RESEARCH: CANCER
FOLDING PROJECT #17910 PROFILE
PROJECT TEAM
Manager(s): Austin WeigleInstitution: University of Illinois at Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 66,134Core: 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 have the same building blocks. By studying their movements, scientists hope to understand how these enzymes evolved to recognize specific targets without needing identical 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
substrate specificity
The ability of an enzyme to selectively bind and act upon a particular substrate.
Substrate specificity refers to an enzyme's ability to recognize and bind to specific molecules called substrates. This selectivity is crucial for biological processes as it ensures that enzymes catalyze the desired reactions without affecting other molecules in the cell.
acyltransferase
An enzyme that catalyzes the transfer of an acyl group (an organic molecule containing a carbonyl group) from one molecule to another.
Acyltransferases are enzymes responsible for transferring acyl groups between molecules. This process plays a vital role in various metabolic pathways, including lipid synthesis, signal transduction, and protein modification.
organismal evolutionary stage
The developmental point or stage of an organism within its evolutionary history.
Organismal evolutionary stage refers to the position an organism holds in its evolutionary lineage. Different stages reflect varying levels of complexity and adaptation to environmental pressures throughout the organism's history.
gene expression
The process by which information encoded in a gene is used to synthesize functional products, such as proteins or RNA molecules.
Gene expression is the fundamental process by which genetic information is converted into functional molecules. It involves two main steps: transcription, where DNA is copied into RNA, and translation, where RNA is used to build proteins.
cancer development
The multi-step process by which normal cells transform into cancerous cells.
Cancer development is a complex process involving genetic mutations and alterations in cellular signaling pathways. These changes allow cells to grow uncontrollably, invade surrounding tissues, and spread throughout the body.
sequence identity
The percentage of amino acid residues that are identical between two protein sequences.
Sequence identity is a measure of similarity between protein sequences. A higher sequence identity indicates greater evolutionary relatedness between the proteins.
topology
The three-dimensional arrangement of atoms and molecules within a protein.
Protein topology refers to the overall spatial structure of a protein molecule. Understanding protein topology is crucial for comprehending its function and interactions with other molecules.
intrinsically disordered loop (IDL)
A region of a protein that lacks a defined three-dimensional structure.
Intrinsically disordered loops (IDLs) are flexible regions within proteins that lack a stable conformation. These loops play important roles in protein function by mediating interactions with other molecules and adapting to changing cellular environments.
substrate permissiveness
The ability of an enzyme to accept a wide range of substrates.
Substrate permissiveness refers to the broad spectrum of molecules an enzyme can effectively catalyze. Enzymes with high substrate permissiveness are versatile and can participate in multiple metabolic pathways.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:33|
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 | 10,229,343 | 78,091 | 130.99 | 0 hrs 11 mins |
| 2 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 8,318,214 | 74,279 | 111.99 | 0 hrs 13 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 8,049,258 | 74,241 | 108.42 | 0 hrs 13 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 7,576,589 | 74,468 | 101.74 | 0 hrs 14 mins |
| 5 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 7,048,332 | 177,022 | 39.82 | 0 hrs 36 mins |
| 6 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,682,655 | 68,033 | 98.23 | 0 hrs 15 mins |
| 7 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,079,101 | 67,062 | 90.65 | 0 hrs 16 mins |
| 8 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,106,965 | 63,387 | 80.57 | 0 hrs 18 mins |
| 9 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,442,644 | 59,924 | 74.14 | 0 hrs 19 mins |
| 10 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,229,485 | 59,262 | 71.37 | 0 hrs 20 mins |
| 11 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,826,102 | 56,366 | 67.88 | 0 hrs 21 mins |
| 12 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,787,572 | 57,888 | 65.43 | 0 hrs 22 mins |
| 13 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,305,692 | 54,502 | 60.65 | 0 hrs 24 mins |
| 14 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,104,347 | 51,068 | 60.79 | 0 hrs 24 mins |
| 15 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,985,385 | 51,829 | 57.60 | 0 hrs 25 mins |
| 16 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,940,048 | 52,065 | 56.47 | 0 hrs 26 mins |
| 17 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,901,917 | 52,377 | 55.40 | 0 hrs 26 mins |
| 18 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,632,361 | 50,507 | 52.12 | 0 hrs 28 mins |
| 19 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,560,866 | 50,750 | 50.46 | 0 hrs 29 mins |
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|
|||||||
| 20 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,394,207 | 49,225 | 48.64 | 0 hrs 30 mins |
| 21 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,371,124 | 49,111 | 48.28 | 0 hrs 30 mins |
| 22 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,110,868 | 46,787 | 45.12 | 0 hrs 32 mins |
| 23 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,922,601 | 44,505 | 43.20 | 0 hrs 33 mins |
| 24 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,785,767 | 44,087 | 40.51 | 0 hrs 36 mins |
| 25 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,562,982 | 42,209 | 37.03 | 0 hrs 39 mins |
| 26 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,338,080 | 40,512 | 33.03 | 0 hrs 44 mins |
| 27 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,294,400 | 40,086 | 32.29 | 0 hrs 45 mins |
| 28 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,281,977 | 39,683 | 32.31 | 0 hrs 45 mins |
| 29 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,231,706 | 39,187 | 31.43 | 0 hrs 46 mins |
| 30 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,072,636 | 37,144 | 28.88 | 0 hrs 50 mins |
| 31 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,004,554 | 36,761 | 27.33 | 0 hrs 53 mins |
| 32 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 998,070 | 36,376 | 27.44 | 0 hrs 52 mins |
| 33 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 951,682 | 35,411 | 26.88 | 0 hrs 54 mins |
| 34 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 648,178 | 31,463 | 20.60 | 1 hrs 10 mins |
| 35 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 604,315 | 30,775 | 19.64 | 1 hrs 13 mins |
| 36 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 571,743 | 30,656 | 18.65 | 1 hrs 17 mins |
| 37 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 330,371 | 24,980 | 13.23 | 1 hrs 49 mins |
| 38 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 235,147 | 22,972 | 10.24 | 2 hrs 21 mins |
| 39 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 229,077 | 22,641 | 10.12 | 2 hrs 22 mins |
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|
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| 40 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 138,161 | 19,002 | 7.27 | 3 hrs 18 mins |
| 41 | GeForce GTX 750 GM107 [GeForce GTX 750] 1111 |
Nvidia | GM107 | 94,565 | 16,622 | 5.69 | 4 hrs 13 mins |
| 42 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 53,547 | 13,617 | 3.93 | 6 hrs 6 mins |
| 43 | GeForce GT 710 GK208B [GeForce GT 710] 366 |
Nvidia | GK208B | 7,235 | 8,284 | 0.87 | 27 hrs 29 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:33|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|