RESEARCH: CANCER
FOLDING PROJECT #17906 PROFILE
PROJECT TEAM
Manager(s): Austin WeigleInstitution: University of Illinois at Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 65,720Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how proteins called acyltransferases recognize different molecules. These proteins have a flexible loop that changes shape when they bind to a molecule, helping them become specialized. By looking at how these loops move, scientists hope to understand how proteins evolve to recognize specific targets without needing identical building blocks.
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
substrate
A substance acted upon by an enzyme.
In biochemistry, a substrate is the molecule that an enzyme acts upon to catalyze a chemical reaction. Enzymes are proteins that speed up these reactions by binding to substrates and lowering the activation energy required for the reaction to occur.
acyltransferase
An enzyme that catalyzes the transfer of an acyl group (e.g., fatty acid) from one molecule to another.
Acyltransferases are a crucial class of enzymes involved in various metabolic processes. They facilitate the transfer of acyl groups, such as fatty acids, from one molecule to another. These reactions play vital roles in lipid metabolism, signal transduction, and protein modification.
protein family
A group of proteins that share a common evolutionary origin and structural/functional similarities.
Proteins belonging to the same family often exhibit conserved domains, motifs, and overall structures. This shared ancestry reflects their functional relatedness and suggests that they may have evolved from a common ancestral protein.
topology
The overall three-dimensional arrangement of atoms or subunits in a molecule.
In the context of proteins, topology refers to their spatial arrangement. It describes how different parts of the protein chain are connected and folded into specific shapes. Understanding protein topology is crucial for comprehending their function and interactions.
intrinsically disordered loop (IDL)
Intrinsically Disordered Loop
An intrinsically disordered loop (IDL) is a region within a protein that lacks a defined three-dimensional structure. These loops are often flexible and play important roles in protein function, such as binding to other molecules or mediating interactions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:39|
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 | 10,897,090 | 77,523 | 140.57 | 0 hrs 10 mins |
| 2 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 6,784,521 | 175,844 | 38.58 | 0 hrs 37 mins |
| 3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,631,203 | 67,741 | 97.89 | 0 hrs 15 mins |
| 4 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,908,603 | 64,356 | 91.81 | 0 hrs 16 mins |
| 5 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,982,088 | 62,709 | 79.45 | 0 hrs 18 mins |
| 6 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,784,797 | 60,918 | 78.54 | 0 hrs 18 mins |
| 7 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,448,401 | 58,121 | 76.54 | 0 hrs 19 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,295,022 | 58,746 | 73.11 | 0 hrs 20 mins |
| 9 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,176,628 | 58,009 | 72.00 | 0 hrs 20 mins |
| 10 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,074,673 | 57,292 | 71.12 | 0 hrs 20 mins |
| 11 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 4,019,788 | 32,568 | 123.43 | 0 hrs 12 mins |
| 12 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,851,868 | 56,131 | 68.62 | 0 hrs 21 mins |
| 13 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,806,033 | 50,326 | 55.76 | 0 hrs 26 mins |
| 14 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,797,869 | 50,463 | 55.44 | 0 hrs 26 mins |
| 15 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,718,855 | 50,609 | 53.72 | 0 hrs 27 mins |
| 16 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,588,562 | 49,569 | 52.22 | 0 hrs 28 mins |
| 17 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,584,084 | 49,873 | 51.81 | 0 hrs 28 mins |
| 18 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,282,291 | 47,578 | 47.97 | 0 hrs 30 mins |
| 19 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,156,970 | 46,909 | 45.98 | 0 hrs 31 mins |
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|||||||
| 20 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,095,597 | 45,985 | 45.57 | 0 hrs 32 mins |
| 21 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,948,945 | 45,114 | 43.20 | 0 hrs 33 mins |
| 22 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,803,239 | 43,779 | 41.19 | 0 hrs 35 mins |
| 23 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,388,851 | 39,984 | 34.74 | 0 hrs 41 mins |
| 24 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,319,823 | 38,736 | 34.07 | 0 hrs 42 mins |
| 25 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,191,647 | 38,458 | 30.99 | 0 hrs 46 mins |
| 26 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,181,788 | 38,011 | 31.09 | 0 hrs 46 mins |
| 27 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,161,409 | 36,330 | 31.97 | 0 hrs 45 mins |
| 28 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 945,349 | 34,926 | 27.07 | 0 hrs 53 mins |
| 29 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 869,728 | 35,171 | 24.73 | 0 hrs 58 mins |
| 30 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 642,671 | 31,185 | 20.61 | 1 hrs 10 mins |
| 31 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 579,961 | 30,633 | 18.93 | 1 hrs 16 mins |
| 32 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 342,357 | 25,071 | 13.66 | 1 hrs 45 mins |
| 33 | Quadro K620 GM107GL [Quadro K620] |
Nvidia | GM107GL | 53,868 | 13,682 | 3.94 | 6 hrs 6 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:39|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|