RESEARCH: DUD-E
FOLDING PROJECT #12235 PROFILE

PROJECT TEAM

Manager(s): Louis Smith
Institution: University of Pennsylvania

WORK UNIT INFO

Atoms: 92,660
Core: 0x23
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how drugs interact with proteins. They're focusing on a protein called Acetylcholinesterase, which is important for the nervous system and is also targeted by pesticides and some medications. By simulating how these molecules bind, scientists hope to develop better drugs and understand how they work.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

In this series of projects we are simulating proteins that are part of the DUD-E benchmark data set for protein-ligand interactions, using simulations initialized from Alpha Fold. Simulation methods to study protein-small molecule interactions are of critical importance to the early stages of drug discovery, but most methods have a poor balance of accuracy relative to cost.

Much of the development process for new compounds happens via screening large libraries of compounds for activity against target proteins believed to be relevant for a disease.

Lending focus to this search makes developing new molecules into drugs more economical and faster. In order to do this kind of methods development, good reference data that is widely available is essential.

A classic dataset for benchmarking structural methods attempting to predict protein-ligand interactions known as DUD-E has been widely used because it has diverse proteins, and each protein is bound to a fairly large collection (usually more than fifty) of small molecules for which the ability to bind the receptor have been measured experimentally.

Using Folding@Home, we will create large reference quality simulations of these proteins.

Because we know how such simulations, and the binding methods we or others may test on them, should look and function we have a great yardstick for improving the methods we have and developing new ones. In this project series we have the following systems, many of which are known for their medical relevance in addition to having been extensively studied with both simulation and experiment in the past. 12234 - ACES: Acetylcholinesterase that is critical to nervous system function in animals.

It is the target of pesticides, and also numerous drugs.

If targeted in the correct way, it can reduce neural swelling.

This sequence happens to be from the Pacific Electric ray, Torpedo Californica, which was a landmark discovery in biomedical efforts to isolate neurotransmitter receptors and led to a mechanistic understanding of myasthenia gravis. 12235 - AKT2: serine-threonine kinase taking part in the insulin signal transduction pathway.

Implicated in some cancers, it has been a target of drug development campaigns in the past. 12236 - AMPC: A critical antibiotic resistance gene, it is a beta lactamase capable of opening the critical structural feature of celphalosporin-type antibiotics, rendering them ineffective. 12237 - BACE1: Beta secretase 1, an aspartic acid protease that helps form myelin sheaths in neurons.

It is the major generator of amyloid-beta peptides in neurons, and therefore is implicated in Alzheimer's disease. 12238 - BRAF: B-raf is involved in sending signals involved in cell growth, and as such is considered a proto-oncogen.

It is a serine/threonine kinase that has several known inhibitors, some of which are now anti-cancer medications. 12239 - CASP3: a caspase-type protease that participates in the execution of apoptosis, the process of programmed cell death.

It also acts to cleave one of the amyloid forming proteins and is therefore implicated in Alzheimer's dementia. 1240 - CDK2: one of the cyclin dependent kinases, this protein is a checkpoint kinase that signals transitions between growth and DNA synthesis phases in the cell cycle.

Dysfunction in this checkpoint is associated with cancer; inhibiting CDK2 can arrest cell cycle in cases of abmormal growth, so it has been an anti-cancer target for some time. 12241 - CSF1R: Colony stimulating factor 1 receptor, when bound by cognate ligands, will promote survival, proliferation and differentiation of many myeloid cell types.

It is thus involved in disease and is targeted in therapies for cancer, neurodegenerative diseases, nad inflammatory bone diseases. 12242 - DPP4: Dipeptidyl peptidase-4, a protein that cuts up certain other proteins on the surfaces of most cells.

Important in immune regulation, signal transduction, and apoptosis, molecules inhibiting its enzymatic activity can help treat type 2 diabetes because the peptide hormones (GLP-1, and GIP) are degraded by DPP4.

Thus, inhibiting DPP4 prolongs the effects of these hormones. 12243 - EGFR: Epidermal growth factor receptor; its deficient signaling is associated with Alzheimer's dementia, whereas its over-expression is a common characteristic of tumor cells.

It is thus an oncogene that is targeted by numerous anti-cancer molecules and drugs.

Many of these are targeted at the tyrosine kinase domain, because hampering its function prevents excessive transduction of the signals these receptors would otherwise send to the nucleus of the tumor cell. 12244 - ESR1: Estrogen Receptor Alpha is critical to many tissue differentiation processes across the body, and has been targeted by various drugs to both enhance and suppress its effects depending on associated conditions.

12245 - FA10: Coagulation factor X is an enzyme in the coagulation signaling cascade for forming blood clots.

