Structural Bioinformatics of Membrane Proteins

Abstract

Membrane proteins constitute approximately 30 percent of all proteomes and represent over 60 percent of current drug targets, yet their structural characterisation lags far behind soluble proteins due to the difficulty of extracting, stabilising, and crystallising them outside their native lipid environment. We evaluated 212 structural bioinformatics programmes for membrane proteins across centres in Switzerland, Austria, and Spain between 2016 and 2021, spanning five methodological categories: homology modelling with membrane-specific templates, deep-learning ab initio prediction, coarse-grained molecular dynamics in lipid bilayers, cryo-EM-guided computational refinement, and transmembrane topology and contact prediction. A Membrane Protein Structural Bioinformatics Index (MPSBI) was constructed from five sub-scores -- backbone prediction accuracy, transmembrane helix placement fidelity, lipid-protein interface modelling quality, functional site prediction accuracy, and experimental validation concordance -- with weights from regression against sustained adoption into structural biology or drug discovery pipelines. MPSBI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted methods with an AUC of 0.882. Deep-learning ab initio prediction scored highest (mean MPSBI 0.824), while coarse-grained MD trailed at 0.596. Only 35.4 percent of programmes exceeded the 0.75 threshold. Backbone accuracy carried the largest regression weight (beta = +0.280), followed by functional site prediction (beta = +0.228).