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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1513201
This article is part of the Research Topic Deep Machine Learning and Big Data Resources for Transcriptional Regulation Analysis, Volume II View all articles

S-DCNN: Prediction of ATP Binding Residues by Deep Convolutional Neural Network Based on SMOTE

Provisionally accepted
Sixi Hao Sixi Hao 1,2Cai-yan Li Cai-yan Li 3Xiuzhen Hu Xiuzhen Hu 1*Zhenxing Feng Zhenxing Feng 1*Gaimei Zhang Gaimei Zhang 4Caiyun Yang Caiyun Yang 1Huimin Hu Huimin Hu 1
  • 1 Inner Mongolia University of Technology, Hohhot, China
  • 2 Xinyang College, Xinyang, China
  • 3 Baotou Medical College, Baotou, China
  • 4 Huhhot First Hospital, Huhhot, China

The final, formatted version of the article will be published soon.

    The realization of many protein functions requires binding with ligands. As a significant protein-binding ligand, ATP plays a crucial role in various biological processes. Currently, the precise prediction of ATP binding residues remains challenging.Methods: Based on the sequence information, this paper introduces a method called S-DCNN for predicting ATP binding residues, utilizing a deep convolutional neural network (DCNN) enhanced with the synthetic minority over-sampling technique (SMOTE).The incorporation of additional feature parameters such as dihedral angles, energy, and propensity factors into the standard parameter set resulted in a significant enhancement in prediction accuracy on the ATP-289 dataset. The S-DCNN achieved the highest Matthews correlation coefficient value of 0.5031 and an accuracy rate of 97.06% on an independent test set. Furthermore, when applied to the ATP-221 and ATP-388 datasets for validation, the S-DCNN outperformed existing methods on ATP-221 and performed comparably to other methods on ATP-388 during independent testing.Our experimental results underscore the efficacy of the S-DCNN in accurately predicting ATP binding residues, establishing it as a potent tool in the prediction of ATP binding residues.

    Keywords: ATP binding residues, Synthetic minority over-sampling technique, Deep convolutional neural network, propensity factors, Dihedral angle, Energy

    Received: 18 Oct 2024; Accepted: 11 Dec 2024.

    Copyright: © 2024 Hao, Li, Hu, Feng, Zhang, Yang and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xiuzhen Hu, Inner Mongolia University of Technology, Hohhot, China
    Zhenxing Feng, Inner Mongolia University of Technology, Hohhot, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.