RESEARCH: DUD-E
FOLDING PROJECT #12260 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 64,372
Core: 0x23
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how proteins interact with small molecules (like drugs). They're focusing on a protein called Acetylcholinesterase, which is important for the nervous system and is targeted by both pesticides and medicines. By simulating these interactions, they hope to develop better ways to design new drugs.

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 Enhanced.

Technical: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a dataset used in drug discovery to benchmark computational methods for predicting how well molecules bind to proteins. It contains diverse proteins and a large collection of small molecules with known binding affinities.


Protein-Ligand Interactions

The binding of a protein to a small molecule ligand, often resulting in a biological effect.

Scientific: Pharmaceuticals
Drug Discovery / Biochemistry

Protein-ligand interactions are fundamental to many biological processes. In drug discovery, these interactions are studied to understand how drugs bind to their target proteins and exert their effects.


Drug Discovery

The process of identifying and developing new pharmaceutical drugs.

Industry: Biotechnology
Pharmaceuticals / Medicinal Chemistry

Drug discovery is a complex and lengthy process that involves multiple stages, from identifying potential drug candidates to conducting clinical trials. The goal is to develop safe and effective medications for treating diseases.


Alpha Fold

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

Technical: Biotechnology
Drug Discovery / Computational Biology

AlphaFold is a revolutionary tool that has transformed our ability to predict protein structures. This knowledge is crucial for understanding how proteins function and developing new drugs.


Folding@Home

A distributed computing project that uses volunteered computer processing power to simulate protein folding.

Technical: Biotechnology
Drug Discovery / Computational Biology

Folding@Home harnesses the power of thousands of computers worldwide to simulate the complex process of protein folding. This research helps us understand how proteins work and develop new drugs.


Acetylcholinesterase

An enzyme that breaks down the neurotransmitter acetylcholine.

Technical: Biotechnology
Pharmacology / Neurobiology

Acetylcholinesterase plays a vital role in the nervous system by regulating the transmission of nerve impulses. It is the target of drugs used to treat Alzheimer's disease and other conditions.


Pesticides

Chemicals used to control pests, such as insects, rodents, and weeds.

Technical: Chemicals
Agriculture / Environmental Science

Pesticides are widely used in agriculture to protect crops from damage. However, they can also have negative impacts on the environment and human health.


Neurotransmitter Receptors

Proteins on the surface of nerve cells that bind to neurotransmitters and transmit signals.

