RESEARCH: CANCER
FOLDING PROJECT #17911 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 72,854
Core: 0x22
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

This project studies how enzymes called acyltransferases change shape to recognize different molecules. These enzymes are important for processes like gene expression and cancer. By looking at how the flexible part of these enzymes moves when it binds to molecules, scientists hope to understand how they evolved to have different specificities 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

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

protein family

A group of proteins with similar structure and function.

Scientific: Biotechnology
Biochemistry / Structural Biology

Protein families are groups of proteins that share common characteristics like structure and function. They often evolved from a common ancestor and play similar roles in biological processes.


acyltransferase

An enzyme that catalyzes the transfer of an acyl group from one molecule to another.

Technical: Pharmaceuticals
Biochemistry / Enzyme Kinetics

Acyltransferases are a type of enzyme that help move chemical groups called acyl groups between molecules. They play important roles in processes like metabolism and cell signaling.


substrate specificity

The ability of an enzyme to preferentially bind and catalyze a specific substrate.

Scientific: Biotechnology
Biochemistry / Enzyme Catalysis

Substrate specificity refers to how well an enzyme can work with a particular molecule (the substrate). Some enzymes are very picky, only working with one type of substrate, while others are more flexible.


organismal evolutionary stage

The point in an organism's life cycle where it has evolved certain traits.

Scientific: Biotechnology
Biology / Evolutionary Biology

Organismal evolutionary stage describes where a species is in its evolutionary journey. Different stages are marked by specific adaptations and characteristics that help the organism survive in its environment.


homology modeling

A computational method for predicting the three-dimensional structure of a protein based on its amino acid sequence and the known structures of related proteins.

Technical: Biotechnology
Bioinformatics / Protein Structure Prediction

Homology modeling is a way to predict the shape of a protein using information from similar proteins whose structures are already known. It's like using a blueprint to build a model.


intrinsically disordered loop (IDL)

A region of a protein that lacks a defined three-dimensional structure.

Scientific: Pharmaceuticals
Biochemistry / Protein Structure

Intrinsically disordered loops (IDLs) are flexible and unstructured parts of proteins. They often play important roles in interactions with other molecules.


substrate permissiveness

The ability of an enzyme to catalyze a reaction with a variety of different substrates.

Scientific: Drug Discovery
Biochemistry / Enzyme Kinetics

Substrate permissiveness describes how flexible an enzyme is in accepting different molecules as its fuel. Some enzymes are very picky, while others can work with a wider range of inputs.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:34:31
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 10,118,385 85,193 118.77 0 hrs 12 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 8,863,637 82,135 107.92 0 hrs 13 mins
3 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 7,056,639 78,954 89.38 0 hrs 16 mins
4 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 6,511,057 185,082 35.18 0 hrs 41 mins
5 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 6,340,889 75,764 83.69 0 hrs 17 mins
6 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,232,642 73,208 85.14 0 hrs 17 mins
7 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,459,414 70,726 77.19 0 hrs 19 mins
8 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,056,604 68,144 74.20 0 hrs 19 mins
9 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,973,809 69,386 71.68 0 hrs 20 mins
10 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,199,454 64,472 65.14 0 hrs 22 mins
11 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,827,278 62,375 61.36 0 hrs 23 mins
12 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,810,813 63,066 60.43 0 hrs 24 mins
13 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 3,762,319 62,511 60.19 0 hrs 24 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,409,586 60,577 56.29 0 hrs 26 mins
15 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 2,913,757 55,862 52.16 0 hrs 28 mins
16 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,909,651 57,729 50.40 0 hrs 29 mins
17 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,694,255 55,726 48.35 0 hrs 30 mins
18 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,650,355 55,697 47.59 0 hrs 30 mins
19 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,572,379 55,046 46.73 0 hrs 31 mins
20 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,561,495 54,612 46.90 0 hrs 31 mins
21 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,451,280 54,395 45.06 0 hrs 32 mins
22 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,088,202 50,904 41.02 0 hrs 35 mins
23 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,081,515 51,199 40.66 0 hrs 35 mins
24 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,806,555 49,052 36.83 0 hrs 39 mins
25 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,727,558 48,132 35.89 0 hrs 40 mins
26 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,168,883 42,581 27.45 0 hrs 52 mins
27 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,155,709 41,871 27.60 0 hrs 52 mins
28 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,145,414 41,085 27.88 0 hrs 52 mins
29 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,126,031 42,098 26.75 0 hrs 54 mins
30 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 994,219 39,861 24.94 0 hrs 58 mins
31 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 934,620 38,942 24.00 0 hrs 60 mins
32 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 752,580 36,600 20.56 1 hrs 10 mins
33 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 627,244 34,343 18.26 1 hrs 19 mins
34 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 502,003 31,745 15.81 1 hrs 31 mins
35 P106-090
GP106 [P106-090]
Nvidia GP106 334,775 27,905 12.00 2 hrs 0 mins
36 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 257,439 26,125 9.85 2 hrs 26 mins
37 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 206,854 23,664 8.74 2 hrs 45 mins
38 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 56,386 15,515 3.63 6 hrs 36 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:34:31
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make