AcACS: improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition.

Guo-Liang Fan,Yan-Ling Liu,Yong-Chun Zuo,Yan Zhao,Yi Rang,Bao-Yan Hou,Han-Xue Mei

doi:10.1155/2014/864135

Guo-Liang Fan, Yan-Ling Liu + Show 5 more

Open Access

https://doi.org/10.1155/2014/864135

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Journal: TheScientificWorldJournal	Publication Date: Jan 1, 2014
Citations: 57	License type: CC BY 3.0

Affiliation: Inner Mongolia University

Abstract

The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.

Highlights

Knowledge of subcellular localization information of a protein may help to unravel its normal cellular function [1]
We selected Autocovariance of Average Chemical Shift (acACS) combined with amino acid composition (AAC), DC, PSSM, and gene ontology (GO) and reduced physicochemical properties (Hn) as feature vectors for representing the proteins and trained the model
90.74% accuracy was obtained for SML3-983 data set with Jackknife cross-validation, which was 1.63% higher than SubMitoPSPCP

Summary

Introduction

Knowledge of subcellular localization information of a protein may help to unravel its normal cellular function [1]. The proteins within the different compartments have different biological activity and functions; in turn, knowing the subcellular localization of a given protein helps in elucidating its functional role. Many computational approaches for subcellular localization predictions have been developed and plenty of methods for improving the accuracy of the prediction were applied. The structure information of a protein is very important, especially when it is used for representing the subcellular locations of a protein. The structure information of a protein cannot be described, and few methods using the structure information can be learned to our knowledge

Methods

Results

Conclusion