Scientific: Biotechnology
Pharmacology / Neuroscience

Neurotransmitter receptors are essential for communication between nerve cells. Understanding how these receptors work is crucial for developing drugs that treat neurological disorders.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:04
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 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 7,132,825 185,120 38.53 0 hrs 37 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,075,986 174,063 40.65 0 hrs 35 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 6,422,644 180,872 35.51 0 hrs 41 mins
4 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,292,982 178,855 35.18 0 hrs 41 mins
5 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,479,835 171,731 31.91 0 hrs 45 mins
6 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 5,416,785 170,832 31.71 0 hrs 45 mins
7 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,968,000 15,816 314.11 0 hrs 5 mins
8 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 4,130,097 156,031 26.47 0 hrs 54 mins
9 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,844,905 150,060 25.62 0 hrs 56 mins
10 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,814,096 150,931 25.27 0 hrs 57 mins
11 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,745,831 150,546 24.88 0 hrs 58 mins
12 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 3,609,415 139,938 25.79 0 hrs 56 mins
13 RTX A4500
GA102GL [RTX A4500]
Nvidia GA102GL 3,574,728 148,557 24.06 0 hrs 60 mins
14 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 3,482,747 146,581 23.76 1 hrs 1 mins
15 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 3,451,773 103,739 33.27 0 hrs 43 mins
16 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 3,349,324 125,649 26.66 0 hrs 54 mins
17 TITAN RTX
TU102 [TITAN RTX] 16310
Nvidia TU102 3,269,577 144,398 22.64 1 hrs 4 mins
18 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,247,568 143,469 22.64 1 hrs 4 mins
19 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,115,061 140,889 22.11 1 hrs 5 mins
20 Radeon RX 7700XT/7800XT
Navi 32 [Radeon RX 7700XT/7800XT]
AMD Navi 32 3,052,795 63,050 48.42 0 hrs 30 mins
21 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,896,964 139,144 20.82 1 hrs 9 mins
22 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,636,805 126,513 20.84 1 hrs 9 mins
23 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,408,719 128,208 18.79 1 hrs 17 mins
24 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 2,398,288 124,240 19.30 1 hrs 15 mins
25 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,236,238 124,980 17.89 1 hrs 20 mins
26 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,184,820 107,018 20.42 1 hrs 11 mins
27 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,173,741 125,970 17.26 1 hrs 23 mins
28 GeForce RTX 2070 Mobile
TU106BM [GeForce RTX 2070 Mobile]
Nvidia TU106BM 2,160,737 125,802 17.18 1 hrs 24 mins
29 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,153,107 116,798 18.43 1 hrs 18 mins
30 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 2,042,537 123,054 16.60 1 hrs 27 mins
31 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,890,518 67,117 28.17 0 hrs 51 mins
32 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,848,946 96,708 19.12 1 hrs 15 mins
33 GeForce RTX 2070 Mobile
TU106M [GeForce RTX 2070 Mobile]
Nvidia TU106M 1,619,693 114,503 14.15 1 hrs 42 mins
34 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,490,634 63,452 23.49 1 hrs 1 mins
35 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,470,145 110,719 13.28 1 hrs 48 mins
36 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 1,417,254 92,531 15.32 1 hrs 34 mins
37 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,387,624 77,287 17.95 1 hrs 20 mins
38 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,384,451 96,260 14.38 1 hrs 40 mins
39 Radeon RX 6600/6600 XT/6600M
Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 XT-XL 1,332,068 101,632 13.11 1 hrs 50 mins
40 RTX A2000 12GB
GA106 [RTX A2000 12GB]
Nvidia GA106 1,319,542 106,846 12.35 1 hrs 57 mins
41 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,273,655 56,791 22.43 1 hrs 4 mins
42 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,263,297 101,142 12.49 1 hrs 55 mins
43 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,250,935 64,003 19.54 1 hrs 14 mins
44 GeForce RTX 3050 Ti Mobile
GA107M [GeForce RTX 3050 Ti Mobile]
Nvidia GA107M 1,185,937 102,857 11.53 2 hrs 5 mins
45 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 1,043,085 87,551 11.91 2 hrs 1 mins
46 Radeon PRO W6600
Navi 23 XL [Radeon PRO W6600]
AMD Navi 23 XL 1,014,898 86,900 11.68 2 hrs 3 mins
47 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 950,400 15,816 60.09 0 hrs 24 mins
48 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 894,126 83,994 10.65 2 hrs 15 mins
49 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 848,128 81,228 10.44 2 hrs 18 mins
50 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 793,855 80,084 9.91 2 hrs 25 mins
51 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 723,597 79,816 9.07 2 hrs 39 mins
52 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 637,105 84,384 7.55 3 hrs 11 mins
53 Radeon RX 6650XT
Navi 23 [Radeon RX 6650XT]
AMD Navi 23 625,032 72,803 8.59 2 hrs 48 mins
54 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 546,223 80,236 6.81 3 hrs 32 mins
55 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 512,304 69,058 7.42 3 hrs 14 mins
56 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 481,240 67,819 7.10 3 hrs 23 mins
57 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 476,445 64,489 7.39 3 hrs 15 mins
58 RTX A500 Laptop GPU
GA107GLM [RTX A500 Laptop GPU]
Nvidia GA107GLM 409,868 63,560 6.45 3 hrs 43 mins
59 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 397,005 70,082 5.66 4 hrs 14 mins
60 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 345,600 15,816 21.85 1 hrs 6 mins
61 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 308,226 65,814 4.68 5 hrs 7 mins
62 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 224,510 52,524 4.27 5 hrs 37 mins
63 GeForce MX150
GP107M [GeForce MX150]
Nvidia GP107M 146,356 51,152 2.86 8 hrs 23 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:04
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 7 5800X3D 8-CORE 16 AMD