1
|
Bux K, Asim I, Ismail Z, Hussain S, Herwig R. Structural and dynamical insights revealed the anti-glioblastoma potential of withanolides from Withania coagulans against vascular endothelial growth factor receptor (VEGFR). J Mol Model 2024; 30:383. [PMID: 39443392 DOI: 10.1007/s00894-024-06178-7] [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: 06/29/2024] [Accepted: 10/12/2024] [Indexed: 10/25/2024]
Abstract
CONTEXT Glioblastoma (GBM), well known as grade 4 tumors due to its progressive malignant features such as vascular proliferation and necrosis, is the most aggressive form of primary brain tumor found in adults. Mutations and amplifications in the vascular endothelial growth factor receptor (VEGFR) contribute to almost 25% of GBM tumors. And thus, VEGFR has been declared the primary target in glioblastoma therapeutic strategies. However, many studies have been previously reported that include GBM as global therapeutics challenge, but they lack the molecular level insights that could help in understanding the biological function of a therapeutically important protein playing a major role in the disease and design the best strategies to develop the potential drugs. METHODS Therefore, to the best of our knowledge, the present study is the first time of kind, which involves multi-in silico approaches to predict the inhibition potential of withanolides from Withania coagulan against VEGFR. The study is actually based on determining the mode of action of five isolates: withanolide J, withaperuvin, 27-hydroxywithanolide I, coagule E, and coagule E, along with their respective binding energies. Molecular docking simulations revealed primarily four ligands, withanolide J (- 7.33 kJ/mol), 27-withanolide (- 7.01 kJ/mol), ajugine, withaperuvin (- 6.89 kJ/mol), and ajugine E (- 6.39 kJ/mol), to have significant binding potencies against the protein. Ligand binding was found to enhance the confirmational stability of the protein revealed through RMSD analysis, and RMSF assessment revealed the protein residues especially from 900-1000 surrounding the binding of the protein. Structural and dynamics of the protein via dynamics cross-correlation movement (DCCM) and principal component analysis (PCA) in both the unbound form and complexed with most potent ligand, withanolide J, reveal the ligand binding affecting the entire conformational integrity of the protein stabilized by hydrogen bonds and electrostatic attractions. Free energy of binding estimations by means of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) method further revealed the withanolide J to have maximum binding potency of the all ligands. Withanolide J in final was also found to have suitable molecular characterizations to cross the blood-brain barrier (BBB +) and reasonable human intestinal absorption ability determined by ADMET profiling via admetSAR tools.
Collapse
Affiliation(s)
- Khair Bux
- Faculty of Life Sciences, Department of Biosciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST) University, Karachi, Pakistan.
| | - Irsa Asim
- Faculty of Life Sciences, Department of Biosciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST) University, Karachi, Pakistan
| | - Zainab Ismail
- Faculty of Life Sciences, Department of Biosciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST) University, Karachi, Pakistan
| | - Samaha Hussain
- Faculty of Life Sciences, Department of Biosciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST) University, Karachi, Pakistan
| | - Ralf Herwig
- Laboratories PD Dr. R. Herwig, 80337, Munich, Germany
- Heimerer-College, 10000, Pristina, Kosovo
| |
Collapse
|
2
|
Sheoran S, Arora S, Basu T, Negi S, Subbarao N, Kumar A, Singh H, Prabhu D, Upadhyay AK, Kumar N, Vuree S. In silico analysis of Diosmetin as an effective chemopreventive agent against prostate cancer: molecular docking, validation, dynamic simulation and pharmacokinetic prediction-based studies. J Biomol Struct Dyn 2024; 42:9105-9117. [PMID: 37615411 DOI: 10.1080/07391102.2023.2250451] [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: 05/23/2023] [Accepted: 08/12/2023] [Indexed: 08/25/2023]
Abstract
