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Alignment using genetic programming with causal trees for identification of protein functions.

Chun-Min Hung1, Yueh-Min Huang1, Ming-Shi Chang2

  • 1Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC.

Nonlinear Analysis, Theory, Methods & Applications
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid evolutionary model for predicting protein functions by analyzing hierarchical protein sequence homology. This approach improves accuracy for novel proteins where traditional alignment methods falter.

Keywords:
Bioinformatics protein databasesEvolutionary computing and genetic algorithmsInvariantsMomentsSplines

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Existing methods for predicting novel protein functions are inconsistent.
  • Maximizing sequence alignment scores is insufficient for proteins lacking conserved domains.

Purpose of the Study:

  • To propose a hybrid evolutionary model for systematic protein function identification using hierarchical homology.
  • To develop a decision model that minimizes costs in predicting protein functions.

Main Methods:

  • Utilizes a hybrid evolutionary model with genetic programming and multiple fitness functions.
  • Incorporates modified robust point matching with moment invariants and thin-plate spline theorems.
  • Employs hierarchical homologies represented as a causal tree to show protein relationships.

Main Results:

  • The model effectively predicts protein functions for distantly related protein families.
  • Demonstrates accurate comparison of feature points for protein sequence analysis.
  • Hierarchical homologies provide a clear framework for understanding protein relationships.

Conclusions:

  • The proposed hybrid model offers significant potential for accurate and systematic protein function prediction.
  • This approach addresses limitations of traditional methods, especially for novel proteins.
  • The model was successfully applied to compare nucleocapsid proteins across different coronaviruses.