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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 2023:10.1007/s12010-023-04430-z. [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] [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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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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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] [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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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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5
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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: 20] [Impact Index Per Article: 6.7] [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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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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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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8
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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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9
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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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10
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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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11
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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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12
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Sharda S, Khandelwal R, Adhikary R, Sharma D, Majhi M, Hussain T, Nayarisseri A, Singh SK. A Computer - Aided Drug Designing for Pharmacological Inhibition of Mutant ALK for the Treatment of Non-small Cell Lung Cancer. Curr Top Med Chem 2019; 19:1129-1144. [DOI: 10.2174/1568026619666190521084941] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 04/12/2019] [Accepted: 04/15/2019] [Indexed: 12/13/2022]
Abstract
Background:
Lung cancer is the most common among all the types of cancer worldwide with
1.8 million people diagnosed every year, leading to 1.6 million deaths every year according to the American
cancer society. The involvement of mutated Anaplasic Lymphoma Kinase (ALK) positive fusion
protein in the progression of NSCLC has made a propitious target to inhibit and treat NSCLC. In the
present study, the main motif is to screen the most effective inhibitor against ALK protein with the potential
pharmacological profile. The ligands selected were docked with Molegro Virtual Docker (MVD) and
CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with a permissible
pharmacological profile.
Methods:
The selected ligands were docked with Molegro Virtual Docker (MVD). With reference to the
obtained compound with the lowest re-rank score, PubChem database was virtually screened to retrieve a
large set of similar compounds which were docked to find the compound with higher affinity. Further
comparative studies and in silico prediction included pharmacophore studies, proximity energy parameters,
ADMET and BOILED-egg plot analysis.
Results:
CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with
preferable pharmacological profile and PubChem compound CID-123449015 came out as the most efficient
virtually screened inhibitor. Interestingly, the contours of the virtual screened compound PubChem
CID- 123449015 fall within our desired high volume cavity of protein having admirable property to control
the ALK regulation to prevent carcinogenesis in NSCLC. BOILED-Egg plot analysis depicts that
both the compounds have analogous characteristics in the divergent aspects. Moreover, in the evaluations
of Blood Brain Barrier, Human Intestinal Absorption, AMES toxicity, and LD50, the virtually screened
compound (PubChem CID-123449015) was found within high optimization.
Conclusion:
These investigations denote that the virtually screened compound (PubChem CID-
123449015) is more efficient to be a better prospective candidate for NSCLC treatment having good
pharmacological profile than the pre-established compound CEP-37440 (PubChem CID- 71721648) with
low re-rank score. The identified virtually screened compound has high potential to act as an ALK inhibitor
and can show promising results in the research of non-small cell lung cancer (NSCLC).
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Affiliation(s)
- Saphy Sharda
- 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
| | - Ritu Adhikary
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Diksha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore – 452010, Madhya Pradesh, India
| | - Manisha Majhi
- 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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13
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Udhwani T, Mukherjee S, Sharma K, Sweta J, Khandekar N, Nayarisseri A, Singh SK. Design of PD-L1 inhibitors for lung cancer. Bioinformation 2019; 15:139-150. [PMID: 31435160 PMCID: PMC6677907 DOI: 10.6026/97320630015139] [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] [Received: 01/27/2019] [Revised: 02/10/2019] [Accepted: 02/19/2019] [Indexed: 12/31/2022] Open
Abstract
The progression of lung cancer is associated with inactivation of programmed cell death protein 1, abbreviated as PD- 1 which regulates
the suppression of the body's immune system by suppressing T- cell inflammatory activity and is responsible for preventing cancer cell
growth. It is of interest to identify inhibitors for PD-L1 dimeric structure through molecular docking and virtual screening. The virtual
screened compound XGIQBUNWFCCMAS-UHFFFAOYSA-N (PubChem CID: 127263272) displays a high affinity with the target protein.
