RESEARCH: ALZHEIMERS
FOLDING PROJECT #18251 PROFILE

PROJECT TEAM

Manager(s): Justin Miller
Institution: University of Pennsylvania

WORK UNIT INFO

Atoms: 1,224,788
Core: 0x24
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

Alzheimer's disease damages brain cells and causes memory loss. A key protein called tau misbehaves in this disease, making it hard to study. Scientists are using computer simulations with different settings to better understand how tau works and find ways to treat Alzheimer's.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

Alzheimer's disease is a significant cause of death and memory loss and there are no effective treatments to halt or reverse disease progression.

One of the late hallmarks and primary biomarkers of Alzheimer's disease is the presence of neurofibrillary tangles, intracellular aggregates of the tau protein.

When behaving properly, tau interacts with microtubules- a critical portion of the cytoskeleton of cells- to help regulate their growth and stability.

However, tau misbehavior and aggregation is also closely linked to Alzheimer's disease among many other neurodegenerative diseases. Studying tau experimentally has been difficult as it is an Intrinsically Disordered Protein (IDP).

As such, traditional structural biology approaches are unable to capture the conformational states of tau in atomistic detail.

Recently, our collaborators have utilized single molecule FRET experiments to experimentally characterize tau by measuring the pairwise distance between different regions.

While simulations of tau could provide atomistic detail of the tau conformational ensemble, historically simulations of IDPs have been challenging as force fields (the parameters which govern the underlying physics of a simulation) and their accompyning models of waters have favored well-folded proteins.

In this project series we embark on an effort to characterize which force field and water models most accurately recapitulate tau experimental results.

We believe these findings will be broadly applicable to all researchers studying intrinsically disordered proteins, and aspire to keep performing these benchmarking simulations as new force field and waters are released.

We also expect these simulations to yield useful information about the tau conformational ensemble. p18251 - amber99sb-disp with tip4pd water.

RELATED TERMS GLOSSARY AI BETA

Note: Glossary items are a high level summary and may not be 100% accurate.

Alzheimer's disease

A progressive neurodegenerative disease characterized by memory loss and cognitive decline.

Technical: Healthcare
Medicine / Neurology

Alzheimer's disease is a serious brain disorder that causes problems with memory, thinking, and behavior. It slowly gets worse over time and eventually leads to death.


neurofibrillary tangles

Abnormal accumulations of tau protein inside neurons.

Technical: Healthcare
Medicine / Neurology

Neurofibrillary tangles are clumps of a protein called tau found inside brain cells. They are a hallmark feature of Alzheimer's disease and other neurodegenerative disorders.


tau protein

A protein that helps stabilize microtubules in neurons.

Technical: Healthcare
Medicine / Neurology

Tau is a protein that plays an important role in the structure and function of nerve cells. In Alzheimer's disease, tau becomes abnormal and forms tangles.


microtubules

Protein filaments that provide structural support and facilitate transport within cells.

Technical: Healthcare
Medicine / Cell Biology

Microtubules are tiny tubes made of protein that help give cells their shape and act as tracks for transporting materials inside the cell.


Intrinsically Disordered Protein (IDP)

A protein that lacks a stable three-dimensional structure.

Technical: Research
Biotechnology / Structural Biology

Intrinsically disordered proteins (IDPs) are proteins that don't have a fixed shape. Their flexibility allows them to perform many different functions in the cell.


single molecule FRET experiments

A technique used to study the structure and dynamics of biomolecules at the single-molecule level.

Technical: Research
Biotechnology / Structural Biology

Single molecule FRET experiments use fluorescent tags to track the movements of individual molecules. This allows researchers to study how proteins fold and interact with other molecules.


force fields

Parameters that govern the interactions between atoms in a molecular simulation.

Technical: Research
Biotechnology / Computational Biology

Force fields are mathematical models that describe how atoms interact with each other. They are used in computer simulations to predict the behavior of molecules.


water models

Representations of water molecules used in molecular simulations.

