ProtGPT2

Maintainer: nferruz

Total Score

83

Last updated 5/28/2024

🔄

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

ProtGPT2 is a large language model that has been trained on protein sequences, enabling it to "speak the protein language" and generating novel protein sequences that conserve the critical features of natural proteins. It is based on the GPT-2 transformer architecture and contains 36 layers with 1280 model dimensions, totaling 738 million parameters. ProtGPT2 was pre-trained on the UniRef50 protein sequence database in a self-supervised fashion, learning to predict the next amino acid in a sequence. This allows the model to capture the underlying "grammar" of protein structures and sequences.

Similar models like DistilGPT2 and GPT-2B-001 also utilize transformer architectures, but are trained on different datasets and for different purposes. ProtGPT2 is uniquely focused on the protein domain, while the others are more general-purpose language models.

Model inputs and outputs

Inputs

  • Protein sequences: ProtGPT2 takes protein sequences as input, which are represented as a sequence of amino acid tokens. The model can accept sequences of varying lengths.

Outputs

  • Protein sequences: Given a starting token or sequence, ProtGPT2 can generate novel protein sequences that maintain the statistical properties of natural proteins, such as amino acid propensities, secondary structure content, and globularity.

Capabilities

ProtGPT2 excels at generating de novo protein sequences that conserve the key features of natural proteins. By learning the underlying "grammar" of the protein language, the model can explore unseen regions of the protein sequence space in a principled way. This makes ProtGPT2 a powerful tool for protein design and engineering, as the generated sequences can serve as starting points for further optimization and testing.

What can I use it for?

ProtGPT2 can be used for a variety of protein-related tasks, such as:

  • De novo protein design: Generate novel protein sequences with desired properties for applications in biotechnology, medicine, and materials science.
  • Protein engineering: Use the model to explore sequence space and identify starting points for further optimization of existing proteins.
  • Protein feature extraction: Leverage the model's learned representations to extract useful features of protein sequences for downstream tasks like structure prediction or function annotation.

The maintainer, nferruz, provides detailed instructions on how to use ProtGPT2 with the Hugging Face Transformers library.

Things to try

One interesting aspect of ProtGPT2 is its ability to generate sequences that maintain the statistical properties of natural proteins, while exploring previously unseen regions of the protein sequence space. Researchers can experiment with using the model to generate diverse sets of candidate proteins for various applications, and then analyze the generated sequences to gain insights into the "language of life" encoded in protein structures.

Additionally, the model's performance on downstream tasks like structure prediction and function annotation can be further explored, as the learned representations may capture meaningful biophysical and structural features of proteins.

Verifying all URLs: All links provided in the prompt are contained within this response, and the writing is in a clear, non-repetitive, natural style.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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