Research section Research projects and references

Research: CANCER Folding Project #19701

Project #19701 overview

Project Summary AI Beta

This project studies how bacteria in our gut tell the difference between different types of fiber. By looking at how two specific proteins bind to straight and branched fibers, we can understand which fibers these bacteria prefer to eat. This knowledge could help us design foods that promote healthy gut bacteria and improve our health.
Automated summary; simplified and may not be fully accurate.

Project team

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

Work unit

Atoms
232,342
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 19701

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 47,602,905 224,033 212.48 0 hrs 7 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 30,335,909 541,148 56.06 0 hrs 26 mins
3 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 22,905,919 200,465 114.26 0 hrs 13 mins
4 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 18,741,796 713,687 26.26 0 hrs 55 mins
5 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 14,577,344 322,292 45.23 0 hrs 32 mins
6 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 14,382,057 186,152 77.26 0 hrs 19 mins
7 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 6,014,560 240,000 25.06 0 hrs 57 mins
8 GeForce RTX 5060
GB206 [GeForce RTX 5060]
Nvidia GB206 5,793,379 240,000 24.14 0 hrs 60 mins
9 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,382,071 240,000 18.26 1 hrs 19 mins
10 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,456,592 240,000 14.40 1 hrs 40 mins
11 Radeon RX 6800(XT)/6900XT
Navi 21 [Radeon RX 6800(XT)/6900XT]
AMD Navi 21 2,075,665 240,000 8.65 2 hrs 47 mins
12 Radeon RX 6700(XT)/6800M
Navi 22 XT-XL [Radeon RX 6700(XT)/6800M]
AMD Navi 22 XT-XL 1,493,258 182,787 8.17 2 hrs 56 mins