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

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10608 - Biochemistry and molecular biology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Nucleic Acids Research

ISSN

0305-1048

e-ISSN

1362-4962

Volume of the periodical

53

Issue of the periodical within the volume

22

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

gkaf1348

UT code for WoS article

001637985400001

EID of the result in the Scopus database

2-s2.0-105024587762