RESEARCH: CANCER
FOLDING PROJECT #17909 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 59,579
Core: 0x22
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

This project looks at how different proteins change shape when they encounter their specific 'food source'. Some enzymes are picky eaters, while others can handle a wider variety. By studying these shapes, we hope to learn how proteins evolve to recognize different 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 specificity

The ability of an enzyme to preferentially bind and act on a particular substrate.

scientific: Pharmaceutical Research
Biochemistry / Enzyme Kinetics

Substrate specificity describes how well an enzyme works with a specific molecule. It's important because enzymes are like tiny machines that carry out chemical reactions in our bodies, and each enzyme is designed to work best with certain molecules.


acyltransferase

An enzyme that catalyzes the transfer of an acyl group (R-CO-) from one molecule to another.

technical: Biotechnology
Biochemistry / Enzyme Catalysis

Acyltransferases are enzymes that move small chemical groups called acyl groups between molecules. They are essential for many biological processes, such as building and breaking down fats.


gene expression

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

scientific: Biomedical Research
Molecular Biology / Gene Regulation

Gene expression is how our genes are turned on and off. It's the process of making proteins based on the instructions in our DNA.


cancer development

The multi-step process by which normal cells transform into cancerous cells.

medical: Healthcare
Oncology / Tumor Biology

Cancer development is a complex process where healthy cells start to grow and divide uncontrollably. This can lead to the formation of tumors and spread of cancer.


sequence identity

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

scientific: Genomics
Bioinformatics / Sequence Alignment

Sequence identity measures how similar two protein sequences are. It helps scientists understand evolutionary relationships and protein function.


topology

The overall three-dimensional arrangement of a molecule or protein.

scientific: Drug Discovery
Structural Biology / Protein Folding

Topology describes the shape and structure of molecules. For proteins, it helps us understand how they fold into functional shapes.


intrinsically disordered loop (IDL)

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

scientific: Biotechnology
Biochemistry / Protein Dynamics

Intrinsically disordered loops (IDLs) are flexible parts of proteins that can change shape and interact with other molecules.


substrate permissiveness

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

scientific: Pharmaceutical Research
Biochemistry / Enzyme Kinetics

Substrate permissiveness means that an enzyme can work with many different molecules. This is important for enzymes that need to be versatile and adapt to changing conditions.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:34:34
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 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 12,020,001 67,300 178.60 0 hrs 8 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 10,244,396 67,649 151.43 0 hrs 10 mins
3 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 8,579,622 176,883 48.50 0 hrs 30 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 8,199,094 66,313 123.64 0 hrs 12 mins
5 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 6,861,039 61,820 110.98 0 hrs 13 mins
6 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 5,651,949 56,772 99.56 0 hrs 14 mins
7 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,072,553 56,887 89.17 0 hrs 16 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,686,198 55,399 84.59 0 hrs 17 mins
9 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,407,966 53,941 81.72 0 hrs 18 mins
10 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 3,997,518 56,461 70.80 0 hrs 20 mins
11 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,764,782 51,194 73.54 0 hrs 20 mins
12 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 3,698,888 51,373 72.00 0 hrs 20 mins
13 GeForce RTX 4070
AD104 [GeForce RTX 4070]
Nvidia AD104 3,553,082 50,395 70.50 0 hrs 20 mins
14 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,548,339 50,782 69.87 0 hrs 21 mins
15 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,077,925 48,061 64.04 0 hrs 22 mins
16 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 2,943,701 47,539 61.92 0 hrs 23 mins
17 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,771,748 46,061 60.18 0 hrs 24 mins
18 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,544,219 44,381 57.33 0 hrs 25 mins
19 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,517,423 44,514 56.55 0 hrs 25 mins
20 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,459,185 44,706 55.01 0 hrs 26 mins
21 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,323,238 43,939 52.87 0 hrs 27 mins
22 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,181,328 41,968 51.98 0 hrs 28 mins
23 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 2,089,027 41,713 50.08 0 hrs 29 mins
24 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,054,039 41,990 48.92 0 hrs 29 mins
25 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,860,657 40,917 45.47 0 hrs 32 mins
26 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,648,501 39,183 42.07 0 hrs 34 mins
27 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,234,849 33,374 37.00 0 hrs 39 mins
28 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,142,536 34,121 33.48 0 hrs 43 mins
29 P104-100
GP104 [P104-100]
Nvidia GP104 984,055 32,885 29.92 0 hrs 48 mins
30 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 826,510 30,611 27.00 0 hrs 53 mins
31 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 822,684 30,834 26.68 0 hrs 54 mins
32 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 679,531 29,129 23.33 1 hrs 2 mins
33 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 611,684 28,318 21.60 1 hrs 7 mins
34 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 553,876 26,924 20.57 1 hrs 10 mins
35 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 523,790 21,873 23.95 1 hrs 0 mins
36 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 485,920 25,979 18.70 1 hrs 17 mins
37 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 425,303 13,322 31.92 0 hrs 45 mins
38 P106-090
GP106 [P106-090]
Nvidia GP106 330,549 22,744 14.53 1 hrs 39 mins
39 Quadro K2200
GM107GL [Quadro K2200]
Nvidia GM107GL 111,727 16,079 6.95 3 hrs 27 mins
40 Quadro K620
GM107GL [Quadro K620]
Nvidia GM107GL 53,337 12,351 4.32 5 hrs 33 mins
41 GTX 650 Ti Boost
GK106 [GTX 650 Ti Boost]
Nvidia GK106 41,654 11,396 3.66 6 hrs 34 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

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