptotic and Cell Cycle Effects of Triterpenes Isolated from Phoradendron wattii on Leukemia Cell Lines. Molecules 2022; 27:molecules27175616. [PMID: 36080390 PMCID: PMC9458143 DOI: 10.3390/molecules27175616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 12/02/2022] Open
Abstract
Current antineoplastic agents present multiple disadvantages, driving an ongoing search for new and better compounds. Four lupane-type triterpenes, 3α,24-dihydroxylup-20(29)-en-28-oic acid (1), 3α,23-dihydroxy-30-oxo-lup-20(29)-en-28-oic acid (2), 3α,23-O-isopropylidenyl-3α,23-dihydroxylup-20(29)-en-28-oic acid (3), and 3α,23-dihydroxylup-20(29)-en-28-oic acid (4), previously isolated from Phoradendron wattii, were evaluated on two cell lines of chronic (K562) and acute (HL60) myeloid leukemia. Compounds 1, 2, and 4 decreased cell viability and inhibit proliferation, mainly in K562, and exhibited an apoptotic effect from 24 h of treatment. Of particular interest is compound 2, which caused arrest in active phases (G2/M) of the cell cycle, as shown by in silico study of the CDK1/Cyclin B/Csk2 complex by molecular docking. This compound [3α,23-dihydroxy-30-oxo-lup-20(29)-en-28-oic acid] s a promising candidate for incorporation into cancer treatments and deserves further study.
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Yadav M, Abdalla M, Madhavi M, Chopra I, Bhrdwaj A, Soni L, Shaheen U, Prajapati L, Sharma M, Sikarwar MS, Albogami S, Hussain T, Nayarisseri A, Singh SK. Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation and Pharmacokinetic modelling of Cyclooxygenase-2 (COX-2) inhibitor for the clinical treatment of Colorectal Cancer. MOLECULAR SIMULATION 2022. [DOI: 10.1080/08927022.2022.2068799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Manasi Yadav
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Mohnad Abdalla
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, PR People’s Republic of China
| | - Maddala Madhavi
- Department of Zoology, Osmania University, Hyderabad, Telangana State, India
| | - Ishita Chopra
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Indore, Madhya Pradesh, India
| | - Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Lovely Soni
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Uzma Shaheen
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Leena Prajapati
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Megha Sharma
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | | | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, Taif, Saudi Arabia
| | - Tajamul Hussain
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Indore, Madhya Pradesh, India
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
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9
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Mukherjee S, Abdalla M, Yadav M, Madhavi M, Bhrdwaj A, Khandelwal R, Prajapati L, Panicker A, Chaudhary A, Albrakati A, Hussain T, Nayarisseri A, Singh SK. Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer. J Mol Model 2022; 28:100. [PMID: 35325303 DOI: 10.1007/s00894-022-05081-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 03/08/2022] [Indexed: 11/28/2022]
Abstract
Vascular endothelial growth factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identified in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential vascular endothelial growth factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumor cells of the ovary and to examine the effectiveness of the identified inhibitor for the treatment of ovarian cancer using various in silico approaches. Twelve established VEGF inhibitors were collected from various literatures. The compound AEE788 displays great affinity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high affinity. Among the 80 virtual screened compounds, CID 88265020 explicates much better affinity than the established compound AEE788. Based on molecular dynamics simulation, pharmacophore and comparative toxicity analysis of both the best established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 has a high affinity with the lowest re-rank score and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in ovarian cancer.
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Affiliation(s)
- Sourav Mukherjee
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Mohnad Abdalla
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 Cultural West Road, Jinan, Shandong Province, 250012, People's Republic of China
| | - Manasi Yadav
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad, 500001, Telangana, India
| | - Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Leena Prajapati
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Aravind Panicker
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Aashish Chaudhary
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Ashraf Albrakati
- Department of Human Anatomy, College of Medicine, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India.
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia.
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India.
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630 003, Tamil Nadu, India.
