Tokyo Institute of Technology · Computer Science
Professor Masahito Ohue's research lab specializes in computational structural biology and bioinformatics, focusing on predicting protein-protein and protein-RNA interactions with high accuracy. The lab develops advanced in silico methods that integrate structural modeling, evolutionary information, and consensus scoring to enhance the reliability of interaction predictions, reducing the need for costly wet-lab validation. Key research directions include leveraging deep learning-based structure prediction tools like AlphaFold Multimer for interaction inference and refining protein docking solutions through statistical consensus approaches.
Figures are computed from collected data and may differ slightly.
Supplementary data are available at Bioinformatics online.
Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.
Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether th
Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pai
Scoring is a challenging step in protein-protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface s
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