Research in our lab includes both collaborative projects with experimental biologists as well as bioinformatics software development. Collaborative research helps us understand the workings of the immune system in greater details and also guides development of novel bioinformatics tools.

Two areas where we are actively working both collaboratively and developing new methods are B/T cell repertoires and protein-nucleotide interactions. Both projects rely heavily on multiple sequence alignment (MSA), structural modeling and machine learning. We are actively developing the MAFFT multiple alignment software, which has numerous features for supporting specific use-case scenarios (Katoh, K. and Standley, DM Mol. Biol. Evol. (2013)).

Computational Biology and Bioinformatics

Our lab specializes in a diverse array of cutting-edge scientific disciplines that span both computational and biological sciences. His expertise includes coarse-grained molecular dynamics simulation, a technique that simplifies molecular systems to understand large-scale behaviors and interactions over longer timescales. This is complemented by their proficiency in immune repertoire analysis and gene expression, where they investigate the complex dynamics of the immune system and its genetic underpinnings to uncover disease signatures and inform diagnostic strategies. Additionally, the professor applies machine learning techniques to analyze vast datasets, identify patterns, and make predictive models that can enhance understanding and treatment of diseases. His skill in structural modeling of biological macromolecules further allows them to explore the intricate three-dimensional structures of proteins, nucleic acids, and other macromolecules, providing critical insights into their functions and interactions. By integrating these diverse methodologies, the professor is able to approach biological problems from multiple angles, offering comprehensive solutions and advancing the frontiers of medical and biological research.

Advancing Bioinformatics for Phylogenetics, Virus Alignment, and Immune Gene Annotation

the MAFFT program have significantly enhanced its functionality. One key feature is the interactive selection of sequences for phylogenetic tree inference, which allows researchers to easily choose and analyze relevant sequences. The program also now supports parallel processing, enabling the use of more accurate options for larger datasets and meeting the demands of large-scale analyses.

Additionally, MAFFT has introduced new options specifically for virus genome alignment, including for SARS-CoV-2. These options are designed to handle the unique challenges of viral genetic variability.

Alongside these advancements, our team is also working on annotating immune receptor genes in eutherian genomes. This research aims to better understand the evolution and function of the mammalian immune system, potentially leading to new insights in immunotherapy and diagnostics.

Protein-Nucleotide Interactions

The interaction between proteins and nucleotides (DNA or RNA) is critical for proper regulation of immune responses as well as for direct detection and elimination of viral infections. Protein-nucleotide interactions can also be hijacked by pathogens or tumors to thwart detection by the immune system.

Because of their importance in various aspects of immunology, we have developed a tool called aaRNA to identify RNA binding sites on RNA-binding proteins (Li, S. et al. Nucleic Acids Res (2014)). We have extended aaRNA to the prediction of DNA-binding sites (aaDNA) and incorporated the binding propensities in flexible docking simulations.

As demonstrated by several studies, the predicted nucleotide binding sites agree well with experiment and provide valuable insight into the molecular mechanisms of protein-nucleotide interactions  (Hanieh, H. et al. Eur. J. Immunol.; Nyati, K. K. et al. Nucleic Acids Res (2017); Yokogawa et al. Si Rep (2016); Masuda, K. et al. J Exp Med (2016); Mino, T. et al. Cell (2015)).