Technical: Research
Biotechnology / Computational Biology

Water models are mathematical representations of water molecules. They are used in computer simulations to account for the effects of water on biomolecules.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:30:50
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 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 29,139,542 118,000 246.95 0 hrs 6 mins
2 GeForce GTX Titan X
GM200 [GeForce GTX Titan X] 6144
Nvidia GM200 8,835,952 118,000 74.88 0 hrs 19 mins
3 Radeon RX 9070(XT)
Navi 48 [Radeon RX 9070(XT)]
AMD Navi 48 3,728,301 184,393 20.22 1 hrs 11 mins
4 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 3,598,184 219,118 16.42 1 hrs 28 mins
5 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 3,134,871 197,748 15.85 1 hrs 31 mins
6 Radeon RX 6800(XT)/6900XT
Navi 21 [Radeon RX 6800(XT)/6900XT]
AMD Navi 21 3,000,128 423,389 7.09 3 hrs 23 mins
7 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 2,981,895 221,835 13.44 1 hrs 47 mins
8 Radeon RX 7700XT/7800XT
Navi 32 [Radeon RX 7700XT/7800XT]
AMD Navi 32 2,575,461 118,000 21.83 1 hrs 6 mins
9 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,834,230 174,385 10.52 2 hrs 17 mins
10 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 1,761,164 118,000 14.93 1 hrs 36 mins
11 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,671,076 647,300 2.58 9 hrs 18 mins
12 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,634,220 307,852 5.31 4 hrs 31 mins
13 Radeon RX 6700(XT)/6800M
Navi 22 XT-XL [Radeon RX 6700(XT)/6800M]
AMD Navi 22 XT-XL 1,524,070 129,236 11.79 2 hrs 2 mins
14 Radeon RX 7700S/7600S
Navi 33 [Radeon RX 7700S/7600S]
AMD Navi 33 1,485,377 118,000 12.59 1 hrs 54 mins
15 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,431,923 118,000 12.13 1 hrs 59 mins
16 Radeon RX 6650XT
Navi 23 [Radeon RX 6650XT]
AMD Navi 23 1,412,141 429,829 3.29 7 hrs 18 mins
17 P104-100
GP104 [P104-100]
Nvidia GP104 1,389,594 118,000 11.78 2 hrs 2 mins
18 Radeon RX 6600(XT/M)
Navi 23 XT-XL [Radeon RX 6600(XT/M)]
AMD Navi 23 XT-XL 1,346,349 382,463 3.52 6 hrs 49 mins
19 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,287,445 407,015 3.16 7 hrs 35 mins
20 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,271,672 371,507 3.42 7 hrs 1 mins
21 Radeon RX 7700S/7600(S)
Navi 33 [Radeon RX 7700S/7600(S)]
AMD Navi 33 1,247,075 377,807 3.30 7 hrs 16 mins
22 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,216,844 355,546 3.42 7 hrs 1 mins
23 Radeon RX 6600/6600 XT/6600M
Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 XT-XL 1,163,140 193,415 6.01 3 hrs 59 mins
24 GeForce RTX 3050 8GB
GA107 [GeForce RTX 3050 8GB]
Nvidia GA107 1,046,267 404,811 2.58 9 hrs 17 mins
25 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,030,804 383,159 2.69 8 hrs 55 mins
26 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 952,134 366,948 2.59 9 hrs 15 mins
27 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 909,366 163,488 5.56 4 hrs 19 mins
28 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 887,586 343,831 2.58 9 hrs 18 mins
29 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 806,066 118,000 6.83 3 hrs 31 mins
30 GeForce RTX 2070 Mobile / Max-Q Refresh
TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh]
Nvidia TU106M 787,977 337,467 2.33 10 hrs 17 mins
31 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 752,878 118,000 6.38 3 hrs 46 mins
32 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 607,870 236,238 2.57 9 hrs 20 mins
33 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 598,884 118,000 5.08 4 hrs 44 mins
34 GeForce RTX 3050 Ti Mobile
GA107M [GeForce RTX 3050 Ti Mobile]
Nvidia GA107M 578,182 326,011 1.77 13 hrs 32 mins
35 GeForce RTX 3050 6GB
GA107 [GeForce RTX 3050 6GB]
Nvidia GA107 466,099 118,000 3.95 6 hrs 5 mins
36 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 432,667 118,000 3.67 6 hrs 33 mins
37 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 407,801 120,636 3.38 7 hrs 6 mins
38 RX 5600 OEM/5600XT/5700/5700XT
Navi 10 [RX 5600 OEM/5600XT/5700/5700XT]
AMD Navi 10 392,306 118,000 3.32 7 hrs 13 mins
39 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 333,348 118,000 2.82 8 hrs 30 mins
40 Radeon Pro W5700
Navi 10 [Radeon Pro W5700]
AMD Navi 10 297,337 276,746 1.07 22 hrs 20 mins
41 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 276,917 156,141 1.77 13 hrs 32 mins
42 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 275,815 232,549 1.19 20 hrs 14 mins
43 Radeon RX 6400/6500XT
Navi 24 [Radeon RX 6400/6500XT]
AMD Navi 24 268,098 118,000 2.27 10 hrs 34 mins
44 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 231,616 146,027 1.59 15 hrs 8 mins
45 GeForce GTX 1650 Ti Mobile
TU117M [GeForce GTX 1650 Ti Mobile]
Nvidia TU117M 215,527 225,047 0.96 25 hrs 4 mins
46 Quadro M6000
GM200GL [Quadro M6000]
Nvidia GM200GL 198,128 201,200 0.98 24 hrs 22 mins
47 RX 5500(M)/Pro 5500M
Navi 14 [RX 5500(M)/Pro 5500M]
AMD Navi 14 195,906 118,000 1.66 14 hrs 27 mins
48 GeForce GTX 1650
TU106 [GeForce GTX 1650]
Nvidia TU106 121,435 118,000 1.03 23 hrs 19 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:30:50
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 5 3600X 6-CORE 12 AMD