A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Approaching Optimal pH Enzyme Prediction with Large Language Models (2024)

Zaretckii, M., Buslaev, P., Kozlovskii, I., Morozov, A., & Popov, P. (2024). Approaching Optimal pH Enzyme Prediction with Large Language Models. ACS Synthetic Biology, Early online. https://doi.org/10.1021/acssynbio.4c00465

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Zaretckii, Mark; Buslaev, Pavel; Kozlovskii, Igor; Morozov, Alexander; Popov, Petr

Lehti tai sarja: ACS Synthetic Biology

eISSN: 2161-5063

Julkaisuvuosi: 2024

Ilmestymispäivä: 28.08.2024

Volyymi: Early online

Kustantaja: American Chemical Society

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1021/acssynbio.4c00465

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/97023

Tiivistelmä

Enzymes are widely used in biotechnology due to their ability to catalyze chemical reactions: food making, laundry, pharmaceutics, textile, brewing─all these areas benefit from utilizing various enzymes. Proton concentration (pH) is one of the key factors that define the enzyme functioning and efficiency. Usually there is only a narrow range of pH values where the enzyme is active. This is a common problem in biotechnology to design an enzyme with optimal activity in a given pH range. A large part of this task can be completed in silico, by predicting the optimal pH of designed candidates. The success of such computational methods critically depends on the available data. In this study, we developed a language-model-based approach to predict the optimal pH range from the enzyme sequence. We used different splitting strategies based on sequence similarity, protein family annotation, and enzyme classification to validate the robustness of the proposed approach. The derived machine-learning models demonstrated high accuracy across proteins from different protein families and proteins with lower sequence similarities compared with the training set. The proposed method is fast enough for the high-throughput virtual exploration of protein space for the search for sequences with desired optimal pH levels.

YSO-asiasanat: entsyymit; pH; koneoppiminen; kielimallit; biotekniikka; laskennallinen kemia; in silico -menetelmä

Vapaat asiasanat: enzyme optimal pH; large language models; machine learning; protein engineering; protein engineering

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2024

Alustava JUFO-taso: 2