RESEARCH: ALZHEIMERS
FOLDING PROJECT #18261 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 1,224,788Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Alzheimer's disease causes memory loss and there's no cure. A protein called tau goes bad in the brain, forming harmful clumps. This project uses computer simulations to study how tau works, finding the best way to model it. This will help scientists understand Alzheimer's and other diseases better.
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. N.B.
because tau is an intrinsically disordered protein, it can fully unfold and refold quite rapidly.
To ensure the protein remains in water the entire simulation, we have included a large number of waters in the system.
As a result these simulations are a good deal more RAM intensive than prior FAH simulations.
Accordingly, we have implemented a minimum system memory requirement of 8000 MiB to run 182[51-56,58].
and 12000 MiB to run 182[57,60] p18251 - amber99sb-disp with tip4pd water p18255- amber14sb with tip3p water p18256- amber03 with tip3p water p18257- amber19sb with opc water p18258- amber19sb with opc3 water p18260- amber99sb-star-ILDN with tip4pd water p18261- amber19sb with opc3 pol water p18262 - charmm36m with tip3p water.
RELATED TERMS GLOSSARY AI BETA
Alzheimer's disease
A progressive neurodegenerative disorder affecting memory and cognitive function.
Alzheimer's disease is a serious brain disorder that causes memory loss, thinking problems, and behavioral changes. It is the most common cause of dementia.
neurofibrillary tangles
Twisted fibers of tau protein found in brain cells of people with Alzheimer's disease.
Neurofibrillary tangles are abnormal accumulations of tau protein inside nerve cells. They are a hallmark of Alzheimer's disease and contribute to the damage of brain cells.
tau protein
A microtubule-associated protein involved in stabilizing and regulating the structure of neurons.
Tau is a protein that plays an important role in maintaining the structure of nerve cells. In Alzheimer's disease, tau becomes abnormal and forms tangles, which damage brain cells.
microtubules
Long, hollow protein fibers that form part of the cytoskeleton and are involved in cell shape, transport, and division.
Microtubules are tiny tubes made of proteins that act as a scaffolding within cells. They help maintain cell shape, transport materials, and play a role in cell division.
cytoskeleton
A network of protein fibers that provides support and structure to cells.
The cytoskeleton is a complex network of protein filaments that gives cells their shape, helps them move, and plays a role in transporting materials within the cell.
Intrinsically Disordered Protein (IDP)
A protein that lacks a well-defined three-dimensional structure.
Intrinsically disordered proteins (IDPs) are proteins that do not have a fixed shape. They can adopt different conformations depending on their environment and interactions with other molecules.
force field
A set of mathematical equations that describe the interactions between atoms in a molecular simulation.
Force fields are used in computer simulations to model the behavior of molecules. They provide a way to calculate the forces acting between atoms and predict how molecules will move and interact.
FRET
Förster Resonance Energy Transfer.
