Prediction of Protein Submitochondria Locations by Hybridizing Pseudo-amino Acid Composition with Various Physicochemical Features of Segmented Sequence

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2006 Dec 1

PMID 17134515

Citations 22

Authors

Pufeng Du

Yanda Li

Affiliations

Soon will be listed here.

Abstract

Background: Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins.

Results: By using leave-one-out cross validation, the prediction accuracy is 85.5% for inner membrane, 94.5% for matrix and 51.2% for outer membrane. The overall prediction accuracy for submitochondria location prediction is 85.2%. For proteins predicted to localize at inner membrane, the accuracy is 94.6% for membrane protein type prediction.

Conclusion: Our method is an effective method for predicting protein submitochondria location. But even with our method or the methods at subcellular level, the prediction of protein submitochondria location is still a challenging problem. The online service SubMito is now available at: http://bioinfo.au.tsinghua.edu.cn/subMito.

Citing Articles

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PMID: 37740195 PMC: 10517492. DOI: 10.1186/s12859-023-05475-x.

Large-scale prediction and analysis of protein sub-mitochondrial localization with DeepMito.

Savojardo C, Martelli P, Tartari G, Casadio R BMC Bioinformatics. 2020; 21(Suppl 8):266.

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SubmitoLoc: Identification of mitochondrial sub cellular locations of proteins using support vector machine.

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Classification of adaptor proteins using recurrent neural networks and PSSM profiles.

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Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks.

Haghighat Hoseini A, Mirzarezaee M Iran J Biotechnol. 2019; 16(3):e1933.

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