TSNAPred: predicting type-specific nucleic acid binding residues via an ensemble approach. Issue 4 (27th June 2022)

• Record Type: Journal Article
• Authors: Nie, Wenjuan; Deng, Lei
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 4(2022)
• Publication Date: 2022-06-27
• Subjects: DNA-binding residues -- RNA-binding residues -- LightGBM -- Capsule network; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac244
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: The interplay between protein and nucleic acid participates in diverse biological activities. Accurately identifying the interaction between protein and nucleic acid can strengthen the understanding of protein function. However, conventional methods are too time-consuming, and computational methods are type-agnostic predictions. We proposed an ensemble predictor termed TSNAPred and first used it to identify residues that bind to A-DNA, B-DNA, ssDNA, mRNA, tRNA and rRNA. TSNAPred combines LightGBM and capsule network, both learned on the feature derived from protein sequence. TSNAPred utilizes the sliding window technique to extract long-distance dependencies between residues and a weighted ensemble strategy to enhance the prediction performance. The results show that TSNAPred can effectively identify type-specific nucleic acid binding residues in our test set. What is more, it also can discriminate DNA-binding and RNA-binding residues, which has improved 5% to 10% on the AUC value compared with other state-of-the-art methods. The dataset and code of TSNAPred are available at: https://github.com/niewenjuan-csu/TSNAPred .