RESEARCH: ALZHEIMERS
FOLDING PROJECT #18251 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 1,224,788Core: 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
Alzheimer's disease
A progressive neurodegenerative disease characterized by memory loss and cognitive decline.
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.
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.
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.
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.
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.
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.
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.
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 |
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|||||||
| 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 |
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|
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| 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 |