RESEARCH: ALZHEIMERS
FOLDING PROJECT #18262 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 913,882Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Alzheimer's disease is linked to abnormal tau protein buildup in the brain. Studying tau is hard because it's flexible and doesn't have a fixed shape. This project tests different computer simulation methods to see which best mimics how tau behaves, hoping to shed light on Alzheimer's and help researchers study other similar 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].
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 tip4pd water p18261- amber19sb with opc3 pol water p18262 - 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. It gets worse over time and can lead to difficulties with daily activities. 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 of a protein called tau found in the brains of people with Alzheimer's. They interfere with the function of brain cells and contribute to the disease's progression.
Tau protein
A microtubule-associated protein involved in stabilizing and regulating neuronal structure.
Tau is a protein that helps maintain the shape and stability of nerve cells. In Alzheimer's disease, tau becomes abnormal and forms tangles, which damage brain cells.
Microtubules
Long, hollow protein fibers that provide structural support and transport within cells.
Microtubules are essential components of the cell's internal framework. They help maintain cell shape, facilitate movement of materials within cells, and play a role in cell division.
Intrinsically Disordered Protein (IDP)
A protein that lacks a defined three-dimensional structure in solution.
Intrinsically disordered proteins are flexible and can adopt different shapes depending on their environment. This makes them challenging to study using traditional methods.
Single molecule FRET
A technique used to study protein interactions and conformational changes at the single-molecule level.
Single molecule FRET measures the fluorescence resonance energy transfer between two fluorescent molecules attached to a protein. This allows researchers to track the movement and interactions of individual protein molecules.
Force fields
Mathematical models that describe the interactions between atoms in a molecule.
Force fields are used in molecular dynamics simulations to calculate the forces acting on atoms. Accurate force fields are essential for simulating biological systems.
Water models
Mathematical models that describe the interactions between water molecules.
Water models are used in molecular dynamics simulations to account for the role of water in biological systems. Accurate water models are crucial for simulating protein folding and other biomolecular processes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:30:36|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | B200 GB100 [B200] |
Nvidia | GB100 | 38,171,486 | 61,000 | 625.76 | 0 hrs 2 mins |
| 2 | GeForce RTX 5090 GB202 [GeForce RTX 5090] |
Nvidia | GB202 | 37,327,370 | 61,000 | 611.92 | 0 hrs 2 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 24,714,592 | 227,657 | 108.56 | 0 hrs 13 mins |
| 4 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 18,982,109 | 282,773 | 67.13 | 0 hrs 21 mins |
| 5 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 18,550,304 | 61,000 | 304.10 | 0 hrs 5 mins |
| 6 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 18,180,697 | 396,792 | 45.82 | 0 hrs 31 mins |
| 7 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 15,805,333 | 61,000 | 259.10 | 0 hrs 6 mins |
| 8 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 14,563,674 | 65,358 | 222.83 | 0 hrs 6 mins |
| 9 | GeForce RTX 5090 Max-Q / Mobile GB203M / GN22 [GeForce RTX 5090 Max-Q / Mobile] |
Nvidia | GB203M / GN22 | 13,541,359 | 61,000 | 221.99 | 0 hrs 6 mins |
| 10 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 13,381,171 | 182,613 | 73.28 | 0 hrs 20 mins |
| 11 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 10,670,157 | 139,159 | 76.68 | 0 hrs 19 mins |
| 12 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 9,706,136 | 157,433 | 61.65 | 0 hrs 23 mins |
| 13 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 9,480,789 | 167,408 | 56.63 | 0 hrs 25 mins |
