Folding project #17725, CANCER research

Research section Research projects and references

Project #17725 overview

Project Summary AI Beta

This project looks at how proteins use ion gradients to move molecules across cell membranes. These proteins are found in all living things and are important for treating diseases like cancer, diabetes, and neurological disorders. By studying these proteins, we can learn more about how they work and develop new treatments.

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Matthew Chan
Institution: University of Illinois at Urbana-Champaign

Work unit

Atoms: 85,162
Core: OPENMM_22
Status: Public

Source material

Official Project Description

Projects 17711-17724 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 disease like cancer, diabetes, and neurological disorders.

These simulations will allow us to understand a universal role of ion-coupling across different families of proteins.

Reading reference

Related Terms Glossary AI Beta

Automated high-level definitions; simplified and may not be fully accurate.

transporters

Proteins that move molecules across cell membranes.

Scientific: Pharmaceutical ResearchBiotechnology / Membrane Biology

Transporters are essential proteins found in all living organisms. They facilitate the movement of various substances, such as nutrients, ions, and drugs, across cell membranes. This process is crucial for numerous biological functions, including nutrient uptake, waste removal, and signal transduction. Many diseases are caused by defects in transporter function.

secondary active transporters

Transporters that use an ion gradient to move molecules across cell membranes.

Scientific: Pharmaceutical ResearchBiotechnology / Membrane Biology

Secondary active transporters are a class of membrane proteins that utilize the energy stored in an electrochemical gradient of ions to drive the transport of other molecules. They play a vital role in maintaining cellular homeostasis, nutrient uptake, and drug efflux. These transporters are often targeted by pharmaceuticals to treat various diseases.

membrane

A thin layer that surrounds cells and organelles.

Scientific: Pharmaceutical ResearchBiotechnology / Cell Biology

The membrane is a fundamental component of all living cells. It serves as a barrier that separates the internal environment of the cell from its surroundings. The membrane is composed of lipids and proteins, which allow for selective transport of molecules in and out of the cell. It also plays a crucial role in cell signaling and communication.

proteins

Large biomolecules that perform various functions in cells.

Scientific: Pharmaceutical ResearchBiotechnology / Molecular Biology

Proteins are essential macromolecules found in all living organisms. They play a diverse range of roles, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes. Proteins are made up of amino acids, which are linked together in specific sequences to form polypeptide chains.

ions

Atoms or molecules that have an electrical charge.

Scientific: Pharmaceutical ResearchBiotechnology / Cellular Physiology

Ions are atoms or molecules that have gained or lost electrons, resulting in a net electric charge. They play a crucial role in various cellular processes, such as nerve impulse transmission, muscle contraction, and enzyme activity. The movement of ions across cell membranes is essential for maintaining electrochemical gradients and regulating cellular function.

simulations

Computer models used to mimic real-world processes.

Technical: Drug DiscoveryBiotechnology / Computational Biology

Simulations are computer programs that create virtual representations of real-world systems. They are widely used in various fields, including biotechnology, to study complex biological processes, design new drugs, and predict the behavior of molecules.

GPU PPD Averages

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 3090 GA102 [GeForce RTX 3090]	Nvidia	GA102	7,285,846	127,765	57.03	0 ^hrs 25 ^mins
2	GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB]	Nvidia	GA102	5,701,205	118,732	48.02	0 ^hrs 30 ^mins
3	GeForce RTX 3080 GA102 [GeForce RTX 3080]	Nvidia	GA102	5,675,205	118,233	48.00	0 ^hrs 30 ^mins
4	GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448	Nvidia	TU102	5,002,392	113,653	44.01	0 ^hrs 33 ^mins
5	GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448	Nvidia	TU102	4,145,184	106,772	38.82	0 ^hrs 37 ^mins
6	Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000]	Nvidia	TU102GL	4,047,015	105,880	38.22	0 ^hrs 38 ^mins
7	GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068	Nvidia	TU104	3,208,637	98,508	32.57	0 ^hrs 44 ^mins
8	GeForce RTX 3070 GA104 [GeForce RTX 3070]	Nvidia	GA104	3,197,627	98,095	32.60	0 ^hrs 44 ^mins
9	GeForce RTX 2080 TU104 [GeForce RTX 2080]	Nvidia	TU104	3,148,574	97,702	32.23	0 ^hrs 45 ^mins
10	GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER]	Nvidia	TU104	3,022,347	96,254	31.40	0 ^hrs 46 ^mins
11	GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti]	Nvidia	GA104	2,926,358	95,239	30.73	0 ^hrs 47 ^mins
12	GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218	Nvidia	TU104	2,568,275	90,354	28.42	0 ^hrs 51 ^mins
13	GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465	Nvidia	TU106	2,396,317	89,540	26.76	0 ^hrs 54 ^mins
14	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,282,870	88,192	25.89	0 ^hrs 56 ^mins
15	GeForce RTX 2080 SUPER Mobile / Max-Q TU104M [GeForce RTX 2080 SUPER Mobile / Max-Q]	Nvidia	TU104M	2,240,550	88,170	25.41	0 ^hrs 57 ^mins
16	GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497	Nvidia	TU106	2,209,945	86,965	25.41	0 ^hrs 57 ^mins
17	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER]	Nvidia	TU106	2,126,875	85,912	24.76	0 ^hrs 58 ^mins
18	GeForce RTX 3060 GA106 [GeForce RTX 3060]	Nvidia	GA106	1,998,052	83,832	23.83	1 ^hrs 0 ^mins
19	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	1,725,928	78,683	21.94	1 ^hrs 6 ^mins

