Folding project #17727, CANCER research

Research section Research projects and references

Project #17727 overview

Project Summary AI Beta

This project looks at how proteins use ions to move molecules across cell membranes. These proteins are found everywhere and help us understand how things like drugs work in the body. The simulations will show how these proteins function across different types of cells.

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: 81,196
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.

secondary active transporters

Proteins that use ions to move molecules across cell membranes.

Scientific: BiotechnologyCellular Biology / Membrane Transport

Secondary active transporters are essential proteins found in all living organisms. They utilize the energy stored in an ion gradient to move other molecules across cell membranes. This process is crucial for various cellular functions, including nutrient uptake, waste removal, and signal transduction. Many secondary active transporters are targets for drugs used to treat diseases like cancer, diabetes, and neurological disorders.

ion gradient

A difference in ion concentration across a cell membrane.

Scientific: BiotechnologyCellular Biology / Membrane Transport

An ion gradient is the unequal distribution of charged particles (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that drives the movement of ions across the membrane. Ion gradients are essential for many cellular processes, including nerve impulse transmission, muscle contraction, and nutrient uptake.

drug targets

Molecules or biological processes that are targeted by drugs to treat diseases.

Scientific: BiotechnologyPharmacology / Drug Discovery

Drug targets are specific molecules or pathways involved in disease progression. By inhibiting or modulating these targets, drugs can effectively treat various illnesses. Examples of drug targets include enzymes, receptors, and signaling proteins.

cancer

A disease characterized by uncontrolled cell growth and spread.

Pathological: HealthcareMedicine / Oncology

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. It arises from genetic mutations that disrupt normal cell regulation and proliferation.

diabetes

A metabolic disorder characterized by high blood sugar levels.

Pathological: HealthcareMedicine / Endocrinology

Diabetes is a chronic condition affecting how the body regulates blood sugar. It occurs when the pancreas doesn't produce enough insulin or when cells become resistant to insulin's effects. This leads to elevated blood glucose levels, which can damage various organs.

neurological disorders

Conditions affecting the nervous system.

Pathological: HealthcareMedicine / Neurology

Neurological disorders encompass a wide range of diseases and conditions affecting the brain, spinal cord, and nerves. They can cause diverse symptoms, such as impaired movement, sensory disturbances, cognitive decline, and mood changes.

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	7,267,555	119,973	60.58	0 ^hrs 24 ^mins
2	GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB]	Nvidia	GA102	5,852,716	111,822	52.34	0 ^hrs 28 ^mins
3	GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti]	Nvidia	GA102	5,274,543	103,782	50.82	0 ^hrs 28 ^mins
4	GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448	Nvidia	TU102	5,030,455	105,858	47.52	0 ^hrs 30 ^mins
5	Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000]	Nvidia	TU102GL	4,732,179	104,064	45.47	0 ^hrs 32 ^mins
6	GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448	Nvidia	TU102	4,252,313	101,634	41.84	0 ^hrs 34 ^mins
7	GeForce RTX 3070 GA104 [GeForce RTX 3070]	Nvidia	GA104	3,218,107	92,018	34.97	0 ^hrs 41 ^mins
8	GeForce RTX 2080 TU104 [GeForce RTX 2080]	Nvidia	TU104	3,176,227	92,141	34.47	0 ^hrs 42 ^mins
9	GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068	Nvidia	TU104	3,122,120	90,680	34.43	0 ^hrs 42 ^mins
10	GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER]	Nvidia	TU104	3,093,524	90,950	34.01	0 ^hrs 42 ^mins
11	GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti]	Nvidia	GA104	2,947,829	89,305	33.01	0 ^hrs 44 ^mins
12	GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218	Nvidia	TU104	2,567,429	83,540	30.73	0 ^hrs 47 ^mins
13	GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465	Nvidia	TU106	2,434,688	83,902	29.02	0 ^hrs 50 ^mins
14	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,376,915	83,489	28.47	0 ^hrs 51 ^mins
15	GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB]	Nvidia	GA104M	2,290,281	80,852	28.33	0 ^hrs 51 ^mins
16	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER]	Nvidia	TU106	2,031,809	78,681	25.82	0 ^hrs 56 ^mins
17	GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497	Nvidia	TU106	1,993,593	78,390	25.43	0 ^hrs 57 ^mins
18	GeForce RTX 3060 GA106 [GeForce RTX 3060]	Nvidia	GA106	1,819,488	74,438	24.44	0 ^hrs 59 ^mins
19	GeForce RTX 2060 TU104 [GeForce RTX 2060]	Nvidia	TU104	1,816,272	76,964	23.60	1 ^hrs 1 ^mins

