RESEARCH: DUD-E
FOLDING PROJECT #12242 PROFILE
PROJECT TEAM
Manager(s): Louis SmithInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 120,837Core: 0x23
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 using a special dataset called DUD-E, which has lots of different proteins and their known interactions. The goal is to improve our understanding of how drugs work and make the drug discovery process faster and more efficient.
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
proteins
large biomolecules essential for various bodily functions.
Proteins are complex molecules found in all living things. They play many important roles in the body, such as building and repairing tissues, transporting substances, and catalyzing chemical reactions.
DUD-E
Directory of Useful Decoys - Enhanced
DUD-E is a database of protein-ligand interactions used to evaluate and benchmark computational methods for predicting binding affinities. It contains diverse proteins with various ligands, providing a comprehensive platform for assessing the accuracy and efficiency of drug design algorithms.
Alpha Fold
an artificial intelligence system for predicting protein structures
AlphaFold is a groundbreaking AI system developed by DeepMind that can accurately predict the 3D structure of proteins. This has revolutionized our understanding of protein function and has immense implications for drug discovery, disease research, and biotechnology.
drug discovery
the process of identifying and developing new medications
Drug discovery is a complex and multi-stage process that involves identifying promising drug candidates, testing their efficacy and safety in preclinical studies, and ultimately bringing them to market for patient use. It requires extensive research, collaboration between scientists and clinicians, and rigorous regulatory oversight.
target proteins
proteins involved in a disease process that are targeted by drugs
Target proteins are specific molecules within the body that are implicated in disease pathways. Drugs are designed to interact with these target proteins, modulating their function and ultimately treating the underlying condition.
Folding@Home
a distributed computing project for protein folding simulations
Folding@Home is a global initiative that utilizes donated computing power to perform complex simulations of protein folding. These simulations provide valuable insights into the structure and function of proteins, aiding in drug discovery, disease research, and our understanding of biological systems.
acetylcholinesterase
an enzyme that breaks down acetylcholine in the nervous system
Acetylcholinesterase is an enzyme crucial for neurotransmission. It catalyzes the breakdown of acetylcholine, a neurotransmitter involved in muscle contraction and nerve impulse transmission. Inhibitors of acetylcholinesterase are used to treat Alzheimer's disease and other neurological conditions.
neurotransmitter
a chemical messenger that transmits signals between nerve cells
Neurotransmitters are specialized chemicals that allow communication between neurons in the brain and throughout the nervous system. They play a vital role in regulating mood, behavior, cognition, and bodily functions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:14|
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 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,365,132 | 311,252 | 23.66 | 1 hrs 1 mins |
| 2 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 6,949,344 | 249,028 | 27.91 | 0 hrs 52 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,311,258 | 332,896 | 18.96 | 1 hrs 16 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,789,628 | 306,028 | 18.92 | 1 hrs 16 mins |
| 5 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,425,192 | 308,812 | 17.57 | 1 hrs 22 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,409,985 | 319,124 | 16.95 | 1 hrs 25 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,342,455 | 295,495 | 14.70 | 1 hrs 38 mins |
| 8 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 4,243,135 | 288,017 | 14.73 | 1 hrs 38 mins |
| 9 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 4,118,510 | 278,131 | 14.81 | 1 hrs 37 mins |
| 10 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 3,564,535 | 279,551 | 12.75 | 1 hrs 53 mins |
| 11 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,434,802 | 275,714 | 12.46 | 1 hrs 56 mins |
| 12 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,262,009 | 270,515 | 12.06 | 1 hrs 59 mins |
| 13 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,208,884 | 270,631 | 11.86 | 2 hrs 1 mins |
| 14 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 3,044,346 | 189,613 | 16.06 | 1 hrs 30 mins |
| 15 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,877,857 | 260,211 | 11.06 | 2 hrs 10 mins |
| 16 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 2,704,955 | 255,106 | 10.60 | 2 hrs 16 mins |
| 17 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,685,003 | 257,828 | 10.41 | 2 hrs 18 mins |
| 18 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,549,826 | 144,284 | 17.67 | 1 hrs 21 mins |
| 19 | GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q] |
Nvidia | GA104M | 2,485,623 | 244,384 | 10.17 | 2 hrs 22 mins |
|
|
|||||||
| 20 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,438,527 | 226,915 | 10.75 | 2 hrs 14 mins |
| 21 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,430,476 | 193,497 | 12.56 | 1 hrs 55 mins |
| 22 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,207,682 | 238,443 | 9.26 | 2 hrs 36 mins |
| 23 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,202,713 | 239,019 | 9.22 | 2 hrs 36 mins |
| 24 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 2,189,879 | 236,905 | 9.24 | 2 hrs 36 mins |
| 25 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,150,098 | 92,560 | 23.23 | 1 hrs 2 mins |
| 26 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,064,545 | 221,807 | 9.31 | 2 hrs 35 mins |
| 27 | GeForce RTX 4050 Max-Q / Mobile AD107M [GeForce RTX 4050 Max-Q / Mobile] |
Nvidia | AD107M | 2,012,106 | 234,299 | 8.59 | 2 hrs 48 mins |
| 28 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 1,999,512 | 129,287 | 15.47 | 1 hrs 33 mins |
| 29 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,974,228 | 227,178 | 8.69 | 2 hrs 46 mins |
| 30 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,906,829 | 228,966 | 8.33 | 2 hrs 53 mins |
| 31 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,881,596 | 226,727 | 8.30 | 2 hrs 54 mins |
| 32 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,850,303 | 194,261 | 9.52 | 2 hrs 31 mins |
| 33 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,824,096 | 223,188 | 8.17 | 2 hrs 56 mins |
| 34 | Radeon RX 6600/6600 XT/6600M Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 XT-XL | 1,546,400 | 212,748 | 7.27 | 3 hrs 18 mins |
| 35 | Radeon PRO W6600 Navi 23 XL [Radeon PRO W6600] |
AMD | Navi 23 XL | 1,311,958 | 201,707 | 6.50 | 3 hrs 41 mins |
| 36 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,284,940 | 200,736 | 6.40 | 3 hrs 45 mins |
| 37 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,203,077 | 196,135 | 6.13 | 3 hrs 55 mins |
| 38 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,194,398 | 194,772 | 6.13 | 3 hrs 55 mins |
| 39 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,109,856 | 187,477 | 5.92 | 4 hrs 3 mins |
|
|
|||||||
| 40 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,025,871 | 186,657 | 5.50 | 4 hrs 22 mins |
| 41 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 1,023,023 | 184,980 | 5.53 | 4 hrs 20 mins |
| 42 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 870,256 | 172,556 | 5.04 | 4 hrs 46 mins |
| 43 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 823,267 | 172,220 | 4.78 | 5 hrs 1 mins |
| 44 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 602,194 | 156,674 | 3.84 | 6 hrs 15 mins |
| 45 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 426,240 | 40,819 | 10.44 | 2 hrs 18 mins |
| 46 | GeForce GTX Titan Black GK110 [GeForce GTX Titan Black] 5121 |
Nvidia | GK110 | 387,506 | 146,255 | 2.65 | 9 hrs 3 mins |
| 47 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 274,986 | 132,477 | 2.08 | 11 hrs 34 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:14|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|