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Tang CY, Gao C, Prasai K, Li T, Dash S, McElroy JA, Hang J, Wan XF. Prediction models for COVID-19 disease outcomes. Emerg Microbes Infect 2024:2361791. [PMID: 38828796 DOI: 10.1080/22221751.2024.2361791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
GRAPHICAL ABSTRACT: MODEL SUMMARY AND MOTIVATION Individuals infected with SARS-CoV-2 experience a wide spectrum of clinical manifestations ranging from no symptoms to death. Using the Virus-Human Outcomes Prediction (ViHOP) algorithm, we aim to utilize the individual's clinical characteristics, the individual's location, and the infecting SARS-CoV-2 virus characteristics obtained by whole genome sequencing to determine their likelihood of admission to the hospital, admission to the intensive care unit (ICU), or experiencing long COVID. This model allows clinicians to identify at-risk patients for further monitoring and/or early treatment.
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Affiliation(s)
- Cynthia Y Tang
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Cheng Gao
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
| | - Kritika Prasai
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
| | - Tao Li
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Shreya Dash
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Jane A McElroy
- Family and Community Medicine, University of Missouri, Columbia, Missouri, USA
| | - Jun Hang
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Xiu-Feng Wan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
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An K, Yang X, Luo M, Yan J, Xu P, Zhang H, Li Y, Wu S, Warshel A, Bai C. Mechanistic study of the transmission pattern of the SARS-CoV-2 omicron variant. Proteins 2024; 92:705-719. [PMID: 38183172 PMCID: PMC11059747 DOI: 10.1002/prot.26663] [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: 10/18/2023] [Revised: 11/25/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024]
Abstract
The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) characterized by 30 mutations in its spike protein, has rapidly spread worldwide since November 2021, significantly exacerbating the ongoing COVID-19 pandemic. In order to investigate the relationship between these mutations and the variant's high transmissibility, we conducted a systematic analysis of the mutational effect on spike-angiotensin-converting enzyme-2 (ACE2) interactions and explored the structural/energy correlation of key mutations, utilizing a reliable coarse-grained model. Our study extended beyond the receptor-binding domain (RBD) of spike trimer through comprehensive modeling of the full-length spike trimer rather than just the RBD. Our free-energy calculation revealed that the enhanced binding affinity between the spike protein and the ACE2 receptor is correlated with the increased structural stability of the isolated spike protein, thus explaining the omicron variant's heightened transmissibility. The conclusion was supported by our experimental analyses involving the expression and purification of the full-length spike trimer. Furthermore, the energy decomposition analysis established those electrostatic interactions make major contributions to this effect. We categorized the mutations into four groups and established an analytical framework that can be employed in studying future mutations. Additionally, our calculations rationalized the reduced affinity of the omicron variant towards most available therapeutic neutralizing antibodies, when compared with the wild type. By providing concrete experimental data and offering a solid explanation, this study contributes to a better understanding of the relationship between theories and observations and lays the foundation for future investigations.
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Affiliation(s)
- Ke An
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P. R. China
- Warshel Institute for Computational Biology
- Chenzhu (MoMeD) Biotechnology Co., Ltd, Hangzhou, Zhejiang, 310005, P.R. China
| | - Xianzhi Yang
- Institute of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen 518000, China
| | - Mengqi Luo
- College of Management, Shenzhen University, Shenzhen, 518060, China
| | - Junfang Yan
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P. R. China
- Warshel Institute for Computational Biology
| | - Peiyi Xu
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P. R. China
- Warshel Institute for Computational Biology
| | - Honghui Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P. R. China
- Warshel Institute for Computational Biology
| | - Yuqing Li
- Department of Urology, South China Hospital of Shenzhen University, Shenzhen 518116, China
| | - Song Wu
- Department of Urology, South China Hospital of Shenzhen University, Shenzhen 518116, China
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, California 90089-1062, United States
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P. R. China
- Warshel Institute for Computational Biology
- Chenzhu (MoMeD) Biotechnology Co., Ltd, Hangzhou, Zhejiang, 310005, P.R. China
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Ortiz-Gómez T, Gomez AC, Chuima B, Zevallos A, Ocampo K, Torres D, Pinto JA. Frequency of SARS-CoV-2 variants identified by real-time PCR in the AUNA healthcare network, Peru. Front Public Health 2024; 11:1244662. [PMID: 38410127 PMCID: PMC10894931 DOI: 10.3389/fpubh.2023.1244662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/26/2023] [Indexed: 02/28/2024] Open
