33 citations · 39 across the 7 of their papers we have counts for
7 papers
Auto-WHATMD : Automated Wasserstein-based High-dimensional feature extraction Analysis of Trajectories from Molecular Dynamics
Sosuke Asano, Ikki Yasuda, Katsuhiro Endo +2
Comparing multiple protein systems with variation such as different binding ligands or mutations, and understanding their effects is one of the objectives in molecular dynamics sim…
Enhanced premelting at the ice-rubber interface using all-atom molecular dynamics simulation
Takumi Kojima, Ikki Yasuda, Takumi Sato +2
The ice-rubber interface is critical in applications such as tires and shoe outsoles, yet its molecular tribology remains unclear. Using all-atom molecular dynamics simulations, we…
TDP-43 multidomains and RNA modulate interactions and viscoelasticity in biomolecular condensates
Yui Matsushita, Ikki Yasuda, Fuga Watanabe +1
RNA-binding proteins form biomolecular condensates with RNA through phase separation, playing crucial roles in various cellular processes. While intrinsically disordered regions (I…
Molecular Dynamics Investigation of Static and Dynamic Interfacial Properties in Ice-Polymer Premelting Layers
Takumi Sato, Ikki Yasuda, Noriyoshi Arai +1
Premelting at the ice-polymer interfaces, in which a quasi-liquid layer (QLL) forms below the melting point, is strongly influenced by polymer surface chemistry; however, the molec…
Software package for simulations using the coarse-grained CALVADOS model
Sören von Bülow, Ikki Yasuda, Fan Cao +6
We present the CALVADOS package for performing simulations of biomolecules using OpenMM and the coarse-grained CALVADOS model. The package makes it easy to run simulations using th…
Prediction of transport property via machine learning molecular movements
Ikki Yasuda, Yusei Kobayashi, Katsuhiro Endo +5
Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are rep…
Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach
Ikki Yasuda, Katsuhiro Endo, Eiji Yamamoto +2
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). T…