Tsukasa Nakamura

Distance-AF Multimer improves protein complex structure prediction by incorporating user-defined residue distance constraints, achieving competitive or better performance than existing methods.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

CryoZeta improves cryo-EM structure modeling by integrating density features with sequence data using a diffusion-based deep learning model, achieving higher accuracy than existing methods.

The paper investigates the structural differences between two capsid sizes in bacteriophage P1, revealing the role of protein appendages in DNA packaging and capsid morphogenesis.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

NuFold predicts RNA tertiary structures using deep learning with a nucleobase center representation to bridge the gap between sequence data and structural insights.

This paper develops a novel RNN architecture, YamRNN, with fewer parameters and faster learning performance compared to LSTM and GRU on sequence classification tasks.