Abstract
Introduction In Peru, on 11 February 2023, the Ministry of Health registered 4 million patients infected with COVID-19 and around 219,260 deaths. In 2020, the SARS-CoV-2 virus was acquiring mutations that impacted the properties of transmissibility, infectivity, and immune evasion, leading to new lineages. In the present study, the frequency of COVID-19 variants was determined during 2021 and 2022 in patients treated in the AUNA healthcare network. Methods The methodology used to detect mutations and identify variants was the Allplex™ SARS-CoV-2 Variants Assay I, II, and VII kit RT-PCR. The frequency of variants was presented by epidemiological weeks. Results In total, 544 positive samples were evaluated, where the Delta, Omicron, and Gamma variants were identified. The Delta variant was found in 242 (44.5%) patients between epidemiological weeks 39 and 52 in 2021. In the case of Gamma, it was observed in 8 (1.5%) patients at weeks 39, 41, 43, 45, and 46 of 2021. The Omicron variant was the most frequent with 289 (53.1%) patients during weeks 49 to 52 of 2021 and 1 to 22 of 2022. During weeks 1 through 22 of 2022, it was possible to discriminate between BA. 1 (n = 32) and BA.2 (n = 82). Conclusion The rapid identification of COVID-19 variants through the RT-PCR methodology contributes to timely epidemiological surveillance, as well as appropriate patient management.
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Affiliation(s)
| | - Andrea C. Gomez
- Centro de Investigación Básica y Translacional, AUNA IDEAS, Lima, Peru
| | | | - Alejandra Zevallos
- Escuela Profesional de Medicina Humana, Universidad Privada San Juan Bautista, Lima, Peru
| | - Karen Ocampo
- Laboratorios AUNA, Área de Biología Molecular, Lima, Peru
| | - Diana Torres
- Laboratorios AUNA, Área de Biología Molecular, Lima, Peru
- Escuela Profesional de Medicina Humana, Universidad Privada Norbert Wiener, Lima, Peru
| | - Joseph A. Pinto
- Centro de Investigación Básica y Translacional, AUNA IDEAS, Lima, Peru
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Rancati S, Nicora G, Prosperi M, Bellazzi R, Salemi M, Marini S. Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.24.563721. [PMID: 37961168 PMCID: PMC10634784 DOI: 10.1101/2023.10.24.563721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and lineages outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape1-3. We devised an unsupervised deep learning AutoEncoder for viral genomes anomaly detection to predict future dominant lineages (FDLs), i.e., lineages or sublineages comprising ≥10% of viral sequences added to the GISAID database on a given week4. The algorithm was trained and validated by assembling global and country-specific data sets from 16,187,950 Spike protein sequences sampled between December 24th, 2019, and November 8th, 2023. The AutoEncoder flags low frequency FDLs (0.01% - 3%), with median lead times of 4-16 weeks. Over time, positive predictive values oscillate, decreasing linearly with the number of unique sequences per data set, showing average performance up to 30 times better than baseline approaches. The B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than one year earlier of being considered for an updated COVID-19 vaccine. Our AutoEncoder, applicable in principle to any pathogen, also pinpoints specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.
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Affiliation(s)
- Simone Rancati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Giovanna Nicora
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Marco Salemi
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Simone Marini
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
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5
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Thadani NN, Gurev S, Notin P, Youssef N, Rollins NJ, Ritter D, Sander C, Gal Y, Marks DS. Learning from prepandemic data to forecast viral escape. Nature 2023; 622:818-825. [PMID: 37821700 PMCID: PMC10599991 DOI: 10.1038/s41586-023-06617-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/06/2023] [Indexed: 10/13/2023]
Abstract
Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design. However, current strategies for viral evolution prediction are not available early in a pandemic-experimental approaches require host polyclonal antibodies to test against1-16, and existing computational methods draw heavily from current strain prevalence to make reliable predictions of variants of concern17-19. To address this, we developed EVEscape, a generalizable modular framework that combines fitness predictions from a deep learning model of historical sequences with biophysical and structural information. EVEscape quantifies the viral escape potential of mutations at scale and has the advantage of being applicable before surveillance sequencing, experimental scans or three-dimensional structures of antibody complexes are available. We demonstrate that EVEscape, trained on sequences available before 2020, is as accurate as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including influenza, HIV and understudied viruses with pandemic potential such as Lassa and Nipah. We provide continually revised escape scores for all current strains of SARS-CoV-2 and predict probable further mutations to forecast emerging strains as a tool for continuing vaccine development ( evescape.org ).
