Mitsugu Araki 研究室

主宰者：Mitsugu Araki

京都大学

AI 要約（直近 5 年の研究成果）

本研究室は、タンパク質と薬剤や生体分子の相互作用を原子レベルで解明することを目指しています。特に、病原菌の感染防御に関わる免疫タンパク質（MR1）と天然化合物の結合機構、がん治療薬の耐性獲得メカニズム、ならびに機能未解明の遺伝子変異の影響評価を研究対象としています。これらの現象は実験観察が困難な場合が多いため、分子動力学シミュレーションという計算手法を用いて、分子がどのように動き、相互作用しているのかを詳細に追跡します。分子動力学シミュレーションを高速化する独自の手法開発も重要な取り組みです。従来は数百万分子対の相互作用の全体像を把握することが難しかったのに対し、スーパーコンピュータと加速度シミュレーション技術を組み合わせることで、数千を超える多くの分子ペアの結合過程を統計的に解析できるようになりました。これにより、分子認識の普遍的な原理を抽出することが可能になります。さらに研究室では、シミュレーション結果を細胞や動物を用いた実験検証と組み合わせ、新規治療薬の候補物質探索や既存薬の耐性機構の予測も行っています。深層学習とドッキングシミュレーションを統合した計算機科学的アプローチにより、治療応用を目指した分子設計にも携わっています。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

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研究成果（28 件）

[2026] BPS2026 – Genome-informed molecular simulations reveal mechanistic links between oncogenic kinase mutations, structural dynamics, and drug resistance
DOI: https://doi.org/10.1016/j.bpj.2025.11.2075
[2026] Elucidating Ligand Charge Effects in MR1 Cell-Surface Translocation Using Molecular Simulations
DOI: https://doi.org/10.1021/acs.jcim.5c02141
[2025] BPS2025 – Deep-learning approaches for protein dynamics extraction from cryo-EM maps and structure-based de novo molecular generation
DOI: https://doi.org/10.1016/j.bpj.2024.11.1865
[2025] Functional and structural analysis of KK-LC-1-specific T cell receptors from patients with lung Cancer for immunotherapy
DOI: https://doi.org/10.1016/j.cellimm.2025.105059
[2025] Molecular recognition of natural compounds by MR1 and their implication in MAIT cell activation elucidated through McMD-based dynamic docking simulations
DOI: https://doi.org/10.1038/s41598-025-28682-3
[2025] Mutation of CMTR2 in Lung Adenocarcinoma Alters RNA Alternative Splicing and Reveals Therapeutic Vulnerabilities
DOI: https://doi.org/10.1038/s41467-025-64821-0
[2025] BPS2025 - Deep learning approaches for protein dynamics extraction from cryo-EM maps and structure-based de novo molecular generation
DOI: https://doi.org/10.1016/j.bpj.2024.11.1790
[2025] Precision spatiotemporal analysis of large-scale compound–protein interactions through molecular dynamics simulation
DOI: https://doi.org/10.1093/pnasnexus/pgaf094
[2024] Molecular Dynamics Unveils Multiple-Site Binding of Inhibitors with Reduced Activity on the Surface of Dihydrofolate Reductase
DOI: https://doi.org/10.1021/jacs.4c04648
[2024] Binding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking
DOI: https://doi.org/10.1021/acs.jcim.4c00100

続きを表示（残り 18 件）

[2024] LTK mutations responsible for resistance to lorlatinib in non-small cell lung cancer harboring CLIP1-LTK fusion
DOI: https://doi.org/10.1038/s42003-024-06116-6
[2024] A macrocyclic kinase inhibitor overcomes triple resistant mutations in EGFR-positive lung cancer
DOI: https://doi.org/10.1038/s41698-024-00542-9
[2024] A multiscale molecular simulation approach for assessing the effects of mutation on protein activity and drug sensitivity of oncogenic kinases
DOI: https://doi.org/10.1016/j.bpj.2023.11.1972
[2023] Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL
DOI: https://doi.org/10.1038/s42003-023-04720-6
[2023] Establishment of an MR1 Presentation Reporter Screening System and Identification of Phenylpropanoid Derivatives as MR1 Ligands
DOI: https://doi.org/10.1021/acs.jmedchem.3c01122
[2023] Hypersound-perturbed molecular dynamics simulation for accelerating slow biomolecular interaction processes
DOI: https://doi.org/10.1016/j.bpj.2022.11.2278
[2022] Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis
DOI: https://doi.org/10.1158/0008-5472.can-22-0834
[2022] Protein–ligand binding affinity prediction of cyclin‐dependent kinase‐2 inhibitors by dynamically averaged fragment molecular orbital‐based interaction energy
DOI: https://doi.org/10.1002/jcc.26940
[2022] Sensitivity to dabrafenib and trametinib treatments in patients with non-small-cell cancer harboring BRAF compound mutations: A pooled analysis of BRAF p.V600E-positive advanced non-small-cell lung cancer
DOI: https://doi.org/10.1016/j.cancergen.2022.05.001
[2022] A multiscale molecular simulation approach for assessing the effects of mutations on functional activity and drug sensitivity in kinases
DOI: https://doi.org/10.1016/j.bpj.2021.11.553
[2022] Integrated approach to functional analysis of an ERBB2 variant of unknown significance detected by a cancer gene panel test
DOI: https://doi.org/10.1007/s13402-021-00656-3
[2021] Accurate Binding Configuration Prediction of a G-Protein-Coupled Receptor to Its Antagonist Using Multicanonical Molecular Dynamics-Based Dynamic Docking
DOI: https://doi.org/10.1021/acs.jcim.1c00712
[2021] Structure-Based <i>de Novo</i> Molecular Generator Combined with Artificial Intelligence and Docking Simulations
DOI: https://doi.org/10.1021/acs.jcim.1c00679
[2021] Oncogenic mutations Q61L and Q61H confer active form-like structural features to the inactive state (state 1) conformation of H-Ras protein
DOI: https://doi.org/10.1016/j.bbrc.2021.05.084
[2021] Exploring ligand binding pathways on proteins using hypersound-accelerated molecular dynamics
DOI: https://doi.org/10.1038/s41467-021-23157-1
[2021] Microsecond-timescale MD simulation of EGFR minor mutation predicts the structural flexibility of EGFR kinase core that reflects EGFR inhibitor sensitivity
DOI: https://doi.org/10.1038/s41698-021-00170-7
[2021] Gilteritinib overcomes lorlatinib resistance in ALK-rearranged cancer
DOI: https://doi.org/10.1038/s41467-021-21396-w
[2021] Extraction of protein dynamics information from cryo-EM maps using deep learning
DOI: https://doi.org/10.1038/s42256-020-00290-y

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AI 要約（直近 5 年の研究成果）

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関連研究室(8 件)

研究成果（28 件）

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