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Updated: Aug 23, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse
Muhammad Taseer Suleman1, Tamim Alkhalifah2, Fahad Alturise2
1Department of Computer Science, School of Systems and Technology, University of Management & Technology, Lahore, Pakistan.
Background:
Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans.
Objective:
For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites.
Methodology:
The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches.
Results:
The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors.
Availability And Implementation:
A user-friendly web server for the proposed model was also developed and is freely available for the researchers.
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