TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence

Zhe Liu; Yingli Gong; Yuanzhao Guo; Xiao Zhang; Chang Lu; Li Zhang; Han Wang

doi:10.3389/fgene.2021.656140

TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence

Front Genet. 2021 Mar 15:12:656140. doi: 10.3389/fgene.2021.656140. eCollection 2021.

Authors

Zhe Liu^{1

2

3}, Yingli Gong⁴, Yuanzhao Guo², Xiao Zhang⁵, Chang Lu², Li Zhang¹, Han Wang²

Affiliations

¹ School of Computer Science and Engineering, Changchun University of Technology, Changchun, China.
² School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China.
³ Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
⁴ College of Intelligence and Computing, Tianjin University, Tianjin, China.
⁵ College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA, United States.

Abstract

Transmembrane protein (TMP) is an important type of membrane protein that is involved in various biological membranes related biological processes. As major drug targets, TMPs' surfaces are highly concerned to form the structural biases of their material-bindings for drugs or other biological molecules. However, the quantity of determinate TMP structures is still far less than the requirements, while artificial intelligence technologies provide a promising approach to accurately identify the TMP surfaces, merely depending on their sequences without any feature-engineering. For this purpose, we present an updated TMP surface residue predictor TMP-SSurface2 which achieved an even higher prediction accuracy compared to our previous version. The method uses an attention-enhanced Bidirectional Long Short Term Memory (BiLSTM) network, benefiting from its efficient learning capability, some useful latent information is abstracted from protein sequences, thus improving the Pearson correlation coefficients (CC) value performance of the old version from 0.58 to 0.66 on an independent test dataset. The results demonstrate that TMP-SSurface2 is efficient in predicting the surface of transmembrane proteins, representing new progress in transmembrane protein structure modeling based on primary sequences. TMP-SSurface2 is freely accessible at https://github.com/NENUBioCompute/TMP-SSurface-2.0.

Keywords: attention mechanism; deep learning; long short term memory; relative accessible surface area; transmembrane protein.