Accurate predictions of complex structure and affinity between proteins and drugs

2018 Fiscal Year Final Research Report

• Project/Area Number: 16K07331
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Biophysics
• Research Institution: University of Hyogo
• Principal Investigator: Kamiya Narutoshi 兵庫県立大学, シミュレーション学研究科, 特任教授 (80420462)
• Project Period (FY): 2016-04-01 – 2019-03-31
• Keywords: 自由エネルギー / 分子動力学シミュレーション / 構造予測

Outline of Final Research Achievements

The author has developed methods to predict the binding structure between proteins and drugs and to accurately calculate the binding affinity between them. These methods consist of multicanonical molecular dynamics simulation with a high sampling efficiency for the complex structure prediction and of umbrella sampling for the affinity prediction. To validate these methods, the author applied them to the systems, neuraminidase-tamiflu, β-secretase-mid-sized drug, antibody drug of solanezmab-amyloid β peptide. In all of the systems, the author succeeded in accurately generating the binding structures and association/dissociation path starting from the binding structures. The umbrella sampling along these path successfully reproduced the experimentally-obtained binding free energy.

Free Research Field

生物物理学

Academic Significance and Societal Importance of the Research Achievements

創薬の現場で用いられてきたタンパク質と薬剤の複合体構造や親和性の予測法は、その精度に問題である。本申請で開発した方法は、最先端のシミュレーション技術を適用し、タンパク質と薬剤の結合構造や親和性を高精度で予測可能である。本研究の対象は、インフルエンザウイルスの酵素ノイラミニダーゼとタミフル、アルツハイマー型認知症の原因として考えられているアミロイドβペプチドの生成に関わる酵素βセクレターゼとその中分子阻害剤、アミロイドβペプチドと高い親和性を持つ抗体薬ソラネズマブとアミロイドβペプチドであり、本法が感染症や認知症関連の薬剤の開発に役立つものと期待される。