DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues.
In: Computers in biology and medicine, Jg. 170 (2024-03-01), S. 108081
academicJournal
Zugriff:
DNA-binding and RNA-binding proteins are essential to an organism's normal life cycle. These proteins have diverse functions in various biological processes. DNA-binding proteins are crucial for DNA replication, transcription, repair, packaging, and gene expression. Likewise, RNA-binding proteins are essential for the post-transcriptional control of RNAs and RNA metabolism. Identifying DNA- and RNA-binding residue is essential for biological research and understanding the pathogenesis of many diseases. However, most DNA-binding and RNA-binding proteins still need to be discovered. This research explored various properties of the protein sequences, such as amino acid composition type, Position-Specific Scoring Matrix (PSSM) values of amino acids, Hidden Markov model (HMM) profiles, physiochemical properties, structural properties, torsion angles, and disorder regions. We utilized a sliding window technique to extract more information from a target residue's neighbors. We proposed an optimized Light Gradient Boosting Machine (LightGBM) method, named DRBpred, to predict DNA-binding and RNA-binding residues from the protein sequence. DRBpred shows an improvement of 112.00 %, 33.33 %, and 6.49 % for the DNA-binding test set compared to the state-of-the-art method. It shows an improvement of 112.50 %, 16.67 %, and 7.46 % for the RNA-binding test set regarding Sensitivity, Mathews Correlation Coefficient (MCC), and AUC metric.
Competing Interests: Declaration of competing interest There is no conflict of interest with any of the authors or the suggested reviewers (if any).
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Titel: |
DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues.
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Autor/in / Beteiligte Person: | Kabir, MWU ; Alawad, DM ; Pokhrel, P ; Hoque, MT |
Zeitschrift: | Computers in biology and medicine, Jg. 170 (2024-03-01), S. 108081 |
Veröffentlichung: | New York : Elsevier ; <i>Original Publication</i>: New York, Pergamon Press., 2024 |
Medientyp: | academicJournal |
ISSN: | 1879-0534 (electronic) |
DOI: | 10.1016/j.compbiomed.2024.108081 |
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