RESEARCH: CANCER
FOLDING PROJECT #17909 PROFILE
PROJECT TEAM
Manager(s): Austin WeigleInstitution: University of Illinois at Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 59,579Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how different proteins change shape when they encounter their specific 'food source'. Some enzymes are picky eaters, while others can handle a wider variety. By studying these shapes, we hope to learn how proteins evolve to recognize different 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 specificity
The ability of an enzyme to preferentially bind and act on a particular substrate.
Substrate specificity describes how well an enzyme works with a specific molecule. It's important because enzymes are like tiny machines that carry out chemical reactions in our bodies, and each enzyme is designed to work best with certain molecules.
acyltransferase
An enzyme that catalyzes the transfer of an acyl group (R-CO-) from one molecule to another.
Acyltransferases are enzymes that move small chemical groups called acyl groups between molecules. They are essential for many biological processes, such as building and breaking down fats.
gene expression
The process by which information from a gene is used to create a functional product, such as a protein.
Gene expression is how our genes are turned on and off. It's the process of making proteins based on the instructions in our DNA.
cancer development
The multi-step process by which normal cells transform into cancerous cells.
Cancer development is a complex process where healthy cells start to grow and divide uncontrollably. This can lead to the formation of tumors and spread of cancer.
sequence identity
The percentage of identical amino acid residues between two protein sequences.
Sequence identity measures how similar two protein sequences are. It helps scientists understand evolutionary relationships and protein function.
topology
The overall three-dimensional arrangement of a molecule or protein.
Topology describes the shape and structure of molecules. For proteins, it helps us understand how they fold into functional shapes.
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.
substrate permissiveness
The ability of an enzyme to act on a wide range of substrates.
Substrate permissiveness means that an enzyme can work with many different molecules. This is important for enzymes that need to be versatile and adapt to changing conditions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:34|
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 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 12,020,001 | 67,300 | 178.60 | 0 hrs 8 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 10,244,396 | 67,649 | 151.43 | 0 hrs 10 mins |
| 3 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 8,579,622 | 176,883 | 48.50 | 0 hrs 30 mins |
| 4 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 8,199,094 | 66,313 | 123.64 | 0 hrs 12 mins |
| 5 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 6,861,039 | 61,820 | 110.98 | 0 hrs 13 mins |
| 6 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 5,651,949 | 56,772 | 99.56 | 0 hrs 14 mins |
| 7 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,072,553 | 56,887 | 89.17 | 0 hrs 16 mins |
| 8 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,686,198 | 55,399 | 84.59 | 0 hrs 17 mins |
| 9 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,407,966 | 53,941 | 81.72 | 0 hrs 18 mins |
| 10 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 3,997,518 | 56,461 | 70.80 | 0 hrs 20 mins |
| 11 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,764,782 | 51,194 | 73.54 | 0 hrs 20 mins |
| 12 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,698,888 | 51,373 | 72.00 | 0 hrs 20 mins |
| 13 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 3,553,082 | 50,395 | 70.50 | 0 hrs 20 mins |
| 14 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,548,339 | 50,782 | 69.87 | 0 hrs 21 mins |
| 15 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,077,925 | 48,061 | 64.04 | 0 hrs 22 mins |
| 16 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,943,701 | 47,539 | 61.92 | 0 hrs 23 mins |
| 17 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,771,748 | 46,061 | 60.18 | 0 hrs 24 mins |
| 18 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,544,219 | 44,381 | 57.33 | 0 hrs 25 mins |
| 19 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,517,423 | 44,514 | 56.55 | 0 hrs 25 mins |
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| 20 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,459,185 | 44,706 | 55.01 | 0 hrs 26 mins |
| 21 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,323,238 | 43,939 | 52.87 | 0 hrs 27 mins |
| 22 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,181,328 | 41,968 | 51.98 | 0 hrs 28 mins |
| 23 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 2,089,027 | 41,713 | 50.08 | 0 hrs 29 mins |
| 24 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,054,039 | 41,990 | 48.92 | 0 hrs 29 mins |
| 25 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,860,657 | 40,917 | 45.47 | 0 hrs 32 mins |
| 26 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,648,501 | 39,183 | 42.07 | 0 hrs 34 mins |
| 27 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,234,849 | 33,374 | 37.00 | 0 hrs 39 mins |
| 28 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,142,536 | 34,121 | 33.48 | 0 hrs 43 mins |
| 29 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 984,055 | 32,885 | 29.92 | 0 hrs 48 mins |
| 30 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 826,510 | 30,611 | 27.00 | 0 hrs 53 mins |
| 31 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 822,684 | 30,834 | 26.68 | 0 hrs 54 mins |
| 32 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 679,531 | 29,129 | 23.33 | 1 hrs 2 mins |
| 33 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 611,684 | 28,318 | 21.60 | 1 hrs 7 mins |
| 34 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 553,876 | 26,924 | 20.57 | 1 hrs 10 mins |
| 35 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 523,790 | 21,873 | 23.95 | 1 hrs 0 mins |
| 36 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 485,920 | 25,979 | 18.70 | 1 hrs 17 mins |
| 37 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 425,303 | 13,322 | 31.92 | 0 hrs 45 mins |
| 38 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 330,549 | 22,744 | 14.53 | 1 hrs 39 mins |
| 39 | Quadro K2200 GM107GL [Quadro K2200] |
Nvidia | GM107GL | 111,727 | 16,079 | 6.95 | 3 hrs 27 mins |
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| 40 | Quadro K620 GM107GL [Quadro K620] |
Nvidia | GM107GL | 53,337 | 12,351 | 4.32 | 5 hrs 33 mins |
| 41 | GTX 650 Ti Boost GK106 [GTX 650 Ti Boost] |
Nvidia | GK106 | 41,654 | 11,396 | 3.66 | 6 hrs 34 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:34|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|