# Deep-learning methods based computational modeling

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2023 · $50,000

## Abstract

Project summary
Recent advancements in deep learning-based computational protein structure prediction by
AlphaFold, RoseTTAFold, ESMFold, and OpenFold methods offer promising opportunities to
advance our ongoing NINDS-funded research project entitled “Activation and Inhibition
Mechanisms of Calcium-Activated Nonselective Cation Channels” (1R01NS128180). We plan to
use current and future deep learning-based computational protein structure prediction methods
to predict gating conformational changes in TRP channels with high accuracy to complement
our functional studies. We aim to capitalize on these opportunities with this administrative
supplement. To fully utilize the extensive capabilities of deep learning-based computational
protein structure prediction method capabilities, powerful computational resources are needed
with advanced GPU, CPU, RAM, and disk capacity.
We are requesting $50,000 NINDS supplement funding to purchase an AI system capable of
running all current and future deep learning-based computational protein structure prediction
methods. We have identified the ideal AI system configuration from Bizon Technostore. A quote
is submitted together with this application.

## Key facts

- **NIH application ID:** 10816248
- **Project number:** 3R01NS128180-02S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Juan Du
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $50,000
- **Award type:** 3
- **Project period:** 2022-06-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10816248

## Citation

> US National Institutes of Health, RePORTER application 10816248, Deep-learning methods based computational modeling (3R01NS128180-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10816248. Licensed CC0.

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