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Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.
Gao, Kaifu; Wang, Rui; Chen, Jiahui; Cheng, Limei; Frishcosy, Jaclyn; Huzumi, Yuta; Qiu, Yuchi; Schluckbier, Tom; Wei, Xiaoqi; Wei, Guo-Wei.
Afiliación
  • Gao K; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Wang R; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Chen J; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Cheng L; Clinical Pharmacology and Pharmacometrics, Bristol Myers Squibb, Princeton, New Jersey 08536, United States.
  • Frishcosy J; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Huzumi Y; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Qiu Y; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Schluckbier T; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Wei X; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • Wei GW; Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
Chem Rev ; 122(13): 11287-11368, 2022 07 13.
Article en En | MEDLINE | ID: mdl-35594413
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Chem Rev Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Chem Rev Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos