Modellentwicklung und maschinelles Lernen erhöhen die Proteinausbeute

Trösemeier, Jan-Hendrik; Rudorf, Sophia; Lößner, Holger; Hofner, Benjamin; Kamp, Christel

doi:10.1007/s12268-020-1369-3

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Modellentwicklung und maschinelles Lernen erhöhen die Proteinausbeute

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Rudorf, Sophia
Sophia Rudorf, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Trösemeier, J.-H., Rudorf, S., Lößner, H., Hofner, B., & Kamp, C. (2020). Modellentwicklung und maschinelles Lernen erhöhen die Proteinausbeute. Biospektrum, 26(3), 262-264. doi:10.1007/s12268-020-1369-3.

Cite as: https://hdl.handle.net/21.11116/0000-0006-6C6E-7

Abstract

Heterologous expression of genes requires their adaptation to the host organism to achieve adequate protein synthesis rates. Typically codons are adjusted to resemble those seen in highly expressed genes of the host organism which lacks a deeper understanding of codon optimality. The codon-specific elongation model (COSEM) identifies optimal codon choices by simulating ribosome dynamics during mRNA translation. COSEM is used in combination with machine learning techniques to predict protein abundance and to optimize codon usage.