RESEARCH: CANCER
FOLDING PROJECT #17908 PROFILE
PROJECT TEAM
Manager(s): Austin WeigleInstitution: University of Illinois at Urbana-Champaign
Project URL: View Project Website
WORK UNIT INFO
Atoms: 72,649Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how enzymes called acyltransferases recognize different molecules. These enzymes have a flexible loop that changes shape when it binds to a molecule, allowing them to specialize in attaching specific tags to various targets. By comparing these loops, researchers hope to understand how proteins evolve to perform diverse functions 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
Large biomolecules essential for various biological functions.
Proteins are the workhorses of cells, carrying out a vast array of functions. They are complex molecules made up of chains of amino acids that fold into unique shapes, allowing them to perform specific tasks such as catalyzing reactions, transporting molecules, and providing structural support.
acyltransferase
An enzyme that catalyzes the transfer of an acyl group (e.g., fatty acid) to a molecule.
Acyltransferases are enzymes that play crucial roles in various metabolic processes by transferring acyl groups from one molecule to another. This transfer is important for building and breaking down lipids, synthesizing hormones, and regulating gene expression.
substrate specificity
The ability of an enzyme to selectively bind and catalyze a specific reaction with its substrate.
Substrate specificity refers to the unique ability of enzymes to recognize and interact with specific molecules called substrates. This selectivity is crucial for regulating biochemical reactions and maintaining cellular order.
homology modeling
A computational method to predict the 3D structure of a protein based on its amino acid sequence and known structures of similar proteins.
Homology modeling is a powerful technique used to predict the three-dimensional shape of proteins. It relies on comparing the amino acid sequence of an unknown protein with those of proteins whose structures are already known.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:36|
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 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,861,148 | 76,947 | 89.17 | 0 hrs 16 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,243,262 | 69,178 | 75.79 | 0 hrs 19 mins |
| 3 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 4,615,347 | 136,425 | 33.83 | 0 hrs 43 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,605,480 | 66,566 | 69.19 | 0 hrs 21 mins |
| 5 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,373,404 | 65,804 | 66.46 | 0 hrs 22 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,951,884 | 64,035 | 61.71 | 0 hrs 23 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,941,671 | 63,109 | 62.46 | 0 hrs 23 mins |
| 8 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,814,596 | 63,192 | 60.37 | 0 hrs 24 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,360,730 | 59,839 | 56.16 | 0 hrs 26 mins |
| 10 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,222,026 | 59,667 | 54.00 | 0 hrs 27 mins |
| 11 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,824,397 | 56,880 | 49.66 | 0 hrs 29 mins |
| 12 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,577,808 | 55,109 | 46.78 | 0 hrs 31 mins |
| 13 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,312,160 | 53,251 | 43.42 | 0 hrs 33 mins |
| 14 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,092,921 | 51,288 | 40.81 | 0 hrs 35 mins |
| 15 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,650,059 | 47,655 | 34.63 | 0 hrs 42 mins |
| 16 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,634,399 | 47,292 | 34.56 | 0 hrs 42 mins |
| 17 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,378,129 | 45,489 | 30.30 | 0 hrs 48 mins |
| 18 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,163,100 | 42,600 | 27.30 | 0 hrs 53 mins |
| 19 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,158,592 | 41,178 | 28.14 | 0 hrs 51 mins |
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| 20 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,119,956 | 41,617 | 26.91 | 0 hrs 54 mins |
| 21 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,030,783 | 40,944 | 25.18 | 0 hrs 57 mins |
| 22 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 697,558 | 33,537 | 20.80 | 1 hrs 9 mins |
| 23 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 588,157 | 33,657 | 17.48 | 1 hrs 22 mins |
| 24 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 565,587 | 27,038 | 20.92 | 1 hrs 9 mins |
| 25 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 476,357 | 31,562 | 15.09 | 1 hrs 35 mins |
| 26 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 337,824 | 28,174 | 11.99 | 2 hrs 0 mins |
| 27 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 165,438 | 18,992 | 8.71 | 2 hrs 45 mins |
| 28 | Quadro K2200 GM107GL [Quadro K2200] |
Nvidia | GM107GL | 126,371 | 21,053 | 6.00 | 3 hrs 60 mins |
| 29 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 105,272 | 19,048 | 5.53 | 4 hrs 21 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:36|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|