Improved Prediction of Signal Peptides: SignalP 3.0
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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Cleavage by signal peptidase is predicted to occur following serine-17, resulting in a 328 amino acid mature protein with a calculated molecular mass of 36 kDa.
“Cleavage by signal peptidase is predicted to occur following serine-17 (Bendtsen et al., 2004), resulting in a 328 amino acid mature protein with a calculated molecular mass of 36 kDa, excluding any contribution of carbohydrates.”
From LRAD3, A Novel Low-Density Lipoprotein Receptor Family Member That Modulates Amyloid Precursor Protein Trafficking (2011) · cites this paper as Bendtsen et al., 2004
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- Authors
- Dyrløv Bendtsen, J., Nielsen, H., von Heijne, G., & Brunak, S.
- Affiliations
- Technical University of Denmark (DK)
- Corresponding author
- Brunak, Søren
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2004-07
- Volume
- 340
- Issue
- 4
- Pages
- 783-795
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W2161746138
- Cited by
- 6,350
- References
- 2
- Citations by year
- 2026: 31 · 2025: 42 · 2024: 73 · 2023: 72 · 2022: 83 · 2021: 110 · 2020: 129 · 2019: 135 · 2018: 162 · 2017: 169 · 2016: 199 · 2015: 245 · 2014: 287 · 2013: 387 · 2012: 546
- FWCI
- 153.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Funders
- Danmarks Grundforskningsfond
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- signal peptide prediction, SignalP, secreted proteins, cleavage site prediction, artificial neural networks, hidden Markov models, amino acid composition, false positive reduction, protein secretion
- MeSH terms
- Algorithms, Amino Acid Sequence, Amino Acids, Chemistry, Physical, Computer Systems, Eukaryotic Cells, False Positive Reactions, Gram-Negative Bacteria, Gram-Positive Bacteria, Isoelectric Point, Markov Chains, Peptide Hydrolases, Protein Precursors, Proteins, Sensitivity and Specificity, Databases, Factual, Neural Networks, Computer, Internet, Protein Sorting Signals, Chemical Phenomena
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