Classifying protein structures using advanced AI methods

ECOD: Large scale classification of predicted and experimental protein structures

['FUNDING_R01'] · UT SOUTHWESTERN MEDICAL CENTER · NIH-11088135

This study is looking at how to better understand protein shapes and functions using both lab data and smart AI tools, which could eventually help us learn more about diseases linked to protein problems, benefiting patients in the long run.

Quick facts

Phase['FUNDING_R01']
Study typeNih_funding
SexAll
SponsorUT SOUTHWESTERN MEDICAL CENTER (nih funded)
Locations1 site (DALLAS, UNITED STATES)
Trial IDNIH-11088135 on ClinicalTrials.gov

What this research studies

This research focuses on classifying protein structures by leveraging both experimental data and advanced AI predictions. It aims to enhance the existing database of protein domains, known as ECOD, by integrating a vast number of predicted protein structures generated by cutting-edge technologies like AlphaFold. By doing so, the project seeks to provide a comprehensive understanding of protein functions and evolutionary relationships, which can be crucial for various biological applications. Patients may benefit indirectly as this research could lead to new insights in understanding diseases caused by protein malfunctions.

Who could benefit from this research

Good fit: Ideal candidates for benefiting from this research include individuals with genetic disorders or diseases related to protein misfolding or malfunction.

Not a fit: Patients with conditions unrelated to protein structures or functions may not receive direct benefits from this research.

Why it matters

Potential benefit: If successful, this research could lead to improved understanding and treatment of diseases linked to protein dysfunction.

How similar studies have performed: Other research has shown success in utilizing AI for protein structure prediction, indicating a promising approach in this field.

Where this research is happening

DALLAS, UNITED STATES

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.

View on NIH RePORTER →

Last reviewed 2026-05-15 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.