ArticleBMC bioinformatics2009
LipocalinPred: a SVM-based method for prediction of lipocalins.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Improved cytokine-receptor interaction prediction by exploiting the negative sample space.BMC bioinformatics · 2020Article
- VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques.Journal of biosciences · 2020Article
- Article
- ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval.BMC bioinformatics · 2012Article
- Amino Acid Features of P1B-ATPase Heavy Metal Transporters Enabling Small Numbers of Organisms to Cope with Heavy Metal Pollution.Bioinformatics and biology insights · 2011Article
- Machine learning methods for prediction of CDK-inhibitors.PloS one · 2010Article
- High resolution X-ray structures of mouse major urinary protein nasal isoform in complex with pheromones.Protein science : a publication of the Protein Society · 2010Article
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2 authors.
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Abstract
backgroundFunctional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures.
resultsIn the present study we propose a SVM based method for identification of lipocalin protein sequences. The SVM models were trained with the input features generated using amino acid, dipeptide and secondary structure compositions as well as PSSM profiles. The model derived using both PSSM and secondary structure emerged as the best model in the study. Apart from achieving a high prediction accuracy (>90% in leave-one-out), lipocalinpred correctly differentiates closely related fatty acid-binding proteins and triabins as non-lipocalins.
conclusionThe method offers a promising approach as a lipocalin prediction tool, complementing PROSITE, Pfam and homology modelling methods.
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