{"title":"Research progress on prediction of RNA-protein binding sites in the past five years","authors":"Yun Zuo , Huixian Chen , Lele Yang, Ruoyan Chen, Xiaoyao Zhang, Zhaohong Deng","doi":"10.1016/j.ab.2024.115535","DOIUrl":null,"url":null,"abstract":"<div><p>Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018–2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.</p></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"691 ","pages":"Article 115535"},"PeriodicalIF":2.6000,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical biochemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003269724000794","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018–2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.
期刊介绍:
The journal''s title Analytical Biochemistry: Methods in the Biological Sciences declares its broad scope: methods for the basic biological sciences that include biochemistry, molecular genetics, cell biology, proteomics, immunology, bioinformatics and wherever the frontiers of research take the field.
The emphasis is on methods from the strictly analytical to the more preparative that would include novel approaches to protein purification as well as improvements in cell and organ culture. The actual techniques are equally inclusive ranging from aptamers to zymology.
The journal has been particularly active in:
-Analytical techniques for biological molecules-
Aptamer selection and utilization-
Biosensors-
Chromatography-
Cloning, sequencing and mutagenesis-
Electrochemical methods-
Electrophoresis-
Enzyme characterization methods-
Immunological approaches-
Mass spectrometry of proteins and nucleic acids-
Metabolomics-
Nano level techniques-
Optical spectroscopy in all its forms.
The journal is reluctant to include most drug and strictly clinical studies as there are more suitable publication platforms for these types of papers.