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Updated: May 16, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
NAD_MCNN: Combining Protein Language Models and Multiwindow Convolutional Neural Networks for Deacetylase NAD+
Van-The Le1, Yu-Chen Liu1, Yan-Yun Chang1
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.
Abstract:
Sirtuins, a class of NAD+ -dependent deacetylases, play a key role in aging, metabolism, and longevity. Their interaction with NAD+ at the catalytic site is crucial for function, but experimental methods to map NAD+ binding sites are time consuming. To address this, we developed a computational method integrating pretrained protein language models with multiwindow convolutional neural networks (CNNs). This method captures sequence information and diverse local patterns, achieving state-of-the-art performance, with AUC of 0.9733 for human sirtuin proteins and 0.9701 for other NAD-dependent deacylation enzymes. These findings offer insights into the role of sirtuins in aging and their broader biological functions while providing a new path for identifying therapeutic targets in aging-related diseases.
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