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10
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Kucuk HB, Kanturk G, Yerlikaya S, Yildiz T, Senturk AM, Guzel M. Novel β‑hydroxy ketones: Synthesis, spectroscopic characterization, molecular docking, and anticancer activity studies. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2021.131772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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11
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Nayarisseri A. Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery. Curr Top Med Chem 2021; 20:1651-1660. [PMID: 32614747 DOI: 10.2174/156802662019200701164759] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
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12
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Qureshi S, Khandelwal R, Madhavi M, Khurana N, Gupta N, Choudhary SK, Suresh RA, Hazarika L, Srija CD, Sharma K, Hindala MR, Hussain T, Nayarisseri A, Singh SK. A Multi-target Drug Designing for BTK, MMP9, Proteasome and TAK1 for the Clinical Treatment of Mantle Cell Lymphoma. Curr Top Med Chem 2021; 21:790-818. [PMID: 33463471 DOI: 10.2174/1568026621666210119112336] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/18/2020] [Accepted: 12/24/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Mantle cell lymphoma (MCL) is a type of non-Hodgkin lymphoma characterized by the mutation and overexpression of the cyclin D1 protein by the reciprocal chromosomal translocation t(11;14)(q13:q32). AIM The present study aims to identify potential inhibition of MMP9, Proteasome, BTK, and TAK1 and determine the most suitable and effective protein target for the MCL. METHODOLOGY Nine known inhibitors for MMP9, 24 for proteasome, 15 for BTK and 14 for TAK1 were screened. SB-3CT (PubChem ID: 9883002), oprozomib (PubChem ID: 25067547), zanubrutinib (PubChem ID: 135565884) and TAK1 inhibitor (PubChem ID: 66760355) were recognized as drugs with high binding capacity with their respective protein receptors. 41, 72, 102 and 3 virtual screened compounds were obtained after the similarity search with compound (PubChem ID:102173753), PubChem compound SCHEMBL15569297 (PubChem ID:72374403), PubChem compound SCHEMBL17075298 (PubChem ID:136970120) and compound CID: 71814473 with best virtual screened compounds. RESULT MMP9 inhibitors show commendable affinity and good interaction profile of compound holding PubChem ID:102173753 over the most effective established inhibitor SB-3CT. The pharmacophore study of the best virtual screened compound reveals its high efficacy based on various interactions. The virtual screened compound's better affinity with the target MMP9 protein was deduced using toxicity and integration profile studies. CONCLUSION Based on the ADMET profile, the compound (PubChem ID: 102173753) could be a potent drug for MCL treatment. Similar to the established SB-3CT, the compound was non-toxic with LD50 values for both the compounds lying in the same range.
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Affiliation(s)
- Shahrukh Qureshi
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India
| | - Naveesha Khurana
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Neha Gupta
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Saurav K Choudhary
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Revathy A Suresh
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Lima Hazarika
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Chillamcherla D Srija
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Khushboo Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Mali R Hindala
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Sanjeev K Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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13
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Nayarisseri A, Khandelwal R, Tanwar P, Madhavi M, Sharma D, Thakur G, Speck-Planche A, Singh SK. Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. Curr Drug Targets 2021; 22:631-655. [PMID: 33397265 DOI: 10.2174/1389450122999210104205732] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 08/21/2020] [Accepted: 09/14/2020] [Indexed: 11/22/2022]
Abstract
Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Poonam Tanwar
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India
| | - Diksha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Garima Thakur
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. 8940577, San Joaquin, Santiago, Chile
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
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14
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Nayarisseri A. Most Promising Compounds for Treating COVID-19 and Recent Trends in Antimicrobial & Antifungal Agents. Curr Top Med Chem 2020; 20:2119-2125. [PMID: 33153418 DOI: 10.2174/156802662023201001094634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Multidrug resistance in microbes poses a major health crisis and demands for the discovery of novel antimicrobial agents. The recent pandemic of SARS-CoV-2 has raised a public health emergency in almost all the countries of the world. Unlike viruses, a bacterium plays a significant role in various environmental issues such as bioremediation. Furthermore, biosurfactants produced by various bacterial species have an edge over traditionally produced chemical surfactants for its biodegradability, low toxicity and better interfacial activity with various applications in agriculture and industry. This special issue focuses on the global perspective of drug discovery for various antimicrobial, antiviral, and antifungal agents for infectious diseases. The issue also emphasizes the ongoing developments and the role of microbes in environmental remediation. We wish the articles published in this issue will enhance the current understanding in microbiology among the readers, and serve as the "seed of an idea" for drug development for ongoing COVID-19 pandemic.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Indore-452 010, Madhya Pradesh, India,Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Indore-452010, Madhya Pradesh,
India
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15
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Nayarisseri A, Khandelwal R, Madhavi M, Selvaraj C, Panwar U, Sharma K, Hussain T, Singh SK. Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation. Curr Top Med Chem 2020; 20:2146-2167. [PMID: 32621718 DOI: 10.2174/1568026620666200704135327] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/20/2020] [Accepted: 04/25/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments. OBJECTIVE This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations. METHODS 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses. RESULTS Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19. CONCLUSION Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India,Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd., Mahalakshmi Nagar, Indore-452010, Madhya
Pradesh, India,Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King
Saud University, Riyadh, Saudi Arabia,Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad-500001, Telangana State, India
| | - Chandrabose Selvaraj
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Khushboo Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia,Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King
Saud University, Riyadh, Saudi Arabia
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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16
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Aher A, Udhwani T, Khandelwal R, Limaye A, Hussain T, Nayarisseri A, Singh SK. In silico Insights on IL-6: A Potential Target for Multicentric Castleman Disease. Curr Comput Aided Drug Des 2020; 16:641-653. [DOI: 10.2174/1573409915666190902142524] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 06/01/2019] [Accepted: 07/11/2019] [Indexed: 12/29/2022]
Abstract
Background:
Multicentric Castleman Disease (MCD) is a confrontational lymphoproliferative
disorder described by symptoms such as lymph node proliferation, unwarranted secretion of
inflammatory cytokines, hyperactive immune system, and in severe cases, multiple organ dysfunction.
