NatureBiochemistryPeer-reviewed journal article
Bottom-up design of calcium channels from defined selectivity filter geometry
Yulai LiuiD, Connor WeidleiD, Ljubica MihaljevićiD, Joseph L. WatsoniD, Zhe LiiD, Le Tracy YuiD, Sagardip MajumderiD, Andrew J. BorstiD, Kenneth D. CarriD, Ryan D. KibleriD, Tamer M. Gamal El-DiniD, William A. CatteralliD, David A. BakeriD
Abstract
Native ion channels play key roles in biological systems, and engineered versions are widely used as chemogenetic tools and in sensing devices1,2. Protein design has been harnessed to generate pore-containing transmembrane proteins, but the capability to design ion selectivity based on the interactions between ions and selectivity filter residues, a crucial feature of native ion channels3, has been constrained by the lack of methods to place the metal-coordinating residues with atomic-level precision. Here we describe a bottom-up RFdiffusion-based approach to construct Ca2+ channels from defined selectivity filter residue geometries, and use this approach to design symmetric oligomeric channels with Ca2+ selectivity filters having different coordination numbers and different geometries at the entrance of a wide pore buttressed by multiple transmembrane helices. The designed channel proteins assemble into homogenous pore-containing particles, and for both tetrameric and hexameric ion-coordinating configurations, patch-clamp experiments show that the designed channels have higher conductances for Ca2+ than for Na+ and other divalent ions (Sr2+ and Mg2+). Cryo-electron microscopy indicates that the design method has high accuracy: the structure of the hexameric Ca2+ channel is nearly identical to the design model. Our bottom-up design approach now enables the testing of hypotheses relating filter geometry to ion selectivity by direct construction, and provides a roadmap for creating selective ion channels for a wide range of applications.
- assay: protein structure
- research focus: protein design
- approach: deep learning
- approach: diffusion model
- Citations
- 20
- FWCI
- 5.52
- Field percentile
- top 3%
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