| 14 | RTX PRO 2000 Blackwell GB206GL [RTX PRO 2000 Blackwell] |
Nvidia | GB206GL | 8,979,088 | 61,000 | 147.20 | 0 hrs 10 mins |
| 15 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 8,910,824 | 61,000 | 146.08 | 0 hrs 10 mins |
| 16 | RTX 4000 Ada Generation AD104GL [RTX 4000 Ada Generation] |
Nvidia | AD104GL | 8,041,732 | 61,000 | 131.83 | 0 hrs 11 mins |
| 17 | GeForce RTX 4060 Ti AD104 [GeForce RTX 4060 Ti] |
Nvidia | AD104 | 8,011,374 | 61,000 | 131.33 | 0 hrs 11 mins |
| 18 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 7,903,235 | 61,000 | 129.56 | 0 hrs 11 mins |
| 19 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 7,493,425 | 61,000 | 122.84 | 0 hrs 12 mins |
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| 20 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 7,023,268 | 155,698 | 45.11 | 0 hrs 32 mins |
| 21 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 6,914,105 | 61,000 | 113.35 | 0 hrs 13 mins |
| 22 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 6,432,507 | 354,728 | 18.13 | 1 hrs 19 mins |
| 23 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,097,368 | 168,665 | 36.15 | 0 hrs 40 mins |
| 24 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 6,068,066 | 69,818 | 86.91 | 0 hrs 17 mins |
| 25 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 5,911,617 | 61,000 | 96.91 | 0 hrs 15 mins |
| 26 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,695,185 | 61,000 | 93.36 | 0 hrs 15 mins |
| 27 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 5,516,776 | 61,000 | 90.44 | 0 hrs 16 mins |
| 28 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 5,414,862 | 396,399 | 13.66 | 1 hrs 45 mins |
| 29 | GeForce RTX 5070 Ti Mobile GB205M [GeForce RTX 5070 Ti Mobile] |
Nvidia | GB205M | 5,399,740 | 61,000 | 88.52 | 0 hrs 16 mins |
| 30 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 4,843,542 | 61,000 | 79.40 | 0 hrs 18 mins |
| 31 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 4,762,242 | 61,000 | 78.07 | 0 hrs 18 mins |
| 32 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 4,628,267 | 141,719 | 32.66 | 0 hrs 44 mins |
| 33 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 4,469,237 | 61,000 | 73.27 | 0 hrs 20 mins |
| 34 | TITAN RTX TU102 [TITAN RTX] 16310 |
Nvidia | TU102 | 4,146,893 | 61,000 | 67.98 | 0 hrs 21 mins |
| 35 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 4,144,615 | 61,000 | 67.94 | 0 hrs 21 mins |
| 36 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 4,115,042 | 61,000 | 67.46 | 0 hrs 21 mins |
| 37 | RTX 4000 SFF Ada Generation AD104GL [RTX 4000 SFF Ada Generation] |
Nvidia | AD104GL | 3,857,229 | 61,000 | 63.23 | 0 hrs 23 mins |
| 38 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,818,239 | 167,679 | 22.77 | 1 hrs 3 mins |
| 39 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 3,693,387 | 61,000 | 60.55 | 0 hrs 24 mins |
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| 40 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,624,576 | 64,807 | 55.93 | 0 hrs 26 mins |
| 41 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 3,549,345 | 252,086 | 14.08 | 1 hrs 42 mins |
| 42 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,482,813 | 103,023 | 33.81 | 0 hrs 43 mins |
| 43 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,242,033 | 123,720 | 26.20 | 0 hrs 55 mins |
| 44 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 3,202,505 | 61,000 | 52.50 | 0 hrs 27 mins |
| 45 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,186,794 | 61,000 | 52.24 | 0 hrs 28 mins |
| 46 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 3,161,224 | 61,000 | 51.82 | 0 hrs 28 mins |
| 47 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 2,966,307 | 61,000 | 48.63 | 0 hrs 30 mins |
| 48 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,941,996 | 61,000 | 48.23 | 0 hrs 30 mins |
| 49 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,940,939 | 340,653 | 8.63 | 2 hrs 47 mins |
| 50 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,714,995 | 245,424 | 11.06 | 2 hrs 10 mins |
| 51 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,680,440 | 61,000 | 43.94 | 0 hrs 33 mins |
| 52 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 2,608,761 | 61,000 | 42.77 | 0 hrs 34 mins |
