RESEARCH: ALZHEIMERS
FOLDING PROJECT #18263 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 bad for your memory and there's no cure. This project studies tau protein, which gets tangled up in Alzheimer's brains. Scientists are using computer models to see how different settings in the model affect how well it mimics real tau protein. They hope this will help everyone studying these tricky proteins.
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[51-56,58,61,62].
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 tip4p water p18261- amber19sb with opc3 pol water p18262 - charmm36m with tip3p water p18263 - amber99sb-star-ILDN with tip4pd 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 causes memory problems, thinking difficulties, and changes in behavior. It affects millions of people worldwide and currently has no cure. Researchers are working hard to understand the disease and develop effective treatments.
Neurofibrillary tangles
Abnormal accumulations of tau protein inside nerve cells.
Neurofibrillary tangles are a hallmark of Alzheimer's disease. They are clumps of twisted fibers made up of a protein called tau that build up inside brain cells. These tangles disrupt normal cell function and contribute to the progression of the disease.
Tau protein
A protein that helps stabilize microtubules in nerve cells.
Tau is a protein crucial for the healthy function of brain cells. It plays a vital role in maintaining the structure and stability of microtubules, which are essential for transporting nutrients and other materials within neurons. In Alzheimer's disease, tau becomes abnormal and forms tangles, disrupting normal cell function.
Microtubules
Hollow tubes made of protein that help maintain cell shape and transport materials.
Microtubules are essential components of the cytoskeleton, a network of protein fibers that provides structure and support to cells. They play a crucial role in various cellular processes, including cell division, movement, and the transport of organelles and other molecules.
Cytoskeleton
A network of protein fibers that provides support and shape to cells.
The cytoskeleton is a dynamic network of protein filaments that extends throughout the cytoplasm of cells. It plays a vital role in maintaining cell shape, facilitating movement, transporting materials within cells, and organizing cellular structures.
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. Unlike most proteins, which fold into specific structures, IDPs exist in a flexible and dynamic state. This allows them to interact with other molecules in various ways and perform diverse functions.
Single molecule FRET experiments
A method used to study the distance between two molecules in real time.
Single molecule FRET experiments involve attaching fluorescent tags to specific molecules and measuring the energy transfer between them. By analyzing the changes in fluorescence, researchers can determine the distance between the tagged molecules and gain insights into their interactions and dynamics.
Force field
A set of mathematical equations that describe the interactions between atoms in a simulation.
Force fields are essential components of molecular dynamics simulations. They provide the rules governing how atoms interact with each other, allowing researchers to simulate the behavior of molecules over time.
Water models
Representations of water molecules used in simulations.
Water models are crucial for accurately simulating biological systems. They capture the dynamic behavior of water molecules and their interactions with other atoms.
Atomistic detail
A level of detail that represents individual atoms in a simulation.
Atomistic detail refers to the ability of a simulation to capture the movements and interactions of individual atoms. It allows researchers to study complex molecular systems at a very precise level.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:30:35|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | RTX PRO 6000 Blackwell Workstation Edition GB202GL [RTX PRO 6000 Blackwell Workstation Edition] |
Nvidia | GB202GL | 65,929,554 | 671,614 | 98.17 | 0 hrs 15 mins |
| 2 | GeForce RTX 5090 GB202 [GeForce RTX 5090] |
Nvidia | GB202 | 46,497,820 | 671,614 | 69.23 | 0 hrs 21 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 24,657,413 | 864,472 | 28.52 | 0 hrs 50 mins |
| 4 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 21,517,070 | 671,614 | 32.04 | 0 hrs 45 mins |
| 5 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 19,715,468 | 2,899,602 | 6.80 | 3 hrs 32 mins |
| 6 | GeForce RTX 5090 Max-Q / Mobile GB203M / GN22 [GeForce RTX 5090 Max-Q / Mobile] |
Nvidia | GB203M / GN22 | 17,581,647 | 671,614 | 26.18 | 0 hrs 55 mins |
| 7 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 17,493,013 | 1,864,586 | 9.38 | 2 hrs 33 mins |
| 8 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 17,271,556 | 893,050 | 19.34 | 1 hrs 14 mins |
| 9 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 13,382,102 | 700,362 | 19.11 | 1 hrs 15 mins |
| 10 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 11,374,023 | 671,614 | 16.94 | 1 hrs 25 mins |
| 11 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 10,141,623 | 1,818,543 | 5.58 | 4 hrs 18 mins |
| 12 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 10,081,855 | 1,905,475 | 5.29 | 4 hrs 32 mins |
| 13 | GeForce RTX 4090 Laptop GPU AD103M / GN21-X11 [GeForce RTX 4090 Laptop GPU] |
