2024年
10/23
開催
開催日時
2024/10/23(水)16:00〜17:30
開催場所
Hybrid (CryoEM-Lab KEK and Zoom)
講演者
Tsukasa Nakamura, PhD :Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), Japan formerly: Biological Sciences, Purdue University, USA
言語
English
お問い合わせ
構造生物学研究センター
Abstract
Cryogenic electron microscopy (cryo-EM) has become one of the main experimental methods for determining protein structures. In cryo-EM, protein structure modeling is in general more difficult than X-ray crystallography as the resolution of maps is often not high enough to specify atom positions. We have been developing computational methods for modeling protein structures from cryo-EM maps determined at a medium resolution (2.5-5 Å). For maps at medium resolution, it turned out that deep learning can provide useful structure information for modeling and protein model quality assessment. In this seminar, we present deep learning applications in protein structure modeling (DeepMainmast) and protein model quality assessment (DAQ score) along with the large-scale analysis results of the quality of protein models from cryo-EM in the Protein Data Bank, which is accessible at DAQ-Score Database https://daqdb.kiharalab.org/. All the tools we developed are easy to use at https://em.kiharalab.org/.
Profile
2019 Ph.D. (Science) in Bioinformatics, The University of Tokyo, Japan
2019-2022 Postdoctoral Fellow (JSPS(PD)), Tohoku University, Japan
2022-2024.6 Postdoctoral Research Associate, Purdue University, USA
2024.7-current Postdoctoral Research Fellow, KEK IMSS SBRC, Japan