PlDBPred: a novel computational model for discovery of DNA binding proteins in plants. Issue 1 (22nd November 2022)

• Record Type: Journal Article
• Authors: Pradhan, Upendra Kumar; Meher, Prabina Kumar; Naha, Sanchita; Pal, Soumen; Gupta, Ajit; Parsad, Rajender
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-11-22
• Subjects: nucleic acid binding -- deep learning -- shallow learning -- computational model -- DNA binding proteins; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac483
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: DNA-binding proteins (DBPs) play crucial roles in numerous cellular processes including nucleotide recognition, transcriptional control and the regulation of gene expression. Majority of the existing computational techniques for identifying DBPs are mainly applicable to human and mouse datasets. Even though some models have been tested on Arabidopsis, they produce poor accuracy when applied to other plant species. Therefore, it is imperative to develop an effective computational model for predicting plant DBPs. In this study, we developed a comprehensive computational model for plant specific DBPs identification. Five shallow learning and six deep learning models were initially used for prediction, where shallow learning methods outperformed deep learning algorithms. In particular, support vector machine achieved highest repeated 5-fold cross-validation accuracy of 94.0% area under receiver operating characteristic curve (AUC-ROC) and 93.5% area under precision recall curve (AUC-PR). With an independent dataset, the developed approach secured 93.8% AUC-ROC and 94.6% AUC-PR. While compared with the state-of-art existing tools by using an independent dataset, the proposed model achieved much higher accuracy. Overall results suggest that the developed computational model is more efficient and reliable as compared to the existing models for the prediction of DBPs in plants. For the convenience of the majority of experimental scientists, the developed prediction server P l DBPred is publicly accessible at https://iasri-sg.icar.gov.in/pldbpred/ .The source code is also provided at https://iasri-sg.icar.gov.in/pldbpred/source_code.php for prediction using a large-size dataset.