RESEARCH: DUD-E
FOLDING PROJECT #12215 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 59,584
Core: 0x22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project uses computer simulations to study how proteins interact with small molecules. These interactions are important for drug discovery. Scientists will use a well-known dataset called DUD-E, which contains information about many different proteins and their binding partners. By simulating these interactions, researchers can develop new methods for predicting how drugs might work. One example is acetylcholinesterase, a protein involved in nerve function that is also targeted by pesticides and some 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: Pharmaceuticals
Biotechnology / Drug Discovery

DUD-E is a database of protein-ligand interactions used to benchmark computational methods for predicting how molecules bind to proteins. It contains diverse proteins and many small molecules, with known binding affinities measured experimentally.


Protein-Ligand Interactions

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

Scientific: Pharmaceuticals
Biotechnology / Drug Discovery

Protein-ligand interactions are essential for many biological processes. In drug discovery, researchers study these interactions to design new drugs that bind to specific proteins and modulate their activity.


Drug Discovery

The process of identifying and developing new drugs.

Technical: Pharmaceuticals
Biotechnology / Pharmaceuticals

Drug discovery is a complex process that involves identifying promising drug candidates, testing them in the laboratory and in clinical trials, and ultimately bringing approved medications to market.


Acetylcholinesterase

An enzyme that breaks down the neurotransmitter acetylcholine.

Scientific: Pharmaceuticals
Medicine / Neurology

Acetylcholinesterase is a crucial enzyme in the nervous system. It helps regulate nerve impulses by breaking down acetylcholine, a neurotransmitter involved in muscle movement, learning, and memory.


Pesticides

Chemicals used to kill pests.

Technical: Chemicals
Agriculture / Chemical Industry

Pesticides are widely used in agriculture to protect crops from insects, weeds, and diseases. They can be effective but also pose risks to human health and the environment.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:29
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 6,806,623 155,666 43.73 0 hrs 33 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,339,824 151,104 41.96 0 hrs 34 mins
3 Radeon RX 7900XT/XTX
Navi 31 [Radeon RX 7900XT/XTX]
AMD Navi 31 4,010,033 130,064 30.83 0 hrs 47 mins
4 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,089,504 101,135 20.66 1 hrs 10 mins
5 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,027,530 103,232 19.64 1 hrs 13 mins
6 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,938,398 101,619 19.08 1 hrs 15 mins
7 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,188,612 85,541 13.90 1 hrs 44 mins
8 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,132,745 85,002 13.33 1 hrs 48 mins
9 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,098,651 83,764 13.12 1 hrs 50 mins
10 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 861,605 77,352 11.14 2 hrs 9 mins
11 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 789,152 75,426 10.46 2 hrs 18 mins
12 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 648,636 70,449 9.21 2 hrs 36 mins
13 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 630,272 63,504 9.92 2 hrs 25 mins
14 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 561,511 67,167 8.36 2 hrs 52 mins
15 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 461,152 60,375 7.64 3 hrs 9 mins
16 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 398,422 60,063 6.63 3 hrs 37 mins
17 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 334,117 58,329 5.73 4 hrs 11 mins
18 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 186,048 46,699 3.98 6 hrs 1 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:29
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make