RESEARCH: DUD-E
FOLDING PROJECT #12228 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 74,289Core: 0x22
Status: Public
Related Projects
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 used in some medications. By creating accurate simulations, they hope to improve drug development methods.
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. 12201 - 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. 12202 - 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. 12203 - 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. 12204 - 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. 12205 - 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. 12206 - 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. 12207 - 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. 12208 - 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. 12209 - 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. 12210 - 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. 12211 - 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.
12212 - 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.
12213 - 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. 12214 - 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. 12215 - 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. 12216 - 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. 12217 - 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. 12218 - 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. 12219 - 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. 12220 - 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. 12221 - 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.
12222 - 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. 12223 - 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.
12224 - 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. 12225 - 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. 12226 - 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. 12227 - RXRA: Retinoid x receptor alpha is a nuclear receptor that binds retinoic acid, causing transcription of a large number of genes.
12228 - 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. 12229 - 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.
12230 - 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. 12231 - 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. 12232 - 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
DUD-E
Directory of Useful Decoys - Expanded
DUD-E is a widely used benchmark dataset for evaluating protein-ligand interaction prediction methods. It contains diverse proteins with experimentally measured binding affinities to various small molecules.
Protein-Ligand Interactions
The binding of a protein and a small molecule (ligand) together.
Protein-ligand interactions are essential for many biological processes, including drug action. They occur when a protein binds to a small molecule, such as a drug or a natural product. Understanding these interactions is crucial for developing new drugs and therapies.
Alpha Fold
An AI system for predicting protein structures.
AlphaFold is a groundbreaking artificial intelligence system developed by DeepMind that can accurately predict the 3D structures of proteins. This has revolutionized our understanding of protein function and has wide-ranging applications in drug discovery, disease research, and biotechnology.
Drug Discovery
The process of identifying and developing new drugs.
Drug discovery is a complex and lengthy process that involves identifying potential drug candidates, evaluating their efficacy and safety, and ultimately bringing them to market. It requires expertise in chemistry, biology, medicine, and other disciplines.
Acetylcholinesterase
An enzyme that breaks down acetylcholine.
Acetylcholinesterase is a crucial enzyme involved in neurotransmission. It breaks down acetylcholine, a neurotransmitter that transmits signals between nerve cells and muscles. Inhibitors of acetylcholinesterase are used as drugs to treat Alzheimer's disease and myasthenia gravis.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:22|
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 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 6,829,014 | 193,888 | 35.22 | 0 hrs 41 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,701,155 | 191,615 | 34.97 | 0 hrs 41 mins |
| 3 | Radeon RX 7900XT/XTX Navi 31 [Radeon RX 7900XT/XTX] |
AMD | Navi 31 | 4,291,026 | 163,503 | 26.24 | 0 hrs 55 mins |
| 4 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 3,539,348 | 155,237 | 22.80 | 1 hrs 3 mins |
| 5 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 3,025,797 | 145,693 | 20.77 | 1 hrs 9 mins |
| 6 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 3,007,027 | 148,909 | 20.19 | 1 hrs 11 mins |
| 7 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,083,214 | 125,261 | 16.63 | 1 hrs 27 mins |
| 8 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,078,493 | 129,955 | 15.99 | 1 hrs 30 mins |
| 9 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,016,020 | 128,724 | 15.66 | 1 hrs 32 mins |
| 10 | Quadro RTX 5000 Mobile / Max-Q TU104GLM [Quadro RTX 5000 Mobile / Max-Q] |
Nvidia | TU104GLM | 1,710,162 | 121,274 | 14.10 | 1 hrs 42 mins |
| 11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,491,209 | 116,146 | 12.84 | 1 hrs 52 mins |
| 12 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,173,076 | 105,636 | 11.10 | 2 hrs 10 mins |
| 13 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,110,495 | 105,576 | 10.52 | 2 hrs 17 mins |
| 14 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,073,708 | 104,038 | 10.32 | 2 hrs 20 mins |
| 15 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 920,599 | 81,436 | 11.30 | 2 hrs 7 mins |
| 16 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 827,801 | 96,284 | 8.60 | 2 hrs 47 mins |
| 17 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 774,676 | 93,528 | 8.28 | 2 hrs 54 mins |
| 18 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 665,961 | 89,572 | 7.43 | 3 hrs 14 mins |
| 19 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 539,138 | 79,620 | 6.77 | 3 hrs 33 mins |
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| 20 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 441,570 | 76,407 | 5.78 | 4 hrs 9 mins |
| 21 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 384,418 | 73,989 | 5.20 | 4 hrs 37 mins |
| 22 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 224,150 | 61,937 | 3.62 | 6 hrs 38 mins |
| 23 | Quadro P600 GP107GL [Quadro P600] |
Nvidia | GP107GL | 75,609 | 49,626 | 1.52 | 15 hrs 45 mins |
| 24 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 62,627 | 39,320 | 1.59 | 15 hrs 4 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:22|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|