Prostate cancer is the second most dangerous cancer type worldwide. While various treatment options are present i.e. agonists and antagonists, their utilization leads to adverse effects and due to this resistance developing, ultimately the outcome is remission. So, to overcome this issue, we have undertaken an in-silico investigation to identify promising and unique flavonoid candidates for combating prostate cancer. Using GOLD software, the study assessed the effectiveness of 560 natural secondary polyphenols against CDKN2. Protein Data Bank was used to retrieve the 3D crystal structure of CDKN2 (PDB Id: 4EK3) and we retrieved the structure of selected secondary polyphenols from the PubChem database. The compound Diosmetin shows the highest GOLD score with the selected Protein i.e. CDKN2 which is 58.72. To better understand the 2-dimensional and 3-dimensional interactions, the interacting amino acid residues were visualised using Discovery Studio 3.5 and Maestro 13.5. Using Schrodinger-Glide, the Diosmetin and CDKN2 were re-docked, and decoy ligands were docked to CDKN2, which was used to further ascertain the study. The ligands with the highest Gold score were forecasted for pharmacokinetics characteristics, and the results were tabulated and analysed. Utilising the Gromacs software and Desmond packages, 100 ns of Diosmetin molecular dynamics simulations were run to evaluate the structural persistence and variations of protein-ligand complexes. Additionally, our investigation revealed that Diosmetin had a better binding affinity with CDKN2 measuring 58.72, and it also showed remarkable stability across a 100-ns simulation. Thus, following in-vitro and in-vivo clinical studies, diosmetin might lead to the Prostate regimen.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Sumit Sheoran
- School of Bioengineering & Biosciences, Lovely Professional University, Jalandhar, India
| | - Swati Arora
- School of Bioengineering & Biosciences, Lovely Professional University, Jalandhar, India
| | - Tanmayee Basu
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | - Swati Negi
- Department of Chemistry, Delhi University, New Delhi, India
| | - Naidu Subbarao
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Anupam Kumar
- School of Bioengineering & Biosciences, Lovely Professional University, Jalandhar, India
| | - Himanshu Singh
- School of Bioengineering & Biosciences, Lovely Professional University, Jalandhar, India
| | - Dhamodharan Prabhu
- Centre for Drug Discovery, Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, India
| | - Atul Kumar Upadhyay
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | - Neeraj Kumar
- Geetanjali Institute of Pharmacy, Udaipur, India
| | - Sugunakar Vuree
- School of Bioengineering & Biosciences, Lovely Professional University, Jalandhar, India
- MNR Foundation for Research and Innovation (MNR-FRI), MNR Medical College and Hospital, Fasalwadi Village, Hyderabad, India
| |
Collapse
|
3
|
Cavalcante BRR, Freitas RD, Siquara da Rocha LO, Santos RSB, Souza BSDF, Ramos PIP, Rocha GV, Gurgel Rocha CA. In silico approaches for drug repurposing in oncology: a scoping review. Front Pharmacol 2024; 15:1400029. [PMID: 38919258 PMCID: PMC11196849 DOI: 10.3389/fphar.2024.1400029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
Collapse
Affiliation(s)
- Bruno Raphael Ribeiro Cavalcante
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | - Raíza Dias Freitas
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Social and Pediatric Dentistry of the School of Dentistry, Federal University of Bahia, Salvador, Brazil
| | - Leonardo de Oliveira Siquara da Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | | | - Bruno Solano de Freitas Souza
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Pablo Ivan Pereira Ramos
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil
| | - Gisele Vieira Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Clarissa Araújo Gurgel Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
- Department of Propaedeutics, School of Dentistry of the Federal University of Bahia, Salvador, Brazil
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Li A, Wu J. High STAT4 expression correlates with poor prognosis in acute myeloid leukemia and facilitates disease progression by upregulating VEGFA expression. Open Med (Wars) 2024; 19:20230840. [PMID: 38737443 PMCID: PMC11087736 DOI: 10.1515/med-2023-0840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/11/2023] [Accepted: 10/22/2023] [Indexed: 05/14/2024] Open
Abstract
The aim of our study is to explore the mechanism of transcription-4 (STAT4) in acute myeloid leukemia (AML). STAT4 level in AML bone marrow samples/cells was analyzed using bioinformatics and quantitative real-time PCR. The correlation between high STAT4 expression and the prognosis of AML patients was analyzed. The viability, apoptosis, and angiogenesis of AML cells were detected. The levels of STAT4, vascular endothelial growth factor A (VEGFA), and apoptosis-related proteins (Bcl-2 and Bax) in transfected AML cells were examined. STAT4 level was upregulated in AML. STAT4 silencing decreased the viability and angiogenesis, yet increased the apoptosis of AML cells, while overexpressed STAT4 did conversely. VEGFA silencing counteracted the impacts of overexpressed STAT4 upon promoting viability and angiogenesis as well as repressing the apoptosis of AML cells. High STAT4 expression was correlated with poor prognosis of AML patients and facilitated disease progression via upregulating VEGFA expression.
