School of Physics and Technology, Nanjing Normal University, Nanjing, 210097, Jiangsu, China.
Sino-U.S. Center for Grazingland Ecosystem Sustainability/Pratacultural Engineering Laboratory of Gansu Province/ Key Laboratory of Grassland Ecosystem, Ministry of Education/College of Pratacultural Science, Gansu Agricultural University, Lanzhou, Gansu, 730070, China.
BMC Plant Biol. 2022 May 2;22(1):227. doi: 10.1186/s12870-022-03607-8.
Creeping bentgrass (Agrostis soionifera) is a perennial grass of Gramineae, belonging to cold season turfgrass, but has poor disease resistance. Up to now, little is known about the induced systemic resistance (ISR) mechanism, especially the relevant functional proteins, which is important to disease resistance of turfgrass. Achieving more information of proteins of infected creeping bentgrass is helpful to understand the ISR mechanism.
With BDO treatment, creeping bentgrass seedlings were grown, and the ISR response was induced by infecting Rhizoctonia solani. High-quality protein sequences of creeping bentgrass seedlings were obtained. Some of protein sequences were functionally annotated according to the database alignment while a large part of the obtained protein sequences was left non-annotated. To treat the non-annotated sequences, a prediction model based on convolutional neural network was established with the dataset from Uniport database in three domains to acquire good performance, especially the higher false positive control rate. With established model, the non-annotated protein sequences of creeping bentgrass were analyzed to annotate proteins relevant to disease-resistance response and signal transduction.
The prediction model based on convolutional neural network was successfully applied to select good candidates of the proteins with functions relevant to the ISR mechanism from the protein sequences which cannot be annotated by database alignment. The waste of sequence data can be avoided, and research time and labor will be saved in further research of protein of creeping bentgrass by molecular biology technology. It also provides reference for other sequence analysis of turfgrass disease-resistance research.
匍匐翦股颖（Agrostis soionifera）是禾本科多年生草本植物，属于冷季型草坪草，但抗病性差。目前，对于其诱导系统抗性（ISR）机制，尤其是相关功能蛋白知之甚少，这对于草坪草的抗病性非常重要。获得更多受侵染匍匐翦股颖的蛋白质信息有助于了解 ISR 机制。
用 BDO 处理匍匐翦股颖幼苗，用立枯丝核菌感染诱导 ISR 反应。获得了匍匐翦股颖幼苗的高质量蛋白质序列。根据数据库比对对部分蛋白质序列进行了功能注释，而大部分获得的蛋白质序列则未注释。为了处理未注释的序列，基于卷积神经网络建立了一个预测模型，使用 Uniport 数据库中的三个域的数据集来获得良好的性能，特别是更高的假阳性控制率。利用建立的模型，对匍匐翦股颖的未注释蛋白质序列进行分析，以注释与抗病反应和信号转导相关的蛋白质。
基于卷积神经网络的预测模型成功地应用于从不能通过数据库比对注释的蛋白质序列中选择与 ISR 机制相关功能的蛋白质的良好候选者。避免了序列数据的浪费，节省了进一步通过分子生物学技术研究匍匐翦股颖蛋白质的研究时间和劳动力。这也为其他草坪草抗病性研究的序列分析提供了参考。
