ArticlePloS one2012
Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 27 citations in OpenAlex.
- ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.ACS omega · 2025Article
- Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost.Protein science : a publication of the Protein Society · 2025Article
- Improving glucose oxidase catalysis in Aspergillus niger via Vitreoscilla hemoglobin fusion protein.Applied microbiology and biotechnology · 2024Article
- HybridGCN for protein solubility prediction with adaptive weighting of multiple features.Journal of cheminformatics · 2023Article
- DeepSol: a deep learning framework for sequence-based protein solubility prediction.Bioinformatics (Oxford, England) · 2018Article
- Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.Chemical Society reviews · 2015Review
- A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.BMC bioinformatics · 2014Article
- SPiCE: a web-based tool for sequence-based protein classification and exploration.BMC bioinformatics · 2014Article
- How to achieve high-level expression of microbial enzymes: strategies and perspectives.BioengineeredReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large set, over 600 homologous and nearly 2,000 heterologous fungal genes, were overexpressed in Aspergillus niger using a standardized expression cassette and scored for high versus no production. Subsequently, sequence-based machine learning techniques were applied for identifying relevant DNA and protein sequence features. The amino-acid composition of the protein sequence was found to be most predictive and interpretation revealed that, for both homologous and heterologous gene expression, the same features are important: tyrosine and asparagine composition was found to have a positive correlation with high-level production, whereas for unsuccessful production, contributions were found for methionine and lysine composition. The predictor is available online at http://bioinformatics.tudelft.nl/hipsec. Subsequent work aims at validating these findings by protein engineering as a method for increasing expression levels per gene copy.
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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.