Using machine learning to analyze protein interactions
Identifying critical protein-protein interactions with ML methods
['FUNDING_TRAINING'] · GEORGIA INSTITUTE OF TECHNOLOGY · NIH-11032279
This study is exploring how to use computer technology to better understand how the virus that causes COVID-19 interacts with our cells, and it's designed for scientists who want to learn how to analyze biological data more easily.
Quick facts
| Phase | ['FUNDING_TRAINING'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | GEORGIA INSTITUTE OF TECHNOLOGY (nih funded) |
| Locations | 1 site (ATLANTA, UNITED STATES) |
| Trial ID | NIH-11032279 on ClinicalTrials.gov |
What this research studies
This research focuses on applying machine learning (ML) techniques to analyze large datasets generated from molecular dynamics simulations of biomolecules, specifically the interactions of the SARS-CoV-2 spike protein with the human ACE2 receptor. By utilizing methods such as logistic regression and neural networks, the project aims to identify key residues that enhance the binding affinity of the virus. The research also aims to provide training and resources for scientists and researchers interested in using ML in quantitative biology, thereby lowering barriers to entry in this field. Participants will learn how to effectively analyze complex biological data using cloud-based tools.
Who could benefit from this research
Good fit: Ideal candidates for participation or benefit from this research include individuals interested in the molecular biology of viruses, particularly those studying SARS-CoV-2.
Not a fit: Patients with conditions unrelated to viral infections or those not engaged in scientific research may not receive direct benefits from this research.
Why it matters
Potential benefit: If successful, this research could lead to improved understanding of viral mechanisms and potentially inform the development of targeted therapies or vaccines.
How similar studies have performed: Other research has successfully utilized machine learning approaches to analyze protein interactions, indicating a promising avenue for this type of investigation.
Where this research is happening
ATLANTA, UNITED STATES
- GEORGIA INSTITUTE OF TECHNOLOGY — ATLANTA, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: QIU, PENG — GEORGIA INSTITUTE OF TECHNOLOGY
- Study coordinator: QIU, PENG
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.