RESEARCH: ALZHEIMERS
FOLDING PROJECT #18256 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 caused by clumps of a protein called tau. Studying tau is hard because it doesn't have a fixed shape. This project uses computer simulations to figure out the best way to model tau, which will help us understand Alzheimer's 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 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 serious brain disorder that causes problems with thinking, memory, and behavior. It is the most common cause of dementia, a condition that affects many aspects of life.
neurofibrillary tangles
Abnormal accumulations of tau protein inside nerve cells.
Neurofibrillary tangles are clumps of twisted fibers found in the brains of people with Alzheimer's disease. They disrupt normal cell function and contribute to brain damage.
tau protein
A protein involved in stabilizing microtubules.
Tau is a protein that helps maintain the structure of nerve cells. In Alzheimer's disease, tau becomes abnormally twisted and clumps together, forming neurofibrillary tangles.
microtubules
Hollow protein tubes that help maintain cell shape and transport materials.
Microtubules are important structural components of cells. They provide support, facilitate movement, and play a role in intracellular transport.
Intrinsically Disordered Protein (IDP)
A protein that lacks a fixed three-dimensional structure.
Intrinsically disordered proteins (IDPs) are unique because they don't have a stable shape. This allows them to be flexible and interact with many different molecules.
force fields
Parameters that govern the interactions between atoms in a molecular simulation.
Force fields are mathematical models used to simulate the behavior of molecules. They help researchers understand how proteins fold and interact with each other.
simulations
Computer models used to predict the behavior of biological systems.
Simulations are powerful tools that allow researchers to study complex biological processes in a virtual environment.
FRET
Förster Resonance Energy Transfer
FRET is a technique used to study the interactions between molecules by measuring the transfer of energy between fluorescent probes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:30:44|
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 | 46,236,307 | 39,000 | 1185.55 | 0 hrs 1 mins |
| 2 | RTX PRO 6000 Blackwell Server Edition GB202GL [RTX PRO 6000 Blackwell Server Edition] |
Nvidia | GB202GL | 38,672,692 | 39,000 | 991.61 | 0 hrs 1 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 24,862,887 | 308,455 | 80.60 | 0 hrs 18 mins |
| 4 | RTX PRO 6000 Blackwell Max-Q Workstation Edition GB202GL [RTX PRO 6000 Blackwell Max-Q Workstation Edition] |
Nvidia | GB202GL | 21,970,177 | 39,000 | 563.34 | 0 hrs 3 mins |
| 5 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 18,911,758 | 335,339 | 56.40 | 0 hrs 26 mins |
| 6 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 17,965,989 | 101,337 | 177.29 | 0 hrs 8 mins |
| 7 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 17,909,060 | 39,000 | 459.21 | 0 hrs 3 mins |
| 8 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 17,748,001 | 90,901 | 195.25 | 0 hrs 7 mins |
| 9 | GeForce RTX 5090 Max-Q / Mobile GB203M / GN22 [GeForce RTX 5090 Max-Q / Mobile] |
Nvidia | GB203M / GN22 | 13,921,939 | 39,000 | 356.97 | 0 hrs 4 mins |
| 10 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 11,552,376 | 333,198 | 34.67 | 0 hrs 42 mins |
| 11 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 11,548,489 | 262,632 | 43.97 | 0 hrs 33 mins |
| 12 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 11,450,118 | 53,726 | 213.12 | 0 hrs 7 mins |
| 13 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 10,792,175 | 99,544 | 108.42 | 0 hrs 13 mins |
| 14 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 10,334,973 | 100,806 | 102.52 | 0 hrs 14 mins |
| 15 | GeForce RTX 4090 Laptop GPU AD103M / GN21-X11 [GeForce RTX 4090 Laptop GPU] |
Nvidia | AD103M / GN21-X11 | 8,689,845 | 39,000 | 222.82 | 0 hrs 6 mins |
| 16 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 8,413,691 | 356,232 | 23.62 | 1 hrs 1 mins |
| 17 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,154,717 | 343,738 | 23.72 | 1 hrs 1 mins |