FRET is a technique used to measure the distance between two molecules labeled with fluorescent probes. It can be used to study protein interactions and conformational changes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:30:38|
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 5090 GB202 [GeForce RTX 5090] |
Nvidia | GB202 | 36,359,201 | 66,000 | 550.90 | 0 hrs 3 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 26,753,965 | 650,554 | 41.12 | 0 hrs 35 mins |
| 3 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 18,917,348 | 66,000 | 286.63 | 0 hrs 5 mins |
| 4 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 15,742,530 | 574,301 | 27.41 | 0 hrs 53 mins |
| 5 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 14,776,040 | 608,543 | 24.28 | 0 hrs 59 mins |
| 6 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 13,547,506 | 198,126 | 68.38 | 0 hrs 21 mins |
| 7 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 10,033,801 | 488,514 | 20.54 | 1 hrs 10 mins |
| 8 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 9,378,662 | 376,903 | 24.88 | 0 hrs 58 mins |
| 9 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 9,233,916 | 355,624 | 25.97 | 0 hrs 55 mins |
| 10 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 8,168,899 | 66,000 | 123.77 | 0 hrs 12 mins |
| 11 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,083,613 | 479,432 | 16.86 | 1 hrs 25 mins |
| 12 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 7,954,541 | 128,252 | 62.02 | 0 hrs 23 mins |
| 13 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,355,813 | 458,498 | 16.04 | 1 hrs 30 mins |
| 14 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 5,778,009 | 66,000 | 87.55 | 0 hrs 16 mins |
| 15 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,576,262 | 427,443 | 13.05 | 1 hrs 50 mins |
| 16 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 5,550,621 | 397,534 | 13.96 | 1 hrs 43 mins |
| 17 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,277,779 | 66,000 | 79.97 | 0 hrs 18 mins |
| 18 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 5,096,531 | 361,831 | 14.09 | 1 hrs 42 mins |
| 19 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 5,083,020 | 66,000 | 77.02 | 0 hrs 19 mins |
|
|
|||||||
| 20 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 5,045,367 | 66,000 | 76.44 | 0 hrs 19 mins |
| 21 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,982,427 | 325,843 | 15.29 | 1 hrs 34 mins |
| 22 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 4,887,925 | 407,727 | 11.99 | 2 hrs 0 mins |
| 23 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 4,734,947 | 421,533 | 11.23 | 2 hrs 8 mins |
| 24 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,713,433 | 401,516 | 11.74 | 2 hrs 3 mins |
| 25 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,757,365 | 328,675 | 11.43 | 2 hrs 6 mins |
| 26 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 3,731,567 | 374,997 | 9.95 | 2 hrs 25 mins |
| 27 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 3,729,124 | 342,960 | 10.87 | 2 hrs 12 mins |
| 28 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 3,692,730 | 325,398 | 11.35 | 2 hrs 7 mins |
| 29 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,179,032 | 369,806 | 8.60 | 2 hrs 48 mins |
| 30 | RTX 4000 SFF Ada Generation AD104GL [RTX 4000 SFF Ada Generation] |
Nvidia | AD104GL | 3,157,279 | 66,000 | 47.84 | 0 hrs 30 mins |
| 31 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 3,082,794 | 66,000 | 46.71 | 0 hrs 31 mins |
| 32 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 2,995,012 | 66,000 | 45.38 | 0 hrs 32 mins |
| 33 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,912,085 | 333,196 | 8.74 | 2 hrs 45 mins |
| 34 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,904,213 | 357,651 | 8.12 | 2 hrs 57 mins |
| 35 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,885,253 | 358,777 | 8.04 | 2 hrs 59 mins |
| 36 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,605,089 | 279,841 | 9.31 | 2 hrs 35 mins |
| 37 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 2,526,510 | 66,000 | 38.28 | 0 hrs 38 mins |
| 38 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,448,104 | 339,002 | 7.22 | 3 hrs 19 mins |
| 39 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,236,609 | 329,321 | 6.79 | 3 hrs 32 mins |
|
|
|||||||
| 40 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,813,633 | 305,616 | 5.93 | 4 hrs 3 mins |
| 41 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,557,128 | 299,001 | 5.21 | 4 hrs 37 mins |
| 42 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)] |
AMD | Navi 23 XT-XL | 1,545,091 | 66,000 | 23.41 | 1 hrs 2 mins |
| 43 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,474,261 | 66,000 | 22.34 | 1 hrs 4 mins |
| 44 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,379,352 | 281,720 | 4.90 | 4 hrs 54 mins |
| 45 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,363,905 | 281,579 | 4.84 | 4 hrs 57 mins |
| 46 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,209,046 | 253,468 | 4.77 | 5 hrs 2 mins |
| 47 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 573,275 | 224,493 | 2.55 | 9 hrs 24 mins |
| 48 | Radeon RX 6400/6500XT Navi 24 [Radeon RX 6400/6500XT] |
AMD | Navi 24 | 387,001 | 183,959 | 2.10 | 11 hrs 24 mins |
| 49 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 362,314 | 66,000 | 5.49 | 4 hrs 22 mins |
| 50 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 278,644 | 163,892 | 1.70 | 14 hrs 7 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:30:38|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | RYZEN 3 3100 4-CORE | 8 | AMD |