Predicting sub-cellular localization of tRNA synthetases from their primary structures.

Panwar, Bharat and Raghava, G.P.S. (2012) Predicting sub-cellular localization of tRNA synthetases from their primary structures. Amino acids, 42 (5). pp. 1703-13. ISSN 1438-2199

[img] PDF
raghava2012.1.pdf - Published Version
Restricted to Registered users only

Download (727Kb) | Request a copy

Abstract

Since endo-symbiotic events occur, all genes of mitochondrial aminoacyl tRNA synthetase (AARS) were lost or transferred from ancestral mitochondrial genome into the nucleus. The canonical pattern is that both cytosolic and mitochondrial AARSs coexist in the nuclear genome. In the present scenario all mitochondrial AARSs are nucleus-encoded, synthesized on cytosolic ribosomes and post-translationally imported from the cytosol into the mitochondria in eukaryotic cell. The site-based discrimination between similar types of enzymes is very challenging because they have almost same physico-chemical properties. It is very important to predict the sub-cellular location of AARSs, to understand the mitochondrial protein synthesis. We have analyzed and optimized the distinguishable patterns between cytosolic and mitochondrial AARSs. Firstly, support vector machines (SVM)-based modules have been developed using amino acid and dipeptide compositions and achieved Mathews correlation coefficient (MCC) of 0.82 and 0.73, respectively. Secondly, we have developed SVM modules using position-specific scoring matrix and achieved the maximum MCC of 0.78. Thirdly, we developed SVM modules using N-terminal, intermediate residues, C-terminal and split amino acid composition (SAAC) and achieved MCC of 0.82, 0.70, 0.39 and 0.86, respectively. Finally, a SVM module was developed using selected attributes of split amino acid composition (SA-SAAC) approach and achieved MCC of 0.92 with an accuracy of 96.00%. All modules were trained and tested on a non-redundant data set and evaluated using fivefold cross-validation technique. On the independent data sets, SA-SAAC based prediction model achieved MCC of 0.95 with an accuracy of 97.77%. The web-server 'MARSpred' based on above study is available at http://www.imtech.res.in/raghava/marspred/.

Item Type: Article
Additional Information: Copyright of this article belongs to Springer Science.
Uncontrolled Keywords: Amino Acids,Eukaryotic Cells,Computational Biology
Subjects: Q Science > QR Microbiology
Depositing User: Dr. K.P.S.Sengar
Date Deposited: 23 Jan 2013 08:59
Last Modified: 23 Jan 2013 08:59
URI: http://crdd.osdd.net/open/id/eprint/1288

Actions (login required)

View Item View Item