20	Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]	AMD	Navi 21	1,670,178	79,392	21.04	1 ^hrs 8 ^mins
21	GeForce RTX 2060 TU104 [GeForce RTX 2060]	Nvidia	TU104	1,583,011	69,069	22.92	1 ^hrs 3 ^mins
22	GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186	Nvidia	GP104	1,356,242	74,257	18.26	1 ^hrs 19 ^mins
23	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,294,322	72,896	17.76	1 ^hrs 21 ^mins
24	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,196,145	71,450	16.74	1 ^hrs 26 ^mins
25	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	1,140,503	70,465	16.19	1 ^hrs 29 ^mins
26	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,140,363	70,116	16.26	1 ^hrs 29 ^mins
27	GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti]	Nvidia	TU116	910,011	64,996	14.00	1 ^hrs 43 ^mins
28	Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]	AMD	Navi 10	807,865	62,325	12.96	1 ^hrs 51 ^mins
29	GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935	Nvidia	GP106	567,916	55,376	10.26	2 ^hrs 20 ^mins
30	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	557,021	55,156	10.10	2 ^hrs 23 ^mins
31	GeForce GTX 970 GM204 [GeForce GTX 970] 3494	Nvidia	GM204	499,272	53,388	9.35	2 ^hrs 34 ^mins
32	Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64]	AMD	Vega 10 XL/XT	493,914	49,833	9.91	2 ^hrs 25 ^mins
33	P106-100 GP106 [P106-100]	Nvidia	GP106	473,815	52,318	9.06	2 ^hrs 39 ^mins
34	GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984	Nvidia	TU116	422,241	50,220	8.41	2 ^hrs 51 ^mins
35	Radeon R9 200/300X Series Hawaii [Radeon R9 200/300X Series]	AMD	Hawaii	351,797	47,409	7.42	3 ^hrs 14 ^mins
36	GeForce GTX 960 GM206 [GeForce GTX 960] 2308	Nvidia	GM206	292,061	44,690	6.54	3 ^hrs 40 ^mins
37	P106-090 GP106 [P106-090]	Nvidia	GP106	248,562	42,321	5.87	4 ^hrs 5 ^mins
38	Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590]	AMD	Ellesmere XT	239,431	38,793	6.17	3 ^hrs 53 ^mins
39	GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile]	Nvidia	GP107M	216,000	40,417	5.34	4 ^hrs 29 ^mins

40	Radeon R9 280/HD 7900/8950 Tahiti PRO [Radeon R9 280/HD 7900/8950]	AMD	Tahiti PRO	213,413	40,226	5.31	4 ^hrs 31 ^mins
41	GeForce GTX 680 GK104 [GeForce GTX 680] 3250	Nvidia	GK104	180,720	37,984	4.76	5 ^hrs 3 ^mins
42	Radeon R9 200/300 Series Hawaii [Radeon R9 200/300 Series]	AMD	Hawaii	161,706	25,030	6.46	3 ^hrs 43 ^mins
43	Radeon RX Vega M XL [Radeon RX Vega M XL]	AMD	Vega	123,230	33,456	3.68	6 ^hrs 31 ^mins
44	Radeon HD 7800 Pitcairn [Radeon HD 7800]	AMD	Pitcairn	119,889	33,107	3.62	6 ^hrs 38 ^mins
45	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	115,459	32,508	3.55	6 ^hrs 45 ^mins
46	Quadro P620 GP107GL [Quadro P620]	Nvidia	GP107GL	110,937	32,342	3.43	6 ^hrs 60 ^mins
47	Quadro K2200 GM107GL [Quadro K2200]	Nvidia	GM107GL	109,084	32,071	3.40	7 ^hrs 3 ^mins
48	GeForce GTX 760 GK104 [GeForce GTX 760] 2258	Nvidia	GK104	106,239	31,046	3.42	7 ^hrs 1 ^mins
49	Radeon RX 460 Baffin XT [Radeon RX 460]	AMD	Baffin XT	100,750	31,135	3.24	7 ^hrs 25 ^mins
50	GeForce GT 1030 GP108 [GeForce GT 1030] 1127	Nvidia	GP108	85,393	29,581	2.89	8 ^hrs 19 ^mins
51	Quadro K1200 GM107GL [Quadro K1200]	Nvidia	GM107GL	75,357	28,432	2.65	9 ^hrs 3 ^mins
52	GeForce GTX 960M GM107 [GeForce GTX 960M] 1439	Nvidia	GM107	65,309	26,974	2.42	9 ^hrs 55 ^mins
53	Radeon R7 250/HD 7700 R575A [Radeon R7 250/HD 7700]	AMD	R575A	21,623	16,170	1.34	17 ^hrs 57 ^mins

Research: CANCER Folding Project #17725

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 17725

GPU PPD Averages

Research: CANCER Folding Project #17725

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages