Research section Research projects and references

Research: CANCER Folding Project #19703

Project #19703 overview

Project Summary AI Beta

This project explores how good gut bacteria sense and break down different types of fiber. By studying two sensors in *Bacterioides intestinalis*, we aim to understand which fibers they prefer and how they recognize them. This knowledge can help us design better prebiotics and probiotics, potentially improving gut health and managing diseases 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
238,033
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 19703

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 45,236,358 220,935 204.75 0 hrs 7 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 34,904,975 1,656,211 21.08 1 hrs 8 mins
3 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 22,098,323 208,591 105.94 0 hrs 14 mins
4 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 20,208,505 1,534,427 13.17 1 hrs 49 mins
5 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 18,896,914 431,952 43.75 0 hrs 33 mins
6 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 18,724,702 240,000 78.02 0 hrs 18 mins
7 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 14,224,897 532,161 26.73 0 hrs 54 mins
8 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 6,702,315 187,292 35.79 0 hrs 40 mins
9 GeForce RTX 5060
GB206 [GeForce RTX 5060]
Nvidia GB206 5,485,515 240,000 22.86 1 hrs 3 mins
10 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 5,162,565 187,292 27.56 0 hrs 52 mins
11 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,213,569 240,000 17.56 1 hrs 22 mins