Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.
Brief Bioinform
; 25(2)2024 Jan 22.
Article
de En
| MEDLINE
| ID: mdl-38261341
ABSTRACT
Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at https//web.iitm.ac.in/bioinfo2/RSAPred/.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
MicroARN
Type d'étude:
Prognostic_studies
/
Risk_factors_studies
Limites:
Humans
Langue:
En
Journal:
Brief Bioinform
Sujet du journal:
BIOLOGIA
/
INFORMATICA MEDICA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Inde