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ProtGPT2

By University of Bayreuth →

Autoregressive language model generating de novo functional protein sequences

ProtGPT2 is a 738M parameter autoregressive transformer trained on 45 million protein sequences, generating de novo proteins that follow the principles of natural protein design.

Autoregressive unconditional and conditional sequence generationProduces sequences with natural secondary structure propensities and globular foldingTrained on UniRef50 with zero structural supervision requiredAvailable on Hugging Face Model Hub with straightforward pipeline inference
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Overview

ProtGPT2 is a 738M parameter autoregressive transformer trained on 45 million protein sequences, generating de novo proteins that follow the principles of natural protein design.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:10:15

Key Features

  • Autoregressive unconditional and conditional sequence generation
  • Produces sequences with natural secondary structure propensities and globular folding
  • Trained on UniRef50 with zero structural supervision required
  • Available on Hugging Face Model Hub with straightforward pipeline inference
Interactive 3D Structure

ProtGPT2 Predicted Complex

Streams real 3D atomic coordinates from RCSB Protein Data Bank
⇄ Drag to rotate · Scroll to zoom
pLDDT / B-Factor:
>90 Very high 70-90 Confident 50-70 Low <50 Very low

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application
De novo protein sequence generation and enzyme design
Research Workflow
Prompt with motif or sample unconditionally -> evaluate folding with AlphaFold/ESMFold
Compute & Hardware
Standard GPU or multi-core CPU for inference
Licensing & Academic Use
Apache 2.0
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing ProtGPT2 in scientific publications and benchmark papers.

@software{protgpt2_2026,
  title = {{ProtGPT2}},
  author = {{University of Bayreuth}},
  year = {2026},
  url = {https://huggingface.co/nferruz/ProtGPT2},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for ProtGPT2.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeAI Model
Access modeOpen Source
AI roleGenerative Sequence Modeling
Input dataNot recorded
Output dataNot recorded
Licence conditionsApache 2.0
Commercial eligibilityOpen source release
Compute requirementsStandard GPU or multi-core CPU for inference
ValidationNot recorded
TypeAutoregressive protein transformer
Intended useNot recorded
CompatibilityNot recorded
ManufacturerUniversity of Bayreuth
Biological applicationDe novo protein sequence generation and enzyme design
Research workflowPrompt with motif or sample unconditionally -> evaluate folding with AlphaFold/ESMFold
Evidence levelPeer-reviewed research (Nature Communications 2022)
Integration evidencehttps://huggingface.co/nferruz/ProtGPT2
Laboratory handoffSynthesized sequences experimentally confirmed to adopt stable folds
AvailabilityAvailable on Hugging Face and GitHub
Price / accessFree Open Source

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

No licensed product photograph recorded for ProtGPT2 yet. Visit the official resource below for manufacturer imagery.

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FAQ

Where is this product available?

Available on Hugging Face and GitHub

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating ProtGPT2.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5