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Li S, Li H, Lian R, Xie J, Feng R. New perspective of small-molecule antiviral drugs development for RNA viruses. Virology 2024; 594:110042. [PMID: 38492519 DOI: 10.1016/j.virol.2024.110042] [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: 10/21/2023] [Revised: 02/20/2024] [Accepted: 03/01/2024] [Indexed: 03/18/2024]
Abstract
High variability and adaptability of RNA viruses allows them to spread between humans and animals, causing large-scale infectious diseases which seriously threat human and animal health and social development. At present, AIDS, viral hepatitis and other viral diseases with high incidence and low cure rate are still spreading around the world. The outbreaks of Ebola, Zika, dengue and in particular of the global pandemic of COVID-19 have presented serious challenges to the global public health system. The development of highly effective and broad-spectrum antiviral drugs is a substantial and urgent research subject to deal with the current RNA virus infection and the possible new viral infections in the future. In recent years, with the rapid development of modern disciplines such as artificial intelligence technology, bioinformatics, molecular biology, and structural biology, some new strategies and targets for antivirals development have emerged. Here we review the main strategies and new targets for developing small-molecule antiviral drugs against RNA viruses through the analysis of the new drug development progress against several highly pathogenic RNA viruses, to provide clues for development of future antivirals.
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Affiliation(s)
- Shasha Li
- College of Life Science and Engineering, Northwest Minzu University, Lanzhou, 730030, China; Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Huixia Li
- Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Ruiya Lian
- College of Life Science and Engineering, Northwest Minzu University, Lanzhou, 730030, China; Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Jingying Xie
- College of Life Science and Engineering, Northwest Minzu University, Lanzhou, 730030, China; Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China
| | - Ruofei Feng
- Key Laboratory of Biotechnology and Bioengineering of State Ethnic Affairs Commission, Biomedical Research Center, Northwest Minzu University, Lanzhou, 730030, China.
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Behboudi E, Nooreddin Faraji S, Daryabor G, Mohammad Ali Hashemi S, Asadi M, Edalat F, Javad Raee M, Hatam G. SARS-CoV-2 mechanisms of cell tropism in various organs considering host factors. Heliyon 2024; 10:e26577. [PMID: 38420467 PMCID: PMC10901034 DOI: 10.1016/j.heliyon.2024.e26577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 01/30/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
A critical step in the drug design for SARS-CoV-2 is to discover its molecular targets. This study comprehensively reviewed the molecular mechanisms of SARS-CoV-2, exploring host cell tropism and interaction targets crucial for cell entry. The findings revealed that beyond ACE2 as the primary entry receptor, alternative receptors, co-receptors, and several proteases such as TMPRSS2, Furin, Cathepsin L, and ADAM play critical roles in virus entry and subsequent pathogenesis. Additionally, SARS-CoV-2 displays tropism in various human organs due to its diverse receptors. This review delves into the intricate details of receptors, host proteases, and the involvement of each organ. Polymorphisms in the ACE2 receptor and mutations in the spike or its RBD region contribute to the emergence of variants like Alpha, Beta, Gamma, Delta, and Omicron, impacting the pathogenicity of SARS-CoV-2. The challenge posed by mutations raises questions about the effectiveness of existing vaccines and drugs, necessitating consideration for updates in their formulations. In the urgency of these critical situations, repurposed drugs such as Camostat Mesylate and Nafamostat Mesylate emerge as viable pharmaceutical options. Numerous drugs are involved in inhibiting receptors and host factors crucial for SARS-CoV-2 entry, with most discussed in this review. In conclusion, this study may provide valuable insights to inform decisions in therapeutic approaches.
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Affiliation(s)
- Emad Behboudi
- Department of Basic Medical Sciences, Khoy University of Medical Sciences, Khoy, Iran
| | - Seyed Nooreddin Faraji
- Department of Pathology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Gholamreza Daryabor
- Autoimmune Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Seyed Mohammad Ali Hashemi
- Department of Bacteriology & Virology, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Microbiology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Maryam Asadi
- Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Fahime Edalat
- Department of Bacteriology & Virology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Javad Raee
- Center for Nanotechnology in Drug Delivery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Gholamreza Hatam
- Basic Sciences in Infectious Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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García-Aguilar A, Campi-Caballero R, Visoso-Carvajal G, García-Sánchez JR, Correa-Basurto J, García-Machorro J, Espinosa-Raya J. In Vitro Analysis of SARS-CoV-2 Spike Protein and Ivermectin Interaction. Int J Mol Sci 2023; 24:16392. [PMID: 38003581 PMCID: PMC10671224 DOI: 10.3390/ijms242216392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
The spike (S) protein of SARS-CoV-2 is a molecular target of great interest for developing drug therapies against COVID-19 because S is responsible for the interaction of the virus with the host cell receptor. Currently, there is no outpatient safety treatment for COVID-19 disease. Furthermore, we consider it of worthy importance to evaluate experimentally the possible interaction of drugs (approved by the Food and Drug Administration) and the S, considering some previously in silico and clinical use. Then, the objective of this study was to demonstrate the in vitro interaction of ivermectin with S. The equilibrium dialysis technique with UV-Vis was performed to obtain the affinity and dissociation constants. In addition, the Drug Affinity Responsive Target Stability (DARTS) technique was used to demonstrate the in vitro interaction of S with ivermectin. The results indicate the interaction between ivermectin and the S with an association and dissociation constant of Ka = 1.22 µM-1 and Kd = 0.81 µM, respectively. The interaction was demonstrated in ratios of 1:50 pmol and 1:100 pmol (S: ivermectin) by the DARTS technique. The results obtained with these two different techniques demonstrate an interaction between S and ivermectin previously explored in silico, suggesting its clinical uses to stop the viral spread among susceptible human hosts.
