The University of Osaka · Medicine
Professor Daron M. Standley's research lab specializes in computational structural biology and bioinformatics, focusing on the atomic-level modeling of immune receptors such as B cell and T cell receptors. The lab develops advanced computational tools for protein structure prediction, functional site identification, and evolutionary analysis of viral glycoproteins like the SARS-CoV-2 spike protein. By integrating structural data with evolutionary and functional insights, the lab aims to uncover mechanisms of immune recognition and viral immune evasion. Their work supports vaccine design, therapeutic development, and the understanding of protein evolution in host-pathogen interactions.
Figures are computed from collected data and may differ slightly.
Repertoire Builder (https://sysimm.org/rep_builder/) is a method for generating atomic-resolution, three-dimensional models of B cell receptors (BCRs) or T cell receptors (TCRs) from their amino acid sequences.
The Protein Data Bank Japan (PDBj) curates, edits and distributes protein structural data as a member of the worldwide Protein Data Bank (wwPDB) and currently processes approximately 25-30% of all deposited data in the world. Structural information is enhanced by the addition of biological and biochemical functional data as well as experimental details extracted from the literature and other databases. Several applications have been developed at PDBj for structural biology and biomedical studies
The SARS-CoV-2 S protein is a major point of interaction between the virus and the human immune system. As a consequence, the S protein is not a static target but undergoes rapid molecular evolution. In order to more fully understand the selection pressure during evolution, we examined residue positions in the S protein that vary greatly across closely related viruses but are conserved in the subset of viruses that infect humans. These "evolutionarily important" residues were not distributed eve
ASH shows high selectivity and sensitivity with regard to domain classification, an important step in defining distantly related protein sequence families. Moreover, the CPU cost per alignment is competitive with the fastest programs, making ASH a practical option for large-scale structure classification studies.
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