Can protein expression be ‘solved’?

Baranowski, Catherine (author), Garcia Martin, Hector (author), Oyarzún, Diego A. (author), Spinner, Aviv (author), Desai, Bijoy (author), Petzold, Christopher J. (author), Nikolados, Evangelos-Marios (author), Jaaks-Kraatz, Sebastian (author), Gaber, Aljaž (author), Chalkley, Robert J. (author), Scannell, Devin (author), Sevey, Rachel (author), Jewett, Michael C. (author), Kelly, Peter J. (author), DeBenedictis, Erika A. (author)

Heterologous protein expression is a fundamental technique used frequently in modern day biology. It enables scientific exploration of protein function as well as development of lifesaving medicines and economically impactful industrial products. Protein expression experiments primarily remain an experience-guided trial and error situation, even though it is an approach used by nearly all biologists. Generating an openly available, large-scale protein expression dataset that spans organisms and uses a standard experimental approach would provide the machine learning community with a foundation for building a multispecies predictive model of expression. A predictive model of protein expression would have a profound commercial impact and could replace countless hours of experimentation with a higher-probability directed approach.

• Language: English
• Keywords: protein expression, machine learning, predictive models, open datasets
• Work type: Article
• Typology: 1.02 - Review Article
• Organization: FKKT - Faculty of Chemistry and Chemical Technology
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 19 str.
• Numbering: Vol. , iss.
• PID: 20.500.12556/RUL-169760
• UDC: 577.112:004.85
• ISSN on article: 0167-7799
• DOI: 10.1016/j.tibtech.2025.04.021
• COBISS.SI-ID: 238521091
• Publication date in RUL: 09.06.2025

Record is a part of a journal

• Title: Trends in biotechnology
• Shortened title: Trends biotechnol.
• Publisher: Elsevier
• ISSN: 0167-7799
• COBISS.SI-ID: 27429888

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: izražanje proteinov, strojno učenje, napovedni modeli, odprte baze podatkov