Directed Evolution of Proteins via Bayesian Optimization in Embedding Space

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00383592" target="_blank" >RIV/68407700:21230/24:00383592 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/BIBM62325.2024.10822356" target="_blank" >https://doi.org/10.1109/BIBM62325.2024.10822356</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM62325.2024.10822356" target="_blank" >10.1109/BIBM62325.2024.10822356</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space

Popis výsledku v původním jazyce

Directed evolution is an iterative laboratory process of designing proteins with improved function by iteratively synthesizing new protein variants and evaluating their desired property with expensive and time-consuming biochemical screening. Machine learning methods can help select informative or promising variants for screening to increase their quality and reduce the amount of necessary screening. In this paper, we present a novel method for machine-learning-assisted directed evolution of proteins which combines Bayesian optimization with informative representation of protein variants extracted from a pre-trained protein language model. We demonstrate that the new representation based on the sequence embeddings significantly improves the performance of Bayesian optimization yielding better results with the same number of conducted screening in total. At the same time, our method outperforms the state-of-the-art machine-learning-assisted directed evolution methods with regression objective.

Název v anglickém jazyce

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space

Popis výsledku anglicky

Directed evolution is an iterative laboratory process of designing proteins with improved function by iteratively synthesizing new protein variants and evaluating their desired property with expensive and time-consuming biochemical screening. Machine learning methods can help select informative or promising variants for screening to increase their quality and reduce the amount of necessary screening. In this paper, we present a novel method for machine-learning-assisted directed evolution of proteins which combines Bayesian optimization with informative representation of protein variants extracted from a pre-trained protein language model. We demonstrate that the new representation based on the sequence embeddings significantly improves the performance of Bayesian optimization yielding better results with the same number of conducted screening in total. At the same time, our method outperforms the state-of-the-art machine-learning-assisted directed evolution methods with regression objective.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine

ISBN

979-8-3503-8622-6

ISSN

2156-1125

e-ISSN

2156-1133

Počet stran výsledku

8

Strana od-do

91-98

Název nakladatele

IEEE Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Rio de Janeiro

Místo konání akce

Lisbon

Datum konání akce

3. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001446153500024