RESEARCH: ALZHEIMERS
FOLDING PROJECT #18260 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 is marked by tangles of a protein called tau. Tau normally helps cells stay organized, but when it misbehaves, it can cause problems. The project relates to figuring out the best computer models to study how tau behaves. These models could help us understand Alzheimer's and develop new treatments.
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 disease characterized by memory loss and cognitive decline.
Alzheimer's disease is a serious brain disorder that gradually destroys memory and thinking skills. It affects millions of people worldwide and is the most common cause of dementia. There is currently no cure for Alzheimer's, but treatments can help manage symptoms.
neurofibrillary tangles
Abnormal accumulations of tau protein inside nerve cells.
Neurofibrillary tangles are twisted fibers that form inside brain cells in people with Alzheimer's disease. They are one of the hallmarks of the disease and contribute to the death of neurons.
tau protein
A microtubule-associated protein that plays a role in stabilizing microtubules.
Tau is a protein found in brain cells that helps maintain the structure of microtubules. Microtubules are essential for many cellular functions, including transport and cell division. In Alzheimer's disease, tau becomes abnormally tangled and contributes to neuronal damage.
microtubules
Hollow tubes made of protein that form part of the cytoskeleton.
Microtubules are long, thin structures found inside cells that play a vital role in cell shape, movement, and transport. They are essential for many cellular processes, such as cell division and intracellular transport.
Intrinsically Disordered Protein (IDP)
A protein that lacks a fixed three-dimensional structure.
Intrinsically disordered proteins (IDPs) are a unique class of proteins that do not have a stable, defined shape. They exist in a flexible, constantly changing state, allowing them to perform diverse functions.
force field
A set of mathematical equations that describes the interactions between atoms in a molecular simulation.
Force fields are used in computer simulations to model the behavior of molecules. They define how atoms interact with each other, such as through electrostatic forces or van der Waals forces.
FRET
Förster Resonance Energy Transfer.
FRET is a technique used to measure the distance between two molecules labeled with fluorescent tags. It has applications in studying protein interactions and molecular dynamics.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:30:39|
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 | 37,175,535 | 59,000 | 630.09 | 0 hrs 2 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 28,164,874 | 568,925 | 49.51 | 0 hrs 29 mins |
| 3 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 19,239,859 | 59,000 | 326.10 | 0 hrs 4 mins |
| 4 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 16,110,946 | 461,071 | 34.94 | 0 hrs 41 mins |
| 5 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 15,081,658 | 534,988 | 28.19 | 0 hrs 51 mins |
| 6 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 13,246,808 | 59,000 | 224.52 | 0 hrs 6 mins |
| 7 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 12,266,229 | 448,622 | 27.34 | 0 hrs 53 mins |
| 8 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 10,630,271 | 202,358 | 52.53 | 0 hrs 27 mins |
| 9 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 9,594,414 | 218,191 | 43.97 | 0 hrs 33 mins |
| 10 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 8,357,248 | 59,000 | 141.65 | 0 hrs 10 mins |
| 11 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,157,277 | 376,262 | 21.68 | 1 hrs 6 mins |
| 12 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 7,974,056 | 176,060 | 45.29 | 0 hrs 32 mins |
| 13 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,500,590 | 237,061 | 31.64 | 0 hrs 46 mins |
| 14 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,176,522 | 451,007 | 15.91 | 1 hrs 30 mins |
| 15 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 6,652,778 | 59,000 | 112.76 | 0 hrs 13 mins |
| 16 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 6,214,719 | 156,853 | 39.62 | 0 hrs 36 mins |
| 17 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 5,941,369 | 421,348 | 14.10 | 1 hrs 42 mins |
| 18 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 5,667,297 | 328,034 | 17.28 | 1 hrs 23 mins |
| 19 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 5,563,635 | 217,374 | 25.59 | 0 hrs 56 mins |
|
|
|||||||
| 20 | GeForce RTX 4080 Max-Q / Mobile AD104M [GeForce RTX 4080 Max-Q / Mobile] |
Nvidia | AD104M | 5,413,379 | 403,195 | 13.43 | 1 hrs 47 mins |
| 21 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,313,453 | 59,000 | 90.06 | 0 hrs 16 mins |
| 22 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,041,000 | 269,752 | 18.69 | 1 hrs 17 mins |
| 23 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,936,709 | 289,143 | 17.07 | 1 hrs 24 mins |
| 24 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,544,232 | 317,325 | 14.32 | 1 hrs 41 mins |
| 25 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,484,644 | 385,555 | 11.63 | 2 hrs 4 mins |
| 26 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 4,325,396 | 115,125 | 37.57 | 0 hrs 38 mins |
| 27 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 3,884,796 | 327,851 | 11.85 | 2 hrs 2 mins |
| 28 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,840,750 | 287,493 | 13.36 | 1 hrs 48 mins |
| 29 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,535,441 | 355,406 | 9.95 | 2 hrs 25 mins |
| 30 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 3,267,888 | 59,000 | 55.39 | 0 hrs 26 mins |
| 31 | RTX 4000 SFF Ada Generation AD104GL [RTX 4000 SFF Ada Generation] |
Nvidia | AD104GL | 3,056,081 | 59,000 | 51.80 | 0 hrs 28 mins |
| 32 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,001,291 | 300,468 | 9.99 | 2 hrs 24 mins |
| 33 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,879,797 | 285,126 | 10.10 | 2 hrs 23 mins |
| 34 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,833,505 | 321,598 | 8.81 | 2 hrs 43 mins |
| 35 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,824,108 | 59,000 | 47.87 | 0 hrs 30 mins |
| 36 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,811,803 | 59,000 | 47.66 | 0 hrs 30 mins |
| 37 | Radeon 8050S/8060S Strix Halo [Radeon 8050S/8060S] |
AMD | Strix Halo | 2,609,324 | 59,000 | 44.23 | 0 hrs 33 mins |
| 38 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,464,075 | 179,693 | 13.71 | 1 hrs 45 mins |
| 39 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,448,482 | 59,000 | 41.50 | 0 hrs 35 mins |
|
|
|||||||
| 40 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,308,122 | 309,079 | 7.47 | 3 hrs 13 mins |
| 41 | Radeon RX 6650XT Navi 23 [Radeon RX 6650XT] |
AMD | Navi 23 | 1,921,501 | 268,971 | 7.14 | 3 hrs 22 mins |
| 42 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)] |
AMD | Navi 23 XT-XL | 1,719,646 | 244,115 | 7.04 | 3 hrs 24 mins |
| 43 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 1,577,046 | 59,000 | 26.73 | 0 hrs 54 mins |
| 44 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 809,717 | 189,665 | 4.27 | 5 hrs 37 mins |
| 45 | Radeon PRO W7500 Navi 33 [Radeon PRO W7500] |
AMD | Navi 33 | 759,053 | 59,000 | 12.87 | 1 hrs 52 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:30:39|
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 |