Z-Coordinate Prediction of Residues in -Helical Transmembrane Proteins Using Deep Learning (TM- ZC)

Abstract

Z-coordinate, defined as the residue's distance from the center of the biological membrane, is a crucial structural property of -helical transmembrane proteins (-TMPs). Neither experimentally solved nor computationally anticipated -TMP structures can z-coordinate prediction allows us to partially describe -TMP structures based on their sequences, which helps with function annotation and drug target finding, and so meets the needs of the relevant study fields. To enhance prediction accuracy and provide a useful tool, we suggested a deep learning-based predictor (TM-ZC) for the z-coordinate of residues in -TMPs. TM-ZC trained a convolutional neural network (CNN) regression model using the one-hot code and the PSSM as input features. The experimental findings showed that TM-ZC was an effective predictor that is both easy to use and quick to run, with respectable results: an average error of 1.958, a percent of prediction error within 3 of 77.461%, and a correlation coefcient (CC) of 0.922. We went on to explore how the TM-ZC predicted z-coordinate may be helpful, and we discovered that it has a high degree of consistency with topological structure and improves the prediction of surface accessibility.