Research section Research projects and references

Research: CANCER Folding Project #19700

Project #19700 overview

Project Summary AI Beta

This project studies how good gut bacteria detect different types of fiber. By using computer simulations, researchers will figure out which fibers two specific sensors in the bacteria prefer. This knowledge can help create healthier foods and treatments for gut problems like inflammatory bowel disease.
Automated summary; simplified and may not be fully accurate.

Project team

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

Work unit

Atoms
232,436
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 19700

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

Data as of Thursday, 03 September 2026 18:21:49

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 51,031,189 240,000 212.63 0 hrs 7 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 25,444,650 488,224 52.12 0 hrs 28 mins
3 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 25,075,015 227,086 110.42 0 hrs 13 mins
4 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 22,757,614 1,683,589 13.52 1 hrs 47 mins
5 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 21,797,901 1,184,483 18.40 1 hrs 18 mins
6 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 18,771,284 240,000 78.21 0 hrs 18 mins
7 GeForce RTX 4070 SUPER
AD104 [GeForce RTX 4070 SUPER]
Nvidia AD104 14,739,266 240,000 61.41 0 hrs 23 mins
8 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 14,114,134 355,649 39.69 0 hrs 36 mins
9 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 11,106,253 240,000 46.28 0 hrs 31 mins
10 GeForce RTX 5060 Ti
GB206 [GeForce RTX 5060 Ti]
Nvidia GB206 7,302,742 240,000 30.43 0 hrs 47 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 6,881,140 240,000 28.67 0 hrs 50 mins
12 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 5,585,745 240,000 23.27 1 hrs 2 mins
13 GeForce RTX 5060
GB206 [GeForce RTX 5060]
Nvidia GB206 5,348,688 240,000 22.29 1 hrs 5 mins
14 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,377,945 240,000 18.24 1 hrs 19 mins
15 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 4,310,683 213,502 20.19 1 hrs 11 mins
16 Radeon RX 6800(XT)/6900XT
Navi 21 [Radeon RX 6800(XT)/6900XT]
AMD Navi 21 3,883,342 240,000 16.18 1 hrs 29 mins
17 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,689,499 240,000 15.37 1 hrs 34 mins
18 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,271,167 240,000 13.63 1 hrs 46 mins
19 Radeon RX 6700(XT)/6800M
Navi 22 XT-XL [Radeon RX 6700(XT)/6800M]
AMD Navi 22 XT-XL 1,964,317 240,000 8.18 2 hrs 56 mins
20 GeForce GTX 1070 Mobile
GP104BM [GeForce GTX 1070 Mobile] 6463
Nvidia GP104BM 161,975 181,328 0.89 26 hrs 52 mins