Research section Research projects and references

Research: CANCER Folding Project #19706

Project #19706 overview

Project Summary AI Beta

No TLDR available at this time.
Missing summaries require a separate enrichment run.

Project team

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

Work unit

Atoms
238,035
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 19706

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

Data as of Friday, 14 August 2026 15:22:39

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 43,218,314 225,324 191.81 0 hrs 8 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 35,235,597 1,864,849 18.89 1 hrs 16 mins
3 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 24,048,291 217,517 110.56 0 hrs 13 mins
4 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 21,016,833 554,144 37.93 0 hrs 38 mins
5 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 19,135,789 1,441,556 13.27 1 hrs 48 mins
6 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 16,961,403 222,703 76.16 0 hrs 19 mins
7 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 14,705,836 832,247 17.67 1 hrs 21 mins
8 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 5,289,173 240,000 22.04 1 hrs 5 mins
9 GeForce RTX 5060
GB206 [GeForce RTX 5060]
Nvidia GB206 4,474,892 195,972 22.83 1 hrs 3 mins
10 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,589,351 240,000 14.96 1 hrs 36 mins
11 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,483,161 240,000 10.35 2 hrs 19 mins
12 Radeon RX 6800(XT)/6900XT
Navi 21 [Radeon RX 6800(XT)/6900XT]
AMD Navi 21 2,239,422 195,972 11.43 2 hrs 6 mins