Research section Research projects and references

Research: CANCER Folding Project #19700

Project #19700 overview

Project Summary AI Beta

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Project team

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

Work unit

Atoms
232,436
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 Friday, 14 August 2026 15:22:45

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 49,305,459 240,000 205.44 0 hrs 7 mins
2 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 24,512,353 216,733 113.10 0 hrs 13 mins
3 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 23,215,431 602,789 38.51 0 hrs 37 mins
4 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 22,852,510 1,689,266 13.53 1 hrs 46 mins
5 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 20,949,086 817,904 25.61 0 hrs 56 mins
6 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 14,486,842 240,000 60.36 0 hrs 24 mins
7 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 13,933,490 368,040 37.86 0 hrs 38 mins
8 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 6,926,790 240,000 28.86 0 hrs 50 mins
9 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,289,959 240,000 17.87 1 hrs 21 mins
10 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
11 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
12 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 3,657,859 181,328 20.17 1 hrs 11 mins
13 GeForce GTX 1070 Mobile
GP104BM [GeForce GTX 1070 Mobile] 6463
Nvidia GP104BM 161,975 181,328 0.89 26 hrs 52 mins