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Morihiro Hayashida1, Peiying Ruan1, Tatsuya Akutsu1

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This study models protein evolution using domain composition and compression. Incorporating gene fusion alongside duplication significantly reduces proteome size, highlighting fusion

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Proteins are fundamental to life, composed of distinct functional units called domains.
  • Evolutionary processes like gene duplication and fusion lead to protein and domain repertoire expansion and diversification.
  • Understanding proteome evolution requires analyzing the combinatorial patterns of protein domains.

Purpose of the Study:

  • To develop a computational method for analyzing protein domain composition and its evolutionary implications.
  • To quantify the impact of gene duplication and fusion events on proteome compression and entropy.
  • To model the evolutionary history of proteomes using a directed hypergraph approach.

Main Methods:

  • Proteins are represented as multisets of domains.
  • A directed hypergraph model is used to capture protein relationships arising from duplication and fusion.
  • A heuristic approach combining the Edmonds algorithm and integer linear programming is developed for proteome compression.
  • The method is applied to 14 diverse proteomes across different species.

Main Results:

  • Proteome compression was achieved by leveraging references to duplicated and fused proteins.
  • The combined effect of duplication and fusion resulted in a smaller compressed size compared to duplication alone.
  • This indicates that fusion events play a significant role in shaping proteome complexity.
  • The study provides a quantitative measure of evolutionary events through compression entropy.

Conclusions:

  • Gene fusion events are crucial for efficient proteome organization and evolution.
  • The proposed compression method offers insights into the evolutionary history and structural organization of proteomes.
  • This work contributes to understanding the interplay between genomic events and proteome diversity.