RESEARCH: ALZHEIMERS
FOLDING PROJECT #18257 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 linked to abnormal tau protein clumps in the brain. Scientists are using computer simulations to study how tau behaves, aiming to find better treatments for Alzheimer's and other neurodegenerative diseases. They are testing different simulation methods to accurately represent tau's flexibility and interactions with water.
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 characterized by memory loss and cognitive decline.
Alzheimer's disease is a brain disorder that slowly destroys memory and thinking skills. It is the most common cause of dementia, a general term for memory loss and other intellectual abilities severe enough to interfere with daily life. There is no cure for Alzheimer's, but treatments can help manage symptoms.
neurofibrillary tangles
Abnormal accumulations of tau protein within neurons.
Neurofibrillary tangles are clumps of a protein called tau that form inside brain cells. These tangles disrupt the normal function of neurons and contribute to the progression of Alzheimer's disease.
tau protein
A microtubule-associated protein involved in neuronal structure and function.
Tau protein is a vital part of brain cells. It helps stabilize structures called microtubules, which are essential for transporting nutrients and other materials within the cell. In Alzheimer's disease, tau protein malfunctions and forms tangles that damage neurons.
microtubules
Hollow protein filaments that provide structural support and transport within cells.
Microtubules are long, thin tubes made of protein that act like the cell's internal scaffolding. They help maintain cell shape, move materials around inside the cell, and separate chromosomes during cell division.
Intrinsically Disordered Protein (IDP)
A protein that lacks a stable three-dimensional structure.
Intrinsically disordered proteins (IDPs) are unique because they don't have a fixed shape. Instead, their structures are flexible and can change depending on their environment. This makes them challenging to study using traditional methods.
single molecule FRET
A technique used to measure the distance between two points in a molecule.
Single-molecule FRET is a powerful tool used to study the structure and dynamics of biomolecules. It involves using fluorescent molecules (called fluorophores) attached to different parts of a molecule. By measuring the energy transfer between these fluorophores, researchers can determine the distance between them.
force field
A set of mathematical equations that describe the interactions between atoms in a simulation.
Force fields are essential for molecular dynamics simulations. They define how atoms attract and repel each other, allowing researchers to simulate the movement and behavior of molecules over time.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:30:42|
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 | 38,628,352 | 60,000 | 643.81 | 0 hrs 2 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 27,419,231 | 522,176 | 52.51 | 0 hrs 27 mins |
| 3 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 19,660,885 | 250,521 | 78.48 | 0 hrs 18 mins |
| 4 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 16,963,022 | 526,028 | 32.25 | 0 hrs 45 mins |
| 5 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 15,533,855 | 556,012 | 27.94 | 0 hrs 52 mins |
| 6 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 13,293,636 | 60,000 | 221.56 | 0 hrs 6 mins |
| 7 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 12,689,519 | 437,772 | 28.99 | 0 hrs 50 mins |
| 8 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 10,851,638 | 239,342 | 45.34 | 0 hrs 32 mins |
| 9 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 9,989,737 | 194,117 | 51.46 | 0 hrs 28 mins |
| 10 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,432,563 | 479,906 | 17.57 | 1 hrs 22 mins |
| 11 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 8,417,025 | 60,000 | 140.28 | 0 hrs 10 mins |
| 12 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 8,319,988 | 200,772 | 41.44 | 0 hrs 35 mins |
| 13 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,994,951 | 376,007 | 21.26 | 1 hrs 8 mins |
| 14 | RTX 4000 Ada Generation AD104GL [RTX 4000 Ada Generation] |
Nvidia | AD104GL | 6,793,356 | 60,000 | 113.22 | 0 hrs 13 mins |
| 15 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 6,626,779 | 82,635 | 80.19 | 0 hrs 18 mins |
| 16 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,597,633 | 420,329 | 15.70 | 1 hrs 32 mins |
| 17 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 6,030,665 | 202,657 | 29.76 | 0 hrs 48 mins |
| 18 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 5,858,644 | 419,729 | 13.96 | 1 hrs 43 mins |
| 19 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 5,595,861 | 252,604 | 22.15 | 1 hrs 5 mins |
|
|
|||||||
| 20 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,436,152 | 60,000 | 90.60 | 0 hrs 16 mins |
| 21 | GeForce RTX 4080 Max-Q / Mobile AD104M [GeForce RTX 4080 Max-Q / Mobile] |
Nvidia | AD104M | 5,352,771 | 405,186 | 13.21 | 1 hrs 49 mins |
| 22 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 5,166,907 | 371,689 | 13.90 | 1 hrs 44 mins |
| 23 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,980,798 | 393,529 | 12.66 | 1 hrs 54 mins |
| 24 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,901,287 | 157,465 | 31.13 | 0 hrs 46 mins |
| 25 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,848,872 | 359,757 | 13.48 | 1 hrs 47 mins |
| 26 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,476,732 | 389,149 | 11.50 | 2 hrs 5 mins |
| 27 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 4,201,529 | 117,558 | 35.74 | 0 hrs 40 mins |
| 28 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 4,140,542 | 371,243 | 11.15 | 2 hrs 9 mins |
| 29 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,070,045 | 280,432 | 14.51 | 1 hrs 39 mins |
| 30 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,484,366 | 314,752 | 11.07 | 2 hrs 10 mins |
| 31 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,416,153 | 358,075 | 9.54 | 2 hrs 31 mins |
| 32 | RTX 4000 SFF Ada Generation AD104GL [RTX 4000 SFF Ada Generation] |
Nvidia | AD104GL | 3,059,365 | 60,000 | 50.99 | 0 hrs 28 mins |
| 33 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,887,441 | 333,258 | 8.66 | 2 hrs 46 mins |
| 34 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,845,886 | 244,293 | 11.65 | 2 hrs 4 mins |
| 35 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 2,481,063 | 60,000 | 41.35 | 0 hrs 35 mins |
| 36 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,438,186 | 318,492 | 7.66 | 3 hrs 8 mins |
| 37 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,182,870 | 60,000 | 36.38 | 0 hrs 40 mins |
| 38 | Radeon RX 6650XT Navi 23 [Radeon RX 6650XT] |
AMD | Navi 23 | 1,827,426 | 288,029 | 6.34 | 3 hrs 47 mins |
| 39 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)] |
AMD | Navi 23 XT-XL | 1,764,656 | 271,082 | 6.51 | 3 hrs 41 mins |
|
|
|||||||
| 40 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,692,536 | 218,690 | 7.74 | 3 hrs 6 mins |
| 41 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 1,210,704 | 206,552 | 5.86 | 4 hrs 6 mins |
| 42 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 766,763 | 60,000 | 12.78 | 1 hrs 53 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:30:42|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | RYZEN 9 5950X 16-CORE | 32 | AMD | ||
| 2 | RYZEN 3 3100 4-CORE | 8 | AMD | ||
| 3 | RYZEN 9 3900 12-CORE | 24 | AMD |