RESEARCH: ALZHEIMERS
FOLDING PROJECT #18262 PROFILE

PROJECT TEAM

Manager(s): Justin Miller
Institution: University of Pennsylvania

WORK UNIT INFO

Atoms: 913,882
Core: 0x27
Status: Public

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

Note: Glossary items are a high level summary and may not be 100% accurate.

Alzheimer's disease

A progressive neurodegenerative disorder that causes memory loss, cognitive decline, and behavioral changes.

Disease: Healthcare
Neurology / Dementia

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.

Pathological Feature: Healthcare
Neuropathology / Alzheimer's disease

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.

Protein: Biotechnology
Neuroscience / Cytoskeleton

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.

Cellular Structure: Biotechnology
Cell Biology / Cytoskeleton

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.

Protein Type: Biotechnology
Structural Biology / Proteomics

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.

Biophysical Technique: Research
Structural Biology / Protein Dynamics

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.

Computational Model: Biotechnology
Computational Biology / Molecular Dynamics

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.

Computational Model: Biotechnology
Computational Biology / Molecular Dynamics

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
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
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
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
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