It is a serine endopeptidase, and has been targeted by inhibitors to reduce coagulation in medical contexts where that is desirable.
12246 - FABP4: Fatty Acid Binding protein 4 is a protein that imports lipids between intra and extracellular membranes in macrophages and adipocytes.

Inhibiting it is associated with both preventing certain fat-tumor cancers and metabolic syndromes. 12247 - GRIA2: Glutamate ionotropic receptor AMPA type subunit 2 is a glutamate receptor, an essential neurotransmitter in humans.

Its pre-mRNA is A->I edited at a particular site that makes the channel impermeable to calcium.

Editing errors here can result in ALS, and some other diseases. 12248 - HSP90AA1: Heat shock protein 90kDa alpha A1 is a stress inducible protein that refolds misfolded or damaged proteins.

It is a relevant drug target because it interacts with a number of tumor promoting proteins and plays a large role in cellular adaptation to stress. 12249 - IGF1R: Insulin like growth factor 1 is an extracellular receptor with a tyrosine kinase domain.

It is critical for growth and development, and as such if overproduced can contribute to the cancer phenotype and certain other diseases.

Therefore inhibitors have been developed to target this extracellular receptor. 12250 - ITAL (LFA-1): Leukocyte adhesion cglycoprotein LFA-1 alpha is an integrin found on lymphocytes and other leukocytes.

It functions in the process of tissue emigration in lymphocytes, and in cytotoxic T-cell mediated killing of cells. 12251 - KIT: tyrosine-protein kinase KIT is a receptor tyrosine kinase and a proto-oncogene.

It senses cytokines, transducing signals that govern cell proliferation and survival.

As such it is often mutated in cancers, where its excessive activity maintains or enhances the tumor state. 12252 - MAPK2: Mitogen-activated protein kinase kinase is part of the MAPK pathway, which is famously aberrant in many types of cancers, particularly melanomas.

Inhibitors against it would slow the progression of cancer, so it has been targeted by therapies historically. 12253 - MET: tyrosine-protein kinase Met, also known as hepatocyte growth factor receptor, governs embryonic development, organogenesis and wound-healing.

Abnormal activation of MET sustains tumors by causing them to grow and become better supplied by blood vessels.

Extensive research has focused on inhibiting MET because of its correlation with poor prognosis in cancer, and many compounds are in various parts of the regulatory approval process. 12254 - MK10: MAPK-10 or mitogen-activated protein kinase 10 is associated with a wide variety of cellular processes associated with proliferatiation, differentiation, and development.

Mapk-10 is implicated in neuronal development, and when active can inhibit neuronal apoptosis.

12255 - MK14: MAPK-14 or p38-alpha is another stress and differentiation controlling kinase.

Because of its interaction with inflammatory signaling in the immune system, it is a relevant target for immune diseases and heart disease. 12256 - PPARD: Peroxisome proliferator-activated receptor delta is a nuclear hormone receptor that is implicated in the development of several classes of chronic disease.

Drugs stimulating it can act as biochemical substitutes for exercise, and decouple oxidative phosphorylation.

12257 - PPARG: Peroxisome proliferator activated receptor gamma is similar to the delta variant in some ways, but also serves as a master-regulator of fat cell differentiation.

It has been studied as a target for growth inhibition in cancer cell cultures.

It also is targeted by drugs that treat lipid metabolism disorders like hyperlipidemia and hyperglycemia, as well as for type 2 diabetes as an insulin sensitizer. 12258 - PTN1: Tyrosine-protein phosphatase non-receptor type 1 counteracts the effect of certain tyrosine kinases in protein signalling.

One of its targets is the phosphosite on the insulin receptor and several other receptor tyrosine kinases, including some from this list.

As such, it has implications for both the treatemnt of some cancers and also type 2 diabetes. 12259 - RENI: Renin is an endopeptidase that generates angiotensin 1, resulting in a blood pressure increasing signalling cascade that also causes sodium retention by the kidneys.

As such, renin inhibitors can serve to reduce blood pressure. 12260 - RXRA: Retinoid x receptor alpha is a nuclear receptor that binds retinoic acid, causing transcription of a large number of genes.

12261 - TGFR1: Transforming growth factor beta receptor 1 is a TGF-beta receptor that regulates differentiation in a number of endothelial cell types, and seems to have particular bearing on the development of reproductive tissues.

It has been targeted by studies working to develop cancer therapeutics. 12262 - THRB: Thyroid hormone receptor beta is a nuclear receptor that, when activated by thyroid hormone, initiates a large number of different genes.

Deficiencies in activity can result in thyroid hormone resistance which can cause goiter.