| 53 | Radeon 8050S/8060S Strix Halo [Radeon 8050S/8060S] |
AMD | Strix Halo | 2,562,129 | 61,000 | 42.00 | 0 hrs 34 mins |
| 54 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,529,360 | 61,000 | 41.46 | 0 hrs 35 mins |
| 55 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,475,172 | 105,269 | 23.51 | 1 hrs 1 mins |
| 56 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,474,615 | 61,000 | 40.57 | 0 hrs 35 mins |
| 57 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 2,462,130 | 77,180 | 31.90 | 0 hrs 45 mins |
| 58 | GeForce RTX 4060 Max-Q / Mobile AD107M [GeForce RTX 4060 Max-Q / Mobile] |
Nvidia | AD107M | 2,409,435 | 61,000 | 39.50 | 0 hrs 36 mins |
| 59 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 2,263,817 | 76,112 | 29.74 | 0 hrs 48 mins |
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| 60 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,216,574 | 239,597 | 9.25 | 2 hrs 36 mins |
| 61 | Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)] |
AMD | Navi 44 | 2,057,794 | 61,000 | 33.73 | 0 hrs 43 mins |
| 62 | GeForce RTX 4050 Max-Q / Mobile AD107M [GeForce RTX 4050 Max-Q / Mobile] |
Nvidia | AD107M | 2,038,734 | 61,000 | 33.42 | 0 hrs 43 mins |
| 63 | P102-100 GP102 [P102-100] |
Nvidia | GP102 | 1,989,813 | 61,000 | 32.62 | 0 hrs 44 mins |
| 64 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 1,945,200 | 61,000 | 31.89 | 0 hrs 45 mins |
| 65 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,686,637 | 61,000 | 27.65 | 0 hrs 52 mins |
| 66 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,657,449 | 132,057 | 12.55 | 1 hrs 55 mins |
| 67 | GeForce RTX 5080 Max-Q / Mobile GB203M / GN22-X9 [GeForce RTX 5080 Max-Q / Mobile] |
Nvidia | GB203M / GN22-X9 | 1,650,336 | 61,000 | 27.05 | 0 hrs 53 mins |
| 68 | RTX A2000 12GB GA106 [RTX A2000 12GB] |
Nvidia | GA106 | 1,508,540 | 61,000 | 24.73 | 0 hrs 58 mins |
| 69 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,370,568 | 61,000 | 22.47 | 1 hrs 4 mins |
| 70 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,358,556 | 222,888 | 6.10 | 3 hrs 56 mins |
| 71 | Radeon RX 6650 XT Navi 23 [Radeon RX 6650 XT] |
AMD | Navi 23 | 1,350,601 | 61,000 | 22.14 | 1 hrs 5 mins |
| 72 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,078,300 | 75,378 | 14.31 | 1 hrs 41 mins |
| 73 | GeForce RTX 3050 6GB GA107 [GeForce RTX 3050 6GB] |
Nvidia | GA107 | 938,515 | 61,000 | 15.39 | 1 hrs 34 mins |
| 74 | Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)] |
AMD | Navi 23 XT-XL | 912,113 | 61,000 | 14.95 | 1 hrs 36 mins |
| 75 | RX 5600 OEM/5600XT/5700(XT) Navi 10 [RX 5600 OEM/5600XT/5700(XT)] |
AMD | Navi 10 | 855,172 | 61,000 | 14.02 | 1 hrs 43 mins |
| 76 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 847,443 | 61,000 | 13.89 | 1 hrs 44 mins |
| 77 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 745,243 | 61,000 | 12.22 | 1 hrs 58 mins |
| 78 | CMP 30HX TU116 [CMP 30HX] |
Nvidia | TU116 | 741,493 | 61,000 | 12.16 | 1 hrs 58 mins |
| 79 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 516,533 | 61,000 | 8.47 | 2 hrs 50 mins |
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| 80 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 498,731 | 61,000 | 8.18 | 2 hrs 56 mins |
| 81 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 391,964 | 61,000 | 6.43 | 3 hrs 44 mins |
| 82 | GeForce GTX 1650 TU106 [GeForce GTX 1650] |
Nvidia | TU106 | 383,204 | 61,000 | 6.28 | 3 hrs 49 mins |
| 83 | GeForce RTX 3050 6GB Laptop GPU GN20-P0-R-K2 [GeForce RTX 3050 6GB Laptop GPU] |
Nvidia | GN20-P0-R-K2 | 350,584 | 61,000 | 5.75 | 4 hrs 11 mins |
| 84 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 300,734 | 61,000 | 4.93 | 4 hrs 52 mins |
| 85 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 245,311 | 146,229 | 1.68 | 14 hrs 18 mins |
| 86 | Quadro T400 Mobile TU117GLM [Quadro T400 Mobile] |
Nvidia | TU117GLM | 193,805 | 61,000 | 3.18 | 7 hrs 33 mins |
| 87 | RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M] |
AMD | Navi 14 | 149,405 | 64,600 | 2.31 | 10 hrs 23 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:30:36|
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 | ||
| 2 | RYZEN 7 5700X 8-CORE | 16 | AMD |