Cation Chloride Cotransporter NKCC1 Operates through a Rocking-Bundle Mechanism

Abstract

The sodium, potassium, and chloride cotransporter 1 (NKCC1) plays a key role in tightly regulating ion shuttling across cell membranes. Lately, its aberrant expression and function have been linked to numerous neurological disorders and cancers, making it a novel and highly promising pharmacological target for therapeutic interventions. A better understanding of how NKCC1 dynamically operates would therefore have broad implications for ongoing efforts toward its exploitation as a therapeutic target through its modulation. Based on recent structural data on NKCC1, we reveal conformational motions that are key to its function. Using extensive deep-learning-guided atomistic simulations of NKCC1 models embedded into the membrane, we captured complex dynamical transitions between alternate open conformations of the inner and outer vestibules of the cotransporter and demonstrated that NKCC1 has water-permeable states. We found that these previously undefined conformational transitions occur via a rocking-bundle mechanism characterized by the cooperative angular motion of transmembrane helices (TM) 4 and 9, with the contribution of the extracellular tip of TM 10. We found these motions to be critical in modulating ion transportation and in regulating NKCC1's water transporting capabilities. Specifically, we identified interhelical dynamical contacts between TM 10 and TM 6, which we functionally validated through mutagenesis experiments of 4 new targeted NKCC1 mutants. We conclude showing that those 4 residues are highly conserved in most Na + -dependent cation chloride cotransporters (CCCs), which highlights their critical mechanistic implications, opening the way to new strategies for NKCC1's function modulation and thus to potential drug action on selected CCCs.

Machine Learning Derived Collective Variables for the Study of Protein Homodimerization in Membrane

“…The DeepLDA protocol has been successfully used to derive collective variables to study rare events in biomolecules. , We used the DeepLDA protocol to study the dimerization of proteins in the membrane. Data obtained from the unbiased simulations were used to train the NN models.…”