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Affiliation(s)
- Alejandra García-Aguilar
- Laboratorio de Neurofarmacología, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico; (A.G.-A.); (R.C.-C.)
- Laboratorio de Medicina de la Conservación, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico;
| | - Rebeca Campi-Caballero
- Laboratorio de Neurofarmacología, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico; (A.G.-A.); (R.C.-C.)
- Laboratorio de Medicina de la Conservación, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico;
| | - Giovani Visoso-Carvajal
- Laboratorio de Medicina de la Conservación, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico;
| | - José Rubén García-Sánchez
- Laboratorio de Oncología Molecular y Estrés Oxidativo, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico;
| | - José Correa-Basurto
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico;
| | - Jazmín García-Machorro
- Laboratorio de Medicina de la Conservación, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico;
| | - Judith Espinosa-Raya
- Laboratorio de Neurofarmacología, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de México C.P. 11340, Mexico; (A.G.-A.); (R.C.-C.)
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Ma Y, Zhong J, Zhu N. Weighted hypergraph learning and adaptive inductive matrix completion for SARS-CoV-2 drug repositioning. Methods 2023; 219:102-110. [PMID: 37804962 DOI: 10.1016/j.ymeth.2023.10.002] [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: 07/03/2022] [Revised: 09/14/2023] [Accepted: 10/03/2023] [Indexed: 10/09/2023] Open
Abstract
MOTIVATION The outbreak of the human coronavirus (SARS-CoV-2) has placed a huge burden on public health and the world economy. Compared with de novo drug discovery, drug repurposing is a promising therapeutic strategy that facilitates rapid clinical treatment decisions, shortens the development process, and reduces costs. RESULTS In this study, we propose a weighted hypergraph learning and adaptive inductive matrix completion method, WHAIMC, for predicting potential virus-drug associations. Firstly, we integrate multi-source data to describe viruses and drugs from multiple perspectives, including drug chemical structures, drug targets, virus complete genome sequences, and virus-drug associations. Then, WHAIMC establishes an adaptive inductive matrix completion model to improve performance through adaptive learning of similarity relations. Finally, WHAIMC introduces weighted hypergraph learning into adaptive inductive matrix completion to capture higher-order relationships of viruses (or drugs). The results showed that WHAIMC had a strong predictive performance for new virus-drug associations, new viruses, and new drugs. The case study further demonstrates that WHAIMC is highly effective for repositioning antiviral drugs against SARS-CoV-2 and provides a new perspective for virus-drug association prediction. The code and data in this study is freely available at https://github.com/Mayingjun20179/WHAIMC.
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Affiliation(s)
- Yingjun Ma
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen 361024, China.
| | - Junjiang Zhong
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen 361024, China
| | - Nenghui Zhu
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen 361024, China
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Qu J, Song Z, Cheng X, Jiang Z, Zhou J. A new integrated framework for the identification of potential virus-drug associations. Front Microbiol 2023; 14:1179414. [PMID: 37675432 PMCID: PMC10478006 DOI: 10.3389/fmicb.2023.1179414] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 07/31/2023] [Indexed: 09/08/2023] Open
Abstract
Introduction With the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases. Methods In this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses. Results The results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.
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Affiliation(s)
- Jia Qu
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Zihao Song
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Xiaolong Cheng
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Zhibin Jiang
- School of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China
| | - Jie Zhou
- School of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China
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DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization. BMC Bioinformatics 2023; 24:52. [PMID: 36793010 PMCID: PMC9931173 DOI: 10.1186/s12859-023-05181-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks. METHODS We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs. RESULTS In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19. CONCLUSIONS In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.
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Wang Y, Xiang J, Liu C, Tang M, Hou R, Bao M, Tian G, He J, He B. Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization. Front Microbiol 2022; 13:1062281. [PMID: 36545200 PMCID: PMC9762482 DOI: 10.3389/fmicb.2022.1062281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.
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Affiliation(s)
- Yibai Wang
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Ju Xiang
- School of Information Engineering, Changsha Medical University, Changsha, China,Academician Workstation, Changsha Medical University, Changsha, China,*Correspondence: Ju Xiang,
| | - Cuicui Liu
- School of Information Engineering, Changsha Medical University, Changsha, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Rui Hou
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Meihua Bao
- School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jianjun He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Jianjun He,
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China,School of Pharmacy, Changsha Medical University, Changsha, China,Key Laboratory Breeding Base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Changsha Medical University, Changsha, China,Binsheng He,
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Su Q, Tan Q, Liu X, Wu L. Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model. Front Genet 2022; 13:1001608. [PMID: 36186429 PMCID: PMC9521272 DOI: 10.3389/fgene.2022.1001608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 08/15/2022] [Indexed: 12/03/2022] Open
Abstract
Bladder cancer is the most common cancer of the urinary system. Bladder urothelial cancer accounts for 90% of bladder cancer. These two cancers have high morbidity and mortality rates worldwide. The identification of biomarkers for bladder cancer and bladder urothelial cancer helps in their diagnosis and treatment. circRNAs are considered oncogenes or tumor suppressors in cancers, and they play important roles in the occurrence and development of cancers. In this manuscript, we developed an Ensemble model, CDA-EnRWLRLS, to predict circRNA-Disease Associations (CDA) combining Random Walk with restart and Laplacian Regularized Least Squares, and further screen potential biomarkers for bladder cancer and bladder urothelial cancer. First, we compute disease similarity by combining the semantic similarity and association profile similarity of diseases and circRNA similarity by combining the functional similarity and association profile similarity of circRNAs. Second, we score each circRNA-disease pair by random walk with restart and Laplacian regularized least squares, respectively. Third, circRNA-disease association scores from these models are integrated to obtain the final CDAs by the soft voting approach. Finally, we use CDA-EnRWLRLS to screen potential circRNA biomarkers for bladder cancer and bladder urothelial cancer. CDA-EnRWLRLS is compared to three classical CDA prediction methods (CD-LNLP, DWNN-RLS, and KATZHCDA) and two individual models (CDA-RWR and CDA-LRLS), and obtains better AUC of 0.8654. We predict that circHIPK3 has the highest association with bladder cancer and may be its potential biomarker. In addition, circSMARCA5 has the highest association with bladder urothelial cancer and may be its possible biomarker.
