A heuristic derived from analysis of the ion channel structural proteome permits the rapid identification of hydrophobic gates

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 28; pp. 13989 - 13995
• Main Authors: Rao, Shanlin, Klesse, Gianni, Stansfeld, Phillip J., Tucker, Stephen J., Sansom, Mark S. P.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 09.07.2019
• Series: PNAS Plus
• Subjects: Annotations; Biological membranes; Biological Sciences; Cell Membrane - chemistry; Cell Membrane - ultrastructure; Channel pores; Computer simulation; Drying; Gates; Hydrophobic and Hydrophilic Interactions; Hydrophobicity; Ion Channel Gating - genetics; Ion channels; Ion Channels - chemistry; Ion Channels - genetics; Learning algorithms; Liquid phases; Machine Learning; Membranes; Molecular dynamics; Molecular Dynamics Simulation; Nanopores - ultrastructure; Occlusion; Physical Sciences; PNAS Plus; Porosity; Protein Conformation; Proteome - chemistry; Proteome - genetics; Proteomes; Water - chemistry; hydrophobic gating; machine learning; molecular dynamics; ion channel annotation; water
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1902702116

Ion channel proteins control ionic flux across biological membranes through conformational changes in their transmembrane pores. An exponentially increasing number of channel structures captured in different conformational states are now being determined; however, these newly resolved structures are commonly classified as either open or closed based solely on the physical dimensions of their pore, and it is now known that more accurate annotation of their conductive state requires additional assessment of the effect of pore hydrophobicity. A narrow hydrophobic gate region may disfavor liquid-phase water, leading to local dewetting, which will form an energetic barrier to water and ion permeation without steric occlusion of the pore. Here we quantify the combined influence of radius and hydrophobicity on pore dewetting by applying molecular dynamics simulations and machine learning to nearly 200 ion channel structures. This allows us to propose a simple simulation-free heuristic model that rapidly and accurately predicts the presence of hydrophobic gates. This not only enables the functional annotation of new channel structures as soon as they are determined, but also may facilitate the design of novel nanopores controlled by hydrophobic gates.