Interleukin-6 (IL-6) is a pleiotropic cytokine which is involved in a large range of physiological
processes in our body such as pro-inflammation, anti-inflammation, differentiation of T-cells
and is reported to be a key pathological factor in MCD. In the case of MCD, it was observed that
IL-6 is overproduced from T-cells and macrophages which disturb Hepcidin, a vital regulator of
iron trafficking in macrophage. The present study endeavour to expound the inhibitor which binds
to IL-6 protein receptor with high affinity.
Methods:
MolegroVirtual Docker software was employed to find the best-established drug from
the list of selected inhibitors of IL-6. This compound was subjected to virtual screening against
PubChem database to get inhibitors with a very similar structure. These inhibitors were docked to
obtain a compound binding with high affinity to the target protein. The established compound and
the virtual screened compound were subjected to relative analysis of interactivity energy variables
and ADMET profile studies.
Results:
Among all the selected inhibitors, the virtual screened compound PubChem CID:
101119084 is seen to possess the highest affinity with the target protein. Comparative studies and
ADMET analysis further implicate this compound as a better inhibitor of the IL-6 protein.
Conclusion:
Hence, this compound recognized in the study possesses high potential as an IL-6 inhibitor
which might assist in the treatment of Multicentric Castleman Disease and should be examined
for its efficiency by in vivo studies.
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Affiliation(s)
- Abhishek Aher
- In Silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Trishang Udhwani
- In Silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In Silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Akanksha Limaye
- In Silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anuraj Nayarisseri
- In Silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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17
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Prajapati L, Khandelwal R, Yogalakshmi KN, Munshi A, Nayarisseri A. Computer-Aided Structure Prediction of Bluetongue Virus Coat Protein VP2 Assisted by Optimized Potential for Liquid Simulations (OPLS). Curr Top Med Chem 2020; 20:1720-1732. [DOI: 10.2174/1568026620666200516153753] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/13/2019] [Accepted: 12/17/2019] [Indexed: 12/13/2022]
Abstract
Background:
The capsid coated protein of Bluetongue virus (BTV) VP2 is responsible for
BTV transmission by the Culicoides vector to vertebrate hosts. Besides, VP2 is responsible for BTV
entry into permissive cells and hence plays a major role in disease progression. However, its mechanism
of action is still unknown.
Objective:
The present investigation aimed to predict the 3D structure of Viral Protein 2 of the bluetongue
virus assisted by Optimized Potential for Liquid Simulations (OPLS), structure validation, and an
active site prediction.
Methods:
The 3D structure of the VP2 protein was built using a Python-based Computational algorithm.
The templates were identified using Smith waterman’s Local alignment. The VP2 protein structure validated
using PROCHECK. Molecular Dynamics Simulation (MDS) studies were performed using an
academic software Desmond, Schrodinger dynamics, for determining the stability of a model protein.
The Ligand-Binding site was predicted by structure comparison using homology search and proteinprotein
network analysis to reveal their stability and inhibition mechanism, followed by the active site
identification.
Results:
The secondary structure of the VP2 reveals that the protein contains 220 alpha helix atoms,
40 310 helix, 151 beta sheets, 134 coils and 424 turns, whereas the 3D structure of Viral Protein 2 of
BTV has been found to have 15774 total atoms in the structure. However, 961 amino acids were found
in the final model. The dynamical cross-correlation matrix (DCCM) analysis tool identifies putative protein
domains and also confirms the stability of the predicted model and their dynamical behavior difference
with the correlative fluctuations in motion.
