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      Improved prediction of signal peptides: SignalP 3.0.

      Journal of Molecular Biology

      Algorithms, Amino Acid Sequence, Amino Acids, chemistry, Chemistry, Physical, Computer Systems, Databases, Factual, Eukaryotic Cells, False Positive Reactions, Gram-Negative Bacteria, Gram-Positive Bacteria, Internet, Isoelectric Point, Markov Chains, Neural Networks (Computer), Peptide Hydrolases, Physicochemical Phenomena, Protein Precursors, Protein Sorting Signals, Proteins, secretion, Sensitivity and Specificity

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          Abstract

          We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server

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          Journal
          10.1016/j.jmb.2004.05.028
          15223320

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