ADMET analysis and cytotoxicity studies further add weight to this compound as a potential inhibitor of PD-L1. The established
compound BMS-202 still shows the high re-rank score, but the virtual screened drug possesses a better ADMET profile with a higher
intestinal absorption value and lower toxicity.
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Affiliation(s)
- Trishang Udhwani
- 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
| | - Khushboo Sharma
- 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
| | - Natasha Khandekar
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar,Indore 452010,Madhya Pradesh,India
| | - 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.,Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi 630 003, Tamil Nadu, 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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14
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Jain D, Udhwani T, Sharma S, Gandhe A, Reddy PB, Nayarisseri A, Singh SK. Design of novel JAK3 Inhibitors towards Rheumatoid Arthritis using molecular docking analysis. Bioinformation 2019; 15:68-78. [PMID: 31435152 PMCID: PMC6677909 DOI: 10.6026/97320630015068] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 02/10/2019] [Accepted: 02/19/2019] [Indexed: 01/06/2023] Open
Abstract
Multiple cytokines play a pivotal role in the pathogenesis of Rheumatoid Arthritis by inducing intracellular signaling and it is known that the members of the Janus kinase (JAK) family are essential for such signal transduction. Janus kinase 3 is a tyrosine kinase that belongs to the Janus family of kinases. Drugs targeting JAK3 in the treatment of Rheumatoid arthritis is relevant. Therefore, it is of interest to design suitable inhibitors for JAK3 dimer using molecular docking with Molegro Virtual Docker. The compound possessing the highest affinity score is subjected to virtual screening to retrieve inhibitors. The compound SCHEMBL19100243 (PubChem CID- 76749591) displays a high affinity with the target protein. The affinity scores of this compound are more than known drugs. ADMET analysis and BOILED Egg plot provide insights into this compound as a potent inhibitor of JAK3.
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Affiliation(s)
- Divya Jain
- In silico Research Laboratory,Eminent Biosciences,Mahalakshmi Nagar,Indore-452010,Madhya Pradesh,India.,Department of Biotechnology and Microbiology,Government PG Arts and Science College, Ratlam-457001, Madhya Pradesh,India
| | - Trishang Udhwani
- 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
| | - Aishwarya Gandhe
- In silico Research Laboratory,Eminent Biosciences,Mahalakshmi Nagar,Indore-452010,Madhya Pradesh,India
| | - Palugulla Bhaskar Reddy
- Department of Biotechnology and Microbiology,Government PG Arts and Science College, Ratlam-457001, Madhya Pradesh,India
| | - 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.,Computer Aided Drug Designing and Molecular Modeling Lab,Department of Bioinformatics, Alagappa University,Karaikudi-630 003,Tamil Nadu,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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15
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Gokhale P, Chauhan APS, Arora A, Khandekar N, Nayarisseri A, Singh SK. FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. Bioinformation 2019; 15:104-115. [PMID: 31435156 PMCID: PMC6677903 DOI: 10.6026/97320630015104] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 02/10/2019] [Accepted: 02/19/2019] [Indexed: 02/08/2023] Open
Abstract
Acute Myeloid Leukaemia (AML) is a blood cancer, which affects the red blood cells in the bone marrow. Of the possible proteins that are affected in AML, fms-like tyrosine
kinase 3 (FLT3) has long been recognized as a potential therapeutic target as it affects the other signaling pathways and leads to a cascade of events. First-generation
inhibitors sorafenib and midostaurin, as well as secondgeneration agents such as quizartinib and crenolanib are known. It is of interest to identify new compounds against
FLT3 with improved activity using molecular docking and virtual screening. Molecular docking of existing inhibitors selected a top scoring bestestablished
candidate Quizartinib having PubChem CID: 24889392. Similarity searching resulted in compound XGIQBUNWFCCMASUHFFFAOYSA-NPubChemCID: 44598530 which shows higher affinity
scores. A comparative study of both the compounds using a drug-drug comparison, ADMET studies, boiled egg plot and pharmacophore parameters and properties confirmed the result and
predicted the ligand to be an efficient inhibitor of FLT3.