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Guo H, Li T, Wen H. Identifying shared genetic loci between coronavirus disease 2019 and cardiovascular diseases based on cross-trait meta-analysis. Front Microbiol 2022; 13:993933. [PMID: 36187959 PMCID: PMC9520490 DOI: 10.3389/fmicb.2022.993933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/24/2022] [Indexed: 12/15/2022] Open
Abstract
People with coronavirus disease 2019 (COVID-19) have different mortality or severity, and this clinical outcome is thought to be mainly attributed to comorbid cardiovascular diseases. However, genetic loci jointly influencing COVID-19 and cardiovascular disorders remain largely unknown. To identify shared genetic loci between COVID-19 and cardiac traits, we conducted a genome-wide cross-trait meta-analysis. Firstly, from eight cardiovascular disorders, we found positive genetic correlations between COVID-19 and coronary artery disease (CAD, Rg = 0.4075, P = 0.0031), type 2 diabetes (T2D, Rg = 0.2320, P = 0.0043), obesity (OBE, Rg = 0.3451, P = 0.0061), as well as hypertension (HTN, Rg = 0.233, P = 0.0026). Secondly, we detected 10 shared genetic loci between COVID-19 and CAD, 3 loci between COVID-19 and T2D, 5 loci between COVID-19 and OBE, and 21 loci between COVID-19 and HTN, respectively. These shared genetic loci were enriched in signaling pathways and secretion pathways. In addition, Mendelian randomization analysis revealed significant causal effect of COVID-19 on CAD, OBE and HTN. Our results have revealed the genetic architecture shared by COVID-19 and CVD, and will help to shed light on the molecular mechanisms underlying the associations between COVID-19 and cardiac traits.
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Affiliation(s)
- Hongping Guo
- School of Mathematics and Statistics, Hubei Normal University, Huangshi, China
- *Correspondence: Hongping Guo,
| | - Tong Li
- School of Mathematics and Statistics, Hubei Normal University, Huangshi, China
| | - Haiyang Wen
- School of Computational Science and Electronics, Hunan Institute of Engineering, Xiangtan, China
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Cheng X, Qu J, Song S, Bian Z. Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction. PeerJ 2022; 10:e13848. [PMID: 35990901 PMCID: PMC9387521 DOI: 10.7717/peerj.13848] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/14/2022] [Indexed: 01/18/2023] Open
Abstract
Background Efficient identification of microbe-drug associations is critical for drug development and solving problem of antimicrobial resistance. Traditional wet-lab method requires a lot of money and labor in identifying potential microbe-drug associations. With development of machine learning and publication of large amounts of biological data, computational methods become feasible. Methods In this article, we proposed a computational model of neighborhood-based inference (NI) and restricted Boltzmann machine (RBM) to predict potential microbe-drug association (NIRBMMDA) by using integrated microbe similarity, integrated drug similarity and known microbe-drug associations. First, NI was used to obtain a score matrix of potential microbe-drug associations by using different thresholds to find similar neighbors for drug or microbe. Second, RBM was employed to obtain another score matrix of potential microbe-drug associations based on contrastive divergence algorithm and sigmoid function. Because generalization ability of individual method is poor, we used an ensemble learning to integrate two score matrices for predicting potential microbe-drug associations more accurately. In particular, NI can fully utilize similar (neighbor) information of drug or microbe and RBM can learn potential probability distribution hid in known microbe-drug associations. Moreover, ensemble learning was used to integrate individual predictor for obtaining a stronger predictor. Results In global leave-one-out cross validation (LOOCV), NIRBMMDA gained the area under the receiver operating characteristics curve (AUC) of 0.8666, 0.9413 and 0.9557 for datasets of DrugVirus, MDAD and aBiofilm, respectively. In local LOOCV, AUCs of 0.8512, 0.9204 and 0.9414 were obtained for NIRBMMDA based on datasets of DrugVirus, MDAD and aBiofilm, respectively. For five-fold cross validation, NIRBMMDA acquired AUC and standard deviation of 0.8569 ± -0.0027, 0.9248 ± -0.0014 and 0.9369 ± -0.0020 on the basis of datasets of DrugVirus, MDAD and aBiofilm, respectively. Moreover, case study for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed that 13 out of the top 20 predicted drugs were verified by searching literature. The other two case studies indicated that 17 and 17 out of the top 20 predicted microbes for the drug of ciprofloxacin and minocycline were confirmed by identifying published literature, respectively.
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Affiliation(s)
- Xiaolong Cheng
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Jia Qu
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Shuangbao Song
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Zekang Bian
- School of AI & Computer Science, Jiangnan University, Wuxi, Jiangsu, China
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Miao Y, Zhang X, Chen S, Zhou W, Xu D, Shi X, Li J, Tu J, Yuan X, Lv K, Tian G. Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci. Front Oncol 2022; 12:946552. [PMID: 36016607 PMCID: PMC9396384 DOI: 10.3389/fonc.2022.946552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/24/2022] [Indexed: 11/13/2022] Open
Abstract
Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7-1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future.
