AUTHOR=Sun Tong-Jie , Bu He-Long , Yan Xin , Sun Zhi-Hong , Zha Mu-Su , Dong Gai-Fang 

TITLE=LABAMPsGCN: A framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1062576

DOI=10.3389/fgene.2022.1062576

ISSN=1664-8021

ABSTRACT=Lactic acid bacteria antimicrobial peptides(LABAMPs) are a class of active polypeptides produced during the metabolic process of lactic acid bacteria, which can inhibit or kill pathogenic bacteria or spoilage bacteria in food. LABAMPs have broad application prospects in important practical fields closely related to human beings, such as food production, efficient agricultural planting, and so on. However, screening for antimicrobial peptides by wet lab researchers is time-consuming and laborious. Therefore, it is urgent to develop a model to predict LABAMPs. In this work, we design a graph convolutional neural network framework for identifying of LABAMPs. We build heterogeneous graph based on amino acids, tripeptides, word co-occurrence, and sequence-word relations, then learn a graph convolutional network. Our GCN initialized with one-hot representation for words and sequences; it then jointly learns the embedding for word and sequence, as supervised by the known class labels for sequence. We applied 10-fold cross-validation experiment to two training datasets and acquired accuracies of 0.9163 and 0.9379 respectively. They are higher that of other machine learning and GNN algorithms. In an independent test dataset, accuracies of two datasets are 0.9130 and 0.9291, which are 1.08% and 1.57% higher than the best methods of other online webservers.