RESEARCH: DUD-E
FOLDING PROJECT #12201 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 95,268
Core: 0x22
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 the nervous system and is targeted by pesticides and medicines. By simulating these interactions accurately, 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. 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.
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RELATED TERMS GLOSSARY AI BETA

Note: Glossary items are a high level summary and may not be 100% accurate.

DUD-E

Directory of Useful Drugs - experimental data

Acronym: Biotechnology
Drug Discovery / Benchmarking

DUD-E is a collection of protein and small molecule pairs used to evaluate the accuracy of computer models predicting how well molecules bind to proteins. It's important for drug discovery because it helps researchers test and improve their methods.


protein-ligand interactions

The binding of a protein and a small molecule (ligand)

Scientific: Pharmaceutical
Drug Discovery / Structural Biology

Protein-ligand interactions are essential for many biological processes, including how drugs work. When a drug binds to a specific protein, it can change the protein's function. This is what makes drugs effective in treating diseases.


Alpha Fold

Artificial intelligence system for protein structure prediction

Acronym: Pharmaceutical
Biotechnology / Protein Structure Prediction

AlphaFold is a powerful computer program that can predict the 3D shape of proteins. This is incredibly useful for drug discovery because it allows researchers to see how potential drugs might interact with their target proteins.


drug discovery

The process of identifying and developing new drugs

Technical: Healthcare
Pharmaceutical / Research and Development

Drug discovery is a complex and lengthy process that involves many steps, from identifying potential drug targets to testing and manufacturing the final product. It's a crucial area of research because it leads to new treatments for diseases.


pesticide

A chemical used to kill pests, such as insects, weeds, or fungi

Technical: Chemical Manufacturing
Agriculture / Pest Control

Pesticides are chemicals designed to eliminate unwanted organisms. They're widely used in agriculture to protect crops from damage but can also have negative impacts on the environment and human health.


Acetylcholinesterase (AChE)

An enzyme that breaks down acetylcholine, a neurotransmitter

Scientific: Biotechnology
Pharmacology / Neurotransmission

Acetylcholinesterase is an important enzyme in the nervous system. It breaks down acetylcholine, a chemical messenger that transmits signals between nerve cells. Some drugs work by inhibiting acetylcholinesterase, which increases the levels of acetylcholine in the brain.


Torpedo Californica

The scientific name for the Pacific Electric Ray

Scientific: Life Sciences
Biomedical Research / Animal Models

Torpedo Californica is a species of ray known for its electric organs. Researchers have studied this animal extensively because it provides valuable insights into the nervous system and neurotransmission.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:36
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 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 12,991,369 314,295 41.33 0 hrs 35 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 9,351,851 284,140 32.91 0 hrs 44 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,756,543 269,809 28.75 0 hrs 50 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,383,828 265,100 27.85 0 hrs 52 mins
5 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,633,265 256,450 25.87 0 hrs 56 mins
6 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,667,574 228,040 20.47 1 hrs 10 mins
7 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,438,460 224,277 19.79 1 hrs 13 mins
8 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 4,394,002 222,873 19.72 1 hrs 13 mins
9 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 3,954,609 215,419 18.36 1 hrs 18 mins
10 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,752,969 212,873 17.63 1 hrs 22 mins
11 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,293,317 203,248 16.20 1 hrs 29 mins
12 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,285,191 203,213 16.17 1 hrs 29 mins
13 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,054,418 198,583 15.38 1 hrs 34 mins
14 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,963,444 195,992 15.12 1 hrs 35 mins
15 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,835,660 193,979 14.62 1 hrs 39 mins
16 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,368,241 181,455 13.05 1 hrs 50 mins
17 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,341,648 181,846 12.88 1 hrs 52 mins
18 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,114,555 171,703 12.32 1 hrs 57 mins
19 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,106,875 175,423 12.01 1 hrs 60 mins
20 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,740,312 163,935 10.62 2 hrs 16 mins
21 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,674,478 161,561 10.36 2 hrs 19 mins
22 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,341,507 149,944 8.95 2 hrs 41 mins
23 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,169,616 143,460 8.15 2 hrs 57 mins
24 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,117,676 141,094 7.92 3 hrs 2 mins
25 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,116,478 122,771 9.09 2 hrs 38 mins
26 Geforce RTX 3050
GA106 [Geforce RTX 3050]
Nvidia GA106 927,906 140,006 6.63 3 hrs 37 mins
27 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 878,315 130,705 6.72 3 hrs 34 mins
28 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 781,652 127,351 6.14 3 hrs 55 mins
29 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 683,664 121,401 5.63 4 hrs 16 mins
30 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 588,927 114,696 5.13 4 hrs 40 mins
31 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 503,117 61,903 8.13 2 hrs 57 mins
32 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 446,290 104,541 4.27 5 hrs 37 mins
33 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 392,356 99,765 3.93 6 hrs 6 mins
34 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 296,551 97,263 3.05 7 hrs 52 mins
35 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 235,010 84,444 2.78 8 hrs 37 mins
36 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 59,836 54,549 1.10 21 hrs 53 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:36
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make