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Affiliation(s)
- Yongchang Miao
- Gastroenterology Center, The Second People’s Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College of Xuzhou Medical University, Lianyungang, China
- The Second People’s Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China
| | - Xueliang Zhang
- Fifth Division of Cancer, Jiamusi Cancer Hospital, Jiamusi, China
| | - Sijie Chen
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Wenjing Zhou
- Department of Oncology, Hiser Medical Center of Qingdao, Qingdao, China
| | - Dalai Xu
- Gastrointestinal Surgery, The Second People’s Hospital of Lianyungang, Lianyungang, China
| | - Xiaoli Shi
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jian Li
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Jinhui Tu
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Xuelian Yuan
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Kebo Lv
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Geng Tian
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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12
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Saravanan KA, Panigrahi M, Kumar H, Rajawat D, Nayak SS, Bhushan B, Dutt T. Role of genomics in combating COVID-19 pandemic. Gene 2022; 823:146387. [PMID: 35248659 PMCID: PMC8894692 DOI: 10.1016/j.gene.2022.146387] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 12/20/2022]
Abstract
The coronavirus disease 2019 (COVID-19) quickly swept over the world, becoming one of the most devastating outbreaks in human history. Being the first pandemic in the post-genomic era, advancements in genomics contributed significantly to scientific understanding and public health response to COVID-19. Genomic technologies have been employed by researchers all over the world to better understand the biology of SARS-CoV-2 and its origin, genomic diversity, and evolution. Worldwide genomic resources have greatly aided in the investigation of the COVID-19 pandemic. The pandemic has ushered in a new era of genomic surveillance, wherein scientists are tracking the changes of the SARS-CoV-2 genome in real-time at the international and national levels. Availability of genomic and proteomic information enables the rapid development of molecular diagnostics and therapeutics. The advent of high-throughput sequencing and genome editing technologies led to the development of modern vaccines. We briefly discuss the impact of genomics in the ongoing COVID-19 pandemic in this review.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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13
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Zendehdel A, Bidkhori M, Ansari M, Jamalimoghaddamsiyahkali S, Asoodeh A. Efficacy of oseltamivir in the treatment of patients infected with Covid-19. Ann Med Surg (Lond) 2022; 77:103679. [PMID: 35531426 PMCID: PMC9054703 DOI: 10.1016/j.amsu.2022.103679] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 12/26/2022] Open
Abstract
Objective The recent unprecedented pandemic caused by Sars-Cov-2 (the new coronavirus 2019), is threatening public health around the world. Although several studies have been performed, there is no identified treatment for Covid-19 patients. Here we assessed the efficacy of oseltamivir in combination therapy, by comparing two different therapeutic regimens in hospitalized patients, in improving outcomes and find better treatment for Covid-19 patients. Methods This is a single-center retrospective cohort study of 285 confirmed Covid-19 in patients at (XXX). Depending on the date of admission, the patients were divided into two groups; group 1 (oseltamivir group) from February 20, 2020 to March 15, 2020 received Oseltamivir with routine regimen and group 2 (control group) from March 20, 2020 to April 20, 2020 received routine regimen alone that included Azithromycin 500 mg/day and Hydroxychloroquine 200 mg/12 h. Endpoints including duration of hospitalization, requirement to admission to intensive care unit (ICU) and mechanical ventilation, outcome and mortality rate. Results A total of 285 patients were enrolled in the two months, 120 patients for group 1 and 165 for group 2. The median time from admission to discharge was significantly shorter in the oseltamivir group compared to the control group (4.9 vs 6.6 days, p < 0.001). Additionally, the mortality rate was found to be lower in the oseltamivir group than in the control group (1.7% vs 6,7%, p = 0.06) which was statistically significant by multivariate analysis (p = 0.03). The incidence of admission to the ICU (6.7% vs 11.5%, p = 0.1) and mechanical ventilation (2.5% vs 4.8%, p = 0.3) were also decreased in the oseltamivir group, but was not statistically significant. Conclusions This study showed that administration of oseltamivir was associated with shorter length of hospital stay and earlier recovery and discharge of hospital, and a lower mortality rate. The recent unprecedented pandemic caused by Sars-Cov-2, is threatening public health over the world. Although several studies have been performed there is no identified treatment for Covid-19 patients. This study showed that administration of oseltamivir was associated with shorter length of hospital stay.
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Affiliation(s)
- Abolfazl Zendehdel
- Geriatrics Department, Associate Professor of Internal Medicine, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Family Medicine Department, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Bidkhori
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohsen Ansari
- Radiology Department, Amir al-Momenin Hospital, Islamic Azad University of Medical Sciences, Tehran, Iran
| | | | - Azadeh Asoodeh
- Family Medicine Department, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Corresponding author. Tehran University of Medical Sciences, Tehran, Iran.
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14
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HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins. Comput Biol Med 2022; 145:105395. [PMID: 35334314 DOI: 10.1016/j.compbiomed.2022.105395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 12/24/2022]
Abstract
The identification of DNA-binding proteins (DBPs) has always been a hot issue in the field of sequence classification. However, considering that the experimental identification method is very resource-intensive, the construction of a computational prediction model is worthwhile. This study developed and evaluated a hybrid kernel alignment maximization-based multiple kernel model (HKAM-MKM) for predicting DBPs. First, we collected two datasets and performed feature extraction on the sequences to obtain six feature groups, and then constructed the corresponding kernels. To ensure the effective utilisation of the base kernel and avoid ignoring the difference between the sample and its neighbours, we proposed local kernel alignment to calculate the kernel between the sample and its neighbours, with each sample as the centre. We combined the global and local kernel alignments to develop a hybrid kernel alignment model, and balance the relationship between the two through parameters. By maximising the hybrid kernel alignment value, we obtained the weight of each kernel and then linearly combined the kernels in the form of weights. Finally, the fused kernel was input into a support vector machine for training and prediction. Finally, in the independent test sets PDB186 and PDB2272, we obtained the highest Matthew's correlation coefficient (MCC) (0.768 and 0.5962, respectively) and the highest accuracy (87.1% and 78.43%, respectively), which were superior to the other predictors. Therefore, HKAM-MKM is an efficient prediction tool for DBPs.
