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Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Masand V, Humane V, Mali S, Alzahrani AYA, Rashid S, Elossaily GM. Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in- vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor. J Biomol Struct Dyn 2024:1-31. [PMID: 38385447 DOI: 10.1080/07391102.2024.2319104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/11/2024] [Indexed: 02/23/2024]
Abstract
A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay Masand
- Department of Chemistry, Amravati, Maharashtra, India
| | - Vivek Humane
- Department of Chemistry, Shri R. R. Lahoti Science college, Morshi District: Amravati, Maharashtra, India
| | - Suraj Mali
- School of Pharmacy, D.Y. Patil University (Deemed to be University), Nerul, Navi Mumbai, India
| | | | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Gehan M Elossaily
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia
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Barman S, Sahoo SS, Padhan J, Sudhamalla B. Identification of novel natural product inhibitors of BRD4 using high throughput virtual screening and MD simulation. J Biomol Struct Dyn 2023; 41:10569-10581. [PMID: 36524430 DOI: 10.1080/07391102.2022.2155346] [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: 09/23/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022]
Abstract
Bromodomains are evolutionarily conserved structural motifs that recognize acetylated lysine residues on histone tails. They play a crucial role in shaping chromatin architecture and regulating gene expression in various biological processes. Mutations in bromodomains containing proteins lead to multiple human diseases, which makes them attractive target for therapeutic intervention. Extensive studies have been done on BRD4 as a target for several cancers, such as Acute Myeloid Leukemia (AML) and Burkitt Lymphoma. Several potential inhibitors have been identified against the BRD4 bromodomain. However, most of these inhibitors have drawbacks such as non-specificity and toxicity, decreasing their appeal and necessitating the search for novel non-toxic inhibitors. This study aims to address this need by virtually screening natural compounds from the NPASS database against the Kac binding site of BRD4-BD1 using high throughput molecular docking followed by similarity clustering, pharmacokinetic screening, MD simulation and MM-PBSA binding free energy calculations. Using this approach, we identified five natural product inhibitors having a similar or better binding affinity to the BRD4 bromodomain compared to JQ1 (previously reported inhibitor of BRD4). Further systematic analysis of these inhibitors resulted in the top three hits: NPC268484 (Palodesangren-B), NPC295021 (Candidine) and NPC313112 (Buxifoliadine-D). Collectively, our in silico results identified some promising natural products that have the potential to act as potent BRD4-BD1 inhibitors and can be considered for further validation through future in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Soumen Barman
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia, West Bengal, India
| | - Snehasudha Subhadarsini Sahoo
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia, West Bengal, India
| | - Jyotirmayee Padhan
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia, West Bengal, India
| | - Babu Sudhamalla
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia, West Bengal, India
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Chen L, Gu R, Li Y, Liu H, Han W, Yan Y, Chen Y, Zhang Y, Jiang Y. Epigenetic target identification strategy based on multi-feature learning. J Biomol Struct Dyn 2023:1-17. [PMID: 37827992 DOI: 10.1080/07391102.2023.2259511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/20/2023] [Indexed: 10/14/2023]
Abstract
The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lingfeng Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Rui Gu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yuanyuan Li
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Weijie Han
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yingchao Yan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yulei Jiang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
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Elalouf A. In-silico Structural Modeling of Human Immunodeficiency Virus Proteins. Biomed Eng Comput Biol 2023; 14:11795972231154402. [PMID: 36819710 PMCID: PMC9936402 DOI: 10.1177/11795972231154402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 01/16/2023] [Indexed: 02/18/2023] Open
Abstract
Human immunodeficiency virus (HIV) is an infectious virus that depletes the CD4+ T lymphocytes of the immune system and causes a chronic life-treating disease-acquired immunodeficiency syndrome (AIDS). The HIV genome encodes different structural and accessory proteins involved in viral entry and life cycle. Determining the 3D structure of HIV proteins is essential for new target position finding, structure-based drug designing, and future planning for computational and laboratory experimentations. Hence, the study aims to predict the 3D structures of all the HIV structural and accessory proteins using computational homology modeling to understand better the structural basis of HIV proteins interacting with host cells and viral replication. The sequences of HIV capsid, matrix, nucleocapsid, p6, reverse transcriptase, invertase, protease, gp120, gp41, virus protein r, viral infectivity factor, virus protein unique, RNA splicing regulator, transactivator protein, negative regulating factor, and virus protein x proteins were retrieved from UniProt. The primary and secondary structures of HIV proteins were predicted by Expasy ProtParam and SOPMA web servers. For the homology modeling, the MODELLER predicted the 3D structures of HIV proteins using templates. Then, the modeled structures were validated by the Ramachandran plot, local and global quality estimation scores, QMEAN scores, and Z-scores. Most of the amino acid residues of HIV proteins were present in the most favored and generously allowed regions in the Ramachandran plots. The local and global quality scores and Z-scores of the HIV proteins confirmed the good quality of modeled structures. The 3D modeled structures of HIV proteins might help further investigate the possible treatment.
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Affiliation(s)
- Amir Elalouf
- Amir Elalouf, Department of Management, Bar-Ilan University, Max and Anna, Ramat Gan 5290002, Israel.
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Rahman MM, Islam MR, Rahman F, Rahaman MS, Khan MS, Abrar S, Ray TK, Uddin MB, Kali MSK, Dua K, Kamal MA, Chellappan DK. Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance. Bioengineering (Basel) 2022; 9:bioengineering9080335. [PMID: 35892749 PMCID: PMC9332125 DOI: 10.3390/bioengineering9080335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/09/2022] [Accepted: 07/18/2022] [Indexed: 01/07/2023] Open
Abstract
Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.
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Affiliation(s)
- Md. Mominur Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Rezaul Islam
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Firoza Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Saidur Rahaman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Shajib Khan
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Sayedul Abrar
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Tanmay Kumar Ray
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Mohammad Borhan Uddin
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Most. Sumaiya Khatun Kali
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Kamal Dua
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun 248007, India
| | - Mohammad Amjad Kamal
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Enzymoics, 7 Peterlee Place, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Bukit Jalil, Kuala Lumpur 57000, Malaysia
- Correspondence:
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