RESEARCH: CANCER
FOLDING PROJECT #17907 PROFILE
PROJECT TEAM
Manager(s): Austin WeigleInstitution: University of Illinois at Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 88,938Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how enzymes called acyltransferases change shape when they bind to different molecules. These enzymes are important for things like gene activity and cancer, but they don't all recognize the same molecules. By studying how their flexible parts move, scientists hope to understand how these enzymes evolved to be specific without having 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
protein family
A group of proteins with similar structures and functions.
Proteins belonging to the same family share common characteristics like structure and function. They often evolve from a shared ancestor.
substrate specificity
The ability of an enzyme to selectively bind and act upon a specific substrate.
Each enzyme has a unique ability to work with certain molecules called substrates. This specificity is crucial for biological processes.
acyltransferase
An enzyme that catalyzes the transfer of an acyl group from one molecule to another.
Acyltransferases are enzymes that facilitate the movement of acyl groups (chains of carbon and hydrogen atoms) between molecules. They play essential roles in various metabolic pathways.
gene expression
The process by which information from a gene is used to synthesize a functional product, such as a protein.
Gene expression is how our DNA instructions are used to create the molecules that build and run our bodies. It's a fundamental process in all living organisms.
cancer development
The process by which normal cells transform into cancerous cells.
Cancer development is a complex process involving changes in our DNA that lead to uncontrolled cell growth and spread. It's a major cause of death worldwide.
sequence identity
The percentage of identical amino acid residues between two protein sequences.
Sequence identity helps us understand how similar different proteins are. A higher percentage means they're more closely related.
intrinsically disordered loop (IDL)
A region of a protein that lacks a defined three-dimensional structure.
Intrinsically disordered loops (IDLs) are flexible parts of proteins that can change shape and interact with other molecules. They play important roles in protein function.
substrate permissiveness
The ability of an enzyme to act upon a wide range of substrates.
Substrate permissiveness means that an enzyme can work with multiple different molecules. This flexibility is important for adapting to changing conditions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:37|
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 | 11,845,680 | 112,713 | 105.10 | 0 hrs 14 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 10,399,782 | 107,734 | 96.53 | 0 hrs 15 mins |
| 3 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 8,678,464 | 101,976 | 85.10 | 0 hrs 17 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 8,570,556 | 102,568 | 83.56 | 0 hrs 17 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,521,421 | 96,947 | 77.58 | 0 hrs 19 mins |
| 6 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,012,567 | 90,928 | 66.12 | 0 hrs 22 mins |
| 7 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,752,173 | 88,147 | 65.26 | 0 hrs 22 mins |
| 8 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,682,535 | 87,742 | 64.76 | 0 hrs 22 mins |
| 9 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,726,709 | 82,927 | 57.00 | 0 hrs 25 mins |
| 10 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,549,917 | 81,948 | 55.52 | 0 hrs 26 mins |
| 11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 4,314,286 | 153,901 | 28.03 | 0 hrs 51 mins |
| 12 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,213,074 | 80,563 | 52.30 | 0 hrs 28 mins |
| 13 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,115,057 | 80,727 | 50.97 | 0 hrs 28 mins |
| 14 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,678,791 | 76,013 | 48.40 | 0 hrs 30 mins |
| 15 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,510,052 | 76,543 | 45.86 | 0 hrs 31 mins |
| 16 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,037,969 | 72,976 | 41.63 | 0 hrs 35 mins |
| 17 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,922,034 | 71,326 | 40.97 | 0 hrs 35 mins |
| 18 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,913,421 | 71,468 | 40.77 | 0 hrs 35 mins |
| 19 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,822,127 | 70,473 | 40.05 | 0 hrs 36 mins |
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|||||||
| 20 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 2,627,009 | 70,479 | 37.27 | 0 hrs 39 mins |
| 21 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,326,761 | 65,916 | 35.30 | 0 hrs 41 mins |
| 22 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,200,800 | 65,076 | 33.82 | 0 hrs 43 mins |
| 23 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,084,132 | 63,280 | 32.94 | 0 hrs 44 mins |
| 24 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,021,781 | 63,684 | 31.75 | 0 hrs 45 mins |
| 25 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,726,794 | 59,100 | 29.22 | 0 hrs 49 mins |
| 26 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,392,529 | 56,410 | 24.69 | 0 hrs 58 mins |
| 27 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,304,995 | 53,622 | 24.34 | 0 hrs 59 mins |
| 28 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,154,242 | 52,439 | 22.01 | 1 hrs 5 mins |
| 29 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,075,562 | 50,429 | 21.33 | 1 hrs 8 mins |
| 30 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 924,257 | 47,554 | 19.44 | 1 hrs 14 mins |
| 31 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 870,316 | 44,613 | 19.51 | 1 hrs 14 mins |
| 32 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 732,374 | 44,853 | 16.33 | 1 hrs 28 mins |
| 33 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 653,509 | 43,395 | 15.06 | 1 hrs 36 mins |
| 34 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 599,535 | 42,093 | 14.24 | 1 hrs 41 mins |
| 35 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 575,269 | 41,719 | 13.79 | 1 hrs 44 mins |
| 36 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 383,701 | 37,029 | 10.36 | 2 hrs 19 mins |
| 37 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 362,624 | 35,447 | 10.23 | 2 hrs 21 mins |
| 38 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 358,591 | 35,306 | 10.16 | 2 hrs 22 mins |
| 39 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 142,065 | 26,253 | 5.41 | 4 hrs 26 mins |
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| 40 | Quadro K2200 GM107GL [Quadro K2200] |
Nvidia | GM107GL | 129,970 | 25,399 | 5.12 | 4 hrs 41 mins |
| 41 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 104,338 | 23,329 | 4.47 | 5 hrs 22 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:37|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|