Accurate predictions of enzymatic biochemistry as an enabler for generation of de-novo sequences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00380848" target="_blank" >RIV/68407700:21730/24:00380848 - isvavai.cz</a>

Výsledek na webu

<a href="https://openreview.net/pdf?id=tWL5iQIlMQ" target="_blank" >https://openreview.net/pdf?id=tWL5iQIlMQ</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Accurate predictions of enzymatic biochemistry as an enabler for generation of de-novo sequences

Popis výsledku v původním jazyce

Terpene synthases (TPSs) generate the scaffolds of the largest class of natural products, including several first-line medicines. The amount of available TPS protein sequences is increasing exponentially, but computational characterization of their function remains an unsolved challenge. We assembled a curated dataset of one thousand characterized TPS reactions and developed a method to devise highly accurate machine-learning models for functional annotation in a low-data regime. Our models significantly outperform existing methods for TPS detection and substrate prediction. By applying the models to large protein sequence databases, we discovered seven TPS enzymes previously undetected by state-of-the-art computational tools and experimentally confirmed their activity. Furthermore, we discovered a new TPS structural domain and distinct subtypes of previously known domains. Our work demonstrates the potential of machine learning to speed up the discovery and characterization of novel TPSs. Furthermore, in-silico functional annotations provide the ML community with a large dataset of pseudo-labeled exemplary TPS sequences. The accurate models for TPS detection and substrate prediction can serve as oracles to check the presence of desired biochemical activity in the generated sequences. We envision the published dataset of exemplary TPS sequences and the accurate TPS-annotation models to boost the generation of de-novo enzymatic TPS sequences.

Název v anglickém jazyce

Accurate predictions of enzymatic biochemistry as an enabler for generation of de-novo sequences

Popis výsledku anglicky

Terpene synthases (TPSs) generate the scaffolds of the largest class of natural products, including several first-line medicines. The amount of available TPS protein sequences is increasing exponentially, but computational characterization of their function remains an unsolved challenge. We assembled a curated dataset of one thousand characterized TPS reactions and developed a method to devise highly accurate machine-learning models for functional annotation in a low-data regime. Our models significantly outperform existing methods for TPS detection and substrate prediction. By applying the models to large protein sequence databases, we discovered seven TPS enzymes previously undetected by state-of-the-art computational tools and experimentally confirmed their activity. Furthermore, we discovered a new TPS structural domain and distinct subtypes of previously known domains. Our work demonstrates the potential of machine learning to speed up the discovery and characterization of novel TPSs. Furthermore, in-silico functional annotations provide the ML community with a large dataset of pseudo-labeled exemplary TPS sequences. The accurate models for TPS detection and substrate prediction can serve as oracles to check the presence of desired biochemical activity in the generated sequences. We envision the published dataset of exemplary TPS sequences and the accurate TPS-annotation models to boost the generation of de-novo enzymatic TPS sequences.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceeding The Twelfth International Conference on Learning Representations (ICLR 2024)

ISBN

9781713898658

ISSN

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e-ISSN

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Počet stran výsledku

3

Strana od-do

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Název nakladatele

International Conference on Learning Representations

Místo vydání

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Místo konání akce

Vídeň

Datum konání akce

7. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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