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Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.
TLDR
A new membrane protein topology prediction method, TMHMM, based on a hidden Markov model is described and validated, and it is discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C-in topologies.
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Improved prediction of signal peptides: SignalP 3.0.
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Predicting subcellular localization of proteins based on their N-terminal amino acid sequence.
TLDR
A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed and it is estimated that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%.
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Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method…
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Tissue-based map of the human proteome
TLDR
A genome-wide analysis of the tissue specificity of RNA and protein expression covering more than 90% of the putative protein-coding genes is reported, complemented with analyses of various subproteomes, such as predicted secreted proteins and membrane-bound proteins.
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Feature-based prediction of non-classical and leaderless protein secretion.
TLDR
By scanning the entire human proteome, a sequence-based method, SecretomeP, is presented, it is discovered that certain pathway-independent features are shared among secreted proteins.
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Prediction of lipoprotein signal peptides in Gram‐negative bacteria
TLDR
A method to predict lipoprotein signal peptides in Gram‐negative Eubacteria, LipoP, has been developed and the predictions agree well with the experimentally verified lipoproteins.
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Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method.
TLDR
This so-called dense alignment surface (DAS) method is shown to perform on par with earlier methods that require extra information in the form of multiple sequence alignments or the distribution of positively charged residues outside the transmembrane segments, and thus improves prediction abilities when only single-sequence information is available.
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A new method for predicting signal sequence cleavage sites.
A new method for identifying secretory signal sequences and for predicting the site of cleavage between a signal sequence and the mature exported protein is described. The predictive accuracy is…
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Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule.
TLDR
A straightforward implementation with no attempts at optimization predicts the correct topology for 23 out of 24 inner membrane proteins with experimentally determined topologies, and correctly identifies 135 transmembrane segments with only one overprediction.
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