RESEARCH: CANCER
FOLDING PROJECT #17790 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 58,675Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how proteins use ion power to move molecules across cell membranes. These proteins are important for many processes in the body and are even targets for some medicines! By studying them, we can learn how they work across different types of organisms.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Molecular basis of secondary active transporters. Secondary active membrane transporters are proteins that utilize ions to transport an assortment of molecules across cell membranes.
These proteins are found in all domains in life and surprisingly, despite vastly different structures, operate under the same mechanism by using an ion gradient to assist in small molecule transport.
Furthermore, many of these secondary active transporters are drug targets to treat diseases like cancer, diabetes, and neurological disorders.
The simulations in this project will allow us to understand a universal role of ion-coupling across different families of proteins.
RELATED TERMS GLOSSARY AI BETA
Molecular basis
The fundamental mechanisms underlying molecular structure and function.
Molecular basis refers to the intricate ways molecules interact and behave. In biochemistry, it's crucial for understanding how proteins like transporters work.
Secondary active transporters
Proteins that use an ion gradient to drive the transport of other molecules across cell membranes.
Secondary active transporters are crucial for moving molecules across cell walls. They utilize the energy from an existing ion gradient to transport various substances, like nutrients or drugs.
Ion gradient
The difference in concentration of ions across a cell membrane.
An ion gradient is like an electrical potential created by having different amounts of charged particles (ions) on either side of a membrane. This difference drives many biological processes, including transport.
Drug targets
Molecules or pathways that are involved in disease processes and can be inhibited or modulated by drugs.
Drug targets are specific molecules or cellular processes that contribute to a disease. By targeting these, drugs can effectively treat or manage the condition.
Simulations
Computer models used to mimic biological processes or systems.
Simulations are powerful tools that allow scientists to study complex biological systems without needing to conduct experiments in the real world. They can help predict how molecules interact and how drugs might work.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:13|
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 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,629,275 | 112,930 | 49.85 | 0 hrs 29 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 5,296,236 | 113,530 | 46.65 | 0 hrs 31 mins |
| 3 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,059,369 | 102,823 | 39.48 | 0 hrs 36 mins |
| 4 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,045,941 | 103,021 | 39.27 | 0 hrs 37 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,018,472 | 102,702 | 39.13 | 0 hrs 37 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,585,351 | 98,519 | 36.39 | 0 hrs 40 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,563,310 | 98,981 | 36.00 | 0 hrs 40 mins |
| 8 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,548,936 | 98,582 | 36.00 | 0 hrs 40 mins |
| 9 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 3,529,102 | 98,031 | 36.00 | 0 hrs 40 mins |
| 10 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,530,884 | 87,878 | 28.80 | 0 hrs 50 mins |
| 11 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,160,218 | 83,735 | 25.80 | 0 hrs 56 mins |
| 12 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,941,723 | 80,905 | 24.00 | 0 hrs 60 mins |
| 13 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,932,577 | 80,524 | 24.00 | 0 hrs 60 mins |
| 14 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,469,630 | 73,141 | 20.09 | 1 hrs 12 mins |
| 15 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,408,478 | 72,530 | 19.42 | 1 hrs 14 mins |
| 16 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,208,563 | 66,667 | 18.13 | 1 hrs 19 mins |
| 17 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,180,085 | 68,739 | 17.17 | 1 hrs 24 mins |
| 18 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 631,946 | 55,414 | 11.40 | 2 hrs 6 mins |
| 19 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 317,520 | 44,152 | 7.19 | 3 hrs 20 mins |
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| 20 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 214,469 | 37,885 | 5.66 | 4 hrs 14 mins |
| 21 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 58,289 | 25,207 | 2.31 | 10 hrs 23 mins |
| 22 | GeForce GTX 660 GK106 [GeForce GTX 660] |
Nvidia | GK106 | 52,511 | 24,391 | 2.15 | 11 hrs 9 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:13|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|