RESEARCH: CANCER
FOLDING PROJECT #17911 PROFILE
PROJECT TEAM
Manager(s): Austin WeigleInstitution: University of Illinois at Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 72,854Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how enzymes called acyltransferases change shape to recognize different molecules. These enzymes are important for processes like gene expression and cancer. By looking at how the flexible part of these enzymes moves when it binds to molecules, scientists hope to understand how they evolved to have different specificities without needing identical amino acid sequences.
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 family
A group of proteins with similar structure and function.
Protein families are groups of proteins that share common characteristics like structure and function. They often evolved from a common ancestor and play similar roles in biological processes.
acyltransferase
An enzyme that catalyzes the transfer of an acyl group from one molecule to another.
Acyltransferases are a type of enzyme that help move chemical groups called acyl groups between molecules. They play important roles in processes like metabolism and cell signaling.
substrate specificity
The ability of an enzyme to preferentially bind and catalyze a specific substrate.
Substrate specificity refers to how well an enzyme can work with a particular molecule (the substrate). Some enzymes are very picky, only working with one type of substrate, while others are more flexible.
organismal evolutionary stage
The point in an organism's life cycle where it has evolved certain traits.
Organismal evolutionary stage describes where a species is in its evolutionary journey. Different stages are marked by specific adaptations and characteristics that help the organism survive in its environment.
homology modeling
A computational method for predicting the three-dimensional structure of a protein based on its amino acid sequence and the known structures of related proteins.
Homology modeling is a way to predict the shape of a protein using information from similar proteins whose structures are already known. It's like using a blueprint to build a model.
intrinsically disordered loop (IDL)
A region of a protein that lacks a defined three-dimensional structure.
Intrinsically disordered loops (IDLs) are flexible and unstructured parts of proteins. They often play important roles in interactions with other molecules.
substrate permissiveness
The ability of an enzyme to catalyze a reaction with a variety of different substrates.
Substrate permissiveness describes how flexible an enzyme is in accepting different molecules as its fuel. Some enzymes are very picky, while others can work with a wider range of inputs.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:31|
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 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 10,118,385 | 85,193 | 118.77 | 0 hrs 12 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 8,863,637 | 82,135 | 107.92 | 0 hrs 13 mins |
| 3 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 7,056,639 | 78,954 | 89.38 | 0 hrs 16 mins |
| 4 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 6,511,057 | 185,082 | 35.18 | 0 hrs 41 mins |
| 5 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 6,340,889 | 75,764 | 83.69 | 0 hrs 17 mins |
| 6 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,232,642 | 73,208 | 85.14 | 0 hrs 17 mins |
| 7 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,459,414 | 70,726 | 77.19 | 0 hrs 19 mins |
| 8 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,056,604 | 68,144 | 74.20 | 0 hrs 19 mins |
| 9 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,973,809 | 69,386 | 71.68 | 0 hrs 20 mins |
| 10 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,199,454 | 64,472 | 65.14 | 0 hrs 22 mins |
| 11 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,827,278 | 62,375 | 61.36 | 0 hrs 23 mins |
| 12 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,810,813 | 63,066 | 60.43 | 0 hrs 24 mins |
| 13 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,762,319 | 62,511 | 60.19 | 0 hrs 24 mins |
| 14 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,409,586 | 60,577 | 56.29 | 0 hrs 26 mins |
| 15 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,913,757 | 55,862 | 52.16 | 0 hrs 28 mins |
| 16 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,909,651 | 57,729 | 50.40 | 0 hrs 29 mins |
| 17 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,694,255 | 55,726 | 48.35 | 0 hrs 30 mins |
| 18 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,650,355 | 55,697 | 47.59 | 0 hrs 30 mins |
| 19 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,572,379 | 55,046 | 46.73 | 0 hrs 31 mins |
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| 20 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,561,495 | 54,612 | 46.90 | 0 hrs 31 mins |
| 21 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,451,280 | 54,395 | 45.06 | 0 hrs 32 mins |
| 22 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,088,202 | 50,904 | 41.02 | 0 hrs 35 mins |
| 23 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,081,515 | 51,199 | 40.66 | 0 hrs 35 mins |
| 24 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,806,555 | 49,052 | 36.83 | 0 hrs 39 mins |
| 25 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,727,558 | 48,132 | 35.89 | 0 hrs 40 mins |
| 26 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,168,883 | 42,581 | 27.45 | 0 hrs 52 mins |
| 27 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,155,709 | 41,871 | 27.60 | 0 hrs 52 mins |
| 28 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,145,414 | 41,085 | 27.88 | 0 hrs 52 mins |
| 29 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,126,031 | 42,098 | 26.75 | 0 hrs 54 mins |
| 30 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 994,219 | 39,861 | 24.94 | 0 hrs 58 mins |
| 31 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 934,620 | 38,942 | 24.00 | 0 hrs 60 mins |
| 32 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 752,580 | 36,600 | 20.56 | 1 hrs 10 mins |
| 33 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 627,244 | 34,343 | 18.26 | 1 hrs 19 mins |
| 34 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 502,003 | 31,745 | 15.81 | 1 hrs 31 mins |
| 35 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 334,775 | 27,905 | 12.00 | 2 hrs 0 mins |
| 36 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 257,439 | 26,125 | 9.85 | 2 hrs 26 mins |
| 37 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 206,854 | 23,664 | 8.74 | 2 hrs 45 mins |
| 38 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 56,386 | 15,515 | 3.63 | 6 hrs 36 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:31|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|