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A Protocol for Computer-Based Protein Structure and Function Prediction
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Prediction of protein group function by iterative classification on functional relevance network.

Ishita K Khan1,2, Aashish Jain1, Reda Rawi3,4

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Bioinformatics (Oxford, England)
|September 8, 2018
PubMed
Summary

This study introduces iterative group function prediction (iGFP) to identify protein group functions from biological data. The model accurately predicts individual protein functions, even with missing gene ontology annotations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Proteomics and transcriptomics studies identify protein sets implicated in diseases.
  • Understanding the collective function of protein groups is crucial, especially when individual functions are unknown.

Purpose of the Study:

  • To develop a computational model for inferring group functions of proteins.
  • To predict individual protein functions based on group functional relevance.

Main Methods:

  • Proteins are represented in a graph reflecting functional relevance using known features.
  • Iterative clustering and probabilistic graphical models (conditional random fields) are employed.
  • The iterative group function prediction (iGFP) algorithm is proposed.

Main Results:

  • iGFP accurately identifies group functions and predicts individual protein functions.
  • The method demonstrates robustness even with missing Gene Ontology annotations.
  • Functional relevance networks and iterative inference are key components.

Conclusions:

  • The 'group' function annotation perspective offers novel insights into protein functions in biological systems.
  • iGFP provides a powerful approach for functional characterization of protein sets.
  • The developed algorithm is available at http://kiharalab.org/iGFP/.