| 18 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 7,620,959 | 253,789 | 30.03 | 0 hrs 48 mins |
| 19 | GeForce RTX 5080 Max-Q / Mobile GB203M / GN22-X9 [GeForce RTX 5080 Max-Q / Mobile] |
Nvidia | GB203M / GN22-X9 | 7,435,474 | 39,000 | 190.65 | 0 hrs 8 mins |
|
|
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| 20 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 6,676,669 | 290,128 | 23.01 | 1 hrs 3 mins |
| 21 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,675,449 | 308,868 | 21.61 | 1 hrs 7 mins |
| 22 | GeForce RTX 4080 Max-Q / Mobile AD104M [GeForce RTX 4080 Max-Q / Mobile] |
Nvidia | AD104M | 6,591,925 | 324,747 | 20.30 | 1 hrs 11 mins |
| 23 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 6,007,633 | 140,593 | 42.73 | 0 hrs 34 mins |
| 24 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 5,333,548 | 302,077 | 17.66 | 1 hrs 22 mins |
| 25 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,234,645 | 164,203 | 31.88 | 0 hrs 45 mins |
| 26 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 5,144,424 | 305,668 | 16.83 | 1 hrs 26 mins |
| 27 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 5,122,283 | 43,385 | 118.07 | 0 hrs 12 mins |
| 28 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 4,806,513 | 284,882 | 16.87 | 1 hrs 25 mins |
| 29 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,359,127 | 231,639 | 18.82 | 1 hrs 17 mins |
| 30 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,355,513 | 88,464 | 49.23 | 0 hrs 29 mins |
| 31 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 4,314,376 | 39,000 | 110.63 | 0 hrs 13 mins |
| 32 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,915,942 | 198,752 | 19.70 | 1 hrs 13 mins |
| 33 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,828,273 | 39,000 | 98.16 | 0 hrs 15 mins |
| 34 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 3,687,115 | 39,000 | 94.54 | 0 hrs 15 mins |
| 35 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,317,085 | 104,451 | 31.76 | 0 hrs 45 mins |
| 36 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,302,917 | 258,974 | 12.75 | 1 hrs 53 mins |
| 37 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,950,449 | 39,000 | 75.65 | 0 hrs 19 mins |
| 38 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 2,935,307 | 39,000 | 75.26 | 0 hrs 19 mins |
| 39 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,906,418 | 252,910 | 11.49 | 2 hrs 5 mins |
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| 40 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,887,979 | 39,000 | 74.05 | 0 hrs 19 mins |
| 41 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,775,870 | 249,110 | 11.14 | 2 hrs 9 mins |
| 42 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 2,715,839 | 72,281 | 37.57 | 0 hrs 38 mins |
| 43 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,610,458 | 243,443 | 10.72 | 2 hrs 14 mins |
| 44 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,468,607 | 173,019 | 14.27 | 1 hrs 41 mins |
| 45 | Radeon RX 6650XT Navi 23 [Radeon RX 6650XT] |
AMD | Navi 23 | 2,461,283 | 240,718 | 10.22 | 2 hrs 21 mins |
| 46 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,255,954 | 39,000 | 57.84 | 0 hrs 25 mins |
| 47 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)] |
AMD | Navi 23 XT-XL | 2,005,089 | 195,465 | 10.26 | 2 hrs 20 mins |
| 48 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,958,823 | 219,767 | 8.91 | 2 hrs 42 mins |
| 49 | GeForce RTX 4050 Max-Q / Mobile AD107M [GeForce RTX 4050 Max-Q / Mobile] |
Nvidia | AD107M | 1,941,616 | 39,000 | 49.79 | 0 hrs 29 mins |
| 50 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,670,709 | 39,000 | 42.84 | 0 hrs 34 mins |
| 51 | Radeon RX 6650 XT Navi 23 [Radeon RX 6650 XT] |
AMD | Navi 23 | 1,533,801 | 39,000 | 39.33 | 0 hrs 37 mins |
| 52 | Radeon RX 7700S/7600S Navi 33 [Radeon RX 7700S/7600S] |
AMD | Navi 33 | 1,426,779 | 39,000 | 36.58 | 0 hrs 39 mins |
| 53 | RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M] |
AMD | Navi 14 | 528,454 | 46,711 | 11.31 | 2 hrs 7 mins |
| 54 | Radeon RX 6400 / 6500 XT Navi 24 [Radeon RX 6400 / 6500 XT] |
AMD | Navi 24 | 507,398 | 39,000 | 13.01 | 1 hrs 51 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:30:44|
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 |