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15
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Tian X, Shen L, Gao P, Huang L, Liu G, Zhou L, Peng L. Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion. Front Microbiol 2022; 13:740382. [PMID: 35295301 PMCID: PMC8919055 DOI: 10.3389/fmicb.2022.740382] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/01/2022] [Indexed: 02/06/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.
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Affiliation(s)
- Xiongfei Tian
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Pengfei Gao
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, China
- The Future Laboratory, Tsinghua University, Beijing, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- *Correspondence: Liqian Zhou,
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
- Lihong Peng,
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16
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Shen L, Liu F, Huang L, Liu G, Zhou L, Peng L. VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares. Comput Biol Med 2022; 140:105119. [PMID: 34902608 PMCID: PMC8664497 DOI: 10.1016/j.compbiomed.2021.105119] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 11/08/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. METHODS In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. RESULTS In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. CONCLUSIONS Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.
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Affiliation(s)
- Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10 084, Beijing, China; The Future Laboratory, Tsinghua University, Beijing, 10 084, Beijing, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China.
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China.
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17
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Su X, Hu L, You Z, Hu P, Wang L, Zhao B. A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2. Brief Bioinform 2021; 23:6489102. [PMID: 34965582 DOI: 10.1093/bib/bbab526] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/20/2021] [Accepted: 11/14/2021] [Indexed: 12/15/2022] Open
Abstract
The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task for new viruses, as there are no verified virus-drug associations (VDAs) between them and existing drugs. To efficiently solve the cold-start problem posed by new viruses, a novel constrained multi-view nonnegative matrix factorization (CMNMF) model is designed by jointly utilizing multiple sources of biological information. With the CMNMF model, the similarities of drugs and viruses can be preserved from their own perspectives when they are projected onto a unified latent feature space. Based on the CMNMF model, we propose a deep learning method, namely VDA-DLCMNMF, for repurposing drugs against new viruses. VDA-DLCMNMF first initializes the node representations of drugs and viruses with their corresponding latent feature vectors to avoid a random initialization and then applies graph convolutional network to optimize their representations. Given an arbitrary drug, its probability of being associated with a new virus is computed according to their representations. To evaluate the performance of VDA-DLCMNMF, we have conducted a series of experiments on three VDA datasets created for SARS-CoV-2. Experimental results demonstrate that the promising prediction accuracy of VDA-DLCMNMF. Moreover, incorporating the CMNMF model into deep learning gains new insight into the drug repurposing for SARS-CoV-2, as the results of molecular docking experiments reveal that four antiviral drugs identified by VDA-DLCMNMF have the potential ability to treat SARS-CoV-2 infections.
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Affiliation(s)
- Xiaorui Su
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Pengwei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
| | - Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning, 530007, China
| | - Bowei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
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18
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Elzupir AO. Molecular Docking and Dynamics Investigations for Identifying Potential Inhibitors of the 3-Chymotrypsin-like Protease of SARS-CoV-2: Repurposing of Approved Pyrimidonic Pharmaceuticals for COVID-19 Treatment. Molecules 2021; 26:molecules26247458. [PMID: 34946540 PMCID: PMC8707611 DOI: 10.3390/molecules26247458] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/28/2021] [Accepted: 11/29/2021] [Indexed: 02/07/2023] Open
Abstract
This study demonstrates the inhibitory effect of 42 pyrimidonic pharmaceuticals (PPs) on the 3-chymotrypsin-like protease of SARS-CoV-2 (3CLpro) through molecular docking, molecular dynamics simulations, and free binding energies by means of molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) and molecular mechanics-generalized Born surface area (MM-GBSA). Of these tested PPs, 11 drugs approved by the US Food and Drug Administration showed an excellent binding affinity to the catalytic residues of 3CLpro of His41 and Cys145: uracil mustard, cytarabine, floxuridine, trifluridine, stavudine, lamivudine, zalcitabine, telbivudine, tipiracil, citicoline, and uridine triacetate. Their percentage of residues involved in binding at the active sites ranged from 56 to 100, and their binding affinities were in the range from -4.6 ± 0.14 to -7.0 ± 0.19 kcal/mol. The molecular dynamics as determined by a 200 ns simulation run of solvated docked complexes confirmed the stability of PP conformations that bound to the catalytic dyad and the active sites of 3CLpro. The free energy of binding also demonstrates the stability of the PP-3CLpro complexes. Citicoline and uridine triacetate showed free binding energies of -25.53 and -7.07 kcal/mol, respectively. Therefore, I recommend that they be repurposed for the fight against COVID-19, following proper experimental and clinical validation.
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Affiliation(s)
- Amin Osman Elzupir
- College of Science, Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
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19
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Lang J, Zhu R, Sun X, Zhu S, Li T, Shi X, Sun Y, Yang Z, Wang W, Bing P, He B, Tian G. Evaluation of the MGISEQ-2000 Sequencing Platform for Illumina Target Capture Sequencing Libraries. Front Genet 2021; 12:730519. [PMID: 34777467 PMCID: PMC8578046 DOI: 10.3389/fgene.2021.730519] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/24/2021] [Indexed: 01/19/2023] Open
Abstract
Illumina is the leading sequencing platform in the next-generation sequencing (NGS) market globally. In recent years, MGI Tech has presented a series of new sequencers, including DNBSEQ-T7, MGISEQ-2000 and MGISEQ-200. As a complex application of NGS, cancer-detecting panels pose increasing demands for the high accuracy and sensitivity of sequencing and data analysis. In this study, we used the same capture DNA libraries constructed based on the Illumina protocol to evaluate the performance of the Illumina Nextseq500 and MGISEQ-2000 sequencing platforms. We found that the two platforms had high consistency in the results of hotspot mutation analysis; more importantly, we found that there was a significant loss of fragments in the 101-133 bp size range on the MGISEQ-2000 sequencing platform for Illumina libraries, but not for the capture DNA libraries prepared based on the MGISEQ protocol. This phenomenon may indicate fragment selection or low fragment ligation efficiency during the DNA circularization step, which is a unique step of the MGISEQ-2000 sequence platform. In conclusion, these different sequencing libraries and corresponding sequencing platforms are compatible with each other, but protocol and platform selection need to be carefully evaluated in combination with research purpose.
