RESEARCH: DUD-E
FOLDING PROJECT #12214 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 313,529
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 proteins from the DUD-E dataset, which has a bunch of different proteins and known binding information. One example is Acetylcholinesterase, important for nerve function and targeted by pesticides and some medicines.

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.
.

RELATED TERMS GLOSSARY AI BETA

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

Acetylcholinesterase

An enzyme that breaks down acetylcholine in the nervous system.

Technical: Biotechnology
Pharmacology / Neuropharmacology

Acetylcholinesterase is a crucial enzyme found in the nervous system. It's responsible for breaking down acetylcholine, a neurotransmitter involved in muscle movement and nerve signaling. Medications that inhibit acetylcholinesterase can be used to treat conditions like Alzheimer's disease.


DUD-E

Directory of Useful Decoys Enhanced

Acronym: Pharmaceutical Research
Biochemistry / Drug Discovery

DUD-E is a database of protein-ligand complexes used for benchmarking computational methods in drug discovery. It contains diverse proteins with experimentally determined binding affinities to various small molecules.


Alpha Fold

A deep learning algorithm for predicting protein structures.

Technical: Computational Biology
Bioinformatics / Protein Structure Prediction

AlphaFold is a revolutionary artificial intelligence system developed by DeepMind that can accurately predict the three-dimensional structures of proteins. This breakthrough has immense implications for understanding protein function and advancing drug discovery.


Protein-Ligand Interactions

Interactions between proteins and small molecules (ligands).

Scientific: Pharmaceutical Research
Biochemistry / Molecular Pharmacology

Protein-ligand interactions are essential for many biological processes. When a drug binds to a protein target, it can modulate its activity and have therapeutic effects. Understanding these interactions is crucial for drug development.


Drug Discovery

The process of identifying and developing new drugs.

Technical: Pharmaceutical Industry
Pharmacology / Medicinal Chemistry

Drug discovery is a complex multi-stage process that involves identifying promising drug candidates, optimizing their properties, and conducting rigorous testing to ensure safety and efficacy. It's a crucial step in bringing new therapies to patients.


Folding@Home

A distributed computing project for protein folding research.

Technical: Research Computing
Bioinformatics / Computational Biology

Folding@Home harnesses the power of volunteer computers to simulate protein folding, a fundamental process in biology. This vast computational effort aids in understanding protein structure and function, contributing to advancements in drug discovery and disease treatment.

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 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 18,078,861 1,344,474 13.45 1 hrs 47 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 14,097,600 1,257,359 11.21 2 hrs 8 mins
3 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 9,953,358 1,117,162 8.91 2 hrs 42 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 7,278,113 1,003,570 7.25 3 hrs 19 mins
5 GeForce RTX 4070
AD104 [GeForce RTX 4070]
Nvidia AD104 7,135,881 1,005,211 7.10 3 hrs 23 mins
6 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,768,268 987,273 6.86 3 hrs 30 mins
7 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,462,604 973,972 6.64 3 hrs 37 mins
8 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,743,323 933,940 6.15 3 hrs 54 mins
9 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,520,754 881,633 5.13 4 hrs 41 mins
10 GeForce RTX 4060 Ti
AD106 [GeForce RTX 4060 Ti]
Nvidia AD106 3,964,935 831,717 4.77 5 hrs 2 mins
11 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,925,568 822,711 4.77 5 hrs 2 mins
12 RTX 4000 SFF Ada Generation
AD104GL [RTX 4000 SFF Ada Generation]
Nvidia AD104GL 3,831,698 852,911 4.49 5 hrs 21 mins
13 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,779,171 824,234 4.59 5 hrs 14 mins
14 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,163,182 781,215 4.05 5 hrs 56 mins
15 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 3,117,052 766,697 4.07 5 hrs 54 mins
16 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,023,130 768,251 3.94 6 hrs 6 mins
17 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,609,155 778,572 3.35 7 hrs 10 mins
18 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,305,676 667,580 3.45 6 hrs 57 mins
19 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,289,688 688,226 3.33 7 hrs 13 mins
20 RTX A4000
GA104GL [RTX A4000]
Nvidia GA104GL 2,008,975 687,477 2.92 8 hrs 13 mins
21 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,764,775 620,985 2.84 8 hrs 27 mins
22 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 1,610,831 583,050 2.76 8 hrs 41 mins
23 Geforce RTX 3050
GA106 [Geforce RTX 3050]
Nvidia GA106 1,141,663 546,013 2.09 11 hrs 29 mins
24 RTX A2000
GA106 [RTX A2000]
Nvidia GA106 868,173 568,993 1.53 15 hrs 44 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