RESEARCH: DUD-E
FOLDING PROJECT #12251 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 144,173
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 targeted by both pesticides and medications. By simulating how drugs bind to this protein, they hope to develop better drugs and make the drug discovery process faster and cheaper.

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: Pharmaceutical
Biotechnology / Drug Discovery

DUD-E is a widely used dataset in drug discovery for benchmarking computational models that predict protein-ligand interactions. It contains diverse proteins with experimentally measured binding affinities to various small molecules.


Protein-Ligand Interactions

The binding interactions between a protein and a ligand (typically a small molecule).

Scientific: Biotechnology
Biochemistry / Molecular Pharmacology

Protein-ligand interactions are crucial for biological processes, including enzyme activity, signal transduction, and drug action. Understanding these interactions is essential for drug discovery and development.


Drug Discovery

The process of identifying and developing new medications.

Process: Healthcare
Pharmaceuticals / Research & Development

Drug discovery is a complex and multi-stage process involving the identification of promising drug candidates, preclinical testing, clinical trials, and regulatory approval.


Alpha Fold

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

Technical: Biotech
Biotechnology / Computational Biology

AlphaFold has revolutionized protein structure prediction by achieving unprecedented accuracy. This breakthrough has significant implications for drug discovery, disease research, and understanding fundamental biological processes.


Simulations

Computer-based models that simulate the behavior of systems over time.

Methodological: Biotechnology
Computational Science / Molecular Dynamics

Simulations are widely used in various fields to study complex phenomena, such as protein folding, drug binding, and chemical reactions. They allow researchers to explore different scenarios and gain insights into system dynamics.


Folding@Home

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

Technical: Research
Biotechnology / Distributed Computing

Folding@Home harnesses the power of thousands of volunteer computers worldwide to perform complex protein simulations. This collaborative effort accelerates research in areas like drug discovery and disease modeling.


Acetylcholinesterase

An enzyme that breaks down acetylcholine, a neurotransmitter.

Technical: Pharmaceuticals
Biochemistry / Neuropharmacology

Acetylcholinesterase plays a vital role in nerve impulse transmission by regulating the levels of acetylcholine in synapses. Its inhibition is used in certain medications for conditions like Alzheimer's disease.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:09
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 12GB
GA102 [GeForce RTX 3080 12GB]
Nvidia GA102 8,512,089 466,053 18.26 1 hrs 19 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 8,110,534 371,230 21.85 1 hrs 6 mins
3 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 7,899,340 382,878 20.63 1 hrs 10 mins
4 RTX A6000
GA102GL [RTX A6000]
Nvidia GA102GL 7,652,396 451,449 16.95 1 hrs 25 mins
5 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,361,543 426,107 17.28 1 hrs 23 mins
6 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 6,221,904 418,126 14.88 1 hrs 37 mins
7 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 6,045,337 416,940 14.50 1 hrs 39 mins
8 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 5,659,719 370,454 15.28 1 hrs 34 mins
9 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,356,990 379,487 14.12 1 hrs 42 mins
10 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,671,508 355,406 13.14 1 hrs 50 mins
11 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 4,161,656 366,494 11.36 2 hrs 7 mins
12 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,791,466 354,943 10.68 2 hrs 15 mins
13 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,537,080 347,131 10.19 2 hrs 21 mins
14 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,288,749 337,931 9.73 2 hrs 28 mins
15 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 3,268,022 198,623 16.45 1 hrs 28 mins
16 GeForce RTX 3070 Mobile / Max-Q
GA104M [GeForce RTX 3070 Mobile / Max-Q]
Nvidia GA104M 3,162,231 335,834 9.42 2 hrs 33 mins
17 GeForce RTX 4060 Max-Q / Mobile
AD107M [GeForce RTX 4060 Max-Q / Mobile]
Nvidia AD107M 3,043,244 332,857 9.14 2 hrs 38 mins
18 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,003,241 258,557 11.62 2 hrs 4 mins
19 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,984,303 328,964 9.07 2 hrs 39 mins
20 GeForce RTX 2080
TU104 [GeForce RTX 2080]
Nvidia TU104 2,870,047 325,198 8.83 2 hrs 43 mins
21 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,692,161 309,116 8.71 2 hrs 45 mins
22 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,639,720 224,806 11.74 2 hrs 3 mins
23 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,573,839 314,371 8.19 2 hrs 56 mins
24 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,262,208 308,395 7.34 3 hrs 16 mins
25 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,138,719 128,335 16.67 1 hrs 26 mins
26 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,095,079 297,890 7.03 3 hrs 25 mins
27 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,041,783 263,214 7.76 3 hrs 6 mins
28 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,982,145 286,303 6.92 3 hrs 28 mins
29 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,940,217 285,447 6.80 3 hrs 32 mins
30 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,746,947 276,965 6.31 3 hrs 48 mins
31 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 1,656,766 270,909 6.12 3 hrs 55 mins
32 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,524,075 264,850 5.75 4 hrs 10 mins
33 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,409,940 258,069 5.46 4 hrs 24 mins
34 GeForce RTX 2070 Mobile / Max-Q Refresh
TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh]
Nvidia TU106M 1,397,656 255,549 5.47 4 hrs 23 mins
35 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,279,021 237,273 5.39 4 hrs 27 mins
36 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,277,488 248,447 5.14 4 hrs 40 mins
37 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 1,262,528 248,382 5.08 4 hrs 43 mins
38 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,179,542 212,187 5.56 4 hrs 19 mins
39 P106-100
GP106 [P106-100]
Nvidia GP106 1,176,359 273,535 4.30 5 hrs 35 mins
40 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,119,823 239,331 4.68 5 hrs 8 mins
41 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 1,086,848 243,326 4.47 5 hrs 22 mins
42 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 1,086,495 68,298 15.91 1 hrs 31 mins
43 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 888,348 224,080 3.96 6 hrs 3 mins
44 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 826,447 213,827 3.87 6 hrs 13 mins
45 GeForce GTX 1060 Mobile
GP106M [GeForce GTX 1060 Mobile]
Nvidia GP106M 773,846 210,769 3.67 6 hrs 32 mins
46 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 561,592 142,700 3.94 6 hrs 6 mins
47 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 517,693 185,869 2.79 8 hrs 37 mins
48 GeForce GTX 1650 Ti Mobile
TU117M [GeForce GTX 1650 Ti Mobile]
Nvidia TU117M 443,652 172,256 2.58 9 hrs 19 mins
49 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 432,000 56,915 7.59 3 hrs 10 mins
50 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 282,251 150,615 1.87 12 hrs 48 mins
51 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 264,143 166,442 1.59 15 hrs 7 mins
52 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 253,195 145,217 1.74 13 hrs 46 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:09
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make