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Singh N, Singh AK. In Silico Structural Modeling and Binding Site Analysis of Cerebroside Sulfotransferase (CST): A Therapeutic Target for Developing Substrate Reduction Therapy for Metachromatic Leukodystrophy. ACS OMEGA 2024; 9:10748-10768. [PMID: 38463293 PMCID: PMC10918841 DOI: 10.1021/acsomega.3c09462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/26/2024] [Accepted: 01/31/2024] [Indexed: 03/12/2024]
Abstract
Cerebroside sulfotransferase (CST) is emerging as an important therapeutic target to develop substrate reduction therapy (SRT) for metachromatic leukodystrophy (MLD), a rare neurodegenerative lysosomal storage disorder. MLD develops with progressive impairment and destruction of the myelin sheath as a result of accumulation of sulfatide around the nerve cells in the absence of its recycling mechanism with deficiency of arylsulfatase A (ARSA). Sulfatide is the product of the catalytic action of cerebroside sulfotransferase (CST), which needs to be regulated under pathophysiological conditions by inhibitor development. To carry out in silico-based preliminary drug screening or for designing new drug candidates, a high-quality three-dimensional (3D) structure is needed in the absence of an experimentally derived three-dimensional crystal structure. In this study, a 3D model of the protein was developed using a primary sequence with the SWISS-MODEL server by applying the top four GMEQ score-based templates belonging to the sulfotransferase family as a reference. The 3D model of CST highlights the features of the protein responsible for its catalytic action. The CST model comprises five β-strands, which are flanked by ten α-helices from both sides as well as form the upside cover of the catalytic pocket of CST. CST has two catalytic regions: PAPS (-sulfo donor) binding and galactosylceramide (-sulfo acceptor) binding. The catalytic action of CST was proposed via molecular docking and molecular dynamic (MD) simulation with PAPS, galactosylceramide (GC), PAPS-galactosylceramide, and PAP. The stability of the model and its catalytic action were confirmed using molecular dynamic simulation-based trajectory analysis. CST response against the inhibition potential of the experimentally reported competitive inhibitor of CST was confirmed via molecular docking and molecular dynamics simulation, which suggested the suitability of the CST model for future drug discovery to strengthen substrate reduction therapy for MLD.
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Affiliation(s)
- Nivedita Singh
- Department of Dravyaguna,
Faculty of Ayurveda, Institute of Medical
Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India
| | - Anil Kumar Singh
- Department of Dravyaguna,
Faculty of Ayurveda, Institute of Medical
Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India
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Liang J, Yan Z, Zhang Y, Xu H, Song W. Proteomics analysis of resistance mechanism of Trichoderma harzianum under U(VI) stress. JOURNAL OF ENVIRONMENTAL RADIOACTIVITY 2023; 270:107303. [PMID: 37783189 DOI: 10.1016/j.jenvrad.2023.107303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/04/2023]
Abstract
Trichoderma harzianum has a certain resistance to Hexavalent Uranium (U(VI)), but its resistance mechanism is unknown. Based on proteomics sequencing using DIA mode, differentially expressed proteins (DEPs) of Trichoderma harzianum under U(VI) stress were identified. GO enrichment, KEGG annotation analysis and DEPs annotation were performed. The results showed that 8 DEPs, 8 DEPs and 15 DEPs were obtained in the low-dose, medium-dose and high-dose groups, respectively. The functional classification of GO demonstrated that DEPs were associated with 17 molecular functions, 5 biological processes, and 5 cellular components. Furthermore, DEPs were enriched in transport and catabolism, energy metabolism, translation, and signal transduction. These findings showed that Trichoderma harzianum was significantly changed in protein expression and signaling pathway after U(VI) exposure. Therefore, these results have provided Trichoderma harzianum with a theoretical background that can be applied to environmental cleanup.
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Affiliation(s)
- Jun Liang
- Jianghuai College of Anhui University, Hefei, 230031, China.
| | - Zhuna Yan
- School of Medicine, Anhui University of Science and Technology, Huainan, 232001, China
| | - Yan Zhang
- School of Medicine, Anhui University of Science and Technology, Huainan, 232001, China
| | - Huan Xu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Wencheng Song
- School of Medicine, Anhui University of Science and Technology, Huainan, 232001, China; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health & Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions and School for Radiological and Interdisciplinary Sciences, Soochow University, 215123, Suzhou, 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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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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