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Affiliation(s)
- Jidong Lang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Academician Workstation, Changsha Medical University, Changsha, China
| | - Rongrong Zhu
- Vascular Surgery Department, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Xue Sun
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Siyu Zhu
- Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA, United States
| | - Tianbao Li
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiaoli Shi
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Yanqi Sun
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Zhou Yang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Weiwei Wang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Geng Tian
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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20
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Wang J, Zhang Y, Hu S, Bai H, Xue Z, Liu Y, Ma W. Antiviral drugs suppress infection of 2019-nCoV spike pseudotyped virus by interacting with ACE2 protein. J Biochem Mol Toxicol 2021; 36:e22948. [PMID: 34755435 PMCID: PMC8646714 DOI: 10.1002/jbt.22948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 09/13/2021] [Accepted: 11/01/2021] [Indexed: 11/09/2022]
Abstract
The outbreak of coronavirus disease 2019 (COVID‐19) has induced a large number of deaths worldwide. Angiotensin‐converting enzyme 2 (ACE2) is the entry receptor for the 2019 novel coronavirus (2019‐nCoV) to infect the host cells. Therefore, ACE2 may be an important target for the prevention and treatment of COVID‐19. The aim of this study was to investigate the inhibition effect of valaciclovir hydrochloride (VACV), zidovudine (ZDV), saquinavir (SQV), and efavirenz (EFV) on 2019‐nCoV infection. The results of molecule docking and surface plasmon resonance showed that VACV, ZDV, SQV, and EFV could bind to ACE2 protein, with the KD value of (4.33 ± 0.09) e−8, (6.29 ± 1.12) e−6, (2.37 ± 0.59) e−5, and (4.85 ± 1.57) e−5 M, respectively. But only ZDV and EFV prevent the 2019‐nCoV spike pseudotyped virus to enter ACE2‐HEK293T cells with an EC50 value of 4.30 ± 1.46 and 3.92 ± 1.36 μM, respectively. ZDV and EFV also have a synergistic effect on preventing entry of virus into cells. In conclusion, ZDV and EFV suppress 2019‐nCoV infection of ACE2‐HEK293T cells by interacting with ACE2.
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Affiliation(s)
- Jue Wang
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yongjing Zhang
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shiling Hu
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Haoyun Bai
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zhuoyin Xue
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yanhong Liu
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Weina Ma
- School of Pharmacy, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China
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21
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Spanakis M, Patelarou A, Patelarou E, Tzanakis N. Drug Interactions for Patients with Respiratory Diseases Receiving COVID-19 Emerged Treatments. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:11711. [PMID: 34770225 PMCID: PMC8583457 DOI: 10.3390/ijerph182111711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/31/2021] [Accepted: 11/04/2021] [Indexed: 12/28/2022]
Abstract
Pandemic of coronavirus disease (COVID-19) is still pressing the healthcare systems worldwide. Thus far, the lack of available COVID-19-targeted treatments has led scientists to look through drug repositioning practices and exploitation of available scientific evidence for potential efficient drugs that may block biological pathways of SARS-CoV-2. Till today, several molecules have emerged as promising pharmacological agents, and more than a few medication protocols are applied during hospitalization. On the other hand, given the criticality of the disease, it is important for healthcare providers, especially those in COVID-19 clinics (i.e., nursing personnel and treating physicians), to recognize potential drug interactions that may lead to adverse drug reactions that may negatively impact the therapeutic outcome. In this review, focusing on patients with respiratory diseases (i.e., asthma or chronic obstructive pulmonary disease) that are treated also for COVID-19, we discuss possible drug interactions, their underlying pharmacological mechanisms, and possible clinical signs that healthcare providers in COVID-19 clinics may need to acknowledge as adverse drug reactions due to drug-drug interactions.
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Affiliation(s)
- Marios Spanakis
- Department of Nursing, School of Health Sciences, Hellenic Mediterranean University, GR-71004 Heraklion, Crete, Greece; (A.P.); (E.P.)
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research & Technology-Hellas (FORTH), GR-70013 Heraklion, Crete, Greece
| | - Athina Patelarou
- Department of Nursing, School of Health Sciences, Hellenic Mediterranean University, GR-71004 Heraklion, Crete, Greece; (A.P.); (E.P.)
| | - Evridiki Patelarou
- Department of Nursing, School of Health Sciences, Hellenic Mediterranean University, GR-71004 Heraklion, Crete, Greece; (A.P.); (E.P.)
