Title
An in silico approach to identification, categorization and prediction of nucleic acid binding proteins
Abstract
The interaction between proteins and nucleic acid plays an important role in many processes, such as transcription, translation and DNA repair. The mechanisms of related biological events can be understood by exploring the function of proteins in these interactions. The number of known protein sequences has increased rapidly in recent years, but the databases for describing the structure and function of protein have unfortunately grown quite slowly. Thus, improving such databases is meaningful for predicting protein-nucleic acid interactions. Furthermore, the mechanism of related biological events, such as viral infection or designing novel drug targets, can be further understood by understanding the function of proteins in these interactions. The information for each sequence, including its function and interaction sites, were collected and identified, and a database called PNIDB was built. The proteins in PNIDB were grouped into 27 classes, such as transcription, immune system, and structural protein, etc. The function of each protein was then predicted using a machine learning method. Using our method, the predictor was trained on labeled sequences, and then the function of a protein was predicted based on the trained classifier. The prediction accuracy achieved a score of 77.43% by 10-fold cross validation.
Year
DOI
Venue
2021
10.1093/bib/bbaa171
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
DNA-binding proteins, RNA-binding proteins, classification, gene ontology, PNIDB
Journal
22
Issue
ISSN
Citations 
3
1467-5463
1
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Lei Xu110.68
Shanshan Jiang212920.15
Jin Wu321.70
quan zou455867.61