The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Using subsite coupling to predict signal peptides
Abstract
Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is a first important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy. Based on a model that takes into account the coupling effect among some key subsites, the so-called [-3, -1, +1] coupling model, a new prediction algorithm is developed. The overall rate of correct prediction for 1939 secretory proteins and 1440 non-secretary proteins was over 92%. It has not escaped our attention that the new method may also serve as a useful tool for helping investigate further many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.
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“The metrics are used in evaluating the performance frequently [64,65,66,67,68,69,70,71,72].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [65]
Citations
Cites
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites
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Prediction of Protein Structural Classes
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Self-citationBioinformatics the machine learning approach
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Protein subcellular location prediction
Chou, K., & Elrod, D. W. (1999) · Protein Engineering Design and Selection · doi:10.1093/protein/12.2.107 · Cited by 375
Self-citationTransport of Proteins Across the Endoplasmic Reticulum Membrane
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Prediction of Protein Structural Classes and Subcellular Locations
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Self-citationPrediction of N-terminal protein sorting signals
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Using Discriminant Function for Prediction of Subcellular Location of Prokaryotic Proteins
Chou, K., & Elrod, D. W. (1998) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.1998.9498 · Cited by 90
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Research works printed but not linked: 1
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Extensive feature detection of N-terminal protein sorting signals
Bannai, H., Tamada, Y., Maruyama, O., Nakai, K., & Miyano, S. (2002) · Bioinform. · doi:10.1093/bioinformatics/18.2.298 · Cited by 746
iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition
Chen, W., Feng, P., Lin, H., & Chou, K. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1450 · Cited by 611
Self-citationPrediction of linear B-cell epitopes using amino acid pair antigenicity scale
Chen, J., Liu, H., Yang, J., & Chou, K.-C. (2007) · Amino Acids · doi:10.1007/s00726-006-0485-9 · Cited by 552
Self-citationImpacts of Bioinformatics to Medicinal Chemistry
Chou, K. (2015) · Medicinal Chemistry · doi:10.2174/1573406411666141229162834 · Cited by 518
Self-citationLin, H., Deng, E.-Z., Ding, H., Chen, W., & Chou, K.-C. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1019 · Cited by 508
Self-citation
Metadata
- Authors
- Chou, K.-C.
- Corresponding author
- Chou, Kuo-Chen
- ORCID
- Chou, Kuo-Chen
- Venue
- Protein Engineering, Design and Selection
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-02
- Volume
- 14
- Issue
- 2
- Pages
- 75-79
- ISSN
- 0269-2139 (ISSN-L)
- 1741-0134
- 1741-0126
- OpenAlex
- W1979648586
- Cited by
- 255
- References
- 20
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 9 · 2021: 12 · 2020: 26 · 2019: 34 · 2018: 34 · 2017: 16 · 2016: 16 · 2015: 21 · 2014: 18 · 2013: 9 · 2012: 6
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Protein Engineering Design and Selection (publishedVersion)
- PubMed (publishedVersion)
- 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 › vaccines and immunoinformatics approaches
- Keywords
- signal peptide prediction, signal peptide, secreted proteins, protein sorting, prediction algorithm
- MeSH terms
- Algorithms, Amino Acid Sequence, Animals, Automation, Computer Simulation, Eukaryotic Cells, Humans, Membrane Proteins, Predictive Value of Tests, Prokaryotic Cells, Proteins, Serine Endopeptidases, Structure-Activity Relationship, Models, Statistical, Sequence Alignment, Computer-Aided Design, Protein Transport, Protein Sorting Signals
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