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Affiliation(s)
- Nicole N Thadani
- Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Sarah Gurev
- Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Pascal Notin
- OATML Group, Department of Computer Science, University of Oxford, Oxford, UK
| | - Noor Youssef
- Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Nathan J Rollins
- Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Seismic Therapeutic, Watertown, MA, USA
| | - Daniel Ritter
- Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Chris Sander
- Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yarin Gal
- OATML Group, Department of Computer Science, University of Oxford, Oxford, UK
| | - Debora S Marks
- Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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Volz E. Fitness, growth and transmissibility of SARS-CoV-2 genetic variants. Nat Rev Genet 2023; 24:724-734. [PMID: 37328556 DOI: 10.1038/s41576-023-00610-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2023] [Indexed: 06/18/2023]
Abstract
The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.
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Affiliation(s)
- Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
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Muik A, Lui BG, Quandt J, Diao H, Fu Y, Bacher M, Gordon J, Toker A, Grosser J, Ozhelvaci O, Grikscheit K, Hoehl S, Kohmer N, Lustig Y, Regev-Yochay G, Ciesek S, Beguir K, Poran A, Vogler I, Türeci Ö, Sahin U. Progressive loss of conserved spike protein neutralizing antibody sites in Omicron sublineages is balanced by preserved T cell immunity. Cell Rep 2023; 42:112888. [PMID: 37527039 DOI: 10.1016/j.celrep.2023.112888] [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: 03/17/2023] [Revised: 03/27/2023] [Accepted: 07/13/2023] [Indexed: 08/03/2023] Open
Abstract
Evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has led to the emergence of sublineages with different patterns of neutralizing antibody evasion. We report that Omicron BA.4/BA.5 breakthrough infection of individuals immunized with SARS-CoV-2 wild-type-strain-based mRNA vaccines results in a boost of Omicron BA.4.6, BF.7, BQ.1.1, and BA.2.75 neutralization but does not efficiently boost BA.2.75.2, XBB, or XBB.1.5 neutralization. In silico analyses showed that the Omicron spike glycoprotein lost most neutralizing B cell epitopes, especially in sublineages BA.2.75.2, XBB, and XBB.1.5. In contrast, T cell epitopes are conserved across variants including XBB.1.5. T cell responses of mRNA-vaccinated, SARS-CoV-2-naive individuals against the wild-type strain, Omicron BA.1, and BA.4/BA.5 were comparable, suggesting that T cell immunity against recent sublineages including XBB.1.5 may remain largely unaffected. While some Omicron sublineages effectively evade B cell immunity, spike-protein-specific T cell immunity, due to the nature of polymorphic cell-mediated immune responses, may continue to contribute to prevention/limitation of severe COVID-19 manifestation.
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Affiliation(s)
| | | | | | - Huitian Diao
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | - Yunguan Fu
- InstaDeep, Ltd., 5 Merchant Square, London W2 1AY, UK
| | - Maren Bacher
- BioNTech, An der Goldgrube 12, 55131 Mainz, Germany
| | | | - Aras Toker
- BioNTech, An der Goldgrube 12, 55131 Mainz, Germany
| | | | | | - Katharina Grikscheit
- Institute for Medical Virology, University Hospital, Goethe University Frankfurt, 60596 Frankfurt am Main, Germany
| | - Sebastian Hoehl
- Institute for Medical Virology, University Hospital, Goethe University Frankfurt, 60596 Frankfurt am Main, Germany
| | - Niko Kohmer
- Institute for Medical Virology, University Hospital, Goethe University Frankfurt, 60596 Frankfurt am Main, Germany
| | - Yaniv Lustig
- Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel; Central Virology Laboratory, Public Health Services, Ministry of Health, Tel-Hashomer, Ramat Gan, Israel
| | - Gili Regev-Yochay
- Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel; SPRI-Sheba Pandemic Preparedness Research Institute, Sheba Medical Center Tel Hashomer, Ramat Gan, Israel
| | - Sandra Ciesek
- Institute for Medical Virology, University Hospital, Goethe University Frankfurt, 60596 Frankfurt am Main, Germany; DZIF - German Centre for Infection Research, External Partner Site, 60596 Frankfurt am Main, Germany
| | - Karim Beguir
- InstaDeep, Ltd., 5 Merchant Square, London W2 1AY, UK
| | - Asaf Poran
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | | | - Özlem Türeci
- BioNTech, An der Goldgrube 12, 55131 Mainz, Germany; HI-TRON - Helmholtz Institute for Translational Oncology Mainz by DKFZ, Obere Zahlbacherstr. 63, 55131 Mainz, Germany
| | - Ugur Sahin
- BioNTech, An der Goldgrube 12, 55131 Mainz, Germany; TRON gGmbH - Translational Oncology at the University Medical Center of the Johannes Gutenberg University, Freiligrathstraße 12, 55131 Mainz, Germany.
