RESEARCH: DUD-E
FOLDING PROJECT #12246 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 42,079
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 nerve function and is targeted by pesticides and some medicines. By simulating these interactions, researchers can better understand how drugs work and develop new ones more efficiently.

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

Dataset of proteins and ligands for benchmarking.

Scientific: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a widely used dataset that contains information about protein-ligand interactions. It's helpful for researchers to test and compare different methods for predicting how molecules bind to proteins.


Alpha Fold

An AI system for predicting protein structures.

Scientific: Biotechnology
Drug Discovery / Protein Structure Prediction

AlphaFold is an advanced artificial intelligence system that can accurately predict the 3D shapes of proteins. This is crucial for understanding how proteins work and designing new drugs.


Protein-Ligand Interactions

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

Scientific: Pharmacology
Drug Discovery / Biomolecular Interactions

Protein-ligand interactions are essential for many biological processes. Drugs often work by interfering with these interactions.


Drug Discovery

The process of identifying and developing new drugs.

Technical: Pharmaceuticals
Pharmaceutical Research / Medicinal Chemistry

Drug discovery is a complex and lengthy process that involves finding new molecules with therapeutic potential, testing them in the lab and in animals, and eventually bringing them to market as approved medications.


Pesticides

Chemicals used to control pests.

Technical: Agrochemicals
Agriculture / Pest Control

Pesticides are used to protect crops from insects, weeds, and other organisms that can damage them. They can have both beneficial and harmful effects on the environment.


Acetylcholinesterase

An enzyme that breaks down acetylcholine.

Scientific: Biotechnology
Neuroscience / Neurotransmission

Acetylcholinesterase is a crucial enzyme in the nervous system. It helps regulate nerve impulses by breaking down acetylcholine, a neurotransmitter.


Neurotransmitter

A chemical messenger that transmits signals between neurons.

