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

@article{Panwar2011PredictingSL,
  title={Predicting sub-cellular localization of tRNA synthetases from their primary structures},
  author={Bharat Panwar and Gajendra P S Raghava},
  journal={Amino Acids},
  year={2011},
  volume={42},
  pages={1703-1713}
}
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… 
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References

SHOWING 1-10 OF 38 REFERENCES
Origin and evolution of the mitochondrial aminoacyl-tRNA synthetases.
TLDR
It is hypothesized that the ancestral eukaryotic gene pool hosted primordial "bacterial-like" genes, to which a limited set of alpha-proteobacterial genes, mostly coding for components of the respiratory chain complexes, were added and selectively maintained.
Import of tRNAs and aminoacyl-tRNA synthetases into mitochondria
TLDR
All mitochondrial aminoacyl-tRNA synthetases and many tRNAs are imported from the cytosol into the mitochondria in eukaryotic cells and their origin and their import into the organelle have been studied in evolutionary distinct organisms.
Computational method to predict mitochondrially imported proteins and their targeting sequences.
TLDR
A computational method that facilitates the analysis and objective prediction of mitochondrially imported proteins has been developed and it is revealed that many of the unknown yeast open reading frames that might be mitochondrial proteins have been predicted and are clustered.
A mechanism for functional segregation of mitochondrial and cytosolic genetic codes
TLDR
It is reported that a mitochondria-specific lysyl-tRNA synthetase in Trypanosoma has evolved a mechanism to block the activity of the enzyme during its synthesis and translocation, preventing the possibility of the enzymes being active in the cytosol.
Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains
TLDR
The high accuracy achieved by the SVM models using selected dipeptide composition demonstrates that certain types of di peptide are preferred in aaRSs, providing interesting insights into tRNA synthetases.
Prediction of Mitochondrial Proteins Using Support Vector Machine and Hidden Markov Model*
TLDR
A hybrid approach that combined hidden Markov model profiles of domains (exclusively found in mitochondrial proteins) and the support vector machine-based method was developed, MitPred, developed for predicting mitochondrial proteins with high accuracy.
Prediction of nuclear proteins using SVM and HMM models
TLDR
This study describes a highly accurate method for predicting nuclear proteins and is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predictingnuclear proteins.
Aminoacyl tRNA synthetases and their connections to disease
TLDR
Notably, the etiology of specific diseases—including cancer, neuronal pathologies, autoimmune disorders, and disrupted metabolic conditions—is connected to specific aminoacyl tRNA synthetases, with both dominant and recessive disease-causing mutations being annotated.
Development of tRNA synthetases and connection to genetic code and disease
  • P. Schimmel
  • Biology
    Protein science : a publication of the Protein Society
  • 2008
TLDR
Clearance of mischarged tRNAs by the editing activities of tRNA synthetases was essential for development of the tree of life and has a role in the etiology of diseases that is just now being understood.
Predotar: A tool for rapidly screening proteomes for N‐terminal targeting sequences
TLDR
A neural network‐based approach (Predotar – Prediction of Organelle Targeting sequences) for identifying genes encoding these proteins amongst eukaryotic genome sequences and has been illustrated by the discovery of the pentatricopeptide repeat family.
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