Research section Research projects and references

Research: CANCER Folding Project #19701

Project #19701 overview

Project Summary AI Beta

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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 Friday, 14 August 2026 15:22:44

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 45,490,213 208,792 217.87 0 hrs 7 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 29,646,262 808,835 36.65 0 hrs 39 mins
3 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 22,560,573 197,607 114.17 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 14,414,421 328,964 43.82 0 hrs 33 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 GeForce RTX 5060
GB206 [GeForce RTX 5060]
Nvidia GB206 5,793,379 240,000 24.14 0 hrs 60 mins
8 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
9 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
10 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