Collapse
Affiliation(s)
- Aohang Li
- Clinical Laboratory Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jingxuan Wu
- Research Ward, Beijing Friendship Hospital, Capital Medical University, Xicheng District, Beijing, China
| |
Collapse
|
7
|
Balaji E V, Satarker S, Kumar BH, Pandey S, Birangal SR, Nayak UY, Pai KSR. In-silico lead identification of the pan-mutant IDH1 and IDH2 inhibitors to target glioblastoma. J Biomol Struct Dyn 2024; 42:3764-3789. [PMID: 37227789 DOI: 10.1080/07391102.2023.2215884] [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: 02/03/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Glioblastoma (GBM) is an aggressive malignant type of brain tumor. Targeting one single intracellular pathway might not alleviate the disease, rather it activates the other molecular pathways that lead to the worsening of the disease condition. Therefore, in this study, we attempted to target both isocitrate dehydrogenase 1 (IDH1) and IDH2, which are one of the most commonly mutated proteins in GBM and other cancer types. Here, standard precision and extra precision docking, IFD, MM-GBSA, QikProp, and molecular dynamics (MD) simulation were performed to identify the potential dual inhibitor for IDH1 and IDH2 from the enamine database containing 59,161 ligands. Upon docking the ligands with IDH1 (PDB: 6VEI) and IDH2 (PDB: 6VFZ), the top eight ligands were selected, based on the XP Glide score. These ligands produced favourable MMGBSA scores and ADME characteristics. Finally, the top four ligands 12953, 44825, 51295, and 53210 were subjected to MD analysis. Interestingly, 53210 showed maximum interaction with Gln 277 for 99% in IDH1 and Gln 316 for 100% in IDH2, which are the crucial amino acids for the inhibitory function of IDH1 and IDH2 to target GBM. Therefore, the present study attempts to identify the novel molecules which could possess a pan-inhibitory action on both IDH1 and IDH that could be crucial in the management of GBM. Yet further evaluation involving in vitro and in vivo studies is warranted to support the data in our current study.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Vignesh Balaji E
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Sairaj Satarker
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - B Harish Kumar
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Samyak Pandey
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Sumit Raosaheb Birangal
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Usha Y Nayak
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - K Sreedhara Ranganath Pai
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
Ullah H, Zhang B, Sharma NK, McCrea PD, Srivastava Y. In-silico probing of AML related RUNX1 cancer-associated missense mutations: Predicted relationships to DNA binding and drug interactions. Front Mol Biosci 2022; 9:981020. [PMID: 36090034 PMCID: PMC9454315 DOI: 10.3389/fmolb.2022.981020] [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: 06/29/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022] Open
Abstract
The molecular consequences of cancer associated mutations in Acute myeloid leukemia (AML) linked factors are not very well understood. Here, we interrogated the COSMIC database for missense mutations associated with the RUNX1 protein, that is frequently mis-regulated in AML, where we sought to identify recurrently mutated positions at the DNA-interacting interface. Indeed, six of the mutated residues, out of a total 417 residues examined within the DNA binding domain, evidenced reduced DNA association in in silico predictions. Further, given the prominence of RUNX1’s compromised function in AML, we asked the question if the mutations themselves might alter RUNX1’s interaction (off-target) with known FDA-approved drug molecules, including three currently used in treating AML. We identified several AML-associated mutations in RUNX1 that were calculated to enhance RUNX1’s interaction with specific drugs. Specifically, we retrieved data from the COSMIC database for cancer-associated mutations of RUNX1 by using R package “data.table” and “ggplot2” modules. In the presence of DNA and/or drug, we used docking scores and energetics of the complexes as tools to evaluate predicted interaction strengths with RUNX1. For example, we performed predictions of drug binding pockets involving Enasidenib, Giltertinib, and Midostaurin (AML associated), as well as ten different published cancer associated drug compounds. Docking of wild type RUNX1 with these 13 different cancer-associated drugs indicates that wild-type RUNX1 has a lower efficiency of binding while RUNX1 mutants R142K, D171N, R174Q, P176H, and R177Q suggested higher affinity of drug association. Literature evidence support our prediction and suggests the mutation R174Q affects RUNX1 DNA binding and could lead to compromised function. We conclude that specific RUNX1 mutations that lessen DNA binding facilitate the binding of a number of tested drug molecules. Further, we propose that molecular modeling and docking studies for RUNX1 in the presence of DNA and/or drugs enables evaluation of the potential impact of RUNX1 cancer associated mutations in AML.
Collapse
Affiliation(s)
- Hanif Ullah
- Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
- Key Laboratory of Regenerative Biology, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Baoyun Zhang
- Key Laboratory of Regenerative Biology, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Narendra Kumar Sharma
- Department of Bioscience and Biotechnology, Banasthali Vidyapith, Banasthali, Tonk, Rajasthan, India
| | - Pierre D. McCrea
- University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Yogesh Srivastava
- University of Chinese Academy of Sciences, Beijing, China
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Genome Regulation Laboratory; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- *Correspondence: Yogesh Srivastava,
| |
Collapse
|
10
|
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
| |
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
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.
Collapse
Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|