Folding project #14933, CANCER research

Research section Research projects and references

Project #14933 overview

Project Summary AI Beta

This project looks at Class F Receptors, proteins that help control how cells grow and change. Too much activity in these proteins can cause cancers like Basal Cell Carcinoma and Medulloblastoma. By using computer simulations, researchers hope to understand how these proteins work so they can better study and treat these diseases.

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Prateek Bansal
Institution: University of Illinois Urbana-Champaign

Work unit

Atoms: 105,522
Core: OPENMM_22
Status: Public

Source material

Official Project Description

Class F Receptors Class F Receptors are involved in the control of cell differentiation.

Over-activation of these proteins have links to Basal Cell Carcinoma and Medulloblastoma.

Through Simulations we aim to understand the activation mechanisms of these proteins, giving us a way to probe into the pathogenesis of the disease.

Reading reference

Related Terms Glossary AI Beta

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

Class F Receptors

Membrane proteins that bind to specific ligands and initiate intracellular signaling cascades.

Scientific: BiotechnologyCellular Biology / Receptor Signaling

Class F receptors are a type of protein found on the surface of cells. They act as receptors, binding to specific molecules called ligands. This binding triggers a series of events inside the cell, known as signaling cascades, which ultimately regulate various cellular functions. These receptors play crucial roles in processes like cell growth, differentiation, and survival. Disruptions in their function can contribute to diseases such as cancer.

Basal Cell Carcinoma

A common type of skin cancer that arises from the basal cells in the epidermis.

Medical: HealthcareOncology / Skin Cancer

Basal cell carcinoma is the most prevalent form of skin cancer. It develops from the basal cells, which are located in the deepest layer of the epidermis (the outermost layer of skin). These tumors typically appear as pearly or waxy bumps and often have a visible blood vessel network. Basal cell carcinomas grow slowly and rarely spread to other parts of the body.

Medulloblastoma

A type of malignant brain tumor that originates in the cerebellum.

Medical: HealthcareOncology / Brain Tumors

Medulloblastoma is a highly aggressive form of childhood brain cancer. It arises from cells in the cerebellum, the part of the brain responsible for coordinating movement and balance. Medulloblastomas tend to grow rapidly and can spread to other parts of the nervous system.

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	7,008,299	135,217	51.83	0 ^hrs 28 ^mins
2	GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti]	Nvidia	GA102	6,691,256	135,120	49.52	0 ^hrs 29 ^mins
3	GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate]	Nvidia	GA102	5,889,361	128,621	45.79	0 ^hrs 31 ^mins
4	GeForce RTX 3080 GA102 [GeForce RTX 3080]	Nvidia	GA102	5,585,713	128,338	43.52	0 ^hrs 33 ^mins
5	GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448	Nvidia	TU102	4,676,418	120,278	38.88	0 ^hrs 37 ^mins
6	GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti]	Nvidia	GA104	4,637,973	120,455	38.50	0 ^hrs 37 ^mins
7	RTX A5000 GA102GL [RTX A5000]	Nvidia	GA102GL	4,423,720	118,999	37.17	0 ^hrs 39 ^mins
8	GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448	Nvidia	TU102	4,407,662	116,791	37.74	0 ^hrs 38 ^mins
9	GeForce RTX 3070 GA104 [GeForce RTX 3070]	Nvidia	GA104	4,044,320	115,116	35.13	0 ^hrs 41 ^mins
10	GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068	Nvidia	TU104	3,889,330	113,700	34.21	0 ^hrs 42 ^mins
11	GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218	Nvidia	TU104	3,179,202	106,269	29.92	0 ^hrs 48 ^mins
12	GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate]	Nvidia	GA104	3,015,278	102,214	29.50	0 ^hrs 49 ^mins
13	GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A]	Nvidia	TU106	2,640,591	100,291	26.33	0 ^hrs 55 ^mins
14	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,249,969	94,967	23.69	1 ^hrs 1 ^mins
15	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER]	Nvidia	TU106	2,239,312	94,561	23.68	1 ^hrs 1 ^mins
16	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	2,127,963	93,591	22.74	1 ^hrs 3 ^mins
17	GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate]	Nvidia	GA106	2,042,500	91,484	22.33	1 ^hrs 4 ^mins
18	GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497	Nvidia	TU106	2,020,003	91,181	22.15	1 ^hrs 5 ^mins
19	GeForce RTX 2060 TU104 [GeForce RTX 2060]	Nvidia	TU104	1,949,249	90,719	21.49	1 ^hrs 7 ^mins

20	GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]	Nvidia	GA104M	1,809,944	87,899	20.59	1 ^hrs 10 ^mins
21	GeForce RTX 2070 TU106 [GeForce RTX 2070]	Nvidia	TU106	1,746,503	87,795	19.89	1 ^hrs 12 ^mins
22	GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile]	Nvidia	TU106M	1,353,435	80,352	16.84	1 ^hrs 25 ^mins
23	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,290,917	78,597	16.42	1 ^hrs 28 ^mins
24	GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632	Nvidia	GM200	1,155,007	75,985	15.20	1 ^hrs 35 ^mins
25	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,090,026	72,652	15.00	1 ^hrs 36 ^mins
26	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,050,794	73,933	14.21	1 ^hrs 41 ^mins
27	GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti]	Nvidia	TU116	905,083	69,782	12.97	1 ^hrs 51 ^mins
28	GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186	Nvidia	GP104	857,080	68,447	12.52	1 ^hrs 55 ^mins
29	Tesla M40 GM200GL [Tesla M40] 6844	Nvidia	GM200GL	801,305	67,149	11.93	2 ^hrs 1 ^mins
30	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	681,387	63,825	10.68	2 ^hrs 15 ^mins
31	GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q]	Nvidia	TU106M	657,519	40,941	16.06	1 ^hrs 30 ^mins
32	P104-100 GP104 [P104-100]	Nvidia	GP104	642,927	61,202	10.50	2 ^hrs 17 ^mins
33	GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q]	Nvidia	TU117M	590,804	61,369	9.63	2 ^hrs 30 ^mins
34	GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935	Nvidia	GP106	588,988	61,078	9.64	2 ^hrs 29 ^mins
35	Tesla P40 GP102GL [Tesla P40] 11760	Nvidia	GP102GL	559,104	60,181	9.29	2 ^hrs 35 ^mins
36	GeForce GTX 980 GM204 [GeForce GTX 980] 4612	Nvidia	GM204	557,340	59,520	9.36	2 ^hrs 34 ^mins
37	GeForce GTX 1650 TU117 [GeForce GTX 1650]	Nvidia	TU117	540,077	59,293	9.11	2 ^hrs 38 ^mins
38	GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138	Nvidia	GP107	287,726	47,813	6.02	3 ^hrs 59 ^mins
39	GeForce GTX 950 GM206 [GeForce GTX 950] 1572	Nvidia	GM206	213,734	42,672	5.01	4 ^hrs 47 ^mins

40	Quadro P1000 GP107GL [Quadro P1000]	Nvidia	GP107GL	197,572	42,672	4.63	5 ^hrs 11 ^mins
41	P106-090 GP106 [P106-090]	Nvidia	GP106	164,668	39,037	4.22	5 ^hrs 41 ^mins
42	GeForce GTX 770 GK104 [GeForce GTX 770] 3213	Nvidia	GK104	131,405	36,751	3.58	6 ^hrs 43 ^mins
43	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	128,996	36,612	3.52	6 ^hrs 49 ^mins
44	GeForce GTX 760 GK104 [GeForce GTX 760] 2258	Nvidia	GK104	91,937	32,667	2.81	8 ^hrs 32 ^mins
45	GeForce GT 730 GK208B [GeForce GT 730] 692.7	Nvidia	GK208B	30,128	22,721	1.33	18 ^hrs 6 ^mins
46	GeForce GT 720 GK208 [GeForce GT 720]	Nvidia	GK208	13,291	16,200	0.82	29 ^hrs 15 ^mins

Research: CANCER Folding Project #14933

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 14933

GPU PPD Averages

Research: CANCER Folding Project #14933

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages