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A Survey of Pretrained Protein Language Models.

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Protein language models (PLMs) are revolutionizing bioinformatics, building on large language models (LLMs) from NLP. These models excel at protein representation and design, showing great promise for future biological discoveries.

Keywords:
DecoderDecoder-only modelsEncoderEncoder-onlyProtein language modelsTransformers in bioinformatics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence in Life Sciences

Background:

  • Large language models (LLMs) have transformed Natural Language Processing (NLP).
  • This success inspired the development of Protein Language Models (PLMs) for bioinformatics.
  • PLMs leverage vast protein datasets to capture structural and functional information.

Purpose of the Study:

  • To explore the evolution and impact of PLMs in protein bioinformatics.
  • To summarize key PLM architectures and their applications.
  • To highlight emerging trends and future potential of PLMs.

Main Methods:

  • Reviewing the origins of PLMs from NLP transformers and LLMs.
  • Categorizing notable PLMs by architecture (encoder-only, encoder-decoder, decoder-only).
  • Examining advanced applications like fine-tuning, multimodal architectures, and reduced alphabets.

Main Results:

  • PLMs demonstrate significant capabilities in protein representation and de novo design.
  • They achieve high performance in tasks like classification and function prediction.
  • PLMs effectively capture intrinsic protein information from large-scale training data.

Conclusions:

  • PLMs represent a significant advancement in protein bioinformatics.
  • Ongoing innovations in PLM architectures and applications promise to address complex biological challenges.
  • PLMs are poised to drive future breakthroughs in understanding and engineering proteins.