TMbed

TMbed predicts transmembrane regions in protein sequences using ProtT5 protein language model embeddings to annotate transmembrane helices (TMH), transmembrane beta strands (TMB), signal peptides, and other residues for structural and proteome-scale analyses.

Key Features:

• Embedding source: Uses embeddings from the ProtT5 protein Language Model (pLM) as input features.
• Residue-level classification: Assigns each residue to one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other.
• Post-processing filters: Applies Gaussian and Viterbi filters to refine per-residue predictions.
• Detection performance: Identifies 94±8% of beta barrel TMPs and 98±1% of alpha helical TMPs on a non-redundant dataset.
• False positive rate: Maintains false positive rates well below 1% on non-membrane proteins.
• Segment positioning accuracy: Places transmembrane segments within five residues of experimental observations.
• Comparative efficiency: Achieves computational efficiency comparable to or exceeding methods that rely on multiple sequence alignments (MSAs).
• Scalability: Enables proteome-scale prediction within hours and supports sequences up to 4,200 residues on standard GPUs such as the NVIDIA GeForce RTX 3060.

Scientific Applications:

• Proteome-scale TMP annotation: Rapidly annotates transmembrane regions across entire proteomes for large-scale studies.
• Structural analysis integration: Filters and annotates transmembrane regions in predicted 3D structures, including those from AlphaFold2.
• Proteomics and functional studies: Provides transmembrane region annotations to support investigations of protein function and membrane topology.
• Therapeutic target exploration: Supplies transmembrane region information useful for identifying and characterizing membrane protein drug targets.

Methodology:

Predicts per-residue classes using ProtT5 pLM embeddings and refines predictions with Gaussian and Viterbi filters.