RESEARCH: ALZHEIMERS
FOLDING PROJECT #18255 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 919,221Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Alzheimer's disease is linked to tangles of a protein called tau. The project relates to finding the best computer models to understand how tau behaves because it's hard to study directly. These models could help us learn about Alzheimer's and other diseases.
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 these simulations.
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 p18259 - amber19sb with opc3 pol water p18260- amber99sb-star-ILDN with tip4pd water p18261- charmm36m with tip3p water.
RELATED TERMS GLOSSARY AI BETA
Alzheimer's disease
A progressive neurodegenerative disorder that causes memory loss, cognitive decline, and behavioral changes.
Alzheimer's disease is a serious brain condition that affects thinking, memory, and behavior. People with Alzheimer's may struggle with everyday tasks and eventually lose the ability to care for themselves. There is no cure for Alzheimer's, but treatments can help manage symptoms.
Neurofibrillary tangles
Abnormal accumulations of tau protein inside neurons.
Neurofibrillary tangles are clumps of a protein called tau that form inside brain cells. These tangles are a hallmark of Alzheimer's disease and other neurodegenerative disorders.
Tau protein
A microtubule-associated protein that plays a role in stabilizing neuronal structures.
Tau is a protein found in brain cells. It helps to support the structure of neurons and keep them healthy. In Alzheimer's disease, tau becomes tangled and clumps together, damaging the brain.
Microtubules
Tubular protein structures that provide structural support and facilitate intracellular transport.
Microtubules are long, hollow tubes made of protein that help cells maintain their shape and move materials around. They play a crucial role in cell division and other important cellular processes.
Intrinsically Disordered Protein (IDP)
A protein that lacks a stable, defined three-dimensional structure.
Intrinsically disordered proteins are unique because they don't have a fixed shape. They can adopt different conformations depending on their environment and interactions with other molecules. This flexibility allows them to perform diverse functions in the cell.
Single molecule FRET
A technique used to measure the distance between two molecules labeled with fluorescent probes.
Single molecule FRET (Förster Resonance Energy Transfer) is a powerful tool for studying the structure and dynamics of biomolecules. It allows researchers to track the movement of individual molecules and measure their interactions in real time.
Force fields
Mathematical models that describe the interactions between atoms in a molecule.
Force fields are used in computer simulations to predict how molecules will behave. They provide a way to simulate the forces that act between atoms, allowing researchers to study chemical reactions, protein folding, and other complex phenomena.
Water models
Mathematical representations of water molecules used in computer simulations.
Water models are essential for simulating biological systems because water plays a critical role in many cellular processes. Accurate water models allow researchers to capture the effects of hydration on protein structure, folding, and function.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:30:45|
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 | 46,152,414 | 40,000 | 1153.81 | 0 hrs 1 mins |
| 2 | RTX PRO 6000 Blackwell Server Edition GB202GL [RTX PRO 6000 Blackwell Server Edition] |
Nvidia | GB202GL | 39,354,953 | 40,000 | 983.87 | 0 hrs 1 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 25,015,612 | 345,240 | 72.46 | 0 hrs 20 mins |
| 4 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 19,473,817 | 323,936 | 60.12 | 0 hrs 24 mins |
| 5 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 19,447,981 | 40,000 | 486.20 | 0 hrs 3 mins |
| 6 | RTX PRO 6000 Blackwell Max-Q Workstation Edition GB202GL [RTX PRO 6000 Blackwell Max-Q Workstation Edition] |
Nvidia | GB202GL | 18,827,467 | 40,000 | 470.69 | 0 hrs 3 mins |
| 7 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 18,218,971 | 131,940 | 138.09 | 0 hrs 10 mins |
| 8 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 16,980,226 | 81,347 | 208.74 | 0 hrs 7 mins |
| 9 | GeForce RTX 5090 Max-Q / Mobile GB203M / GN22 [GeForce RTX 5090 Max-Q / Mobile] |
Nvidia | GB203M / GN22 | 15,615,859 | 40,000 | 390.40 | 0 hrs 4 mins |
| 10 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 11,829,887 | 335,902 | 35.22 | 0 hrs 41 mins |
| 11 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 11,366,256 | 40,000 | 284.16 | 0 hrs 5 mins |
| 12 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 11,259,837 | 100,671 | 111.85 | 0 hrs 13 mins |
