Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin 150001, China.
State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150001, China.
Bioinformatics. 2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae581.
Nucleotide-binding leucine-rich repeat (NLR) family is a class of immune receptors capable of detecting and defending against pathogen invasion. They have been widely used in crop breeding. Notably, the correspondence between NLRs and effectors (CNE) determines the applicability and effectiveness of NLRs. Unfortunately, CNE data is very scarce. In fact, we've found a substantial 91 291 NLRs confirmed via wet experiments and bioinformatics methods but only 387 CNEs are recognized, which greatly restricts the potential application of NLRs.
We propose a deep learning algorithm called ProNEP to identify NLR-effector pairs in a high-throughput manner. Specifically, we conceptualized the CNE prediction task as a protein-protein interaction (PPI) prediction task. Then, ProNEP predicts the interaction between NLRs and effectors by combining the transfer learning with a bilinear attention network. ProNEP achieves superior performance against state-of-the-art models designed for PPI predictions. Based on ProNEP, we conduct extensive identification of potential CNEs for 91 291 NLRs. With the rapid accumulation of genomic data, we expect that this tool will be widely used to predict CNEs in new species, advancing biology, immunology, and breeding.
The ProNEP is available at http://nerrd.cn/#/prediction. The project code is available at https://github.com/QiaoYJYJ/ProNEP.
核苷酸结合富含亮氨酸重复（NLR）家族是一类能够检测和抵御病原体入侵的免疫受体。它们已被广泛应用于作物育种。值得注意的是，NLR 与效应子（CNE）的对应关系决定了 NLR 的适用性和有效性。不幸的是，CNE 数据非常稀缺。事实上，我们通过湿实验和生物信息学方法发现了大量 91291 个经证实的 NLR，但仅识别出 387 个 CNE，这极大地限制了 NLR 的潜在应用。
我们提出了一种名为 ProNEP 的深度学习算法，用于高通量识别 NLR-效应子对。具体来说，我们将 CNE 预测任务概念化为蛋白质-蛋白质相互作用（PPI）预测任务。然后，ProNEP 通过结合迁移学习和双线性注意网络来预测 NLR 和效应子之间的相互作用。ProNEP 在针对 PPI 预测设计的最新模型中表现出色。基于 ProNEP，我们对 91291 个 NLR 进行了广泛的潜在 CNE 识别。随着基因组数据的快速积累，我们预计该工具将广泛用于预测新物种中的 CNE，从而推动生物学、免疫学和育种的发展。
ProNEP 可在 http://nerrd.cn/#/prediction 上使用。项目代码可在 https://github.com/QiaoYJYJ/ProNEP 上获得。
