RESEARCH: DUD-E
FOLDING PROJECT #12213 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 42,079
Core: 0x22
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 medicines. By creating accurate simulations, they hope to improve drug discovery methods and develop new treatments.

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.

protein

A large biomolecule composed of amino acids.

Scientific: Pharmaceutical
Biotechnology / Structural Biology

Proteins are the building blocks of life. They perform a wide variety of functions in the body, including transporting molecules, catalyzing chemical reactions, and providing structural support.


ligand

A molecule that binds to a specific receptor or protein.

Scientific: Pharmaceutical
Biotechnology / Drug Discovery

Ligands are molecules that interact with other molecules, like proteins. They can be drugs, hormones, or even small parts of other molecules.


DUD-E

Directory of Useful Decoys for Enhanced Docking.

Acronym: Pharmaceutical
Biotechnology / Drug Discovery

DUD-E is a database of protein-ligand complexes used to evaluate and improve docking algorithms in drug discovery.


AlphaFold

An artificial intelligence system for protein structure prediction.

Acronym: Pharmaceutical
Biotechnology / Structural Biology

AlphaFold is a powerful tool that uses machine learning to predict the 3D structure of proteins from their amino acid sequence.


drug discovery

The process of identifying and developing new drugs.

Technical: Pharmaceutical
Biotechnology / Pharmaceutical Research

Drug discovery is a complex process that involves finding new molecules with therapeutic potential and testing them for safety and efficacy.


simulations

Computer models that mimic real-world processes.

Technical: Pharmaceutical
Biotechnology / Computational Biology

Simulations are used in drug discovery to study how molecules interact with each other and with biological systems.


Folding@Home

A distributed computing project that uses volunteers' computers to perform protein folding simulations.

Acronym: Pharmaceutical
Biotechnology / Computational Biology

Folding@Home is a citizen science project that harnesses the power of many computers to simulate protein folding and other biological processes.


Acetylcholinesterase

An enzyme that breaks down the neurotransmitter acetylcholine.

Scientific: Pharmaceutical
Biotechnology / Neuropharmacology

Acetylcholinesterase is a crucial enzyme in the nervous system that regulates nerve impulses by breaking down acetylcholine.


neurotransmitter

A chemical messenger that transmits signals between nerve cells.

Scientific: Pharmaceutical
Biotechnology / Neuropharmacology

Neurotransmitters are chemicals that allow communication between neurons. They play a vital role in various brain functions, including movement, thought, and emotion.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:30
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 5,713,722 96,781 59.04 0 hrs 24 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 4,971,134 92,918 53.50 0 hrs 27 mins
3 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 2,794,206 77,550 36.03 0 hrs 40 mins
4 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 2,656,099 74,415 35.69 0 hrs 40 mins
5 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,128,862 69,918 30.45 0 hrs 47 mins
6 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 1,985,035 69,267 28.66 0 hrs 50 mins
7 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,879,831 67,287 27.94 0 hrs 52 mins
8 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,558,338 63,456 24.56 0 hrs 59 mins
9 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 1,454,657 64,361 22.60 1 hrs 4 mins
10 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,231,682 58,924 20.90 1 hrs 9 mins
11 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,179,639 57,887 20.38 1 hrs 11 mins
12 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,125,959 56,486 19.93 1 hrs 12 mins
13 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,019,473 55,244 18.45 1 hrs 18 mins
14 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 954,965 53,939 17.70 1 hrs 21 mins
15 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 910,103 52,972 17.18 1 hrs 24 mins
16 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 679,687 48,461 14.03 1 hrs 43 mins
17 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 595,592 46,753 12.74 1 hrs 53 mins
18 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 395,870 35,646 11.11 2 hrs 10 mins
19 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 356,898 38,957 9.16 2 hrs 37 mins
20 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 347,725 38,641 9.00 2 hrs 40 mins
21 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 287,802 28,986 9.93 2 hrs 25 mins
22 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 152,741 29,721 5.14 4 hrs 40 mins
23 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 137,330 28,319 4.85 4 hrs 57 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:30
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make