Conclusion:
The biological interpretation of the Viral Protein 2 was carried out. DCCM maps were calculated,
using a different coordinate reference frame, through which, protein domain boundaries and
protein domain residue constituents were identified. The obtained model shows good reliability. Moreover,
we anticipated that this research should play a promising role in the identification of novel candidates
with the target protein to inhibit their functional significance.
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Affiliation(s)
- Leena Prajapati
- Department of Environmental Science and Technology, Central University of Punjab, Bathinda-151001, Punjab, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | | | - Anjana Munshi
- Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda - 151001 Punjab, India
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
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18
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Limaye A, Sweta J, Madhavi M, Mudgal U, Mukherjee S, Sharma S, Hussain T, Nayarisseri A, Singh SK. In Silico Insights on GD2 : A Potential Target for Pediatric Neuroblastoma. Curr Top Med Chem 2020; 19:2766-2781. [DOI: 10.2174/1568026619666191112115333] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 09/02/2019] [Accepted: 09/25/2019] [Indexed: 02/07/2023]
Abstract
Background:Originating from the abnormal growth of neuroblasts, pediatric neuroblastoma affects the age group below 15 years. It is an aggressive heterogenous cancer with a high morbidity rate. Biological marker GD2 synthesised by the GD2 gene acts as a powerful predictor of neuroblastoma cells. GD2 gangliosides are sialic acid-containing glycosphingolipids. Differential expression during brain development governs the function of the GD2. The present study explains the interaction of the GD2 with its established inhibitors and discovers the compound having a high binding affinity against the target protein. Technically, during the development of new compounds through docking studies, the best drug among all pre-exist inhibitors was filtered. Hence in reference to the best docked compound, the study proceeded further.Methodology:The In silico approach provides a platform to determine and establish potential inhibitor against GD2 in Pediatric neuroblastoma. The 3D structure of GD2 protein was modelled by homology base fold methods using Smith-Watermans’ Local alignment. A total of 18 established potent compounds were subjected to molecular docking and Etoposide (CID: 36462) manifested the highest affinity. The similarity search presented 336 compounds similar to Etoposide.Results:Through virtual screening, the compound having PubChem ID 10254934 showed a better affinity towards GD2 than the established inhibitor. The comparative profiling of the two compounds based on various interactions such as H-bond interaction, aromatic interactions, electrostatic interactions and ADMET profiling and toxicity studies were performed using various computational tools.Conclusion:The docking separated the virtual screened drug (PubChemID: 10254934) from the established inhibitor with a better re-rank score of -136.33. The toxicity profile of the virtual screened drug was also lesser (less lethal) than the established drug. The virtual screened drug was observed to be bioavailable as it does not cross the blood-brain barrier. Conclusively, the virtual screened compound obtained in the present investigation is better than the established inhibitor and can be further augmented by In vitro analysis, pharmacodynamics and pharmacokinetic studies.
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Affiliation(s)
- Akanksha Limaye
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Jajoriya Sweta
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India
| | - Urvy Mudgal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Sourav Mukherjee
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Shreshtha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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19
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Nayarisseri A. Prospects of Utilizing Computational Techniques for the Treatment of Human Diseases. Curr Top Med Chem 2019; 19:1071-1074. [PMID: 31490742 DOI: 10.2174/156802661913190827102426] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory Eminent Biosciences Mahalakshmi Nagar, Indore - 452010 Madhya Pradesh, India.,Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd., Indore - 452010 Madhya Pradesh, India
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20
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Yadav M, Khandelwal R, Mudgal U, Srinitha S, Khandekar N, Nayarisseri A, Vuree S, Singh SK. Identification of Potent VEGF Inhibitors for the Clinical Treatment of Glioblastoma, A Virtual Screening Approach. Asian Pac J Cancer Prev 2019; 20:2681-2692. [PMID: 31554364 PMCID: PMC6976853 DOI: 10.31557/apjcp.2019.20.9.2681] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 09/02/2019] [Indexed: 02/04/2023] Open
Abstract
Vascular endothelial growth factor (VEGF) expression could be found in all glioblastomas. VEGF takes part in numerous changes including the endothelial cell proliferation, the vasculature of solid tumor: its survival invasion, and migration, chemotaxis of bone marrow-derived progenitor cells, vasodilation and vascular permeability. VEGF inhibition can be a smart therapeutic strategy because it is extremely specific and less toxic than cytotoxic therapy. To establish better inhibition of VEGF than the current inhibitors, present study approach is by molecular docking, virtual screening to illustrate the inhibitor with superior affinity against VEGF to have a cautious pharma profile. To retrieve the best established and high-affinity high affinity molecule, Molegro Virtual Docker software was executed. The high-affinity scoring compounds were subjected to further similarity search to retrieve the drugs with similar properties from pubchem database. The completion of virtual screening reveals that PubChem compound SCHEMBL1250485 (PubChem CID: 66965667) has the highest affinity. The study of the drug-likeness was verified using OSIRIS Property Explorer software which supported the virtual screened result. Further ADMET study and drug comparative study strongly prove the superiority of the new established inhibitor with lesser rerank score and toxicity. Overall, the new inhibitor has higher potential to stop the expression of VEGF in glioblastoma and positively can be further analysed through In vitro studies.
