Omori, S. | bioRxiv Science

bioRxiv2023

Elevator motion of chloride ion transporter SLC26A9 induced by STAS domain

The anion exchanger SLC26A9, consisting of the transmembrane (TM) domain and the cytoplasmic STAS domain, plays an essential role in regulating chloride transport across cell membranes. Recent studies have indicated that C-terminal helices block the entrance of the putative ion transport pathway. However, the precise functions of the STAS domain and C-terminal helix, as well as the underlying molecular mechanisms governing the transport process, remain poorly understood. In this study, we performed molecular dynamics simulations of three distinct models of human SLC26A9: full-length (FL), STAS domain removal ({Delta}STAS), and C-terminus removal ({Delta}C), to investigate their conformational dynamics and ion binding properties. Stable binding of ions to the binding sites was exclusively observed in the {Delta}C model in these simulations. Comparing the FL and {Delta}C simulations, the {Delta}C model displayed enhanced motion of the STAS domain. Furthermore, comparing the {Delta}STAS and {Delta}C simulations, the {Delta}STAS simulation failed to exhibit stable ion bindings to the sites despite the absence of the C-terminus blocking the ion transmission pathway in both systems. These results suggest that the removal of the C-terminus not only unblocks the access of ions to the permeation pathway but also triggers STAS domain motion, gating the TM domain to promote ions entry into their binding site. Further analysis revealed that the asymmetric motion of STAS domain leads to the expansion of the ion permeation pathway within the TM domain, resulting in the stiffening of the flexible TM12 helix near the ion binding site. This structural change in the TM12 helix stabilizes chloride ion binding, which is essential for SLC26A9 elevator motion. Overall, our study provides new insights into the molecular mechanisms of SLC26A9 transport and may pave the way for the development of novel treatments for diseases associated with dysregulated ion transport. SIGNIFICANCEWe explored the mechanism by which the human protein SLC26A9 transports chloride in the cell. SLC26A9 is a potential therapeutic target for patients with cystic fibrosis, as by targeting drugs to it, it may be possible to restore chloride ion transport in epithelial cells. To design therapeutic drugs, it is essential to understand how the protein works. Our findings support an elevator-type mechanism, in which chloride ions bind to SLC26A9 inside the cell and are then released by the protein to the extracellular environment. We find that the STAS domain of SLC26A9 has critical roles in binding chloride and induces conformational changes in the transmembrane domain that facilitate chloride transport.

biophysics↗

bioRxiv2021

Prediction and Characterization of Disorder-Order Transition Regions in Proteins by Deep Learning

The biological functions of proteins are traditionally thought to depend on well-defined three-dimensional structures, but many experimental studies have shown that disordered regions lacking fixed three-dimensional structures also have crucial biological roles. In some of these regions, disorder-order transitions are also involved in various biological processes, such as protein-protein interaction and ligand binding. Therefore, it is crucial to study disordered regions and structural transitions for further understanding of protein functions and folding. Owing to the costs and time requirements of experimental identification of natively disordered or transitional regions, the development of effective computational methods is a key research goal. In this study, we used overall residue dependencies and deep representation learning for prediction and reused the obtained disordered regions for the prediction of disorder-order transitions. Two similar and related prediction tasks were combined. Firstly, we developed a novel deep learning method, Res-BiLstm, for residue-wise disordered region prediction. Our method outperformed other predictors with respect to almost all criteria, as evaluated using an independent test set. For disorder-order transition prediction, we proposed a transfer learning method, Res-BiLstm-NN, with an acceptable but unbalanced performance, yielding reasonable results. To grasp underlining biophysical principles of disorder-order transitions, we performed qualitative analyses on the obtained results and discovered that most transitions have strong disordered or ordered preferences, and more transitions are consistent with the ordered state than the disordered state, different from conventional wisdom. To the best of our knowledge, this is the first sizable-scale study of transition prediction. Availabilityhttps://github.com/Yanzziang/Transition_Disorder_Prediction Contactkengo@ecei.tohoku.ac.jp

bioinformatics↗