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Wang G, Liu X, Wang K, Gao Y, Li G, Baptista-Hon DT, Yang XH, Xue K, Tai WH, Jiang Z, Cheng L, Fok M, Lau JYN, Yang S, Lu L, Zhang P, Zhang K. Deep-learning-enabled protein-protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution. Nat Med 2023; 29:2007-2018. [PMID: 37524952 DOI: 10.1038/s41591-023-02483-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 06/28/2023] [Indexed: 08/02/2023]
Abstract
Host-pathogen interactions and pathogen evolution are underpinned by protein-protein interactions between viral and host proteins. An understanding of how viral variants affect protein-protein binding is important for predicting viral-host interactions, such as the emergence of new pathogenic SARS-CoV-2 variants. Here we propose an artificial intelligence-based framework called UniBind, in which proteins are represented as a graph at the residue and atom levels. UniBind integrates protein three-dimensional structure and binding affinity and is capable of multi-task learning for heterogeneous biological data integration. In systematic tests on benchmark datasets and further experimental validation, UniBind effectively and scalably predicted the effects of SARS-CoV-2 spike protein variants on their binding affinities to the human ACE2 receptor, as well as to SARS-CoV-2 neutralizing monoclonal antibodies. Furthermore, in a cross-species analysis, UniBind could be applied to predict host susceptibility to SARS-CoV-2 variants and to predict future viral variant evolutionary trends. This in silico approach has the potential to serve as an early warning system for problematic emerging SARS-CoV-2 variants, as well as to facilitate research on protein-protein interactions in general.
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Affiliation(s)
- Guangyu Wang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Xiaohong Liu
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
- UCL Cancer Institute, University College London, London, UK
| | - Kai Wang
- Department of Big Data and Biomedical Artificial Intelligence, National Biomedical Imaging Center, College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing, China
| | - Yuanxu Gao
- Guangzhou National Laboratory, Guangzhou, China
| | - Gen Li
- Guangzhou National Laboratory, Guangzhou, China
- Guangzhou Women and Children's Medical Center, Guangzhou, China
| | - Daniel T Baptista-Hon
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
- Zhuhai International Eye Center and Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Guangdong, China
| | - Xiaohong Helena Yang
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Kanmin Xue
- Nuffield Laboratory of Ophthalmology, Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Wa Hou Tai
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Zeyu Jiang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Linling Cheng
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
- Zhuhai International Eye Center and Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Guangdong, China
| | - Manson Fok
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Johnson Yiu-Nam Lau
- Departments of Biology and Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Shengyong Yang
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ligong Lu
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China
- Zhuhai International Eye Center and Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Guangdong, China
| | - Ping Zhang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kang Zhang
- Instutite for Artificial Intelligence in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China.
- Department of Big Data and Biomedical Artificial Intelligence, National Biomedical Imaging Center, College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing, China.
- Guangzhou National Laboratory, Guangzhou, China.
- Zhuhai International Eye Center and Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Guangdong, China.
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9
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Kidambi Raju S, Ramaswamy S, Eid MM, Gopalan S, Karim FK, Marappan R, Khafaga DS. Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection. Bioengineering (Basel) 2023; 10:880. [PMID: 37508907 PMCID: PMC10376564 DOI: 10.3390/bioengineering10070880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/01/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission's stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.
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Affiliation(s)
| | | | - Marwa M Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt
| | | | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Raja Marappan
- School of Computing, SASTRA Deemed University, Thanjavur 613401, India
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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