ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution.
- 1Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Abstract
Plant ATP-binding proteins play a central role in various physiological processes in plants, including growth and development, energy metabolism, signal transduction, and environmental adaptation. Accurately identifying ATP-binding proteins from plant protein sequences is crucial for a deeper understanding of plant physiology and their applications in agricultural biotechnology. In this study, we propose ATPPred, a tool based on the protein large language model Evolutionary Scale Modeling 2 (ESM2) and a 1-dimensional convolutional neural network for the high-precision prediction of plant ATP-binding proteins. The tool extracts global features of plant ATP-binding proteins using ESM2 and then further extracts feature information with the 1-dimensional convolutional neural network. Comprehensive performance metrics demonstrate ATPPred's competitive prediction capability. A systematic evaluation of the impact of protein sequence similarity thresholds on ATPPred yielded an average AUC ranging from 0.9191 ± 0.0161 to 0.9669 ± 0.0030 across thresholds from 0.4 to 0.9, highlighting its robustness. Cross-dataset benchmarking of ATPPred further validated the effectiveness of our framework, showcasing superior performance compared to existing methods. In summary, ATPPred provides a feasible computational approach for sequence-based prediction of plant ATP-binding proteins and may serve as a useful auxiliary tool for the preliminary screening and prioritization of candidate ATP-binding proteins.
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BibTeX
@article{zhang2026atppred,
title = {{ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution.}},
author = {Hong-Qi Zhang and Zi-Xuan Zhang and Yi-Xuan Qi and Cheng Chen and Jian Huang and Hao Lin and Ke-Jun Deng and Juan Feng},
journal = {International journal of molecular sciences},
year = {2026},
month = sep,
doi = {10.3390/ijms27198566},
url = {https://europepmc.org/article/MED/42883812},
note = {CC BY},
}
RIS
TY - JOUR
TI - ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution.
AU - Hong-Qi Zhang
AU - Zi-Xuan Zhang
AU - Yi-Xuan Qi
AU - Cheng Chen
AU - Jian Huang
AU - Hao Lin
AU - Ke-Jun Deng
AU - Juan Feng
PY - 2026
DA - 2026/09/25
JO - International journal of molecular sciences
DO - 10.3390/ijms27198566
UR - https://europepmc.org/article/MED/42883812
AB - Plant ATP-binding proteins play a central role in various physiological processes in plants, including growth and development, energy metabolism, signal transduction, and environmental adaptation. Accurately identifying ATP-binding proteins from plant protein sequences is crucial for a deeper understanding of plant physiology and their applications in agricultural biotechnology. In this study, we propose ATPPred, a tool based on the protein large language model Evolutionary Scale Modeling 2 (ESM2) and a 1-dimensional convolutional neural network for the high-precision prediction of plant ATP-binding proteins. The tool extracts global features of plant ATP-binding proteins using ESM2 and then further extracts feature information with the 1-dimensional convolutional neural network. Comprehensive performance metrics demonstrate ATPPred's competitive prediction capability. A systematic evaluation of the impact of protein sequence similarity thresholds on ATPPred yielded an average AUC ranging from 0.9191 ± 0.0161 to 0.9669 ± 0.0030 across thresholds from 0.4 to 0.9, highlighting its robustness. Cross-dataset benchmarking of ATPPred further validated the effectiveness of our framework, showcasing superior performance compared to existing methods. In summary, ATPPred provides a feasible computational approach for sequence-based prediction of plant ATP-binding proteins and may serve as a useful auxiliary tool for the preliminary screening and prioritization of candidate ATP-binding proteins.
SN - 1422-0067
ER -