ArticlePloS one2014
Protein sub-nuclear localization prediction using SVM and Pfam domain information.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Comprehensive Prediction of Protein Localization and Signal Peptides Using MULocDeep.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology.Journal of proteome research · 2024Review
- MULocDeep web service for protein localization prediction and visualization at subcellular and suborganellar levels.Nucleic acids research · 2023Article
- Computational methods for protein localization prediction.Computational and structural biotechnology journal · 2021Review
- Bird Eye View of Protein Subcellular Localization Prediction.Life (Basel, Switzerland) · 2020Review
- Identification and evolutionary analysis of the nucleolar proteome of Giardia lamblia.BMC genomics · 2020Article
- DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks.Bioinformatics (Oxford, England) · 2020Article
- Article
- SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments.Bioinformatics (Oxford, England) · 2017Article
- Article
- PredHSP: Sequence Based Proteome-Wide Heat Shock Protein Prediction and Classification Tool to Unlock the Stress Biology.PloS one · 2016Article
- Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.International journal of molecular sciences · 2015Article
- Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites.Scientific reports · 2015Article
- NRfamPred: a proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families.Scientific reports · 2014Article
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4 authors.
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Abstract
The nucleus is the largest and the highly organized organelle of eukaryotic cells. Within nucleus exist a number of pseudo-compartments, which are not separated by any membrane, yet each of them contains only a specific set of proteins. Understanding protein sub-nuclear localization can hence be an important step towards understanding biological functions of the nucleus. Here we have described a method, SubNucPred developed by us for predicting the sub-nuclear localization of proteins. This method predicts protein localization for 10 different sub-nuclear locations sequentially by combining presence or absence of unique Pfam domain and amino acid composition based SVM model. The prediction accuracy during leave-one-out cross-validation for centromeric proteins was 85.05%, for chromosomal proteins 76.85%, for nuclear speckle proteins 81.27%, for nucleolar proteins 81.79%, for nuclear envelope proteins 79.37%, for nuclear matrix proteins 77.78%, for nucleoplasm proteins 76.98%, for nuclear pore complex proteins 88.89%, for PML body proteins 75.40% and for telomeric proteins it was 83.33%. Comparison with other reported methods showed that SubNucPred performs better than existing methods. A web-server for predicting protein sub-nuclear localization named SubNucPred has been established at http://14.139.227.92/mkumar/subnucpred/. Standalone version of SubNucPred can also be downloaded from the web-server.
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