Yuki Kagaya

The paper introduces NuFold, a tool for large-scale RNA tertiary structure prediction, and provides a database with precomputed structures and confidence metrics.

PathFold predicts protein folding pathways from sequence using a deep learning framework combining AlphaFold and diffusion models, showing agreement with experimental data.

Prot-LAMBDA improves protein structure prediction by explicitly incorporating spatial relationships, achieving better performance than ESM2-3B in contact prediction and 3D structure prediction.

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

Emap2lig detects ligand densities and builds atomic structures from cryo-EM maps using a two-stage deep learning framework.

PL-PatchSurfer3 improves structure-based virtual screening by incorporating hydrogen bond complementarity and visibility features, enhancing performance while maintaining tolerance to receptor structure changes.

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.

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.

DistPepFold improves protein-peptide complex docking using a privileged knowledge distillation approach based on AlphaFold-Multimer, outperforming AFM on two datasets.

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

The paper introduces Domain-PFP, a method for protein function prediction using function-aware domain embeddings, which outperforms existing methods and large-scale protein language models in GO prediction tasks.

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