Can protein expression be ‘solved’?

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

Povzetek

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.

Ključne besede

izražanje proteinov;strojno učenje;napovedni modeli;odprte baze podatkov;protein expression;machine learning;predictive models;open datasets;

Podatki

Jezik:	Angleški jezik
Leto izida:	2025
Tipologija:	1.02 - Pregledni znanstveni članek
Organizacija:	UL FKKT - Fakulteta za kemijo in kemijsko tehnologijo
UDK:	577.112:004.85
COBISS:	238521091
ISSN:	0167-7799
Št. ogledov:	176
Št. prenosov:	26
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	izražanje proteinov;strojno učenje;napovedni modeli;odprte baze podatkov;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 1-19
Letnik:	ǂVol. ǂ
Zvezek:	ǂiss. ǂ
Čas izdaje:	2025
DOI:	10.1016/j.tibtech.2025.04.021
ID:	26558431