Nvidia | AD103M / GN21-X11 | 9,276,494 | 671,614 | 13.81 | 1 hrs 44 mins |
| 14 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 9,020,472 | 671,614 | 13.43 | 1 hrs 47 mins |
| 15 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 8,229,465 | 1,049,697 | 7.84 | 3 hrs 4 mins |
| 16 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,072,224 | 717,660 | 11.25 | 2 hrs 8 mins |
| 17 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 7,880,028 | 671,614 | 11.73 | 2 hrs 3 mins |
| 18 | GeForce RTX 4070 Ti SUPER AD102 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD102 | 7,795,647 | 671,614 | 11.61 | 2 hrs 4 mins |
| 19 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,693,380 | 797,586 | 9.65 | 2 hrs 29 mins |
|
|
|||||||
| 20 | Radeon PRO W7900 Dual Slot Navi 31 [Radeon PRO W7900 Dual Slot] |
AMD | Navi 31 | 7,616,499 | 671,614 | 11.34 | 2 hrs 7 mins |
| 21 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,581,893 | 671,614 | 11.29 | 2 hrs 8 mins |
| 22 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 7,555,166 | 671,614 | 11.25 | 2 hrs 8 mins |
| 23 | GeForce RTX 5070 Ti Mobile GB205M [GeForce RTX 5070 Ti Mobile] |
Nvidia | GB205M | 6,615,372 | 671,614 | 9.85 | 2 hrs 26 mins |
| 24 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 6,453,244 | 671,614 | 9.61 | 2 hrs 30 mins |
| 25 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 6,018,793 | 892,171 | 6.75 | 3 hrs 33 mins |
| 26 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,743,449 | 671,614 | 8.55 | 2 hrs 48 mins |
| 27 | GeForce RTX 4060 Ti AD104 [GeForce RTX 4060 Ti] |
Nvidia | AD104 | 5,595,251 | 671,614 | 8.33 | 2 hrs 53 mins |
| 28 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 5,512,196 | 932,634 | 5.91 | 4 hrs 4 mins |
| 29 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 5,317,086 | 967,122 | 5.50 | 4 hrs 22 mins |
| 30 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,866,311 | 1,484,141 | 3.28 | 7 hrs 19 mins |
| 31 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,760,912 | 671,614 | 7.09 | 3 hrs 23 mins |
| 32 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 4,596,724 | 671,614 | 6.84 | 3 hrs 30 mins |
| 33 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,512,982 | 1,215,759 | 3.71 | 6 hrs 28 mins |
| 34 | Intel Arc B580 Graphics Battlemage G21 [Intel Arc B580 Graphics] |
Intel | Battlemage G21 | 4,441,804 | 671,614 | 6.61 | 3 hrs 38 mins |
| 35 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,278,099 | 671,614 | 6.37 | 3 hrs 46 mins |
| 36 | Radeon 8050S/8060S Strix Halo [Radeon 8050S/8060S] |
AMD | Strix Halo | 4,272,684 | 671,614 | 6.36 | 3 hrs 46 mins |
| 37 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,706,194 | 671,614 | 5.52 | 4 hrs 21 mins |
| 38 | RTX 4000 SFF Ada Generation AD104GL [RTX 4000 SFF Ada Generation] |
Nvidia | AD104GL | 3,545,913 | 671,614 | 5.28 | 4 hrs 33 mins |
| 39 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,519,045 | 702,378 | 5.01 | 4 hrs 47 mins |
|
|
|||||||
| 40 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,258,836 | 783,938 | 4.16 | 5 hrs 46 mins |
| 41 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 3,170,819 | 1,724,184 | 1.84 | 13 hrs 3 mins |
| 42 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 3,147,266 | 671,614 | 4.69 | 5 hrs 7 mins |
| 43 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 3,012,724 | 671,614 | 4.49 | 5 hrs 21 mins |
| 44 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,968,008 | 910,021 | 3.26 | 7 hrs 22 mins |
| 45 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 2,948,041 | 671,614 | 4.39 | 5 hrs 28 mins |
| 46 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,900,809 | 671,614 | 4.32 | 5 hrs 33 mins |
| 47 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,806,093 | 671,614 | 4.18 | 5 hrs 45 mins |
| 48 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,786,461 | 826,682 | 3.37 | 7 hrs 7 mins |
| 49 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 2,728,678 | 671,614 | 4.06 | 5 hrs 54 mins |
| 50 | GeForce RTX 4060 Max-Q / Mobile AD107M [GeForce RTX 4060 Max-Q / Mobile] |
Nvidia | AD107M | 2,727,023 | 671,614 | 4.06 | 5 hrs 55 mins |
| 51 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,667,047 | 671,614 | 3.97 | 6 hrs 3 mins |
| 52 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 2,583,079 | 671,614 | 3.85 | 6 hrs 14 mins |
| 53 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,441,211 | 671,614 | 3.63 | 6 hrs 36 mins |
| 54 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,321,415 | 671,614 | 3.46 | 6 hrs 57 mins |
| 55 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 2,240,793 | 671,614 | 3.34 | 7 hrs 12 mins |
| 56 | Radeon RX 6650 XT Navi 23 [Radeon RX 6650 XT] |
AMD | Navi 23 | 2,082,777 | 671,614 | 3.10 | 7 hrs 44 mins |
| 57 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,166,138 | 671,614 | 1.74 | 13 hrs 49 mins |
| 58 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)] |
AMD | Navi 23 XT-XL | 1,122,191 | 671,614 | 1.67 | 14 hrs 22 mins |
| 59 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 342,466 | 671,614 | 0.51 | 47 hrs 4 mins |
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|||||||
| 60 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 278,638 | 671,614 | 0.41 | 57 hrs 51 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:30:35|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|