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Affiliation(s)
- Padmini Gokhale
- In silico Research Laboratory,Eminent Biosciences,Mahalakshmi Nagar,Indore-452010,Madhya Pradesh,India
| | | | - Anushka Arora
- In silico Research Laboratory,Eminent Biosciences,Mahalakshmi Nagar,Indore-452010,Madhya Pradesh,India
| | - Natasha Khandekar
- In silico Research Laboratory,Eminent Biosciences,Mahalakshmi Nagar,Indore-452010,Madhya Pradesh,India
| | - 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.,Computer Aided Drug Designing and Molecular Modeling Lab,Department of Bioinformatics,Alagappa University,Karaikudi-630 003,Tamil Nadu,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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16
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Shukla P, Khandelwal R, Sharma D, Dhar A, Nayarisseri A, Singh SK. Virtual Screening of IL-6 Inhibitors for Idiopathic Arthritis. Bioinformation 2019; 15:121-130. [PMID: 31435158 PMCID: PMC6677908 DOI: 10.6026/97320630015121] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 02/10/2019] [Accepted: 02/19/2019] [Indexed: 12/16/2022] Open
Abstract
Juvenile idiopathic arthritis (JIA) is a heterogeneous disease characterized by the arthritis of unknown origin and IL6 is a known target for JIA. 20 known inhibitors towards IL-6 were screened and Methotrexate (MTX) having PubChem ID: 126941 showed high binding capacity with the receptor IL-6. The similarity searching with this compound gave 269 virtual screened compounds. The said screening presented 269 possible drugs having structural similarity to Methotrexate. The docking studies of the screened drugs separated the compound having PubChem CID: 122677576 (re-rank value of -140.262). Toxicity and interaction profile validated this compound for having a better affinity with the target protein. Conclusively, this study shows that according to ADMET profile and BOILED-Egg plot, the compound (PubChem CID: 122677576) obtained from Virtual Screen could be the best drug in future during the prevention of juvenile idiopathic arthritis. In the current study, the drug CID: 122677576 is a potent candidate for treating JIA. The pharmacophore study revealed that the drug CID: 122677576 is a non-inhibitor of CYP450 microsomal enzymes and was found to be non-toxic, similar to the established drug Methotrexate (CID: 126941). It has a lower LD50 value of 2.6698mol/kg as compared to the established compound having LD50 value as 23.4955mol/kg. Moreover, the compound was found to be non-carcinogenic.
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Affiliation(s)
- Palak Shukla
- 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
| | - Diksha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar,Indore - 452010,Madhya Pradesh, India
| | - Anindya Dhar
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar,Indore - 452010,Madhya Pradesh, India
| | - 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.,Computer Aided Drug Designing and Molecular Modelling Lab,Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modelling Lab,Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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17
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Bandaru S, Alvala M, Nayarisseri A, Sharda S, Goud H, Mundluru HP, Singh SK. Molecular dynamic simulations reveal suboptimal binding of salbutamol in T164I variant of β2 adrenergic receptor. PLoS One 2017; 12:e0186666. [PMID: 29053759 PMCID: PMC5650161 DOI: 10.1371/journal.pone.0186666] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 10/05/2017] [Indexed: 01/09/2023] Open
Abstract
The natural variant C491T (rs1800088) in ADRB2 gene substitutes Threonine to Isoleucine at 164th position in β2AR and results in receptor sequestration and altered binding of agonists. Present investigation pursues to identify the effect of T164I variation on function and structure of β2AR through systematic computational approaches. The study, in addition, addresses altered binding of salbutamol in T164I variant through molecular dynamic simulations. Methods involving changes in free energy, solvent accessibility surface area, root mean square deviations and analysis of binding cavity revealed structural perturbations in receptor to incur upon T164I substitution. For comprehensive understanding of receptor upon substitution, OPLS force field aided molecular dynamic simulations were performed for 10 ns. Simulations revealed massive structural departure for T164I β2AR variant from the native state along with considerably higher root mean square fluctuations of residues near the cavity. Affinity prediction by molecular docking showed two folds reduced affinity of salbutamol in T164I variant. To validate the credibility docking results, simulations for ligand-receptor complex were performed which demonstrated unstable salbutamol-T164I β2AR complex formation. Further, analysis of interactions in course of simulations revealed reduced ligand-receptor interactions of salbutamol in T164I variant. Taken together, studies herein provide structural rationales for suboptimal binding of salbutamol in T164I variant through integrated molecular modeling approaches.
