RESEARCH: DUD-E
FOLDING PROJECT #12236 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 76,500
Core: 0x23
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how proteins interact with small molecules. These interactions are important for drug development. The team is focusing on a set of well-studied proteins, like Acetylcholinesterase, which plays a role in the nervous system. By simulating these interactions, researchers hope to improve methods for designing 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

Database of protein-ligand interactions

Acronym: Pharmaceutical
Drug Discovery / Benchmarking

DUD-E is a widely used dataset for evaluating methods that predict how proteins and small molecules interact. It contains diverse proteins, each bound to many small molecules with experimentally measured binding abilities.


Protein-Ligand Interactions

Interactions between proteins and small molecules

Technical: Biotechnology
Drug Discovery / Molecular Modeling

Protein-ligand interactions are essential for many biological processes, including drug action. They occur when a protein binds to a small molecule (ligand), often affecting the protein's function.


Drug Discovery

The process of identifying and developing new medications

Process: Biotechnology, Pharmaceutical
Pharmaceuticals / Research & Development

Drug discovery is a complex multi-step process involving identifying potential drug targets, screening compounds, testing for efficacy and safety, and ultimately bringing a new medication to market.


Alpha Fold

An AI system for predicting protein structures

Software: Biotechnology, AI
Bioinformatics / Protein Structure Prediction

AlphaFold is a groundbreaking artificial intelligence system developed by DeepMind that can accurately predict the 3D structure of proteins from their amino acid sequences. This has revolutionized structural biology and drug discovery.


Protein Simulation

Computer-based modeling of protein behavior

Method: Biotechnology, Computational Science
Biophysics / Computational Biology

Protein simulations use computer algorithms to model the movements and interactions of atoms within a protein. This allows researchers to study protein function, folding, and dynamics in detail.


Acetylcholinesterase

Enzyme that breaks down acetylcholine

Enzyme: Pharmaceutical, Neuroscience
Neurobiology / Neuromuscular Transmission

Acetylcholinesterase is an enzyme crucial for nerve impulse transmission. It rapidly breaks down the neurotransmitter acetylcholine in the synaptic cleft, ensuring clear communication between nerve cells.


Pesticide

Substance used to control pests

Chemical: Agrochemicals
Agriculture / Pest Control

Pesticides are chemicals designed to kill or repel unwanted organisms like insects, weeds, and fungi. They are widely used in agriculture to protect crops and increase yields.


