S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE

S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE

BackgroundThe 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.MethodsBased on the sequence information...

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Main Authors: Sixi Hao, Cai-Yan Li, Xiuzhen Hu, Zhenxing Feng, Gaimei Zhang, Caiyun Yang, Huimin Hu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Genetics
Subjects:
ATP binding residues
synthetic minority over-sampling technique
deep convolutional neural network
propensity factors
dihedral angle
energy
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2024.1513201/full
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Summary:BackgroundThe 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.MethodsBased 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).ResultsThe 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.ConclusionOur 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.
ISSN:1664-8021

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