RESEARCH: CANCER
FOLDING PROJECT #17905 PROFILE
PROJECT TEAM
Manager(s): Austin WeigleInstitution: University of Illinois at Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 92,996Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how proteins called acyltransferases change shape to recognize different molecules they work with. Acyltransferases are important for things like gene activity and cancer, and the study focuses on a flexible part of these proteins that seems to control their ability to bind to various targets.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Understanding the molecular basis of substrate specificity for a given protein family is fundamental for the biophysical study of any metabolic process and related molecule design.
In this project, we model the apo and holo dynamics of acyltransferase enzymes demonstrating variable substrate specificity and organismal evolutionary stage.
Human acyltransferase activity is generally implicated in gene expression and cancer development.
Averaging ~18-25 sequence identity within their family, our selected acyltransferase enzymes maintain a remarkably conserved topology, where the front and back domains of these proteins form a doughnut-like shape bridged by an ~50 residue intrinsically disordered loop (IDL).
Sequence-based analyses suggest that some correlation exists between the extent of disorder in this IDL region and the extent of demonstrated substrate permissiveness by the respective enzyme.
By comparing loop dynamics in response to substrate recognition between the different modeled proteins, our goal is to offer fundamental insights into how soluble proteins can evolve substrate specificity without converging to a conserved amino acid sequence.
RELATED TERMS GLOSSARY AI BETA
protein
A large biomolecule composed of amino acids.
Proteins are the workhorses of cells, carrying out a vast array of functions. They are made up of chains of amino acids folded into specific 3D shapes. These shapes allow proteins to interact with other molecules and perform their diverse roles in living organisms.
substrate specificity
The ability of an enzyme to preferentially bind and act upon a particular substrate.
Substrate specificity refers to the selective nature of enzymes. Each enzyme has evolved to recognize and interact with specific molecules called substrates. This specificity is crucial for controlling biochemical reactions within cells.
acyltransferase
An enzyme that catalyzes the transfer of an acyl group from one molecule to another.
Acyltransferases are a crucial class of enzymes involved in various metabolic processes. They facilitate the transfer of acyl groups, which are chemical fragments containing an oxygen-linked carbon chain, between different molecules.
gene expression
The process by which information encoded in a gene is used to synthesize a functional product, typically a protein.
Gene expression is the fundamental process by which cells translate genetic information into functional molecules. It involves two main steps: transcription (DNA to RNA) and translation (RNA to protein). This process allows cells to produce the proteins they need to function.
cancer
A disease characterized by the uncontrolled growth and spread of abnormal cells.
Cancer is a complex group of diseases involving the abnormal proliferation of cells. These cancerous cells can invade surrounding tissues and spread to other parts of the body. Cancer development is influenced by genetic mutations, environmental factors, and lifestyle choices.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:40|
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 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,728,654 | 102,439 | 75.45 | 0 hrs 19 mins |
| 2 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,763,946 | 97,094 | 69.66 | 0 hrs 21 mins |
| 3 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 6,269,587 | 208,068 | 30.13 | 0 hrs 48 mins |
| 4 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,969,934 | 93,489 | 63.86 | 0 hrs 23 mins |
| 5 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,955,520 | 95,917 | 62.09 | 0 hrs 23 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,017,050 | 88,098 | 56.95 | 0 hrs 25 mins |
| 7 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,525,120 | 88,506 | 51.13 | 0 hrs 28 mins |
| 8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,288,831 | 83,858 | 51.14 | 0 hrs 28 mins |
| 9 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,171,795 | 83,390 | 50.03 | 0 hrs 29 mins |
| 10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,037,289 | 81,857 | 49.32 | 0 hrs 29 mins |
| 11 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 3,149,890 | 75,116 | 41.93 | 0 hrs 34 mins |
| 12 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,141,786 | 76,032 | 41.32 | 0 hrs 35 mins |
| 13 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,964,031 | 74,124 | 39.99 | 0 hrs 36 mins |
| 14 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,440,222 | 69,665 | 35.03 | 0 hrs 41 mins |
| 15 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,261,592 | 67,820 | 33.35 | 0 hrs 43 mins |
| 16 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,222,903 | 67,329 | 33.02 | 0 hrs 44 mins |
| 17 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,801,014 | 62,261 | 28.93 | 0 hrs 50 mins |
| 18 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,247,589 | 55,709 | 22.39 | 1 hrs 4 mins |
| 19 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,076,108 | 53,043 | 20.29 | 1 hrs 11 mins |
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|||||||
| 20 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,073,466 | 52,152 | 20.58 | 1 hrs 10 mins |
| 21 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 950,777 | 51,163 | 18.58 | 1 hrs 17 mins |
| 22 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 766,068 | 48,000 | 15.96 | 1 hrs 30 mins |
| 23 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 703,531 | 45,757 | 15.38 | 1 hrs 34 mins |
| 24 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 684,128 | 41,769 | 16.38 | 1 hrs 28 mins |
| 25 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 668,801 | 45,073 | 14.84 | 1 hrs 37 mins |
| 26 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 614,396 | 44,221 | 13.89 | 1 hrs 44 mins |
| 27 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 562,986 | 42,354 | 13.29 | 1 hrs 48 mins |
| 28 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 356,121 | 36,568 | 9.74 | 2 hrs 28 mins |
| 29 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 351,459 | 36,875 | 9.53 | 2 hrs 31 mins |
| 30 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 308,478 | 33,901 | 9.10 | 2 hrs 38 mins |
| 31 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 203,539 | 30,622 | 6.65 | 3 hrs 37 mins |
| 32 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 139,982 | 26,914 | 5.20 | 4 hrs 37 mins |
| 33 | GeForce GTX 950M GM107 [GeForce GTX 950M] 1439 |
Nvidia | GM107 | 101,390 | 23,974 | 4.23 | 5 hrs 40 mins |
| 34 | GeForce GTX 750 GM107 [GeForce GTX 750] 1111 |
Nvidia | GM107 | 96,038 | 23,776 | 4.04 | 5 hrs 56 mins |
| 35 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 54,316 | 19,712 | 2.76 | 8 hrs 43 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:40|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|