Using structured libraries, selection, and machine learning to rapidly explore the sequence space of a fluorescent deoxyribozyme

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F25%3A00643722" target="_blank" >RIV/61388963:_____/25:00643722 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11310/25:10507205 RIV/60461373:22310/25:43933629

Výsledek na webu

<a href="https://doi.org/10.1093/nar/gkaf1348" target="_blank" >https://doi.org/10.1093/nar/gkaf1348</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/nar/gkaf1348" target="_blank" >10.1093/nar/gkaf1348</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using structured libraries, selection, and machine learning to rapidly explore the sequence space of a fluorescent deoxyribozyme

Popis výsledku v původním jazyce

Finding ways to more comprehensively explore the sequence space of complex functional motifs is an important and unresolved question in nucleic acid engineering. Standard approaches use libraries in which a single variant of a motif is randomly mutagenized at a low level. This provides comprehensive coverage of sequence space over short mutational distances, but only limited information about more distant variants. Here we describe a new approach that uses libraries made up of sequences consistent with the multiple constraints of a desired target motif. Functional variants are rapidly identified in a single round of selection followed by high-throughput sequencing, and rules relating sequence to function are elucidated using machine learning. This method was tested using a fluorescent deoxyribozyme recently discovered in our group called Aurora. Single-step selections showed that a secondary structure library based on Aurora contained similar to 40-fold more unique catalytic sequences than one generated by random mutagenesis. Furthermore, models developed by machine learning could quantitatively predict read numbers and identify the most active variants using small subsets of sequences as training sets. By combining secondary structure libraries, selection, and machine learning in this way, sequence space can be explored far more quickly and efficiently than in standard approaches.

Název v anglickém jazyce

Using structured libraries, selection, and machine learning to rapidly explore the sequence space of a fluorescent deoxyribozyme

Popis výsledku anglicky

Finding ways to more comprehensively explore the sequence space of complex functional motifs is an important and unresolved question in nucleic acid engineering. Standard approaches use libraries in which a single variant of a motif is randomly mutagenized at a low level. This provides comprehensive coverage of sequence space over short mutational distances, but only limited information about more distant variants. Here we describe a new approach that uses libraries made up of sequences consistent with the multiple constraints of a desired target motif. Functional variants are rapidly identified in a single round of selection followed by high-throughput sequencing, and rules relating sequence to function are elucidated using machine learning. This method was tested using a fluorescent deoxyribozyme recently discovered in our group called Aurora. Single-step selections showed that a secondary structure library based on Aurora contained similar to 40-fold more unique catalytic sequences than one generated by random mutagenesis. Furthermore, models developed by machine learning could quantitatively predict read numbers and identify the most active variants using small subsets of sequences as training sets. By combining secondary structure libraries, selection, and machine learning in this way, sequence space can be explored far more quickly and efficiently than in standard approaches.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Nucleic Acids Research

ISSN

0305-1048

e-ISSN

1362-4962

Svazek periodika

53

Číslo periodika v rámci svazku

22

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

gkaf1348

Kód UT WoS článku

001637985400001

EID výsledku v databázi Scopus

2-s2.0-105024587762