Contents

Publications

1. S. Iida and T. Kameda. "Dissociation Rate Calculation via Constant-Force Steered Molecular Dynamics Simulation". J. Chem. Inf. Model. (2023)

2. S. Iida and T. Kameda "Free energy and kinetic rate calculation via non-equilibrium molecular simulation: application to biomolecules". Biophys. Rev. (2022)

3. J. Higo, K. Kasahara, G. J. Bekker, B. Ma, S. Sakuraba, S. Iida, N. Kamiya, I. Fukuda, H. Kono, Y. Fukunishi, H. Nakamura, "Fly-casting with ligand–sliding and orientational selection to support the complex formation of a GPCR and a middle-sized flexible molecule" 2022, Sci. Rep. 12, 13792

4. S. Iida and Y. Fukunishi "Asymmetric dynamics of dimeric SARS-CoV-2 and SARS-CoV main proteases in an apo form: Molecular dynamics study on fluctuations of active site, catalytic dyad, and hydration water" 2021, 1, 100016, BBA advances

5. B. S. Hanson, S. Iida, D. J. Read, O. G. Harlen, G. Kurisu, H. Nakamura , S. A. Harris "Continuum Mechanical Parameterisation of Cytoplasmic Dynein from Atomistic Simulation" 2021 Methods

6. S. Iida, H. K. Nakamura, T. Mashimo, Y. Fukunishi "Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-based Drug Design" 2020, 124, 45, 9977–9986 Journal of Physical Chemistry B, DOI: 10.1021/acs.jpcb.0c04963

7. S. Iida, T. Kawabata, K. Kasahara, H. Nakamura, and J. Higo, "Multimodal Structural Distribution of the p53 C-terminal Domain Upon Binding to S100B via a Generalised Ensemble Method: From Disorder to Extra- Disorder" , Journal of Chemical Theory and Computation 2019, 15(4), 2597-2607

8. S. Iida, H. Nakamura, and J. Higo, "Enhanced Conformational Sampling to Visualize a Free-energy Landscape of Protein Complex Formation" , Biochemical Journal 2016 473, 1651–62

9. S. Iida, T. Mashimo, T. Kurosawa, H. Hojo, H. Muta, Y. Goto, Y. Fukunishi, H. Nakamura, J. Higo "Variation of Free-energy Landscape of the p53 C-terminal Domain Induced by Acetylation: Enhanced Conformational Sampling" 2016 Journal of Computational Chemistry, 37, 2687–2700

Research Support

1. S.Iida, "Protein Conformational Ensemble Generation Using a Diffusion model", Link , 23827793, Oct 2023 - Mar 2026, funded by JST ACT-X.

2. S.Iida, "Drug Design via Machine Learning: Use of Molecular Dynamics Data",Link, EG-J13-022018, Nov 2018-Mar 2019, funded by The Swiss State Secretariat for Education, Research and Innovation.

3. S.Iida, "Clarification of molecular recognition mechanisms of a disordered region via molecular dynamics simulation", Link, 17J07112, Apr 2017 -Mar 2019, funded by Japan Society for the Promotion of Science (JSPS).

Supercomputer Usage

1. FY2023 "The identification of cryptic binding sites by noble gas: Molecular Dynamics Study", National Institutes of Natural Sciences (NINS), Okazaki Research Facilities

2. FY2023 "Conformational Ensemble Prediction of Intrinsically Disordered Protein via the Combination of Molecular Dynamics Simulation and Generative Model", TSUBAME 3.0

3. FY2021 Adoption of research proposal for supercomputer: “TSUBAME Encouragement Program for Young/Female/Younger Users”

4. FY2020 Adoption of research proposal for supercomputer: “TSUBAME Encouragement Program for Young/Female/Younger Users”

5. FY2018 Adoption of research proposal for supercomputer: “TSUBAME Encouragement Program for Young/Female/Younger Users”

6. FY2016 Adoption of research proposal for supercomputer: “TSUBAME Encouragement Program for Young/Female/Younger Users”

7. FY2015 High Performance Computing Infrastructure Research. Subject: Excellent Achievement Award (Research Organization for Information Science and Technology)