| 13 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 10,255,090 | 144,319 | 71.06 | 0 hrs 20 mins |
| 14 | GeForce RTX 5080 Max-Q / Mobile GB203M / GN22-X9 [GeForce RTX 5080 Max-Q / Mobile] |
Nvidia | GB203M / GN22-X9 | 9,734,274 | 40,000 | 243.36 | 0 hrs 6 mins |
| 15 | GeForce RTX 4090 Laptop GPU AD103M / GN21-X11 [GeForce RTX 4090 Laptop GPU] |
Nvidia | AD103M / GN21-X11 | 8,658,319 | 40,000 | 216.46 | 0 hrs 7 mins |
| 16 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 8,621,266 | 365,380 | 23.60 | 1 hrs 1 mins |
| 17 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 8,497,087 | 173,249 | 49.05 | 0 hrs 29 mins |
| 18 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,394,271 | 357,226 | 23.50 | 1 hrs 1 mins |
| 19 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 7,680,732 | 314,467 | 24.42 | 0 hrs 59 mins |
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| 20 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 6,986,550 | 282,782 | 24.71 | 0 hrs 58 mins |
| 21 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,814,551 | 328,188 | 20.76 | 1 hrs 9 mins |
| 22 | GeForce RTX 4080 Max-Q / Mobile AD104M [GeForce RTX 4080 Max-Q / Mobile] |
Nvidia | AD104M | 6,427,653 | 325,316 | 19.76 | 1 hrs 13 mins |
| 23 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 6,105,492 | 111,667 | 54.68 | 0 hrs 26 mins |
| 24 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,707,390 | 140,098 | 40.74 | 0 hrs 35 mins |
| 25 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 5,288,884 | 56,248 | 94.03 | 0 hrs 15 mins |
| 26 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 5,237,712 | 297,739 | 17.59 | 1 hrs 22 mins |
| 27 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 5,227,436 | 48,633 | 107.49 | 0 hrs 13 mins |
| 28 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 4,932,077 | 278,392 | 17.72 | 1 hrs 21 mins |
| 29 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,901,457 | 261,305 | 18.76 | 1 hrs 17 mins |
| 30 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,489,248 | 234,131 | 19.17 | 1 hrs 15 mins |
| 31 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,368,152 | 113,948 | 38.33 | 0 hrs 38 mins |
| 32 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,281,167 | 40,000 | 107.03 | 0 hrs 13 mins |
| 33 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,052,002 | 231,137 | 17.53 | 1 hrs 22 mins |
| 34 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 3,821,430 | 40,000 | 95.54 | 0 hrs 15 mins |
| 35 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,564,470 | 275,682 | 12.93 | 1 hrs 51 mins |
| 36 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,377,381 | 106,716 | 31.65 | 0 hrs 46 mins |
| 37 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 3,113,034 | 40,000 | 77.83 | 0 hrs 19 mins |
| 38 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,926,204 | 40,000 | 73.16 | 0 hrs 20 mins |
| 39 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 2,878,319 | 40,000 | 71.96 | 0 hrs 20 mins |
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| 40 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 2,857,387 | 64,917 | 44.02 | 0 hrs 33 mins |
| 41 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,845,340 | 253,669 | 11.22 | 2 hrs 8 mins |
| 42 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,641,206 | 247,092 | 10.69 | 2 hrs 15 mins |
| 43 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,510,717 | 166,884 | 15.04 | 1 hrs 36 mins |
| 44 | Radeon RX 6650XT Navi 23 [Radeon RX 6650XT] |
AMD | Navi 23 | 2,481,246 | 244,162 | 10.16 | 2 hrs 22 mins |
| 45 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,354,669 | 238,170 | 9.89 | 2 hrs 26 mins |
| 46 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,293,354 | 40,000 | 57.33 | 0 hrs 25 mins |
| 47 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX ж600(XT/M)] |
AMD | Navi 23 XT-XL | 2,109,051 | 224,963 | 9.38 | 2 hrs 34 mins |
| 48 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)] |
AMD | Navi 23 XT-XL | 2,109,051 | 224,963 | 9.38 | 2 hrs 34 mins |
| 49 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,584,180 | 40,000 | 39.60 | 0 hrs 36 mins |
| 50 | Radeon RX 6650 XT Navi 23 [Radeon RX 6650 XT] |
AMD | Navi 23 | 1,341,433 | 40,000 | 33.54 | 0 hrs 43 mins |
| 51 | GeForce RTX 4060 Max-Q / Mobile AD107M [GeForce RTX 4060 Max-Q / Mobile] |
Nvidia | AD107M | 1,254,238 | 40,000 | 31.36 | 0 hrs 46 mins |
| 52 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 1,000,553 | 40,000 | 25.01 | 0 hrs 58 mins |
| 53 | RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M] |
AMD | Navi 14 | 666,736 | 48,884 | 13.64 | 1 hrs 46 mins |
| 54 | Radeon 880M/890M Strix Point [Radeon 880M/890M] |
AMD | Strix Point | 521,101 | 40,000 | 13.03 | 1 hrs 51 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:30:45|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | 12TH GEN CORE I5-12400F | 12 | Intel |