Research section Research projects and references

Research: CANCER Folding Project #19704

Project #19704 overview

Project Summary AI Beta

This project investigates how certain gut bacteria sense different types of plant fiber. By studying these sensors, we hope to understand how bacteria break down fiber into beneficial compounds that improve our health. This knowledge can help develop new prebiotics and probiotics to treat diseases like IBD and diabetes.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Shivank Nag
Institution
University of Illinois Urbana-Champaign

Work unit

Atoms
238,009
Core
0x27
Status
Beta
Source material

Official Project Description

Substrate Selectivity in Commensal Gut Bacterial Sensors Hybrid Two-Component Sensor (HTCS) proteins help beneficial intestinal bacteria detect complex carbohydrates/fibers in their environment and upregulate Polysaccharide Uitilization Locus (PUL) genes which encode the transporters and enzymes required to degrade them into simpler sugars.

These sugars are fermented to produce beneficial metabolites like Short-Chain Fatty Acids (SFCAs) with several downstream benefits related to lipid and cholesterol metabolism, glucose regulation, and anti-inflammatory and anti-cancer effects. This project will study two β-propeller sensors in Bacterioides intestinalis, BACINT_04208 and BACINT_01044, with predicted preferences for soluble/unbranched versus insoluble/branched xylan-derived fibers.

Atomistic molecular dynamics simulations from docked poses with straight-chain xylan and branched arabinoxylan ligands will reveal binding/unbinding events to validate this hypothesized fiber selectivity in the sensors, along with specific residues implicated in ligand recognition.

The mechanistic insights from this project can support prebiotic and probiotic design and predictive microbiome models to address intestinal and metabolic health disorders like Inflammatory Bowel Diseases (IBDs) and Diabetes.

Performance data

Hardware Performance for Project 19704

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Thursday, 03 September 2026 18:21:47

GPU PPD Averages

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average
1 GeForce RTX 5090
GB202 [GeForce RTX 5090]
Nvidia GB202 50,129,333 237,190 211.35 0 hrs 7 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 32,253,486 800,916 40.27 0 hrs 36 mins
3 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 23,151,525 209,934 110.28 0 hrs 13 mins
4 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 21,860,222 1,678,340 13.02 1 hrs 51 mins
5 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 18,498,159 803,722 23.02 1 hrs 3 mins
6 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 17,025,274 225,151 75.62 0 hrs 19 mins
7 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 14,619,968 370,845 39.42 0 hrs 37 mins
8 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 6,649,081 240,000 27.70 0 hrs 52 mins
9 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 5,612,486 240,000 23.39 1 hrs 2 mins
10 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 4,483,856 240,000 18.68 1 hrs 17 mins
11 GeForce RTX 5060
GB206 [GeForce RTX 5060]
Nvidia GB206 4,300,492 188,029 22.87 1 hrs 3 mins
12 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,202,977 240,000 17.51 1 hrs 22 mins
13 Radeon RX 6800(XT)/6900XT
Navi 21 [Radeon RX 6800(XT)/6900XT]
AMD Navi 21 3,268,607 240,000 13.62 1 hrs 46 mins