12263 - TRY1: Trypsin-1 is the main form of trypsinogen secreted by the pancreas.

It is an enzyme that breaks down proteins, and defective mutations of it can cause pancreatitis.

It is also a workhorse protein in modern biochemical and biophysical labs. 12264 - TRYB1: Tryptase beta-1 is a trypsin like protease that is secreted as part of Mast-cell activation.

As such it has roles in inflammation associated with asthma, and in cleaving flu's hemagglutinin surface protein (which initiates the experience of flu-like symptoms).

Attempts to produce inhibitors hve so far been difficult, but it has relevance to reducing the severity of the inflammatory response in these conditions. 12265 - VGFR2: Vascular endothelial growth factor receptor number 2 is a tyrosine kinase signaling receptor that binds the vascularization hormone, and causes tissue remodeling to form channels for blood vessel growth.

When over-active or over-expressed this protein supports the vascularization of tumor tissue, making inhibitors targeting it helpful in treating some cancers.

RELATED TERMS GLOSSARY AI BETA

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

DUD-E

Directory of Useful Decoys for Enhanced Evaluation

Technical: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a benchmark dataset used to evaluate the accuracy of computational methods for predicting protein-ligand interactions. It contains diverse proteins and a large collection of small molecules with known binding affinities.


Alpha Fold

A deep learning algorithm for predicting the three-dimensional structure of proteins.

Scientific: Biotechnology
Drug Discovery / Protein Structure Prediction

AlphaFold is a powerful AI tool that can predict how proteins fold into their 3D shapes. This information is crucial for understanding protein function and designing new drugs.


Protein-Ligand Interactions

The binding of a small molecule (ligand) to a protein.

Scientific: Biotechnology
Drug Discovery / Molecular Docking

Protein-ligand interactions are essential for many biological processes, including drug action. Understanding how molecules bind to proteins is key to developing new therapies.


Drug Discovery

The process of identifying and developing new drugs.

Technical: Biotechnology
Pharmaceuticals / Medicinal Chemistry

Drug discovery is a complex and lengthy process that involves identifying promising drug candidates, testing their safety and efficacy, and ultimately bringing them to market.


Pesticide

A chemical substance used to control pests.

Technical: Biotechnology
Agriculture / Crop Protection

Pesticides are widely used in agriculture to protect crops from insects, weeds, and diseases. However, their use can have negative impacts on the environment and human health.


Acetylcholinesterase

An enzyme that breaks down acetylcholine, a neurotransmitter.

Scientific: Biotechnology
Neuroscience / Neurotransmission

Acetylcholinesterase plays a crucial role in regulating nerve impulses by breaking down the neurotransmitter acetylcholine. Its dysfunction can lead to various neurological disorders.


Neurotransmitter

A chemical messenger that transmits signals between nerve cells.

