qi, j. | Search bioRxiv

bioRxiv2023

Force sensation by a Gs-coupled adhesion GPCR mediates equilibrioception by increasing vestibular hair cell membrane excitability and CNGA3 coupling

AbstractEquilibrioception is essential for the perception and navigation of mammals in the three-dimensional world. A rapid mechanoelectrical transduction (MET) response in vestibular hair cells plays a critical role in positional and motional perception. Here, we identified that the G protein-coupled receptor LPHN2/ADGRL2, which is expressed in the apical membrane of utricular hair cells, is required for maintenance of normal balance. Hair cell-specific Lphn2 deficiency in mice impaired both balance behaviors and MET responses. Functional analyses using Pou4f3-CreER+/-; Lphn2fl/fl mice and LPHN2-specific inhibitors revealed that LPHN2 regulated the tip link-independent MET current at the apical surface of the utricular hair cell by converting force stimuli into transmembrane channel-like protein 1 (TMC1) activity. Force sensation by LPHN2 also induced glutamate release and calcium signaling in utricular hair cells. Reintroduction of LPHN2 into the hair cells of Lphn2-deficient mice restored the vestibular functions and MET responses. Our data suggest an indispensable role for a mechanosensitive GPCR in equilibrioception.

physiology↗

bioRxiv2023

PGAR-Zernike: an ultra-fast, accurate and fully open-sourcestructure retrieval toolkit for convenient structural database construction

With the release of AlphaFold2, protein model databases are growing at an unprecedented rate. Efficient structure retrieval schemes are becoming more and more important to quickly analyze structure models. The core problem in structural retrieval is how to measure the similarity between structures. Some structure alignment algorithms can solve this problem but at a substantial time cost. At present, the state-of-the-art method is to convert protein structures into 3D Zernike descriptors and evaluate the similarity between structures by Euclidean distance. However, methods for computing 3D Zernike descriptors of protein structures are almost always based on structural surfaces and most are web servers, which is not conducive for users to analyze customized datasets. To overcome this limitation, we propose PGAR-Zernike, a convenient toolkit for computing different types of Zernike descriptors of structures: the user simply needs to enter one line of command to calculate the Zernike descriptors of all structures in a customized datasets. Compared with the state-of-the-art method based on 3D Zernike descriptors and an efficient structural comparison tool, PGAR-Zernike achieves higher retrieval accuracy and binary classification accuracy on benchmark datasets with different attributes. In addition, we show how PGA-Zernike completes the construction of the descriptor database and the protocol used for the PDB dataset so as to facilitate the local deployment of this tool for interested readers. We construct a demonstration containing 590685 structures; at this scale, our retrieval system takes only 4 ~ 9 seconds to complete a retrieval, and experiments show that it reaches the state-of-the-art level in terms of accuracy. PGAR-Zernike is an open-source toolkit, whose source code and related data are accessible at https://github.com/junhaiqi/PGAR-Zernike/.

bioinformatics↗