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Lu L, Qin J, Chen J, Yu N, Miyano S, Deng Z, Li C. Recent computational drug repositioning strategies against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5713-5728. [PMID: 36277237 PMCID: PMC9575573 DOI: 10.1016/j.csbj.2022.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022] Open
Abstract
We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data. We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19. Integrating different levels of data may improve the success rate for drug repositioning.
Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.
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Affiliation(s)
- Lu Lu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiale Qin
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, China
| | - Jiandong Chen
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,School of Public Health, Undergraduate School of Zhejiang University, Hangzhou, China
| | - Na Yu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Zhenzhong Deng
- Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
| | - Chen Li
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
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Picón-Pagès P, Bonet J, García-García J, Garcia-Buendia J, Gutierrez D, Valle J, Gómez-Casuso CE, Sidelkivska V, Alvarez A, Perálvarez-Marín A, Suades A, Fernàndez-Busquets X, Andreu D, Vicente R, Oliva B, Muñoz FJ. Human Albumin Impairs Amyloid β-peptide Fibrillation Through its C-terminus: From docking Modeling to Protection Against Neurotoxicity in Alzheimer's disease. Comput Struct Biotechnol J 2019; 17:963-971. [PMID: 31360335 PMCID: PMC6639691 DOI: 10.1016/j.csbj.2019.06.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 06/03/2019] [Accepted: 06/13/2019] [Indexed: 12/01/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of extracellular deposits of amyloid β-peptide (Aβ), which induces neuronal death. Monomeric Aβ is not toxic but tends to aggregate into β-sheets that are neurotoxic. Therefore to prevent or delay AD onset and progression one of the main therapeutic approaches would be to impair Aβ assembly into oligomers and fibrils and to promote disaggregation of the preformed aggregate. Albumin is the most abundant protein in the cerebrospinal fluid and it was reported to bind Aβ impeding its aggregation. In a previous work we identified a 35-residue sequence of clusterin, a well-known protein that binds Aβ, that is highly similar to the C-terminus (CTerm) of albumin. In this work, the docking experiments show that the average binding free energy of the CTerm-Aβ1-42 simulations was significantly lower than that of the clusterin-Aβ1-42 binding, highlighting the possibility that the CTerm retains albumin's binding properties. To validate this observation, we performed in vitro structural analysis of soluble and aggregated 1 μM Aβ1-42 incubated with 5 μM CTerm, equimolar to the albumin concentration in the CSF. Reversed-phase chromatography and electron microscopy analysis demonstrated a reduction of Aβ1-42 aggregates when the CTerm was present. Furthermore, we treated a human neuroblastoma cell line with soluble and aggregated Aβ1-42 incubated with CTerm obtaining a significant protection against Aβ-induced neurotoxicity. These in silico and in vitro data suggest that the albumin CTerm is able to impair Aβ aggregation and to promote disassemble of Aβ aggregates protecting neurons.
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Key Words
- AD, Alzheimer's disease
- APP, amyloid precursor protein
- Albumin
- Alzheimer's disease
- Amyloid
- Aß, Amyloid-ß peptide
- CD, Circular dichroism
- CSF, cerebrospinal fluid
- CTerm, albumin C-terminus
- Docking
- HPLC, high performance liquid chromatography
- LC-MS, Liquid chromatography-mass spectrometry
- MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide
- NMR, nuclear magnetic resonance
- PBS, phosphate-buffered saline
- PDB, Protein Data Bank
- PPI, protein-protein interactions
- SDS, sodium dodecyl sulfate
- TEM, transmission electron microscopy
- TFA, trifluoroacetic acid
- UV, ultraviolet
- fAβ1–42, HiLyte Fluor488 labelled human Aβ1–42
- β-Sheet
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Affiliation(s)
- Pol Picón-Pagès
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jaume Bonet
- Laboratory of Structural Bioinformatics (GRIB), Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Javier García-García
- Laboratory of Structural Bioinformatics (GRIB), Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Joan Garcia-Buendia
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Daniela Gutierrez
- Cell Signaling Laboratory, Centro UC de Envejecimiento y Regeneración (CARE), Department of Cellular and Molecular Biology, Biological Sciences Faculty, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javier Valle
- Laboratory of Proteomics and Protein Chemistry, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Carmen E.S. Gómez-Casuso
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Valeriya Sidelkivska
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Alejandra Alvarez
- Cell Signaling Laboratory, Centro UC de Envejecimiento y Regeneración (CARE), Department of Cellular and Molecular Biology, Biological Sciences Faculty, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Alex Perálvarez-Marín
- Unitat de Biofísica, Departament de Bioquímica i de Biologia Molecular, Facultat de Medicina, Centre d'Estudis en Biofísica, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Albert Suades
- Unitat de Biofísica, Departament de Bioquímica i de Biologia Molecular, Facultat de Medicina, Centre d'Estudis en Biofísica, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Fernàndez-Busquets
- Nanomalaria Group, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, ES-08028 Barcelona, Spain
- Barcelona Institute for Global Health (ISGlobal, Hospital Clínic-Universitat de Barcelona), Rosselló 149-153, ES-08036 Barcelona, Spain
| | - David Andreu
- Laboratory of Proteomics and Protein Chemistry, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Rubén Vicente
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Baldomero Oliva
- Laboratory of Structural Bioinformatics (GRIB), Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Francisco J. Muñoz
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
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