Functional annotation of creeping bentgrass protein sequences based on convolutional neural network

TL;DR

This study developed a convolutional neural network model to annotate uncharacterized protein sequences in creeping bentgrass, aiding understanding of disease resistance mechanisms and improving research efficiency.

Contribution

The paper introduces a CNN-based prediction model for functional annotation of proteins in creeping bentgrass, especially for sequences unannotated by traditional database methods.

Findings

Successfully annotated non-annotated protein sequences related to disease resistance.

The CNN model effectively reduces research time and labor in protein function analysis.

Provides a reference approach for turfgrass disease-resistance studies.

Abstract

Background: 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. Results: 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. Conclusions: 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.