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Affiliation(s)
- Mohini Yadav
- In silico Research Laboratory, Eminent Biosciences, Indore - 452 010, Madhya Pradesh, India. ,
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Indore - 452 010, Madhya Pradesh, India. ,
| | - Urvy Mudgal
- In silico Research Laboratory, Eminent Biosciences, Indore - 452 010, Madhya Pradesh, India. ,
| | - Sivaraj Srinitha
- In silico Research Laboratory, Eminent Biosciences, Indore - 452 010, Madhya Pradesh, India. ,
| | - Natasha Khandekar
- In silico Research Laboratory, Eminent Biosciences, Indore - 452 010, Madhya Pradesh, India. ,
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Indore - 452 010, Madhya Pradesh, India. ,
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd., Indore-452010, Madhya Pradesh, India
| | - Sugunakar Vuree
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Division of Research and Development, Lovely Professional University, Phagwara, Punjab-144411, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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21
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Ali MA, Vuree S, Goud H, Hussain T, Nayarisseri A, Singh SK. Identification of High-affinity Small Molecules Targeting Gamma Secretase for the Treatment of Alzheimer’s Disease. Curr Top Med Chem 2019; 19:1173-1187. [DOI: 10.2174/1568026619666190617155326] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Revised: 01/12/2019] [Accepted: 04/10/2019] [Indexed: 02/07/2023]
Abstract
Background:
Alzheimers Disease (AD) is a neurodegenerative disease which is characterized by
the deposition of amyloid plaques in the brain- a concept supported by most of the researchers worldwide. The
main component of the plaques being amyloid-beta (Aβ42) results from the sequential cleavage of Amyloid
precursor protein (APP) by beta and gamma secretase. This present study intends to inhibit the formation of
amyloid plaques by blocking the action of gamma secretase protein with Inhibitors (GSI).
Methods:
A number of Gamma Secretase Inhibitors (GSI) were targeted to the protein by molecular docking.
The inhibitor having the best affinity was used as a subject for further virtual screening methods to obtain
similar compounds. The generated compounds were docked again at the same docking site on the protein to
find a compound with higher affinity to inhibit the protein. The highlights of virtually screened compound
consisted of Pharmacophore Mapping of the docking site. These steps were followed by comparative assessments
for both the compounds, obtained from the two aforesaid docking studies, which included interaction
energy descriptors, ADMET profiling and PreADMET evaluations.
Results:
111 GSI classified as azepines, sulfonamides and peptide isosteres were used in the study. By molecular
docking an amorpholino-amide, compound (22), was identified to be the high affinity compound GSI
along with its better interaction profiles.The virtually screened pubchem compound AKOS001083915
(CID:24462213) shows the best affinity with gamma secretase. Collective Pharmacophore mapping (H bonds,
electrostatic profile, binding pattern and solvent accesibility) shows a stable interaction. The resulting ADMETand
Descriptor values were nearly equivalent.
Conclusion:
These compounds identified herein hold a potential as Gamma Secretase inhibitors.According to
PreADMET values the compound AKOS001083915 is effective and specific to the target protein. Its
BOILED-egg plot analysis infers the compound permeable to blood brain barrier.Comparative study for both
the compounds resulted in having nearly equivalent properties. These compounds have the capacity to inhibit
the protein which is indirectly responsible for the formation of amyloid plaques and can be further put to in
vitro pharmacokinetic and dynamic studies.
