RESEARCH: CANCER
FOLDING PROJECT #17907 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 88,938
Core: 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 recognize the same molecules. By studying how their flexible parts move, scientists hope to understand how these enzymes evolved to be specific without having 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.

protein family

A group of proteins with similar structures and functions.

Scientific: Biotechnology
Biochemistry / Structural Biology

Proteins belonging to the same family share common characteristics like structure and function. They often evolve from a shared ancestor.


substrate specificity

The ability of an enzyme to selectively bind and act upon a specific substrate.

Scientific: Pharmaceutical Research
Biochemistry / Enzyme Kinetics

Each enzyme has a unique ability to work with certain molecules called substrates. This specificity is crucial for biological processes.


acyltransferase

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

Technical: Medicine, Biotechnology
Biochemistry / Enzyme Catalysis

Acyltransferases are enzymes that facilitate the movement of acyl groups (chains of carbon and hydrogen atoms) between molecules. They play essential roles in various metabolic pathways.


gene expression

The process by which information from a gene is used to synthesize a functional product, such as a protein.

Scientific: Biotechnology, Healthcare
Molecular Biology / Genetics

Gene expression is how our DNA instructions are used to create the molecules that build and run our bodies. It's a fundamental process in all living organisms.


cancer development

The process by which normal cells transform into cancerous cells.

Medical: Healthcare, Pharmaceuticals
Oncology / Tumor Biology

Cancer development is a complex process involving changes in our DNA that lead to uncontrolled cell growth and spread. It's a major cause of death worldwide.


sequence identity

The percentage of identical amino acid residues between two protein sequences.

Scientific: Biotechnology, Genomics
Bioinformatics / Sequence Alignment

Sequence identity helps us understand how similar different proteins are. A higher percentage means they're more closely related.


intrinsically disordered loop (IDL)

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

Technical: Biotechnology, Drug Discovery
Structural Biology / Protein Folding

Intrinsically disordered loops (IDLs) are flexible parts of proteins that can change shape and interact with other molecules. They play important roles in protein function.


substrate permissiveness

The ability of an enzyme to act upon a wide range of substrates.

Scientific: Pharmaceutical Research
Enzyme Kinetics / Biochemical Mechanisms

Substrate permissiveness means that an enzyme can work with multiple different molecules. This flexibility is important for adapting to changing conditions.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:34:37
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 11,845,680 112,713 105.10 0 hrs 14 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 10,399,782 107,734 96.53 0 hrs 15 mins
3 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 8,678,464 101,976 85.10 0 hrs 17 mins
4 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 8,570,556 102,568 83.56 0 hrs 17 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,521,421 96,947 77.58 0 hrs 19 mins
6 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,012,567 90,928 66.12 0 hrs 22 mins
7 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,752,173 88,147 65.26 0 hrs 22 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,682,535 87,742 64.76 0 hrs 22 mins
9 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,726,709 82,927 57.00 0 hrs 25 mins
10 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,549,917 81,948 55.52 0 hrs 26 mins
11 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 4,314,286 153,901 28.03 0 hrs 51 mins
12 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,213,074 80,563 52.30 0 hrs 28 mins
13 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,115,057 80,727 50.97 0 hrs 28 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,678,791 76,013 48.40 0 hrs 30 mins
15 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,510,052 76,543 45.86 0 hrs 31 mins
16 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,037,969 72,976 41.63 0 hrs 35 mins
17 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,922,034 71,326 40.97 0 hrs 35 mins
18 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,913,421 71,468 40.77 0 hrs 35 mins
19 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,822,127 70,473 40.05 0 hrs 36 mins
20 GeForce RTX 3070 Mobile / Max-Q
GA104M [GeForce RTX 3070 Mobile / Max-Q]
Nvidia GA104M 2,627,009 70,479 37.27 0 hrs 39 mins
21 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,326,761 65,916 35.30 0 hrs 41 mins
22 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,200,800 65,076 33.82 0 hrs 43 mins
23 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,084,132 63,280 32.94 0 hrs 44 mins
24 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,021,781 63,684 31.75 0 hrs 45 mins
25 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,726,794 59,100 29.22 0 hrs 49 mins
26 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,392,529 56,410 24.69 0 hrs 58 mins
27 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,304,995 53,622 24.34 0 hrs 59 mins
28 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,154,242 52,439 22.01 1 hrs 5 mins
29 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,075,562 50,429 21.33 1 hrs 8 mins
30 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 924,257 47,554 19.44 1 hrs 14 mins
31 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 870,316 44,613 19.51 1 hrs 14 mins
32 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 732,374 44,853 16.33 1 hrs 28 mins
33 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 653,509 43,395 15.06 1 hrs 36 mins
34 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 599,535 42,093 14.24 1 hrs 41 mins
35 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 575,269 41,719 13.79 1 hrs 44 mins
36 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 383,701 37,029 10.36 2 hrs 19 mins
37 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 362,624 35,447 10.23 2 hrs 21 mins
38 P106-090
GP106 [P106-090]
Nvidia GP106 358,591 35,306 10.16 2 hrs 22 mins
39 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 142,065 26,253 5.41 4 hrs 26 mins
40 Quadro K2200
GM107GL [Quadro K2200]
Nvidia GM107GL 129,970 25,399 5.12 4 hrs 41 mins
41 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 104,338 23,329 4.47 5 hrs 22 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:34:37
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make