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Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to

Yu-Yen Ou1,2, Quang-Thai Ho1, Heng-Ta Chang1

  • 1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.

Proteomics
|October 20, 2023
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Summary

Computational methods, including multiple sequence alignment (MSA) and pre-trained language models (PLMs), offer powerful ways to analyze membrane proteins. Advancements address computational challenges, enabling deeper insights for drug discovery.

Keywords:
machine learningmembrane proteinspre-trained language model

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Membrane proteins are vital cellular components with complex structures challenging experimental analysis.
  • Traditional sequence analysis methods struggle to capture higher-order patterns in protein sequences.

Purpose of the Study:

  • To review traditional and recent computational methods for generating features from protein sequences.
  • To focus on multiple sequence alignment (MSA) and pre-trained language models (PLMs) for membrane protein analysis.
  • To discuss computational challenges and solutions in feature generation.

Main Methods:

  • Overview of traditional protein sequence analysis features (amino acid types, composition, pair composition).
  • Exploration of Multiple Sequence Alignment (MSA) for generating protein sequence features.
  • Examination of Pre-trained Language Models (PLMs) like BERT for protein sequence embeddings.

Main Results:

  • MSA and PLMs offer advanced feature generation beyond traditional methods.
  • Computational costs of MSA can be a bottleneck; methods exist to accelerate generation.
  • PLMs provide informative embeddings for protein sequence analysis.

Conclusions:

  • Computational methods, especially MSA and PLMs, are crucial for studying membrane proteins.
  • Addressing computational challenges enhances the utility of these methods.
  • Advancements promise deeper understanding of membrane proteins for drug discovery and personalized medicine.