TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

Zhe Liu; Yingli Gong; Yihang Bao; Yuanzhao Guo; Han Wang; Guan Ning Lin

doi:10.3389/fbioe.2020.629937

TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

Front Bioeng Biotechnol. 2021 Jan 25:8:629937. doi: 10.3389/fbioe.2020.629937. eCollection 2020.

Authors

Zhe Liu^{1

2}, Yingli Gong³, Yihang Bao⁴, Yuanzhao Guo⁴, Han Wang⁴, Guan Ning Lin^{1

2}

Affiliations

¹ Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
² Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
³ College of Intelligence and Computing, Tianjin University, Tianjin, China.
⁴ School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China.

Abstract

Alpha transmembrane proteins (αTMPs) profoundly affect many critical biological processes and are major drug targets due to their pivotal protein functions. At present, even though the non-transmembrane secondary structures are highly relevant to the biological functions of αTMPs along with their transmembrane structures, they have not been unified to be studied yet. In this study, we present a novel computational method, TMPSS, to predict the secondary structures in non-transmembrane parts and the topology structures in transmembrane parts of αTMPs. TMPSS applied a Convolutional Neural Network (CNN), combined with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network, to extract the local contexts and long-distance interdependencies from primary sequences. In addition, a multi-task learning strategy was used to predict the secondary structures and the transmembrane helixes. TMPSS was thoroughly trained and tested against a non-redundant independent dataset, where the Q3 secondary structure prediction accuracy achieved 78% in the non-transmembrane region, and the accuracy of the transmembrane region prediction achieved 90%. In sum, our method showcased a unified model for predicting the secondary structure and topology structure of αTMPs by only utilizing features generated from primary sequences and provided a steady and fast prediction, which promisingly improves the structural studies on αTMPs.

Keywords: alpha-helical transmembrane proteins; deep learning; long short-term memory networks; protein secondary structure; protein topology structure.