Improved prediction of signal peptides: SignalP 3.0.

Machine learning approaches for the prediction of signal peptides and other protein sorting signals.

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently, the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, have made it possible to achieve a level of reliability where practical use in, for example automatic database annotation is feasible. ...

Fusion of Conditional Random Field and Signalp for Protein Cleavage Site Prediction

Prediction of protein cleavage sites is an important step in drug design. Recent research has demonstrated that conditional random fields are capable of predicting the cleavage site locations of signal peptides, and their performance is comparable to that of SignalP—a state-of-the-art predictor based on hidden Markov models and neural networks. This paper investigates the degree of complementar...

Computational Molecular Biology

Computational prediction of protein subcellular locations in eukaryotes facilitates experimental design and proteome analysis. We provide a short review on recent development of computational tools and our experience in evaluating some of these tools. Classical secretomes can be relatively accurately predicted using computational tools to predict existence of a secretory signal peptide and to r...

A combined transmembrane topology and signal peptide prediction method.

An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes....

Nature Methods SignalP 4.0: discriminating signal peptides from transmembrane regions