Research section Research projects and references

Research: CANCER Folding Project #19702

Project #19702 overview

Project Summary AI Beta

This project explores how gut bacteria use special sensors to detect different types of fiber. By studying these sensors, scientists hope to better understand how fiber affects our health and develop new ways to improve it. This could lead to better treatments for conditions like inflammatory bowel disease 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
232,540
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 19702

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

Data as of Thursday, 03 September 2026 18:21:48

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 44,337,170 207,306 213.87 0 hrs 7 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 33,945,869 1,575,089 21.55 1 hrs 7 mins
3 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 23,329,258 208,638 111.82 0 hrs 13 mins
4 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 21,832,128 1,627,041 13.42 1 hrs 47 mins
5 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 19,964,313 1,033,492 19.32 1 hrs 15 mins
6 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 19,155,913 331,080 57.86 0 hrs 25 mins
7 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 16,902,853 212,127 79.68 0 hrs 18 mins
8 GeForce RTX 4070 SUPER
AD104 [GeForce RTX 4070 SUPER]
Nvidia AD104 14,761,422 240,000 61.51 0 hrs 23 mins
9 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 6,086,223 211,393 28.79 0 hrs 50 mins
10 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,362,436 240,000 18.18 1 hrs 19 mins
11 Radeon RX 6800(XT)/6900XT
Navi 21 [Radeon RX 6800(XT)/6900XT]
AMD Navi 21 3,813,038 240,000 15.89 1 hrs 31 mins
12 Radeon RX 6700(XT)/6800M
Navi 22 XT-XL [Radeon RX 6700(XT)/6800M]
AMD Navi 22 XT-XL 1,491,623 182,787 8.16 2 hrs 56 mins