PSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties

Deepak Sarda,Gek Huey Chua,Kuo-Bin Li,Arun Krishnan

doi:10.1186/1471-2105-6-152

Deepak Sarda, Gek Huey Chua + Show 2 more

Open Access

https://doi.org/10.1186/1471-2105-6-152

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Journal: BMC bioinformatics	Publication Date: Jan 1, 2005
Citations: 117	License type: CC BY 2.0

Affiliation: Bioinformatics Institute

Abstract

BackgroundProtein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively.ResultsIn this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the dataset provided by Park and Kanehisa and we obtained prediction accuracies for the different classes ranging from 87.7% – 97.0% with an overall accuracy of 93.1%.ConclusionThis study presents a physicochemical property based protein localization prediction algorithm. Unlike other algorithms, contextual information is preserved by dividing the protein sequences into clusters. The prediction accuracy shows an improvement over other algorithms based on various types of amino acid composition (single, pair and gapped pair). We have also implemented a web server to predict protein localization across the six classes (available at ).

Highlights

Protein subcellular localization is an important determinant of protein function and reliable methods for prediction of localization are needed
Later efforts were targeted at incorporating sequence order information in the prediction algorithms [22,23,24,25,26,27]
We divided the dataset into clusters based on sequence length and ran N-fold cross validation (NF-CV) tests for each of the protein clusters

Summary

Introduction

Protein subcellular localization is an important determinant of protein function and reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. Such approaches lead to a loss of contextual information. In humans, the number of proteins for which the structures and functions are unknown makes up more than 40% of the total number of proteins. Initial efforts relied on amino acid compositions [13,14], the prediction of signal peptides [15,16,17,18,19] or a combination of both [20,21]. Later efforts were targeted at incorporating sequence order information (in the form of dipeptide compositions etc.) in the prediction algorithms [22,23,24,25,26,27]

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Results

Conclusion