Computational Protein Design for COVID-19 Research and Emerging Therapeutics
| dc.contributor.author | Kalita, Parismita | |
| dc.contributor.author | Tripathi, Timir | |
| dc.contributor.author | Padhi, Aditya K. | |
| dc.date.accessioned | 2023-04-25T05:25:41Z | |
| dc.date.available | 2023-04-25T05:25:41Z | |
| dc.date.issued | 2022 | |
| dc.description | This paper is submitted by the author of IIT (BHU), Varanasi | en_US |
| dc.description.abstract | As the world struggles with the ongoing COVID-19 pandemic, unprecedented obstacles have continuously been traversed as new SARS-CoV-2 variants continually emerge. Infectious disease outbreaks are unavoidable, but the knowledge gained from the successes and failures will help create a robust health management system to deal with such pandemics. Previously, scientists required years to develop diagnostics, therapeutics, or vaccines; however, we have seen that, with the rapid deployment of high-throughput technologies and unprecedented scientific collaboration worldwide, breakthrough discoveries can be accelerated and insights broadened. Computational protein design (CPD) is a game-changing new technology that has provided alternative therapeutic strategies for pandemic management. In addition to the development of peptide-based inhibitors, miniprotein binders, decoys, biosensors, nanobodies, and monoclonal antibodies, CPD has also been used to redesign native SARS-CoV-2 proteins and human ACE2 receptors. We discuss how novel CPD strategies have been exploited to develop rationally designed and robust COVID-19 treatment strategies. | en_US |
| dc.description.sponsorship | The authors sincerely acknowledge the infrastructure facilities of IIT (BHU) Varanasi and DST-funded I-DAPT Hub Foundation, IIT (BHU) [DST/NMICPS/TIH11/IIT(BHU)2020/02]. Further, the support and the computing resources for the work on computational protein design of SARS-CoV-2 proteins by PARAM Shivay Facility under the National Supercomputing Mission, Government of India, at the IIT (BHU), Varanasi, is gratefully acknowledged. | en_US |
| dc.identifier.issn | 23747943 | |
| dc.identifier.uri | https://idr-sdlib.iitbhu.ac.in/handle/123456789/2240 | |
| dc.language.iso | en | en_US |
| dc.publisher | American Chemical Society | en_US |
| dc.relation.ispartofseries | ACS Central Science; | |
| dc.subject | Coronavirus | en_US |
| dc.subject | Diagnosis | en_US |
| dc.subject | Monoclonal antibodies | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | American Chemical Society | en_US |
| dc.subject | Computational protein design | en_US |
| dc.subject | Health management systems | en_US |
| dc.subject | High throughput technology | en_US |
| dc.subject | Infectious disease outbreaks | en_US |
| dc.subject | Mini-proteins | en_US |
| dc.subject | Peptide-based inhibitors | en_US |
| dc.subject | Rapid deployments | en_US |
| dc.subject | Scientific collaboration | en_US |
| dc.subject | Therapeutic strategy | en_US |
| dc.title | Computational Protein Design for COVID-19 Research and Emerging Therapeutics | en_US |
| dc.type | Article | en_US |
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