# Quantitatively predictive biology of aquaporins 1, 5, and GlpF

> **NIH NIH SC1** · UNIVERSITY OF TEXAS SAN ANTONIO · 2020 · $367,500

## Abstract

Project Summary
The state-of-the-art high performance computing enables researchers to simulate the motions of 
millions of atoms interacting with one another. Now it is feasible to produce quantitative 
predictions of biological functions of a protein that are “deterministic” out of the atomistic 
interactions and motions that are stochastic in nature. In this project, the researchers propose to 
study the functions of two human aquaporins and look for ways to modulate/inhibit them. They will 
build the aquaporins and their biological environments from atoms up, simulate their stochastic 
dynamics, and elucidate their deterministic functional behaviors under various controllable 
conditions. Specifically, they aim to find inhibitors of two water channels (AQP1 and AQP5) and one 
glycerol channel (GlpF) by accurately quantifying the binding affinities of dozens of candidate 
inhibitors.
The PI developed a new method, the hybrid steered molecular dynamics (hSMD) method, for the purpose 
of this project and related research. Using hSMD, the researchers will be free from the problem of 
systematic error amplifications inherent in the current methods of the literature. 5% errors in the 
input will translate into
5% errors in the final results for binding affinities. They will be able to take full advantage of 
the high resolution protein structures and the mature CHARMM force field parameters. They will 
harness the massively parallel computing power of the day to improve the currently investigated 
candidate inhibitors and to find new inhibitors in a quantitatively predictive manner.
Upon completion of the project, two types of aquaporin inhibitors, the extracellular channel entry 
blockers and the deep channel cloggers, will be ready for clinical trials as drugs for treatment of 
hypertension, refractory edema, and elevated airway mucus secretion during anesthesia.

## Key facts

- **NIH application ID:** 9965941
- **Project number:** 5SC1GM121275-04
- **Recipient organization:** UNIVERSITY OF TEXAS SAN ANTONIO
- **Principal Investigator:** LIAO Y CHEN
- **Activity code:** SC1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $367,500
- **Award type:** 5
- **Project period:** 2017-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9965941

## Citation

> US National Institutes of Health, RePORTER application 9965941, Quantitatively predictive biology of aquaporins 1, 5, and GlpF (5SC1GM121275-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9965941. Licensed CC0.

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