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Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

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Predicting protein subcellular location with network embedding and enrichment features.

Xiaoyong Pan¹, Lin Lu², Yu-Dong Cai³

¹School of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China.

Biochimica Et Biophysica Acta. Proteins and Proteomics

|June 29, 2020

Summary

This study introduces a computational method for predicting protein subcellular localization using combined features from protein interaction networks and functional annotations. The model achieves high accuracy, aiding in understanding protein function and biological mechanisms.

Keywords:

Decision tree Gene ontology KEGG pathway Network embedding Protein subcellular location Recurrent neural network

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

Protein subcellular localization is crucial for understanding protein function.
Experimental methods for localization are costly and inefficient.
Computational approaches offer an efficient alternative for predicting protein localization.

Purpose of the Study:

To develop an accurate computational model for predicting protein subcellular localization.
To integrate diverse feature types for improved prediction performance.
To extract interpretable decision rules for understanding protein localization mechanisms.

Main Methods:

Feature extraction from protein-protein interaction networks.
Feature extraction from Gene Ontology and biological pathways.
Feature selection and optimization.
Recurrent Neural Network (RNN) for classification.
Decision tree for rule extraction.

Main Results:

The proposed model achieved a high prediction performance with a Matthews correlation coefficient of 0.844.
Combined features from interaction networks and functional annotations improved prediction accuracy.
Decision rules extracted by decision trees provided insights into molecular mechanisms.

Conclusions:

The developed computational method accurately predicts protein subcellular localization.
Integrating network and functional features is effective for localization prediction.
Extracted decision rules offer valuable biological insights into protein localization.