ArticleBioinformatics (Oxford, England)2017
Machine learning in computational biology to accelerate high-throughput protein expression.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 15 citations in OpenAlex.
- Deciphering the determinants of recombinant protein expression across the human secretome.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Advances in mRNA LNP-Based Cancer Vaccines: Mechanisms, Formulation Aspects, Challenges, and Future Directions.Journal of personalized medicine · 2024Review
- Machine learning modeling for solubility prediction of recombinant antibody fragment in four different E. coli strains.Scientific reports · 2022Article
- Machine and Deep Learning for Prediction of Subcellular Localization.Methods in molecular biology (Clifton, N.J.) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
motivationThe Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility.
resultsCombining computational biology and machine learning identifies protein properties that hinder the HPA high-throughput antibody production pipeline. We predict protein expression and solubility with accuracies of 70% and 80%, respectively, based on a subset of key properties (aromaticity, hydropathy and isoelectric point). We guide the selection of protein fragments based on these characteristics to optimize high-throughput experimentation. AVAILABILITY AND IMPLEMENTATION: We present the machine learning workflow as a series of IPython notebooks hosted on GitHub (https://github.com/SBRG/Protein_ML). The workflow can be used as a template for analysis of further expression and solubility datasets. CONTACT: ebrunk@ucsd.edu or johanr@biotech.kth.se. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.