20	GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q]	Nvidia	GA106M	1,570,288	72,604	21.63	1 ^hrs 7 ^mins
21	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	1,484,691	69,248	21.44	1 ^hrs 7 ^mins
22	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,368,282	69,640	19.65	1 ^hrs 13 ^mins
23	GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186	Nvidia	GP104	1,311,215	68,174	19.23	1 ^hrs 15 ^mins
24	Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]	AMD	Navi 21	1,237,544	66,358	18.65	1 ^hrs 17 ^mins
25	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,138,640	65,468	17.39	1 ^hrs 23 ^mins
26	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,092,479	64,445	16.95	1 ^hrs 25 ^mins
27	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	1,089,129	64,804	16.81	1 ^hrs 26 ^mins
28	GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti]	Nvidia	TU116	962,946	61,891	15.56	1 ^hrs 33 ^mins
29	GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER]	Nvidia	TU116	823,519	58,810	14.00	1 ^hrs 43 ^mins
30	P104-100 GP104 [P104-100]	Nvidia	GP104	775,011	57,408	13.50	1 ^hrs 47 ^mins
31	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	667,121	54,773	12.18	1 ^hrs 58 ^mins
32	GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935	Nvidia	GP106	518,356	50,314	10.30	2 ^hrs 20 ^mins
33	GeForce GTX 970 GM204 [GeForce GTX 970] 3494	Nvidia	GM204	499,792	49,801	10.04	2 ^hrs 23 ^mins
34	Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]	AMD	Navi 10	488,241	47,512	10.28	2 ^hrs 20 ^mins
35	P106-100 GP106 [P106-100]	Nvidia	GP106	483,204	49,266	9.81	2 ^hrs 27 ^mins
36	Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q]	Nvidia	TU117GLM	467,873	48,668	9.61	2 ^hrs 30 ^mins
37	Radeon R9 200/300 Series Hawaii [Radeon R9 200/300 Series]	AMD	Hawaii	460,715	48,524	9.49	2 ^hrs 32 ^mins
38	GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984	Nvidia	TU116	446,826	48,006	9.31	2 ^hrs 35 ^mins
39	Radeon R9 200/300X Series Hawaii [Radeon R9 200/300X Series]	AMD	Hawaii	340,648	43,933	7.75	3 ^hrs 6 ^mins

40	GeForce GTX 960 GM206 [GeForce GTX 960] 2308	Nvidia	GM206	303,004	42,339	7.16	3 ^hrs 21 ^mins
41	Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590]	AMD	Ellesmere XT	279,730	38,937	7.18	3 ^hrs 20 ^mins
42	Radeon Vega Frontier Edition Vega 10 XTX [Radeon Vega Frontier Edition]	AMD	Vega 10 XTX	255,483	39,918	6.40	3 ^hrs 45 ^mins
43	Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64]	AMD	Vega 10 XL/XT	234,641	33,453	7.01	3 ^hrs 25 ^mins
44	GeForce GTX 950 GM206 [GeForce GTX 950] 1572	Nvidia	GM206	214,304	37,262	5.75	4 ^hrs 10 ^mins
45	Radeon R9 280/HD 7900/8950 Tahiti PRO [Radeon R9 280/HD 7900/8950]	AMD	Tahiti PRO	206,740	37,245	5.55	4 ^hrs 19 ^mins
46	GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634	Nvidia	GK104	155,691	33,232	4.68	5 ^hrs 7 ^mins
47	Radeon HD 7800 Pitcairn [Radeon HD 7800]	AMD	Pitcairn	119,370	31,084	3.84	6 ^hrs 15 ^mins
48	Radeon RX Vega M XL [Radeon RX Vega M XL]	AMD	Vega	114,909	30,566	3.76	6 ^hrs 23 ^mins
49	Radeon Rx 270/370 Curacao Pro [Radeon Rx 270/370]	AMD	Curacao Pro	106,647	29,237	3.65	6 ^hrs 35 ^mins
50	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	102,782	29,491	3.49	6 ^hrs 53 ^mins
51	GeForce GT 1030 GP108 [GeForce GT 1030] 1127	Nvidia	GP108	95,282	28,710	3.32	7 ^hrs 14 ^mins
52	Quadro K1200 GM107GL [Quadro K1200]	Nvidia	GM107GL	75,188	26,619	2.82	8 ^hrs 30 ^mins
53	GeForce GTX 960M GM107 [GeForce GTX 960M] 1439	Nvidia	GM107	63,071	25,149	2.51	9 ^hrs 34 ^mins
54	Radeon R7/R6 360 Series Bonaire [Radeon R7/R6 360 Series]	AMD	Bonaire	33,802	15,453	2.19	10 ^hrs 58 ^mins
55	Radeon R7 250/HD 7700 R575A [Radeon R7 250/HD 7700]	AMD	R575A	18,828	13,682	1.38	17 ^hrs 26 ^mins

Research: CANCER Folding Project #17727

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 17727

GPU PPD Averages

Research: CANCER Folding Project #17727

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages