Folding project #17805, CANCER research

Research section Research projects and references

Project #17805 overview

Project Summary AI Beta

This project studies BAX, a protein important for cell death in lymphoma. It's similar to another protein studied before, but this one changes shape dramatically when activated. This makes understanding it harder, but could lead to new cancer treatments.

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Rafal Wiewiora
Institution: Memorial Sloan Kettering Cancer Center
Website: Open project site

Work unit

Atoms: 80,000
Core: OPENMM_22
Status: Public

Source material

Official Project Description

BAX apoptotic protein --- a drug target in lymphoma. This is a homologous (i.e shares some structures) protein to BCL in projects 17800-03 --- as in there, I am testing adaptive sampling strategies --- this project is 'vanilla' (i.e.

no adaptive algorithm) and the problem here is probably much harder than in the previous project, this particular protein experiences 'activation' which is a massive structural change, a long tail unbinds and extends away from the protein.

Reading reference

Related Terms Glossary AI Beta

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

BAX apoptotic protein

A pro-apoptotic protein involved in programmed cell death.

Scientific: BiotechnologyOncology / Lymphoma Research

BAX is a protein that plays a crucial role in triggering apoptosis, the process of programmed cell death. It is found to be dysregulated in various cancers, including lymphoma. Research focuses on BAX as a potential drug target for cancer therapies.

BCL

Bcl-2 family of proteins regulating apoptosis.

Scientific: BiotechnologyOncology / Protein Structure Research

The Bcl-2 protein family consists of various proteins that play a critical role in controlling cell death (apoptosis). Some members promote cell survival, while others, like BAX, induce apoptosis. Understanding the interactions within this family is crucial for developing targeted cancer therapies.

Adaptive Sampling

A technique for selecting protein structures in simulations.

Technical: BiotechnologyComputational Biology / Protein Modeling

Adaptive sampling is a computational method used to improve the efficiency of protein modeling simulations. It involves dynamically adjusting the selection criteria for protein structures during simulations, focusing on regions that are more relevant for understanding protein function or folding.

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	5,234,965	230,117	22.75	1 ^hrs 3 ^mins
2	GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti]	Nvidia	GA102	4,940,000	222,986	22.15	1 ^hrs 5 ^mins
3	GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448	Nvidia	TU102	3,934,145	208,619	18.86	1 ^hrs 16 ^mins
4	GeForce RTX 3080 GA102 [GeForce RTX 3080]	Nvidia	GA102	3,813,710	207,263	18.40	1 ^hrs 18 ^mins
5	GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti]	Nvidia	GA104	2,827,602	187,529	15.08	1 ^hrs 36 ^mins
6	GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448	Nvidia	TU102	2,455,842	179,040	13.72	1 ^hrs 45 ^mins
7	GeForce RTX 3070 GA104 [GeForce RTX 3070]	Nvidia	GA104	2,258,080	173,951	12.98	1 ^hrs 51 ^mins
8	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,142,147	171,067	12.52	1 ^hrs 55 ^mins
9	TITAN Xp GP102 [TITAN Xp] 12150	Nvidia	GP102	2,081,956	169,531	12.28	1 ^hrs 57 ^mins
10	GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER]	Nvidia	TU104	2,081,202	169,564	12.27	1 ^hrs 57 ^mins
11	GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068	Nvidia	TU104	1,986,379	166,506	11.93	2 ^hrs 1 ^mins
12	GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti]	Nvidia	GA104	1,879,007	163,865	11.47	2 ^hrs 6 ^mins
13	GeForce RTX 2080 TU104 [GeForce RTX 2080]	Nvidia	TU104	1,850,532	162,938	11.36	2 ^hrs 7 ^mins
14	GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465	Nvidia	TU106	1,833,398	162,536	11.28	2 ^hrs 8 ^mins
15	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER]	Nvidia	TU106	1,585,674	152,650	10.39	2 ^hrs 19 ^mins
16	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	1,476,840	151,552	9.74	2 ^hrs 28 ^mins
17	GeForce RTX 3060 GA106 [GeForce RTX 3060]	Nvidia	GA106	1,397,173	148,543	9.41	2 ^hrs 33 ^mins
18	GeForce RTX 3070 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB]	Nvidia	GA104M	1,150,599	139,125	8.27	2 ^hrs 54 ^mins
19	GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile]	Nvidia	TU106M	1,090,591	137,061	7.96	3 ^hrs 1 ^mins

20	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,034,177	134,456	7.69	3 ^hrs 7 ^mins
21	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	994,714	132,202	7.52	3 ^hrs 11 ^mins
22	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	985,078	131,379	7.50	3 ^hrs 12 ^mins
23	GeForce GTX 960 GM206 [GeForce GTX 960] 2308	Nvidia	GM206	286,966	87,581	3.28	7 ^hrs 19 ^mins
24	GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile]	Nvidia	GP107M	252,604	83,794	3.01	7 ^hrs 58 ^mins
25	P106-100 GP106 [P106-100]	Nvidia	GP106	247,328	83,370	2.97	8 ^hrs 5 ^mins
26	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	121,153	65,088	1.86	12 ^hrs 54 ^mins
27	GeForce GT 840M GM108 [GeForce GT 840M]	Nvidia	GM108	39,980	43,053	0.93	25 ^hrs 51 ^mins
28	GeForce GT 1030 GP108 [GeForce GT 1030] 1127	Nvidia	GP108	30,580	40,542	0.75	31 ^hrs 49 ^mins
29	GeForce 920M GK208 [GeForce 920M]	Nvidia	GK208	26,734	39,832	0.67	35 ^hrs 46 ^mins

Research: CANCER Folding Project #17805

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 17805

GPU PPD Averages

Research: CANCER Folding Project #17805

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages