KIDDS 월간 세미나 시리즈 - 2023년 9월

2023년 9월 KIDDS 온라인 세미나 소식 알려드립니다.

이번에 모실 연사는 Purdue University Department of Biological Science & Computer Science의 Daisuke Kihara 교수입니다. Kihara 교수는 단백질의 표면 성질을 Zernike 기술자로 변환하여 빠른 속도로 단백질 구조 검색, 단백질-단백질 도킹, 리간드 스크리닝등을 연구하고 있으며 최근에는 딥러닝을 이용한 Cryo-EM map 구조 모델링에 연구를 활발히 하고 있습니다. 이번 세미나에서는 이와 관련하여 최근의 연구 성과에 대하여 들으실 수 있을 것으로 기대합니다.

일시: 9월 22일 금요일 오전 10시 (online)

줌링크: https://snu-ac-kr.zoom.us/j/3907204837

연사: Daisuke Kihara (Purdue University)

Title: Building and Validating Biomolecular Structure Models for cryo-EM Maps Using Deep Learning

Abstract: Cryo-electron microscopy (cryo-EM) has become one of the main experimental methods for determining biomolecular structures including proteins and nucleic acids. Molecular structure modeling from cryo-EM can be challenging when the resolution of maps is around 3 Å or lower, which is not often high enough to specify atom positions. We have been developing a series of computational methods for modeling protein and nucleic acid structures from cryo-EM maps. For maps at medium resolution (up to around 5 Å), deep learning can detect characteristic local density features of amino acids and secondary structures, which can be used to guide structure modeling. We developed DeepMainmast and CryoREAD for protein modeling and DNA/RNA modeling, respectively. Local density features can also be used for validating existing protein structure models in PDB. The protein model quality assessment score, DAQ, which we developed recently, compares local density patterns captured by deep learning with amino acid positions in a model and detects potential errors in the model. In a large-scale analysis of protein models from cryo-EM, we found that a substantial small number of models may have some errors. All the tools we developed are available at https://kiharalab.org/emsuites/.

주최: KIDDS, 차세대 인실리코 단백질 디자인 센터