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METHODS article

Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1489116

Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion

Provisionally accepted
Liuyan Wang Liuyan Wang Rongguang Li Rongguang Li Xuemei Guan Xuemei Guan *Shanchun Yan Shanchun Yan
  • Northeast Forestry University, Harbin, China

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

    Pine Wilt Disease (PWD) is a devastating forest disease that has a serious impact on ecological balance ecological. Since the identification of plant-pathogen protein interactions (PPIs) is a critical step in understanding the pathogenic system of the pine wilt disease, this study proposes a Multi-feature Fusion Graph Attention Convolution (MFGAC-PPI) for predicting plantpathogen PPIs based on deep learning. Compared with methods based on single-feature information, MFGAC-PPI obtains more 3D characterization information by utilizing AlphaFold and combining protein sequence features to extract multi-dimensional features via Transform with improved GCN. The performance of MFGAC-PPI was compared with the current representative methods of sequence-based, structure-based and hybrid characterization, demonstrating its superiority across all metrics. The experiments showed that learning multi-dimensional feature information effectively improved the ability of MFGAC-PPI in plant and pathogen PPI prediction tasks. Meanwhile, a pine wilt disease PPI network consisting of 2,688 interacting protein pairs was constructed based on MFGAC-PPI, which made it possible to systematically discover new disease resistance genes in pine trees and promoted the understanding of plant-pathogen interactions.

    Keywords: protein-protein interaction, pine wilt disease, deep learning, Multi-dimensional feature, Pine wood nematode (Bursaphelenchus xylophilus)

    Received: 31 Aug 2024; Accepted: 12 Nov 2024.

    Copyright: © 2024 Wang, Li, Guan and Yan. 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: Xuemei Guan, Northeast Forestry University, Harbin, 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.