RESEARCH: CANCER
FOLDING PROJECT #12915 PROFILE

PROJECT TEAM

Manager(s): Diego Kleiman
Institution: University of Illinois Urbana-Champaign

WORK UNIT INFO

Atoms: 12,161
Core: 0x22
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

Proteins are like tiny machines that need to fold into specific shapes to work properly. Sometimes, changes in the protein's instructions (DNA) can cause it to misfold, leading to diseases like cancer and Alzheimer's. This project looks at how different mutations affect how proteins fold, hoping to understand and predict these problems better.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

Protein misfolding occurs when a peptide cannot fold into its native structure.

Mutations in the protein sequence may cause alterations of the native folded conformation, leading to diseases such as cancer and Alzheimer's.

Understanding protein misfolding as a function of mutations is currently one of the biggest challenges in the biological sciences.

We aim to systematically study folding rates of diverse mutated proteins to better understand and predict folding dynamics.

RELATED TERMS GLOSSARY AI BETA

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

Protein

Large biological molecules essential for life.

Scientific: Biotechnology
Biochemistry / Molecular Biology

Proteins are complex molecules that perform many vital functions in living organisms. They are involved in processes such as building and repairing tissues, transporting molecules, catalyzing chemical reactions, and defending against disease.


Misfolding

When a protein fails to fold into its correct three-dimensional shape.

Scientific: Pharmaceutical Research
Biochemistry / Protein Structure

Misfolding occurs when proteins don't adopt their intended shapes. This can disrupt their function and lead to diseases like Alzheimer's and Parkinson's.


Mutation

A change in the DNA sequence.

Scientific: Biotechnology
Genetics / Molecular Biology

Mutations are alterations in the genetic code. They can be inherited or occur spontaneously and can sometimes lead to changes in protein structure and function.


Alzheimer's

A progressive neurodegenerative disease.

Pathology: Healthcare
Neurology / Dementia

Alzheimer's disease is a brain disorder that causes memory loss, thinking problems, and behavioral changes. It is characterized by the buildup of abnormal protein deposits in the brain.


Cancer

Uncontrolled cell growth.

Pathology: Healthcare
Oncology / Malignancy

Cancer is a group of diseases characterized by abnormal cell growth and spread. It can affect various organs and tissues in the body.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:33:54
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 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 3,049,693 173,526 17.57 1 hrs 22 mins
2 GeForce RTX 2060 12GB
TU106 [GeForce RTX 2060 12GB]
Nvidia TU106 2,738,928 166,820 16.42 1 hrs 28 mins
3 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,556,085 172,049 14.86 1 hrs 37 mins
4 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,139,803 27,705 77.24 0 hrs 19 mins
5 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,755,350 142,547 12.31 1 hrs 57 mins
6 GeForce RTX 3050 8GB
GA107 [GeForce RTX 3050 8GB]
Nvidia GA107 1,744,678 133,410 13.08 1 hrs 50 mins
7 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,441,221 135,487 10.64 2 hrs 15 mins
8 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 1,385,869 27,705 50.02 0 hrs 29 mins
9 GeForce GTX 1060 6GB
GP104 [GeForce GTX 1060 6GB]
Nvidia GP104 1,012,415 120,276 8.42 2 hrs 51 mins
10 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 948,684 117,505 8.07 2 hrs 58 mins
11 P106-100
GP106 [P106-100]
Nvidia GP106 913,322 116,108 7.87 3 hrs 3 mins
12 Quadro P3200 Mobile
GP104GLM [Quadro P3200 Mobile]
Nvidia GP104GLM 850,235 56,116 15.15 1 hrs 35 mins
13 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 833,600 27,705 30.09 0 hrs 48 mins
14 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 824,718 112,222 7.35 3 hrs 16 mins
15 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 705,443 27,705 25.46 0 hrs 57 mins
16 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 655,938 81,589 8.04 2 hrs 59 mins
17 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 545,597 39,297 13.88 1 hrs 44 mins
18 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 355,729 86,739 4.10 5 hrs 51 mins
19 GeForce GTX 1050 3 GB Max-Q
GP107M [GeForce GTX 1050 3 GB Max-Q]
Nvidia GP107M 343,179 83,578 4.11 5 hrs 51 mins
20 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 297,331 79,827 3.72 6 hrs 27 mins
21 Quadro M4000
GM204GL [Quadro M4000]
Nvidia GM204GL 258,899 76,208 3.40 7 hrs 4 mins
22 GeForce MX150
GP107M [GeForce MX150]
Nvidia GP107M 151,715 64,007 2.37 10 hrs 8 mins
23 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 112,550 53,742 2.09 11 hrs 28 mins
24 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 98,386 55,366 1.78 13 hrs 30 mins
25 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 88,464 51,324 1.72 13 hrs 55 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:33:54
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make