Prediction of membrane protein amphiphilic helix based on horizontal visibility graph and graph convolution network

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 20; no. 6; pp. 1 - 8
• Main Authors: Jia, Baoli, Meng, Qingfang, Chen, Yuehui, Yang, Hongri
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Amino Acid Sequence; Amino acids; Amphipathic helix; Artificial neural networks; Biological activity; Biomembranes; Complex network; Convolution; Datasets; Feature extraction; Graph convolutional network; Helices; Hidden Markov models; Membrane protein; Membrane Proteins; Membranes; Predictions; Protein Domains; Protein folding; Protein structure; Proteins; Research Design; Short term memory; Structure prediction; Time series analysis; Visibility
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2023.3305493

Membrane protein amphiphilic helices play an important role in many biological processes. Based on the graph convolution network and the horizontal visibility graph the prediction method of membrane protein amphiphilic helix structure is proposed in this paper. The new dataset of amphiphilic helix is constructed. In this paper, we propose the novel feature extraction method, which characterize the amphiphilicity of membrane protein. We also extract three commonly used protein features together with the new features as protein node features. The neighbor information and long-distance dependence information of proteins are further extracted by sliding window and bidirectional long-short term memory network respectively. From the perspective of horizontal visibility algorithm, we transform protein sequences into complex networks to obtain the graph features of proteins. Then, graph convolutional network model is employed to predict the amphiphilic helix structure of membrane protein. A rigorous ten-fold cross-validation shows that the proposed method outperforms other AH prediction methods on the newly constructed dataset.