When will RNA get its AlphaFold moment?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652036%3A_____%2F23%3A00582988" target="_blank" >RIV/86652036:_____/23:00582988 - isvavai.cz</a>

Výsledek na webu

<a href="https://academic.oup.com/nar/article/51/18/9522/7272628?login=true" target="_blank" >https://academic.oup.com/nar/article/51/18/9522/7272628?login=true</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/nar/gkad726" target="_blank" >10.1093/nar/gkad726</a>

Jazyk výsledku

angličtina

Název v původním jazyce

When will RNA get its AlphaFold moment?

Popis výsledku v původním jazyce

The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.

Název v anglickém jazyce

When will RNA get its AlphaFold moment?

Popis výsledku anglicky

The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

<a href="/cs/project/LM2023055" target="_blank" >LM2023055: Česká národní infrastruktura pro biologická data</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

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

51

Číslo periodika v rámci svazku

18

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

9522-9352

Kód UT WoS článku

001064570800001

EID výsledku v databázi Scopus

2-s2.0-85175135995