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Xenopus laevis as a Model to Identify Translation Impairment

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Published on: September 27, 2015

Teaching AI to speak protein.

Michael Heinzinger¹, Burkhard Rost²

¹TUM (Technical University of Munich), School of Computation, Information and Technology (CIT), Faculty of Informatics, Chair of Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching, Munich, Germany.

Current Opinion in Structural Biology

|February 22, 2025

Summary

Protein Language Models (pLMs) advance protein prediction and design by interpreting protein sequences. Fine-tuning pLMs improves accuracy, especially with limited data, integrating AI and experimental biology for novel protein discovery.

Keywords:

Artificial intelligence Deep learning Large language models Protein prediction

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Area of Science:

Computational Biology
Artificial Intelligence in Biology
Bioinformatics

Background:

Protein Language Models (pLMs) offer novel ways to encode protein sequence information.
pLMs have shown promise in predicting protein functions, such as binding sites and variant effects.
Protein structure prediction has not yet significantly benefited from single-sequence pLM embeddings compared to other applications.

Purpose of the Study:

To highlight the advancements and applications of protein Language Models (pLMs) in computational biology.
To discuss the potential of pLMs in enhancing protein prediction tasks.
To explore the role of fine-tuning pLMs for improved accuracy and efficiency.

Main Methods:

Leveraging protein Language Models (pLMs) to analyze and interpret protein sequences.
Applying fine-tuning techniques to foundation pLMs for specific prediction tasks.
Integrating computational approaches with experimental biological data.

Main Results:

pLMs are increasingly powerful tools for advancing protein prediction, including molecular function identification.
Fine-tuning pLMs significantly enhances solution efficiency and accuracy, particularly when experimental annotations are scarce.
pLMs are poised to drive a new era of protein design by bridging AI and wet-lab research.

Conclusions:

Protein Language Models represent a significant leap in understanding and utilizing the information within protein sequences.
Fine-tuning pLMs is crucial for maximizing their potential in prediction and design, especially in data-limited scenarios.
The integration of pLMs facilitates a synergistic approach between computational and experimental biology, paving the way for innovative protein engineering.