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Protein Language Models (PLMs) can predict protein stability changes from mutations. An optimized MSA Transformer model, using evolutionary information, significantly improves prediction accuracy and generalization over existing methods.

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Area of Science:

  • Structural biology
  • Computational biology
  • Protein engineering

Background:

  • Protein Language Models (PLMs) show promise in structural biology, using only sequence data.
  • Predicting stability changes from single amino acid mutations is challenging due to limited data.
  • Existing methods struggle with data scarcity and experimental constraints.

Purpose of the Study:

  • To develop a novel approach for predicting protein thermodynamic stability shifts caused by mutations.
  • To leverage evolutionary information via Multiple Sequence Alignments (MSAs) within PLMs.
  • To utilize a large-scale dataset with rigorous pre-processing to enhance model performance and prevent overfitting.

Main Methods:

  • Incorporated Multiple Sequence Alignments (MSAs) into a Protein Language Model (PLM).
  • Utilized a large-scale dataset with stringent data pre-processing and anti-data leakage policies.
  • Conducted comparative analyses, including ablation studies and baseline evaluations, of various fine-tuned pre-trained models.

Main Results:

  • The MSA Transformer, leveraging co-evolution signals from MSAs, demonstrated superior accuracy.
  • The optimized MSA Transformer outperformed existing methods in predicting protein stability changes.
  • The model exhibited enhanced generalization power, improving predictions for point mutations.

Conclusions:

  • Integrating evolutionary information via MSAs significantly enhances PLM performance for stability prediction.
  • The optimized MSA Transformer represents a state-of-the-art method for predicting mutation-induced stability changes.
  • This approach offers a powerful tool for protein engineering and understanding protein function.