Neurotransmitter Receptor

Protein that binds to neurotransmitters

Protein: Pharmaceutical, Neuroscience
Neuroscience / Synaptic Transmission

Neurotransmitter receptors are specialized proteins located on the surface of nerve cells. They bind to specific neurotransmitters, triggering a cascade of events that ultimately transmit signals between neurons.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:17
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 8,923,875 192,157 46.44 0 hrs 31 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 8,170,605 203,585 40.13 0 hrs 36 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 7,534,486 235,566 31.98 0 hrs 45 mins
4 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,501,394 236,286 31.75 0 hrs 45 mins
5 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 6,294,218 222,957 28.23 0 hrs 51 mins
6 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 6,059,876 219,477 27.61 0 hrs 52 mins
7 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 5,349,260 59,930 89.26 0 hrs 16 mins
8 Radeon RX 6950 XT
Navi 21 [Radeon RX 6950 XT]
AMD Navi 21 5,047,310 204,242 24.71 0 hrs 58 mins
9 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,530,752 197,219 22.97 1 hrs 3 mins
10 TITAN RTX
TU102 [TITAN RTX] 16310
Nvidia TU102 4,484,712 195,915 22.89 1 hrs 3 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 4,238,148 190,424 22.26 1 hrs 5 mins
12 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 4,173,369 186,233 22.41 1 hrs 4 mins
13 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 4,081,672 193,245 21.12 1 hrs 8 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,996,056 190,326 21.00 1 hrs 9 mins
15 RTX A4500
GA102GL [RTX A4500]
Nvidia GA102GL 3,924,922 189,300 20.73 1 hrs 9 mins
16 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 3,695,550 137,290 26.92 0 hrs 53 mins
17 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,693,600 21,771 169.66 0 hrs 8 mins
18 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 3,573,070 184,544 19.36 1 hrs 14 mins
19 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,484,854 117,763 29.59 0 hrs 49 mins
20 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,434,371 181,333 18.94 1 hrs 16 mins
21 Radeon RX 7700XT/7800XT
Navi 32 [Radeon RX 7700XT/7800XT]
AMD Navi 32 3,086,668 115,234 26.79 0 hrs 54 mins
22 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,808,420 155,918 18.01 1 hrs 20 mins
23 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 2,778,984 153,851 18.06 1 hrs 20 mins
24 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 2,609,837 162,679 16.04 1 hrs 30 mins
25 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,523,869 163,214 15.46 1 hrs 33 mins
26 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,435,667 138,608 17.57 1 hrs 22 mins
27 GeForce RTX 2070 Mobile
TU106BM [GeForce RTX 2070 Mobile]
Nvidia TU106BM 2,301,923 160,421 14.35 1 hrs 40 mins
28 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,231,132 150,628 14.81 1 hrs 37 mins
29 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,183,746 139,033 15.71 1 hrs 32 mins
30 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,960,977 136,520 14.36 1 hrs 40 mins
31 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,945,646 151,170 12.87 1 hrs 52 mins
32 GeForce RTX 2070 Mobile
TU106M [GeForce RTX 2070 Mobile]
Nvidia TU106M 1,832,508 147,424 12.43 1 hrs 56 mins
33 Radeon RX 6650XT
Navi 23 [Radeon RX 6650XT]
AMD Navi 23 1,626,910 125,657 12.95 1 hrs 51 mins
34 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,609,438 85,931 18.73 1 hrs 17 mins
35 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,608,358 140,698 11.43 2 hrs 6 mins
36 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,572,139 83,221 18.89 1 hrs 16 mins
37 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,496,297 75,281 19.88 1 hrs 12 mins
38 Geforce RTX 3050
GA106 [Geforce RTX 3050]
Nvidia GA106 1,489,588 136,605 10.90 2 hrs 12 mins
39 RTX A2000 12GB
GA106 [RTX A2000 12GB]
Nvidia GA106 1,404,773 134,674 10.43 2 hrs 18 mins
40 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,376,213 81,480 16.89 1 hrs 25 mins
41 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,307,949 91,664 14.27 1 hrs 41 mins
42 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,220,063 109,034 11.19 2 hrs 9 mins
43 Radeon RX 6600/6600 XT/6600M
Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 XT-XL 1,129,727 106,164 10.64 2 hrs 15 mins
44 Radeon PRO W6600
Navi 23 XL [Radeon PRO W6600]
AMD Navi 23 XL 1,099,714 110,193 9.98 2 hrs 24 mins
45 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,061,689 110,050 9.65 2 hrs 29 mins
46 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 1,034,181 21,771 47.50 0 hrs 30 mins
47 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 833,187 100,723 8.27 2 hrs 54 mins
48 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 815,026 113,038 7.21 3 hrs 20 mins
49 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 777,656 97,523 7.97 3 hrs 1 mins
50 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 647,556 88,417 7.32 3 hrs 17 mins
51 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 607,842 61,909 9.82 2 hrs 27 mins
52 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 551,187 86,831 6.35 3 hrs 47 mins
53 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 520,116 100,715 5.16 4 hrs 39 mins
54 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 498,178 85,304 5.84 4 hrs 7 mins
55 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 408,637 74,857 5.46 4 hrs 24 mins
56 GeForce GTX Titan Z
GK110 [GeForce GTX Titan Z] 8122
Nvidia GK110 382,691 80,736 4.74 5 hrs 4 mins
57 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 370,800 19,352 19.16 1 hrs 15 mins
58 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 363,873 84,231 4.32 5 hrs 33 mins
59 RTX A500 Laptop GPU
GA107GLM [RTX A500 Laptop GPU]
Nvidia GA107GLM 327,091 60,696 5.39 4 hrs 27 mins
60 GeForce GTX 1050 3 GB Max-Q
GP107M [GeForce GTX 1050 3 GB Max-Q]
Nvidia GP107M 298,650 71,593 4.17 5 hrs 45 mins
61 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 251,436 69,813 3.60 6 hrs 40 mins
62 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 234,789 65,323 3.59 6 hrs 41 mins
63 GeForce GTX 960M
GM107 [GeForce GTX 960M] 1439
Nvidia GM107 141,852 56,102 2.53 9 hrs 30 mins
64 GeForce MX150
GP107M [GeForce MX150]
Nvidia GP107M 113,911 51,833 2.20 10 hrs 55 mins
65 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 86,400 19,928 4.34 5 hrs 32 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:17
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make