RESEARCH: CANCER
FOLDING PROJECT #17906 PROFILE

PROJECT TEAM

Manager(s): Austin Weigle
Institution: University of Illinois at Urbana-Champaign
Project URL: View Project Website

WORK UNIT INFO

Atoms: 65,720
Core: 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

Note: Glossary items are a high level summary and may not be 100% accurate.

substrate

A substance acted upon by an enzyme.

Scientific: Biotechnology
Biochemistry / Enzyme Kinetics

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.

Scientific: Biotechnology
Biochemistry / Enzyme Catalysis

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.

Scientific: Biotechnology
Biochemistry / Protein Structure

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.

Scientific: Biotechnology
Biochemistry / Protein Structure

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

Scientific: Biotechnology
Biochemistry / Protein Structure

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
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