ArticleScientific reports2017
MatureP: prediction of secreted proteins with exclusive information from their mature regions.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 34 citations in OpenAlex.
- ThemBio · 2026Article
- Automated machine learning for genome wide association studies.Bioinformatics (Oxford, England) · 2023Article
- Evolutionary adaptation of the protein folding pathway for secretability.The EMBO journal · 2022Article
- Just Add Data: automated predictive modeling for knowledge discovery and feature selection.NPJ precision oncology · 2022Article
- Article
- Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.Scientific reports · 2021Article
- Strategies to Enhance Periplasmic Recombinant Protein Production Yields inFrontiers in bioengineering and biotechnology · 2021Review
- Phosphorylation of Extracellular Proteins inFrontiers in microbiology · 2021Article
- Observational
- Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.Journal of clinical medicine · 2020Article
- Evolution ofInternational journal of molecular sciences · 2020Review
- Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning.JMIR medical informatics · 2020Article
- Trigger factor is a bona fide secretory pathway chaperone that interacts with SecB and the translocase.EMBO reports · 2020Article
- Understanding the mammalian TRAP complex function(s).Open biology · 2020Review
- Article
- Structural Basis of the Subcellular Topology Landscape ofFrontiers in microbiology · 2019Article
- Enhancing Recombinant Protein Yields in theFrontiers in microbiology · 2019Article
- Accessory signals in protein translocation.Aging · 2018Article
- Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation.Machine learning · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
More than a third of the cellular proteome is non-cytoplasmic. Most secretory proteins use the Sec system for export and are targeted to membranes using signal peptides and mature domains. To specifically analyze bacterial mature domain features, we developed MatureP, a classifier that predicts secretory sequences through features exclusively computed from their mature domains. MatureP was trained using Just Add Data Bio, an automated machine learning tool. Mature domains are predicted efficiently with ~92% success, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC). Predictions were validated using experimental datasets of mutated secretory proteins. The features selected by MatureP reveal prominent differences in amino acid content between secreted and cytoplasmic proteins. Amino-terminal mature domain sequences have enhanced disorder, more hydroxyl and polar residues and less hydrophobics. Cytoplasmic proteins have prominent amino-terminal hydrophobic stretches and charged regions downstream. Presumably, secretory mature domains comprise a distinct protein class. They balance properties that promote the necessary flexibility required for the maintenance of non-folded states during targeting and secretion with the ability of post-secretion folding. These findings provide novel insight in protein trafficking, sorting and folding mechanisms and may benefit protein secretion biotechnology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.