Scientific: Biotechnology
Neuroscience / Synaptic Transmission

Neurotransmitters are chemicals that allow nerve cells to communicate with each other. They play a vital role in many brain functions, including thinking, feeling, and moving.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:12
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 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 6,375,300 114,441 55.71 0 hrs 26 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,239,975 111,292 56.07 0 hrs 26 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 5,826,160 84,572 68.89 0 hrs 21 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 5,546,436 107,535 51.58 0 hrs 28 mins
5 GeForce RTX 3080 12GB
GA102 [GeForce RTX 3080 12GB]
Nvidia GA102 5,221,751 51,026 102.34 0 hrs 14 mins
6 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,715,950 103,196 45.70 0 hrs 32 mins
7 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,446,494 15,684 283.51 0 hrs 5 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,342,618 100,724 43.11 0 hrs 33 mins
9 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 3,634,516 94,983 38.26 0 hrs 38 mins
10 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,481,780 92,706 37.56 0 hrs 38 mins
11 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,354,713 91,973 36.47 0 hrs 39 mins
12 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,037,806 89,107 34.09 0 hrs 42 mins
13 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 2,965,823 78,647 37.71 0 hrs 38 mins
14 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 2,957,008 85,138 34.73 0 hrs 41 mins
15 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,847,857 87,447 32.57 0 hrs 44 mins
16 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,793,485 72,014 38.79 0 hrs 37 mins
17 RTX A4500
GA102GL [RTX A4500]
Nvidia GA102GL 2,783,990 87,269 31.90 0 hrs 45 mins
18 Radeon RX 7700XT/7800XT
Navi 32 [Radeon RX 7700XT/7800XT]
AMD Navi 32 2,647,864 56,006 47.28 0 hrs 30 mins
19 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,583,326 84,401 30.61 0 hrs 47 mins
20 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,580,374 84,328 30.60 0 hrs 47 mins
21 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,503,569 83,440 30.00 0 hrs 48 mins
22 TITAN RTX
TU102 [TITAN RTX] 16310
Nvidia TU102 2,503,343 83,477 29.99 0 hrs 48 mins
23 GeForce RTX 2080
TU104 [GeForce RTX 2080]
Nvidia TU104 2,407,909 82,781 29.09 0 hrs 50 mins
24 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 2,300,186 79,111 29.08 0 hrs 50 mins
25 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 2,247,672 49,579 45.34 0 hrs 32 mins
26 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,133,138 76,871 27.75 0 hrs 52 mins
27 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 1,988,621 72,274 27.52 0 hrs 52 mins
28 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,956,489 55,249 35.41 0 hrs 41 mins
29 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,915,585 76,600 25.01 0 hrs 58 mins
30 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,887,239 68,016 27.75 0 hrs 52 mins
31 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,839,285 72,081 25.52 0 hrs 56 mins
32 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,830,178 74,671 24.51 0 hrs 59 mins
33 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,828,230 75,654 24.17 0 hrs 60 mins
34 GeForce RTX 2070 Mobile
TU106BM [GeForce RTX 2070 Mobile]
Nvidia TU106BM 1,823,602 75,755 24.07 0 hrs 60 mins
35 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,692,288 43,342 39.04 0 hrs 37 mins
36 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,680,029 64,778 25.94 0 hrs 56 mins
37 GeForce RTX 2070 Mobile
TU106M [GeForce RTX 2070 Mobile]
Nvidia TU106M 1,365,174 66,238 20.61 1 hrs 10 mins
38 Radeon RX 6650XT
Navi 23 [Radeon RX 6650XT]
AMD Navi 23 1,354,657 60,732 22.31 1 hrs 5 mins
39 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,326,874 67,382 19.69 1 hrs 13 mins
40 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,311,778 67,854 19.33 1 hrs 14 mins
41 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,296,000 6,698 193.49 0 hrs 7 mins
42 Radeon RX 7700S/7600(S)
Navi 33 [Radeon RX 7700S/7600(S)]
AMD Navi 33 1,294,723 66,113 19.58 1 hrs 14 mins
43 RTX A2000 12GB
GA106 [RTX A2000 12GB]
Nvidia GA106 1,263,957 66,489 19.01 1 hrs 16 mins
44 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,257,885 46,736 26.91 0 hrs 54 mins
45 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,220,082 36,390 33.53 0 hrs 43 mins
46 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,196,006 26,206 45.64 0 hrs 32 mins
47 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 1,102,321 53,376 20.65 1 hrs 10 mins
48 Radeon RX 6600/6600 XT/6600M
Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 XT-XL 1,081,956 59,088 18.31 1 hrs 19 mins
49 Radeon PRO W6600
Navi 23 XL [Radeon PRO W6600]
AMD Navi 23 XL 936,211 53,220 17.59 1 hrs 22 mins
50 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 933,120 8,037 116.10 0 hrs 12 mins
51 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 917,359 46,876 19.57 1 hrs 14 mins
52 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 910,439 52,547 17.33 1 hrs 23 mins
53 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 881,026 52,268 16.86 1 hrs 25 mins
54 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 785,976 50,648 15.52 1 hrs 33 mins
55 Quadro M6000
GM200GL [Quadro M6000]
Nvidia GM200GL 728,270 49,529 14.70 1 hrs 38 mins
56 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 632,452 46,801 13.51 1 hrs 47 mins
57 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 600,707 43,862 13.70 1 hrs 45 mins
58 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 535,131 49,225 10.87 2 hrs 12 mins
59 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 471,398 42,703 11.04 2 hrs 10 mins
60 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 464,857 46,063 10.09 2 hrs 23 mins
61 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 410,078 44,564 9.20 2 hrs 36 mins
62 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 370,285 6,698 55.28 0 hrs 26 mins
63 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 283,041 18,558 15.25 1 hrs 34 mins
64 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 261,468 38,180 6.85 3 hrs 30 mins
65 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 226,030 33,377 6.77 3 hrs 33 mins
66 GeForce MX150
GP107M [GeForce MX150]
Nvidia GP107M 126,997 29,344 4.33 5 hrs 33 mins
67 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 86,400 6,698 12.90 1 hrs 52 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:12
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