ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution

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.