Genki Terashi

DAQplugin provides real-time deep learning-based validation of cryo-EM protein models in ChimeraX, identifying residue errors and offering correction guidance.

The paper validates a cell-free nucleosome assembly method using ExACT and confirms its structural fidelity through cryo-EM and deep-learning-based model quality scoring.

DMcloud improves fitting of molecular models into cryo-EM maps by local structure fitting, achieving higher accuracy and coverage than existing methods.

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.

Distance-AF improves AlphaFold2 predictions by incorporating user-specified distance constraints to refine protein structures and reduce RMSD.

DiffModeler uses diffusion models to accurately model protein complex structures from low-resolution cryo-EM maps, achieving high TM-Scores across various resolutions.

The paper introduces a method combining deep learning for density tracing with AlphaFold2 to improve protein structure modeling from cryo-EM maps and accurately assign chain identities in homo-multimers.

The paper introduces DAQ-refine, a protocol that improves protein models from cryo-EM maps using a deep learning score and modified AlphaFold2 refinement, showing consistent improvements over existing methods.

The paper introduces 3D-AF-Surfer, a tool for real-time structure-based search of AlphaFold2 protein models using molecular surface representation and 3D Zernike descriptors.

AttentiveDist improves protein inter-residue distance prediction using deep learning with attention on multiple sequence alignments, achieving better performance than existing methods in CASP13.

This paper introduces SuperEM, a deep learning method that improves cryo-EM map resolution, enhancing protein structure modeling.

This paper introduces ContactGAN, a GAN-based method to denoise protein contact maps, improving accuracy over existing methods on CASP13 datasets.