DeepTMHMM2 enables accurate prediction of transmembrane protein topology and subcellular location
TL;DR
DeepTMHMM2 is presented, the first predictor to include re-entrant regions and interfacial helices in its topologies and jointly predict localization across 17 biological membranes and Benchmark results show that DeepTMHMM2 successfully learns to predict the additional elements, while achieving strong performance on canonical α-helical and β-barrel topology prediction.
Abstract
Transmembrane α-helical and β-barrel proteins are a ubiquitous component of proteomes. Topology prediction infers how proteins are embedded in lipid bilayers, identifying membrane-spanning segments and their orientation. While recent methods achieve high performance for membrane-spanning segments, they cannot predict re-entrant regions and interfacial helices – membrane-associated segments that partially insert but do not cross the bilayer – nor identify which biological membrane a protein resides in. Here, we present DeepTMHMM2, the first predictor to include re-entrant regions and interfacial helices in its topologies and jointly predict localization across 17 biological membranes. Benchmark results show that DeepTMHMM2 successfully learns to predict the additional elements, while achieving strong performance on canonical α-helical and β-barrel topology prediction. Applying DeepTMHMM2 to Swiss-Prot reveals that non-crossing segments are a ubiquitous feature of the transmembrane proteome, with interfacial helices present in nearly a quarter of all α-helical transmembrane proteins.