PRED-TMSdeep: Prediction of Transmembrane Topology and Signal Peptides Using Deep Learning

Accurate annotation of secreted and membrane proteins requires detecting N-terminal secretion signals, locating their cleavage sites, and distinguishing secretion-signal classes, while also predicting full transmembrane topology for both alpha-helical and beta-barrel proteins. Current tools typically address either whole-protein topology with a generic signal-peptide category or signal-peptide type classification without integrated topology annotation, leaving end-to-end labels incomplete when both features must be resolved together. Here, we present PRED-TMSdeep, a deep learning method that jointly predicts transmembrane topology and three signal peptide classes: secretory pathway/signal peptidase I (Sec/SPI), secretory pathway/signal peptidase II (Sec/SPII), and twin-arginine translocation/signal peptidase I (Tat/SPI). We introduce a two-step constrained decoding procedure that first detects transmembrane segments and signal peptides and then resolves global orientation and refines boundaries under stricter biological constraints. On redundancy-reduced datasets curated from the Orientation of Proteins in Membranes and the Protein Data Bank of Transmembrane Proteins, PRED-TMSdeep matches leading predictors for segment-level topology while improving signal peptide classification and yielding the highest overall top-1 cleavage-site accuracy. Top-1 cleavage-site accuracy reached 89.2%, compared with 84.7% for TMbed and 86.2% for SignalP 6.0, mainly reflecting strong performance on the predominant Sec/SPI class. The software is available as a web server and a batch command-line tool with pretrained models and reproducible workflows.