① 大阪大学 微生物病研究所
② 大阪大学 免疫学フロンティア研究センター
① Daron M. Standley
② 朴 昭映
|代表者||Daron M. Standley|
B Cell Receptor, T Cell Receptor, Repertoire, Optical Control, Machine Learning
We will support the development of new biomarkers and therapeutics by designing and delivering functional molecules, in particular DNA or RNA derivatives, that can activate or inhibit immune cells.
This project builds on our earlier machine-learning tools that can analyze BCR and TCR repertoires. To accelerate data-driven science, we will develop and maintain new web-accessible databases, including for BCR and TCR repertoire data, SARS-CoV-2 deep mutational scanning data and protein structural alignments derived from the PDB and AlphaFold. These databases will enrich the scientific community and act as a beacon for other researchers to participate in future BINDS programs.
BCR and TCR structural modeling
Functional lustering of BCR/TCR models
Docking simulations of phospholipids
Prediction of nucleotide binding sites
Flexible oligonucleotide-protein docking
MD simulations of protein-membrane systems
We aim to provide a resourceful support system for BINDS researchers through modeling, synthesis, and validation of functional molecules. We focus on design of bioactive molecules as an activator or an inhibitor for targeting extracellular receptors or mRNAs in B or T cells. In addition, we develop methods that can harness repertoire from BCR and TCR sequencing analysis to develop specific biomarkers and therapeutics for a wide range of diseases, including infectious diseases, autoimmunity, and cancer.
For precise spatiotemporal control, we will incorporate optical conformational switches that can induce changes in phase or oligomerization. When applied to extracellular receptors, such as scFAbs on CAR T cells, such changes can be translated into activation/inactivation. These efforts are already of interest to clinical researchers and pharmaceutical companies. We will also carry out modeling of supramolecular complexes in realistic biological membranes with and without bound ligands, as well as perform simulations using molecular dynamics. An example of such a simulation is the simulation of two SARS-CoV-2 spike proteins embedded in a membrane and cross-linked by an infection-enhancing antibody. This simulation is providing new insight into the mechanism of infection-enhancing antibodies. In addition to targeting extracellular receptors, we will design mRNA silencing agents and RNase inhibitors to modulate mRNA expression. For example, inhibition of the endoribonuclease Regnase-1 has been shown to be an effective way of amplifying the strengths and duration of cytotoxic T cells for use in cancer therapy. To this end, we will design RNA mimicking inhibitors based on known Regnase-1 targets. To further strengthen our bioinformatics and simulations, we will continue to interact closely with experimental structural biologists, including experts in X-ray crystallography, Cryo-EM and HDX-MS. We will also support the development of young researchers through hands-on programming and data science training for students and researchers from medical or wet biology backgrounds.