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.identifier.issn |
23747943 |
|
dc.identifier.uri |
http://localhost:8080/xmlui/handle/123456789/2240 |
|
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.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 |