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Biological Foundation Models

ProtGPT2

University of Bayreuth
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CategoryBiological Foundation Models
CompanyUniversity of Bayreuth
PriceFree Open Source
StatusStatus not confirmed
AvailabilityAvailable on Hugging Face and GitHub
Last checked20/09/2026
FeaturesAutoregressive 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
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