Scientific: Biotechnology
Neuroscience / Synaptic Transmission

Neurotransmitters are essential for communication between neurons. They allow nerve cells to send and receive signals, enabling various brain functions.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:18
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 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 8,476,890 255,392 33.19 0 hrs 43 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 8,312,728 297,496 27.94 0 hrs 52 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 7,693,277 290,219 26.51 0 hrs 54 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 6,999,611 164,435 42.57 0 hrs 34 mins
5 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 6,364,439 273,542 23.27 1 hrs 2 mins
6 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,849,228 214,637 27.25 0 hrs 53 mins
7 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 5,460,850 81,422 67.07 0 hrs 21 mins
8 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 5,407,937 257,878 20.97 1 hrs 9 mins
9 TITAN RTX
TU102 [TITAN RTX] 16310
Nvidia TU102 4,856,632 247,424 19.63 1 hrs 13 mins
10 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,641,548 244,944 18.95 1 hrs 16 mins
11 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 4,331,930 213,582 20.28 1 hrs 11 mins
12 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 4,073,332 208,929 19.50 1 hrs 14 mins
13 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 3,909,179 159,764 24.47 0 hrs 59 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,763,828 222,366 16.93 1 hrs 25 mins
15 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,592,537 225,697 15.92 1 hrs 30 mins
16 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,591,441 226,030 15.89 1 hrs 31 mins
17 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,543,974 197,676 17.93 1 hrs 20 mins
18 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 3,510,734 220,146 15.95 1 hrs 30 mins
19 Radeon RX 7700XT/7800XT
Navi 32 [Radeon RX 7700XT/7800XT]
AMD Navi 32 3,451,619 206,972 16.68 1 hrs 26 mins
20 GeForce RTX 3070 Mobile / Max-Q
GA104M [GeForce RTX 3070 Mobile / Max-Q]
Nvidia GA104M 3,417,552 222,947 15.33 1 hrs 34 mins
21 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,211,378 218,722 14.68 1 hrs 38 mins
22 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 3,142,811 205,279 15.31 1 hrs 34 mins
23 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 2,512,665 194,275 12.93 1 hrs 51 mins
24 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,480,618 170,298 14.57 1 hrs 39 mins
25 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,421,228 183,376 13.20 1 hrs 49 mins
26 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,370,556 196,375 12.07 1 hrs 59 mins
27 GeForce RTX 2070 Mobile
TU106BM [GeForce RTX 2070 Mobile]
Nvidia TU106BM 2,315,997 195,734 11.83 2 hrs 2 mins
28 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 2,269,665 194,137 11.69 2 hrs 3 mins
29 GeForce RTX 2060 12GB
TU106 [GeForce RTX 2060 12GB]
Nvidia TU106 2,199,218 170,704 12.88 1 hrs 52 mins
30 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,127,786 186,478 11.41 2 hrs 6 mins
31 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,984,470 161,015 12.32 1 hrs 57 mins
32 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,956,169 150,994 12.96 1 hrs 51 mins
33 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,815,453 103,832 17.48 1 hrs 22 mins
34 GeForce RTX 2070 Mobile
TU106M [GeForce RTX 2070 Mobile]
Nvidia TU106M 1,739,391 173,334 10.03 2 hrs 23 mins
35 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,725,673 91,982 18.76 1 hrs 17 mins
36 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,616,739 153,276 10.55 2 hrs 17 mins
37 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,586,388 172,201 9.21 2 hrs 36 mins
38 RTX A2000 12GB
GA106 [RTX A2000 12GB]
Nvidia GA106 1,448,455 165,398 8.76 2 hrs 44 mins
39 Radeon RX 6600/6600 XT/6600M
Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 XT-XL 1,385,157 165,904 8.35 2 hrs 52 mins
40 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,344,465 139,534 9.64 2 hrs 29 mins
41 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,325,107 103,504 12.80 1 hrs 52 mins
42 GeForce RTX 3080 12GB
GA102 [GeForce RTX 3080 12GB]
Nvidia GA102 1,318,471 137,809 9.57 2 hrs 31 mins
43 Radeon PRO W6600
Navi 23 XL [Radeon PRO W6600]
AMD Navi 23 XL 1,247,240 141,464 8.82 2 hrs 43 mins
44 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,125,445 123,882 9.08 2 hrs 39 mins
45 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 1,041,068 127,134 8.19 2 hrs 56 mins
46 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 1,028,945 29,523 34.85 0 hrs 41 mins
47 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 981,008 128,069 7.66 3 hrs 8 mins
48 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 898,691 126,924 7.08 3 hrs 23 mins
49 Quadro M6000
GM200GL [Quadro M6000]
Nvidia GM200GL 888,324 127,620 6.96 3 hrs 27 mins
50 GeForce GTX 1060 6GB
GP104 [GeForce GTX 1060 6GB]
Nvidia GP104 884,440 144,801 6.11 3 hrs 56 mins
51 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 836,771 124,156 6.74 3 hrs 34 mins
52 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 756,704 116,608 6.49 3 hrs 42 mins
53 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 749,634 113,868 6.58 3 hrs 39 mins
54 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 587,472 104,901 5.60 4 hrs 17 mins
55 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 506,213 100,232 5.05 4 hrs 45 mins
56 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 491,261 104,613 4.70 5 hrs 7 mins
57 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 481,193 118,846 4.05 5 hrs 56 mins
58 GeForce GTX Titan Z
GK110 [GeForce GTX Titan Z] 8122
Nvidia GK110 468,376 106,245 4.41 5 hrs 27 mins
59 GeForce GTX 1650 Ti Mobile
TU117M [GeForce GTX 1650 Ti Mobile]
Nvidia TU117M 446,458 99,787 4.47 5 hrs 22 mins
60 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 428,905 113,455 3.78 6 hrs 21 mins
61 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 399,260 39,716 10.05 2 hrs 23 mins
62 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 345,600 24,603 14.05 1 hrs 43 mins
63 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 326,648 101,930 3.20 7 hrs 29 mins
64 Quadro M4000
GM204GL [Quadro M4000]
Nvidia GM204GL 311,996 89,732 3.48 6 hrs 54 mins
65 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 278,404 91,013 3.06 7 hrs 51 mins
66 GeForce MX150
GP107M [GeForce MX150]
Nvidia GP107M 141,551 75,097 1.88 12 hrs 44 mins
67 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 112,732 63,328 1.78 13 hrs 29 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:18
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make