English message is below Japanese message.
信州大学 教職員の皆様
信州大学 アクア・リジェネレーション機構（ARG）は下記のセミナーを企画いたしました。
皆様のご参加をお待ちしております。
日 時：2025年11月27日（木） 16:00～
場 所：・信州大学長野(工学)キャンパス AICS4階コミュニケーションルーム
・Google Meet: https://meet.google.com/auf-wfgw-zon
講演者：岡崎 圭一 准教授（分子科学研究所 計算科学研究センター）
「Elucidating and Controlling Functional Dynamics of Biomolecular Machines Using Molecular Simulations and Machine Learning」
要 旨：Biomolecular machines, such as motor and transporter proteins, change conformations when they function. First, I will introduce approaches for predicting conformational changes using the structure-prediction AI AlphaFold, with modifications to its procedure. Second, I will present approaches that integrate AlphaFold with molecular dynamics simulations to validate predicted structures and obtain physical properties, such as free energy. Third, I will introduce an approach combining molecular simulations with machine learning for estimating reaction coordinates that precisely capture the transition dynamics of conformational changes. This approach enables us to control the speed of a transporter protein, which was validated by experiments.
講演は英語になります。
お問い合わせ：taborosi_attila@shinshu-u.ac.jp
Dear Members of Shinshu University
Institute for Aqua Regeneration organizes a seminar.
We look forward to having you join us.
WHEN : November 27th, 2025. 16:00~
WHERE : Nagano(Engineering Campus),
AICS 4F Communication Room
Google Meet https://meet.google.com/auf-wfgw-zon
SPEAKER: Dr. Ki-ichi Okazaki
Associate Professor, Research Center for Computational Science, Institute of Molecular Science
TITLE : "Elucidating and Controlling Functional Dynamics of Biomolecular Machines Using Molecular Simulations and Machine Learning"
ABSTRACT: Biomolecular machines, such as motor and transporter proteins, change conformations when they function. First, I will introduce approaches for predicting conformational changes using the structure-prediction AI AlphaFold, with modifications to its procedure. Second, I will present approaches that integrate AlphaFold with molecular dynamics simulations to validate predicted structures and obtain physical properties, such as free energy. Third, I will introduce an approach combining molecular simulations with machine learning for estimating reaction coordinates that precisely capture the transition dynamics of conformational changes. This approach enables us to control the speed of a transporter protein, which was validated by experiments.
The talk is in English.
INQUIRY: taborosi_attila@shinshu-u.ac.jp
We look forward to having you join us.
