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.
Highlights
Membrane proteins (MPs) are pivotal players in several physiological events, such as signal transduction, neurotransmitter adhesion, ion transport, etc. (Goddard et al, 2015; Roy, 2015)
Secondary structure labels were obtained by the DSSP program (Kabsch and Sander, 1983) through Protein Data Bank (PDB) files, and topology structures were collected from PDBTM
We found that the points representing “helices” of secondary structure and the ones representing “transmembrane helices” of topology structure have almost completely overlapping distributions under different labelorientated predictions
Summary
Membrane proteins (MPs) are pivotal players in several physiological events, such as signal transduction, neurotransmitter adhesion, ion transport, etc. (Goddard et al, 2015; Roy, 2015). As the major class of TMPs, alpha-helical TMPs are given great pharmacological importance, Secondary Structures of Transmembrane Protein accounting for about 60% of known drug targets in the current benchmark (Wang et al, 2019). The difficulties of acquiring their crystal structures always stand in our way due to their low solubilities in the buffers typically used in 2D-PAGE (Butterfield and Boyd-Kimball, 2004; Nugent et al, 2011). All of this is calling for accurate computational predictors. Q3 is preferred according to its low cost and close ability in depicting the secondary structure compared with Q8
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