A classification scoring schema to validate protein interactors.

Bioinformation
Prashanth Suravajhala, Vijayaraghava Seshadri Sundararajan

Abstract

Hypothetical protein [HP] annotation poses a great challenge especially when the protein is putatively linked or mapped to another protein. With protein interaction networks (PIN) prevailing, many visualizers still remain unsupported to the HP annotation. Through this work, we propose a six-point classification system to validate protein interactions based on diverse features. The HP data-set was used as a training data-set to find putative functional interaction partners to the remaining proteins that are waiting to be interacting. A Total Reliability Score (TRS) was calculated based on the six-point classification which was evaluated using machine learning algorithm on a single node. We found that multilayer perceptron of neural network yielded 81.08% of accuracy in modelling TRS whereas feature selection algorithms confirmed that all classification features are implementable. Furthermore statistical results using variance and co-variance analyses confirmed the usefulness of these classification metrics. It has been evaluated that of all the classification features, subcellular location (sorting signals) makes higher impact in predicting the function of HPs.

Citations

Jul 3, 2013·Database : the Journal of Biological Databases and Curation·Chunguang LiangThomas Dandekar
Jun 6, 2013·International Journal of Genomics·Chuan-Ching Huang, Chuan Yi Tang
Apr 16, 2015·Frontiers in Genetics·Johny IjaqVijayaraghava S Sundararajan
Aug 26, 2016·Frontiers in Genetics·Lahiru IddamalgodaJayaraman K Valadi

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Methods Mentioned

BETA
Rosetta
Rosetta Stone
phylogenetic profiling
PCA

Software Mentioned

TargetP
cytoscape
Osprey
protein Navigator
WEKA
Pfam
MINT
MATLAB

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