Mitsuo Umetsu

This paper introduces pLM-bVAE, a binary variational autoencoder enhanced with protein language model representations, showing improved sequence reconstruction and design performance in quantum annealing-based protein sequence design.

This paper demonstrates that fusion-protein architecture determines site selectivity in proximity-directed enzymatic modification of antibodies, enabling site-specific functionalization.

This study compares different fine-tuning strategies and classical machine learning approaches for protein function prediction using benchmark datasets.

This study uses deep sequencing and machine learning to optimize antibody discovery by analyzing training data and search space design for high-affinity VHHs.