RESEARCH: DUD-E
FOLDING PROJECT #12210 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 77,173
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how proteins interact with small molecules, which is important for drug discovery. They're focusing on a protein called Acetylcholinesterase, which plays a role in the nervous system and is targeted by both pesticides and medications.

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 Decoys - Enhanced

Technical: Biotechnology
Drug Discovery / Protein-Ligand Interactions

DUD-E is a benchmark dataset used to evaluate computational methods for predicting protein-ligand interactions. It contains diverse proteins and their experimentally determined binding affinities to various small molecules.


Alpha Fold

An artificial intelligence system that predicts the 3D structure of proteins from their amino acid sequence.

Scientific: Biotechnology
Drug Discovery / Protein Structure Prediction

AlphaFold is a groundbreaking AI program developed by DeepMind. It can accurately predict the 3D shape of proteins based on their amino acid sequences. This has significant implications for drug discovery, as it allows researchers to understand how proteins interact with drugs and design more effective therapies.


Drug Discovery

The process of identifying and developing new medications.

Industry: Biotechnology
Pharmaceutical / Therapeutic Development

Drug discovery is a complex and lengthy process that involves identifying potential drug candidates, testing them in laboratory and animal models, and ultimately conducting clinical trials to evaluate their safety and efficacy in humans.


Protein-Ligand Interactions

The binding of a small molecule (ligand) to a protein.

Scientific: Pharmaceutical
Biochemistry / Drug Design

Protein-ligand interactions are essential for many biological processes, including drug action. When a drug binds to a specific protein, it can alter the protein's function and produce a therapeutic effect.


Pesticide

A substance used to kill pests.

Technical: Chemicals
Agriculture / Pest Control

Pesticides are chemicals designed to control unwanted organisms like insects, weeds, and fungi. They are widely used in agriculture to protect crops from damage.


Acetylcholinesterase

An enzyme that breaks down the neurotransmitter acetylcholine.

Scientific: Biotechnology
Neurobiology / Enzyme

Acetylcholinesterase is a crucial enzyme in the nervous system. It regulates the activity of acetylcholine, a neurotransmitter involved in muscle movement, memory, and learning.


Neurotransmitter

A chemical messenger that transmits signals between nerve cells.

Scientific: Biotechnology
Neurobiology / Signal Transduction

Neurotransmitters are essential for communication between nerve cells in the brain and throughout the body. They play a vital role in regulating mood, behavior, thought, and bodily functions.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:31
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 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 11,152,553 234,524 47.55 0 hrs 30 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 10,130,409 226,750 44.68 0 hrs 32 mins
3 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 7,776,507 209,774 37.07 0 hrs 39 mins
4 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 7,544,311 207,382 36.38 0 hrs 40 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,230,669 193,472 32.20 0 hrs 45 mins
6 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,921,170 188,727 31.37 0 hrs 46 mins
7 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,827,633 191,497 30.43 0 hrs 47 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,541,759 175,964 25.81 0 hrs 56 mins
9 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 4,031,903 167,782 24.03 0 hrs 60 mins
10 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,861,140 165,359 23.35 1 hrs 2 mins
11 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,621,878 163,231 22.19 1 hrs 5 mins
12 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 3,141,522 155,569 20.19 1 hrs 11 mins
13 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,045,056 154,264 19.74 1 hrs 13 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,898,676 150,674 19.24 1 hrs 15 mins
15 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,830,475 150,562 18.80 1 hrs 17 mins
16 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,681,454 148,040 18.11 1 hrs 20 mins
17 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,574,921 145,502 17.70 1 hrs 21 mins
18 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,515,703 143,993 17.47 1 hrs 22 mins
19 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,481,115 143,750 17.26 1 hrs 23 mins
20 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,426,791 139,703 17.37 1 hrs 23 mins
21 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,377,788 141,877 16.76 1 hrs 26 mins
22 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,332,374 141,463 16.49 1 hrs 27 mins
23 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,299,436 140,990 16.31 1 hrs 28 mins
24 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,224,689 138,596 16.05 1 hrs 30 mins
25 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,037,652 134,807 15.12 1 hrs 35 mins
26 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,987,328 133,298 14.91 1 hrs 37 mins
27 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,939,699 132,375 14.65 1 hrs 38 mins
28 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,602,769 120,505 13.30 1 hrs 48 mins
29 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,548,159 122,251 12.66 1 hrs 54 mins
30 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 1,170,755 111,860 10.47 2 hrs 18 mins
31 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,113,009 110,065 10.11 2 hrs 22 mins
32 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,040,640 107,420 9.69 2 hrs 29 mins
33 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 838,382 100,999 8.30 2 hrs 53 mins
34 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 827,007 99,279 8.33 2 hrs 53 mins
35 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 813,953 99,672 8.17 2 hrs 56 mins
36 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 655,124 93,461 7.01 3 hrs 25 mins
37 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 609,422 88,077 6.92 3 hrs 28 mins
38 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 478,310 69,061 6.93 3 hrs 28 mins
39 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 375,276 76,855 4.88 4 hrs 55 mins
40 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 301,728 76,043 3.97 6 hrs 3 mins
41 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 113,466 51,411 2.21 10 hrs 52 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:31
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make