| | - Nikolaos Tzanakis
- Department of Respiratory Medicine, University Hospital of Heraklion, Medical School, University of Crete, GR-71303 Heraklion, Crete, Greece;
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22
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Wang J, Wang C, Shen L, Zhou L, Peng L. Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization. Front Genet 2021; 12:749256. [PMID: 34691157 PMCID: PMC8529063 DOI: 10.3389/fgene.2021.749256] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 01/04/2023] Open
Abstract
The novel coronavirus pneumonia COVID-19 infected by SARS-CoV-2 has attracted worldwide attention. It is urgent to find effective therapeutic strategies for stopping COVID-19. In this study, a Bounded Nuclear Norm Regularization (BNNR) method is developed to predict anti-SARS-CoV-2 drug candidates. First, three virus-drug association datasets are compiled. Second, a heterogeneous virus-drug network is constructed. Third, complete genomic sequences and Gaussian association profiles are integrated to compute virus similarities; chemical structures and Gaussian association profiles are integrated to calculate drug similarities. Fourth, a BNNR model based on kernel similarity (VDA-GBNNR) is proposed to predict possible anti-SARS-CoV-2 drugs. VDA-GBNNR is compared with four existing advanced methods under fivefold cross-validation. The results show that VDA-GBNNR computes better AUCs of 0.8965, 0.8562, and 0.8803 on the three datasets, respectively. There are 6 anti-SARS-CoV-2 drugs overlapping in any two datasets, that is, remdesivir, favipiravir, ribavirin, mycophenolic acid, niclosamide, and mizoribine. Molecular dockings are conducted for the 6 small molecules and the junction of SARS-CoV-2 spike protein and human angiotensin-converting enzyme 2. In particular, niclosamide and mizoribine show higher binding energy of −8.06 and −7.06 kcal/mol with the junction, respectively. G496 and K353 may be potential key residues between anti-SARS-CoV-2 drugs and the interface junction. We hope that the predicted results can contribute to the treatment of COVID-19.
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Affiliation(s)
- Juanjuan Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Chang Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China.,College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
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23
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Yu L, Shi X, Liu X, Jin W, Jia X, Xi S, Wang A, Li T, Zhang X, Tian G, Sun D. Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19. Front Microbiol 2021; 12:729455. [PMID: 34650534 PMCID: PMC8507494 DOI: 10.3389/fmicb.2021.729455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/17/2021] [Indexed: 01/14/2023] Open
Abstract
Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people's lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients. Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman's correlation test was applied to study correlations of CT characteristics and laboratory findings. Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features. Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.
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Affiliation(s)
- Lan Yu
- Clinical Medical Research Center/Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, China.,Department of Endocrinology, Inner Mongolia People's Hospital, Hohhot, China
| | - Xiaoli Shi
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiaoling Liu
- Department of Otolaryngology, Inner Mongolia People's Hospital, Hohhot, China
| | - Wen Jin
- Clinical Medical Research Center/Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, China
| | - Xiaoqing Jia
- Baotou City Hospital for Infectious Diseases, Baotou, China
| | - Shuxue Xi
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Ailan Wang
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Tianbao Li
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiao Zhang
- Clinical Medical Research Center/Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Dejun Sun
- Department of Pulmonary and Critical Care Medicine/Key Laboratory of National Health Commission for the Diagnosis & Treatment of COPD, Inner Mongolia People's Hospital, Hohhot, China
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24
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Lee R, Kim V, Chun Y, Kim D. Structure-Functional Analysis of Human Cytochrome P450 2C8 Using Directed Evolution. Pharmaceutics 2021; 13:pharmaceutics13091429. [PMID: 34575505 PMCID: PMC8469462 DOI: 10.3390/pharmaceutics13091429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 11/16/2022] Open
Abstract
The human genome includes four cytochrome P450 2C subfamily enzymes, and CYP2C8 has generated research interest because it is subject to drug-drug interactions and various polymorphic outcomes. To address the structure-functional complexity of CYP2C8, its catalytic activity was studied using a directed evolution analysis. Consecutive rounds of random mutagenesis and screening using 6-methoxy-luciferin produced two mutants, which displayed highly increased luciferase activity. Wild-type and selected mutants were expressed on a large scale and purified. The expression levels of the D349Y and D349Y/V237A mutants were ~310 and 460 nmol per liter of culture, respectively. The steady-state kinetic analysis of paclitaxel 6α-hydroxylation showed that the mutants exhibited a 5-7-fold increase in kcat values and a 3-5-fold increase in catalytic efficiencies (kcat/KM). In arachidonic acid epoxidation, two mutants exhibited a 30-150-fold increase in kcat values and a 40-110-fold increase in catalytic efficiencies. The binding titration analyses of paclitaxel and arachidonic acid showed that the V237A mutation had a lower Kd value, indicating a tighter substrate-binding affinity. The structural analysis of CYP2C8 indicated that the D349Y mutation was close enough to the putative binding domain of the redox partner; the increase in catalytic activity could be partially attributed to the enhancement of the P450 coupling efficiency or electron transfer.
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Affiliation(s)
- Rowoon Lee
- Department of Biological Sciences, Konkuk University, Seoul 05029, Korea; (R.L.); (V.K.)
| | - Vitchan Kim
- Department of Biological Sciences, Konkuk University, Seoul 05029, Korea; (R.L.); (V.K.)
| | - Youngjin Chun
- College of Pharmacy, Chung-Ang University, Seoul 06974, Korea;
| | - Donghak Kim
- Department of Biological Sciences, Konkuk University, Seoul 05029, Korea; (R.L.); (V.K.)
