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  • 05월 20일 18시 이후 : 초록수정 불가능, 일정확인 및 검색만 가능

제125회 대한화학회 학술발표회 및 총회 Prediction of protein structure and interaction by physics and informatics

등록일
2020년 4월 14일 10시 50분 28초
접수번호
2229
발표코드
PHYS3-1 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
화 15시 : 20분
발표형식
분과기념
발표분야
Physical Chemistry - Recent Progress in Theoretical and Computational Chemistry
저자 및
공동저자
Chaok Seok
Division of Chemistry, Seoul National University, Korea
Protein structure prediction problem has challenged theoretical and computational physical scientists since the first protein structure was published in 1958. There have been steady progresses in protein structure prediction since then, but major contributions to the progress came from informatics-based approaches rather than from physics-based approaches. Recently, DeepMind’s AlphaFold made a further contribution by introducing deep learning to extract structural information from the large sequence database. Meaningful contribution of physics-based approaches began to be made only in 2012 in the field of model structure refinement. However, structural improvements that can be achieved by refinement with current physics-based approaches are very limited due to both energy and sampling problems. To overcome this limitation, we are taking an approach that combines physics and informatics, including deep learning. We take similar approaches to predict interactions of proteins with other proteins or small ligands including short peptides and oligosaccharides. One goal is to develop protein structure modeling techniques that can provide useful predictions even in the absence of available information, although currently available experimental data would play important roles in developing such techniques. Such modeling methods would be very useful for applications to a wide range of biomedical research and drug discovery.

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