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Affiliation(s)
- Meer Asif Ali
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Sugunakar Vuree
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India
| | - Himshikha Goud
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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22
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Sweta J, Khandelwal R, Srinitha S, Pancholi R, Adhikary R, Ali MA, Nayarisseri A, Vuree S, Singh SK. Identification of High-Affinity Small Molecule Targeting IDH2 for the Clinical Treatment of Acute Myeloid Leukemia. Asian Pac J Cancer Prev 2019; 20:2287-2297. [PMID: 31450897 PMCID: PMC6852809 DOI: 10.31557/apjcp.2019.20.8.2287] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Indexed: 02/06/2023] Open
Abstract
Acute myeloid leukemia (AML) is symbolized by an increase in the number of myeloid cells in the bone marrow and an arrest in their maturation, frequently resulting in hematopoietic insufficiency (granulocytopenia, thrombocytopenia, or anemia) with or without leukocytosis either by a predominance of immature forms or a loss of normal hematopoiesis. IDH2 gene encodes for isocitrate dehydrogenase enzyme which is involved in the TCA cycle domino effect and converts isocitrate to alpha-ketoglutarate. In the U.S, the annual incidence of AML progressively increases with age to a peak of 12.6 per 100,000 adults of 65 years or older. Mutations in isocitrate dehydrogenase 2 (arginine 132) have been demonstrated to be recurrent gene alterations in acute myeloid leukemia (AML) by forming 2-Hydroxy alpha ketoglutarate which, instead of participating in TCA cycle, accumulates to form AML. The current study approaches by molecular docking and virtual screening to elucidate inhibitor with superior affinity against IDH2 and achieve a pharmacological profile. To obtain the best established drug Molegro Virtual Docker algorithm was executed. The compound AG-221 (Pub CID 71299339) having the high affinity score was subjected to similarity search to retrieve the drugs with similar properties. The virtual screened compound SCHEMBL16391748 (PubChem CID-117816179)shows high affinity for the protein. Comparative study and ADMET study for both the above compounds resulted in equivalent chemical properties. Virtual screened compound SCHEMBL16391748 (PubChem CID-117816179) shows the lowest re-rank score. These drugs are identified as high potential IDH2 inhibitors and can halt AML when validated through further In vitro screening.
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Affiliation(s)
- Jajoriya Sweta
- In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore- 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore- 452010, Madhya Pradesh, India
| | - Sivaraj Srinitha
- In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore- 452010, Madhya Pradesh, India
| | - Rashi Pancholi
- In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore- 452010, Madhya Pradesh, India
| | - Ritu Adhikary
- In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore- 452010, Madhya Pradesh, India
| | - Meer Asif Ali
- In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore- 452010, Madhya Pradesh, India
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore- 452010, Madhya Pradesh, India.,Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India.,Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India. ,
| | - Sugunakar Vuree
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India. ,
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23
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Patidar K, Panwar U, Vuree S, Sweta J, Sandhu MK, Nayarisseri A, Singh SK. An In silico Approach to Identify High Affinity Small Molecule
Targeting m-TOR Inhibitors for the Clinical Treatment of
Breast Cancer. Asian Pac J Cancer Prev 2019; 20:1229-1241. [PMID: 31030499 PMCID: PMC6948900 DOI: 10.31557/apjcp.2019.20.4.1229] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is the most frequent malignancy among women. It is a heterogeneous disease with different subtypes defined by its hormone receptor. A hormone receptor is mainly concerned with the progression of the PI3K/AKT/mTOR pathway which is often dysregulated in breast cancer. This is a major signaling pathway that controls the activities such as cell growth, cell division, and cell proliferation. The present study aims to suppress mTOR protein by its various inhibitors and to select one with the highest binding affinity to the receptor protein. Out of 40 inhibitors of mTOR against breast cancer, SF1126 was identified to have the best docking score of -8.705, using Schrodinger Suite which was further subjected for high throughput screening to obtain best similar compound using Lipinski’s filters. The compound obtained after virtual screening, ID: ZINC85569445 is seen to have the highest affinity with the target protein mTOR. The same result based on the binding free energy analysis using MM-GBSA showed that the compound ZINC85569445 to have the the highest binding free energy. The next study of interaction between the ligand and receptor protein with the pharmacophore mapping showed the best conjugates, and the ZINC85569445 can be further studied for future benefits of treatment of breast cancer.
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Affiliation(s)
- Khushboo Patidar
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India. ,
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi,Tamil Nadu, India
| | - Sugunakar Vuree
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India
| | - Jajoriya Sweta
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India. ,
| | - Manpreet Kaur Sandhu
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India. ,
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India. , ,Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi,Tamil Nadu, India.,Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd., Indore, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi,Tamil Nadu, India
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