- Correspondence: ; Tel.: +82-2-450-3366; Fax: +82-2-3436-5432
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25
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Zhao L, Li Y, Wang Y, Gao Q, Ge Z, Sun X, Li Y. Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study. Front Microbiol 2021; 12:737066. [PMID: 34489922 PMCID: PMC8417384 DOI: 10.3389/fmicb.2021.737066] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Background Hospital mortality is high for patients with encephalopathy caused by microbial infection. Microbial infections often induce sepsis. The damage to the central nervous system (CNS) is defined as sepsis-associated encephalopathy (SAE). However, the relationship between pathogenic microorganisms and the prognosis of SAE patients is still unclear, especially gut microbiota, and there is no clinical tool to predict hospital mortality for SAE patients. The study aimed to explore the relationship between pathogenic microorganisms and the hospital mortality of SAE patients and develop a nomogram for the prediction of hospital mortality in SAE patients. Methods The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of SAE patients was developed by multivariable Cox regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) to evaluate the clinical utility of the model. Results Unfortunately, the results of our study did not find intestinal infection and microorganisms of the gastrointestinal (such as: Escherichia coli) that are related to the prognosis of SAE. Lasso regression and multivariate Cox regression indicated that factors including respiratory failure, lactate, international normalized ratio (INR), albumin, SpO2, temperature, and renal replacement therapy were significantly correlated with hospital mortality. The AUC of 0.812 under the nomogram was more than that of the Simplified Acute Physiology Score (0.745), indicating excellent discrimination. DCA demonstrated that using the nomogram or including the prognostic signature score status was better than without the nomogram or using the SAPS II at predicting hospital mortality. Conclusion The prognosis of SAE patients has nothing to do with intestinal and microbial infections. We developed a nomogram that predicts hospital mortality in patients with SAE according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.
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Affiliation(s)
- Lina Zhao
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.,Department of Critical Care Medicine, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Yun Li
- Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Yunying Wang
- Department of Critical Care Medicine, Chifeng Municipal Hospital, Chifeng Clinical Medical College of Inner Mongolia Medical University, Chifeng, China
| | - Qian Gao
- Department of Neurology, Yidu Central Hospital Affiliated to Weifang Medical University, Weifang, China
| | - Zengzheng Ge
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xibo Sun
- Department of Neurology, Yidu Central Hospital Affiliated to Weifang Medical University, Weifang, China
| | - Yi Li
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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26
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He B, Hou F, Ren C, Bing P, Xiao X. A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds. Front Oncol 2021; 11:711225. [PMID: 34367996 PMCID: PMC8340770 DOI: 10.3389/fonc.2021.711225] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/07/2021] [Indexed: 12/23/2022] Open
Abstract
Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.
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Affiliation(s)
- Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Fangxing Hou
- Queen Mary School, Nanchang University, Jiangxi, China
| | - Changjing Ren
- School of Science, Dalian Maritime University, Dalian, China.,Genies Beijing Co., Ltd., Beijing, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Xiangzuo Xiao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Jiangxi, China
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27
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Scior T, Abdallah HH, Mustafa SFZ, Guevara-García JA, Rehder D. Are vanadium complexes druggable against the main protease M pro of SARS-CoV-2? - A computational approach. Inorganica Chim Acta 2021; 519:120287. [PMID: 33589845 PMCID: PMC7875704 DOI: 10.1016/j.ica.2021.120287] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 01/30/2021] [Accepted: 02/02/2021] [Indexed: 12/16/2022]
Abstract
In silico techniques helped explore the binding capacities of the SARS-CoV-2 main protease (Mpro) for a series of metalloorganic compounds. Along with small size vanadium complexes a vanadium-containing derivative of the peptide-like inhibitor N3 (N-[(5-methylisoxazol-3-yl)carbonyl]alanyl-l-valyl-N1-((1R,2Z)-4-(benzyloxy)-4-oxo-1-{[(3R)-2-oxopyrrolidin-3-yl] methyl }but-2-enyl)-l-leucinamide) was designed from the crystal structure with PDB entry code 6LU7. On theoretical grounds our consensus docking studies evaluated the binding affinities at the hitherto known binding site of Chymotrypsin-like protease (3CLpro) of SARS-CoV-2 for existing and designed vanadium complexes. This main virus protease (Mpro) has a Cys-His dyad at the catalytic site that is characteristic of metal-dependent or metal-inhibited hydrolases. Mpro was compared to the human protein-tyrosine phosphatase 1B (hPTP1B) with a comparable catalytic dyad. HPTP1B is a key regulator at an early stage in the signalling cascade of the insulin hormone for glucose uptake into cells. The vanadium-ligand binding site of hPTP1B is located in a larger groove on the surface of Mpro. Vanadium constitutes a well-known phosphate analogue. Hence, its study offers possibilities to design promising vanadium-containing binders to SARS-CoV-2. Given the favourable physicochemical properties of vanadium nuclei, such organic vanadium complexes could become drugs not only for pharmacotherapy but also diagnostic tools for early infection detection in patients. This work presents the in silico design of a potential lead vanadium compound. It was tested along with 20 other vanadium-containing complexes from the literature in a virtual screening test by docking to inhibit Mpro of SARS-CoV-2.
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Affiliation(s)
- Thomas Scior
- Departamento de Farmacia, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla. 72000 Puebla, Pue., Mexico,Corresponding author
| | - Hassan H. Abdallah
- Chemistry Department, College of Education, Salahaddin University Erbil, 44001 Erbil, Iraq
| | | | - José Antonio Guevara-García
- Facultad de Ciencias Básicas, Campus Ingeniería y Tecnología, Universidad Autónoma de Tlaxcala, 90401 Apizaco, Tlax., Mexico
| | - Dieter Rehder
- Chemistry Department, University of Hamburg, D-22087 Hamburg, Germany
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28
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Ma S, Guo Z, Wang B, Yang M, Yuan X, Ji B, Wu Y, Chen S. A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them. Front Genet 2021; 12:832627. [PMID: 35116059 PMCID: PMC8804649 DOI: 10.3389/fgene.2021.832627] [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: 12/10/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes. Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers. Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit β2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas. Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas.
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Affiliation(s)
- Shuzhi Ma
- Department of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhen Guo
- Academician Workstation, Changsha Medical University, Changsha, China
- Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Bo Wang
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Min Yang
- Geneis (Beijing) Co., Ltd., Beijing, China
| | | | - Binbin Ji
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Yan Wu
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Size Chen
- Department of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Guangdong Provincial Engineering Research Center for Esophageal Cancer Precise Therapy, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Central Laboratory, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- *Correspondence: Size Chen,
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