Abstract
We report the development of LumenP, a new neural network-based predictor for the identification of proteins targeted to the thylakoid lumen of plant chloroplasts and prediction of their cleavage sites. When used together with the previously developed TargetP predictor, LumenP reaches a significantly better performance than what has been recorded for previous attempts at predicting thylakoid lumen location, mostly due to a lower false positive rate. The combination of TargetP and LumenP predicts around 1.5%-3% of all proteins encoded in the genomes of Arabidopsis thaliana and Oryza sativa to be located in the lumen of the thylakoid.
Publication types
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Comparative Study
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Research Support, Non-U.S. Gov't
MeSH terms
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Arabidopsis / genetics
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Arabidopsis / physiology
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Arabidopsis Proteins / analysis
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Arabidopsis Proteins / chemistry
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Arabidopsis Proteins / physiology
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Artificial Intelligence
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Chloroplast Proteins
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Chloroplasts / chemistry
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Chloroplasts / physiology
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Computational Biology / methods*
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Databases, Protein
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Genome, Plant
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Genomic Library
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Neural Networks, Computer*
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Oryza / genetics
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Oryza / physiology
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Pisum sativum / genetics
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Pisum sativum / physiology
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Plant Proteins / analysis*
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Plant Proteins / chemistry
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Plant Proteins / physiology
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Protein Sorting Signals / physiology
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Protein Transport
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Proteome
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Sensitivity and Specificity
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Sequence Alignment
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Thylakoids / chemistry*
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Thylakoids / metabolism
Substances
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Arabidopsis Proteins
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Chloroplast Proteins
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Plant Proteins
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Protein Sorting Signals
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Proteome
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chloroplast transit peptides