RESEARCH: CANCER
FOLDING PROJECT #12915 PROFILE
PROJECT TEAM
Manager(s): Diego KleimanInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 12,161Core: 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
Protein
Large biological molecules essential for life.
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.
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.
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.
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.
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 |
|---|