9n5y
Hemagglutinin CA09 homotrimer bound to AEL31302/AEL31311 Fab
Structural highlights
FunctionA0A3S7XTA4_9INFA Binds to sialic acid-containing receptors on the cell surface, bringing about the attachment of the virus particle to the cell. This attachment induces virion internalization either through clathrin-dependent endocytosis or through clathrin- and caveolin-independent pathway. Plays a major role in the determination of host range restriction and virulence. Class I viral fusion protein. Responsible for penetration of the virus into the cell cytoplasm by mediating the fusion of the membrane of the endocytosed virus particle with the endosomal membrane. Low pH in endosomes induces an irreversible conformational change in HA2, releasing the fusion hydrophobic peptide. Several trimers are required to form a competent fusion pore.[HAMAP-Rule:MF_04072] Binds to sialic acid-containing receptors on the cell surface, bringing about the attachment of the virus particle to the cell. This attachment induces virion internalization of about two third of the virus particles through clathrin-dependent endocytosis and about one third through a clathrin- and caveolin-independent pathway. Plays a major role in the determination of host range restriction and virulence. Class I viral fusion protein. Responsible for penetration of the virus into the cell cytoplasm by mediating the fusion of the membrane of the endocytosed virus particle with the endosomal membrane. Low pH in endosomes induces an irreversible conformational change in HA2, releasing the fusion hydrophobic peptide. Several trimers are required to form a competent fusion pore.[ARBA:ARBA00059860][RuleBase:RU003324] Publication Abstract from PubMedMany proteins are highly flexible and their ability to adapt their shape can be fundamental to their functional properties. For example, the flexibility of antibody complementarity-determining region (CDR) loops influences binding affinity and specificity, making it a key factor in understanding and designing antigen interactions. With methods such as AlphaFold, it is possible to computationally predict a single, static protein structure with high accuracy. However, the reliable prediction of structural flexibility has not yet been achieved. A major factor limiting such predictions is the scarcity of suitable training data. Here we focus on predicting the structural flexibility of functionally important antibody and T cell receptor CDR3 loops. To this end, we constructed ALL-conformations by extracting CDR3s and CDR3-like loop motifs from all structures deposited in the Protein Data Bank. This dataset comprises 1.2 million loop structures representing more than 100,000 unique sequences and captures all experimentally observed conformations of these motifs. Using this dataset, we develop ITsFlexible, a deep learning tool with graph neural network architecture. We trained the model to binary classify CDR loops as 'rigid' or 'flexible' from inputs of antibody structures. ITsFlexible outperforms all alternative approaches on our crystal structure datasets and successfully generalizes to molecular dynamics simulations. We also used ITsFlexible to predict the flexibility of three CDRH3 loops with no solved structures and experimentally determined their conformations using cryogenic electron microscopy. Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions.,Spoendlin FC, Fernandez-Quintero ML, Raghavan SSR, Turner HL, Gharpure A, Loeffler JR, Wong WK, Bujotzek A, Georges G, Ward AB, Deane CM Nat Mach Intell. 2025;7(10):1755-1767. doi: 10.1038/s42256-025-01131-6. Epub 2025 , Oct 16. PMID:41143207[1] From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine. References
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