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Affiliation(s)
- Srinivas Bandaru
- Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Hyderabad, India
- Molecular Modeling Lab, Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Hyderabad, India
| | - Mallika Alvala
- Molecular Modeling Lab, Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Hyderabad, India
| | - Anuraj Nayarisseri
- In Silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
- Bioinformatics Research Laboratory, LeGene Biosciences Private Limited, Indore, Madhya Pradesh, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Saphy Sharda
- In Silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Himshikha Goud
- In Silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Hema Prasad Mundluru
- Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Hyderabad, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
- * E-mail:
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18
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Diao M, Min J, Guo F, Zhang CL. Effects of salbutamol aerosol combined with magnesium sulfate on T-lymphocyte subgroup and Th1/Th2 cytokines of pediatric asthma. Exp Ther Med 2016; 13:117-120. [PMID: 28123478 PMCID: PMC5245138 DOI: 10.3892/etm.2016.3912] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 11/09/2016] [Indexed: 01/18/2023] Open
Abstract
The aim of the study was to analyze the effects of the intravenous infusion of salbutamol aerosol combined with magnesium sulfate in the treatment of pediatric asthma and the subsequent effects on the levels of T-lymphocyte subgroups and Th1/Th2 cytokines. A total of 86 patients with pediatric asthma, first diagnosed and treated at the Xuzhou Children's Hospital, were continuously selected and randomly divided into an observation group of 44 cases and control group of 42 cases. The patients in the control group were treated with budesonide atomization inhalation, while the children in the observation group were treated with intravenous infusion of salbutamol aerosol combined with magnesium sulfate. The therapeutic effects in the groups were compared. After treatment, the levels of serum CD3+ and CD8+ decreased when compared to before treatment; the levels of CD4+ and CD4+/CD8+ also increased, but the observation group had more significant improvement. Differences were statistically significant (P<0.05). After treatment, the levels of serum interleukin-2 (IL-2) and interferon-γ (IFN-γ) increased when compared to before, while levels of IL-4 and IL-6 decreased, and the observation group had more significant improvement. The differences were statistically significant (P<0.05). After treatment, the levels of VT, t-PTEF/t-E, MTIF/MTEF and TEF75/PTEF increased when compared to before; the observation group had more significant improvement. The differences were statistically significant (P<0.05). The effective rate and degree of treatment for the observation group were significantly higher than those of the control group and differences were statistically significant (P<0.05). The intravenous infusion of salbutamol aerosol combined with magnesium sulfate in the treatment of pediatric asthma can significantly improve therapeutic effects and lung functions, improve immune functions and relieve inflammatory reactions. Therefore, it indicates better clinical application and promotion value.
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Affiliation(s)
- Min Diao
- Department of Respiratory Medicine, Xuzhou Children's Hospital, Xuzhou, Jiangsu 221002, P.R. China
| | - Jie Min
- Department of Respiratory Medicine, Xuzhou Children's Hospital, Xuzhou, Jiangsu 221002, P.R. China
| | - Fei Guo
- Department of Respiratory Medicine, Xuzhou Children's Hospital, Xuzhou, Jiangsu 221002, P.R. China
| | - Chong-Lin Zhang
- Department of Respiratory Medicine, Xuzhou Children's Hospital, Xuzhou, Jiangsu 221002, P.R. China
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