Biology subjects

Miao, X.

Publications and source records attributed to Miao, X..

21 records · Page 2Linked to original sources

bioRxiv2020

Comparative study of curvature sensing mediated by F-BAR domain and an intrinsically disordered region of FBP17

Membrane curvature has emerged as an intriguing physical organization principle underlying biological signaling and membrane trafficking. FBP17 of the CIP4/FBP17/Toca-1 F-BAR family is unique in the BAR family because its structurally folded F-BAR domain does not contain any hydrophobic motifs that insert into lipid bilayer. While it has been widely assumed so, whether the banana-shaped F-BAR domain alone can sense curvature has never been experimentally demonstrated. Using a nanopillar-supported lipid bilayer system, we found that the F-BAR domain of FBP17 displayed minimal curvature sensing in vitro. We further identified an alternatively spliced intrinsically disordered region (IDR) of FBP17 next to its F-BAR domain that is conserved in sequence across species. The IDR senses membrane curvature and its sensing ability greatly exceeds that of F-BAR domain alone. In living cells, presence of the IDR domain changed the dynamics of FBP17 recruitment in a curvature-coupled cortical wave system. Collectively, we propose that FBP17 does sense curvature but contrary to the common belief, its curvature sensing capability largely originates from its disordered region, not F-BAR domain itself.

biophysics↗

bioRxiv2020

A novel biparatopic antibody-ACE2 fusion that blocks SARS-CoV-2 infection: implications for therapy

In the absence of a proven effective vaccine preventing infection by SARS-CoV-2, or a proven drug to treat COVID-19, the positive results of passive immune therapy using convalescent serum provides a strong lead. We have developed a new class of tetravalent, biparatopic therapy, 89C8-ACE2. It combines the specificity of a monoclonal antibody (89C8) that recognizes the relatively conserved N-terminal domain (NTD) of the viral S glycoprotein, and the ectodomain of ACE2, which binds to the receptor-binding domain (RBD) of S. This molecule shows exceptional performance in vitro, inhibiting the interaction of recombinant S1 to ACE2 and transduction of ACE2-overexpressing cells by S-pseudotyped lentivirus with IC50s substantially below 100 pM, and with potency approximately 100-fold greater than ACE2-Fc itself. Moreover, 89C8-ACE2 was able to neutralize authentic virus infection in a standard assay at low nanomolar concentrations, making this class of molecule a promising lead for therapeutic applications.

immunology↗

bioRxiv2019

MultiCapsNet: a interpretable deep learning classifier integrate data from multiple sources

Recent advances in experimental biology have generated huge amount of data. Due to differences present in detection targets and detection mechanisms, the produced data comes with different formats and lengths. There is an urgent call for computational methods to integrate these diverse data. Deep learning model is an ideal tool to cope with complex datasets, but its inherent black box nature needs more interpretability. Here, we present MultiCapsNet, a deep learning model built on CapsNet and scCapsNet. The MultiCapsNet model possesses the merits of both easier data integration and higher model interpretability. In the first example, we use the labeled variant call dataset, which is originally used to test the models for automating somatic variant refinement. We divide the 71 features listed in the dataset into eight groups according to data source and data property. Then, the data from those eight groups with different formats and lengths are integrated by our MultiCapsNet to predict the labels associated with each variant call. The performance of our MultiCapsNet matches the previous deep learning model well, given much less parameters than those needed by the previous model. After training, the MultiCapsNet model provides importance scores for each data source directly, while the previous deep learning model needs an extra importance determination step to do so. Despite that our MultiCapsNet model is substantially different from the previous deep learning model and the source importance measuring methods are also different, the importance score correlation between these two models is very high. In the second example, the prior knowledge, including information for protein-protein interactions and protein-DNA interactions, is used to determine the structure of MultiCapsNet model. The single cell RNA sequence data are decoupled into multiple parts according to the structure of MultiCapsNet model that has been integrated with prior knowledge, with each part represents genes influenced by a transcription factor or involved in a protein-protein interaction network and then could be viewed as a data source. The MultiCapsNet model could classify cells with high accuracy as well as reveal the contribution of each data source for cell type recognition. The high ranked contributors are often relevant to the contributed cell type.

bioinformatics↗