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A Protocol for Computer-Based Protein Structure and Function Prediction
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Improving protein structure prediction using templates and sequence embedding.

Fandi Wu1,2,3, Xiaoyang Jing2, Xiao Luo2

  • 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing 626011, China.

Bioinformatics (Oxford, England)
|November 10, 2022
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Summary
This summary is machine-generated.

This study investigates how template information and multiple sequence alignment (MSA) embeddings impact deep learning-based protein structure prediction. Combining both significantly improves accuracy, especially for challenging targets.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Deep learning has advanced protein structure prediction.
  • The specific contributions of template information and multiple sequence alignment (MSA) embeddings remain underexplored.
  • Existing methods like AlphaFold2 and RoseTTAFold utilize templates detected by HHsearch, potentially limiting performance.

Purpose of the Study:

  • To investigate the impact of template-based information (number, quality, generation method) on protein structure prediction accuracy.
  • To assess the influence of sequence embeddings from MSATransformer and ESM-1b on structure prediction.
  • To evaluate a deep learning method incorporating templates and MSA embeddings as inputs.

Main Methods:

  • Implemented a deep learning model for protein structure prediction.
  • Incorporated templates and MSA embeddings as additional input features.
  • Evaluated model performance on CASP13, CASP14, and CAMEO datasets.
  • Analyzed the specific benefits of templates and MSA embeddings for different target types.

Main Results:

  • Templates improved prediction accuracy for 71/110 CASP13 and 47/91 CASP14 targets, particularly those with similar templates.
  • MSA embeddings enhanced prediction for 63/91 CASP14 and 87/183 CAMEO targets, especially for proteins with shallow MSAs.
  • The combined approach predicted correct folds for 16/23 CASP14 FM and 14/18 CAMEO targets, outperforming RoseTTAFold.

Conclusions:

  • Templates and MSA embeddings are valuable inputs for deep learning-based protein structure prediction.
  • The developed method demonstrates superior performance compared to existing tools like RoseTTAFold.
  • Further exploration of these features can lead to more accurate protein structure prediction models.