北京大学 | ENGLISH
讲座信息
Global Optimization by Conformational Space Annealing and its Applications to Protein Structure Prediction/Determination and Machine Learning
发布时间:2018-02-27      点击量:1106
主讲人:Prof. Jooyoung Lee
讲座地点:生命科学学院邓祐才报告厅
讲座日期:2018-03-09
讲座时间:13:00 — 14:30
联系人:苏晓东教授
 
生命科学学院2018年度春季学期学术系列讲座之二
题目:Global Optimization by Conformational Space Annealing and its Applications to Protein Structure Prediction/Determination and Machine Learning
讲座人:Prof. Jooyoung Lee
Director of Center for In Silico Protein Science
Professor, School of Computational Sciences
Korea Institute for Advanced Study
时间:2018年3月9日(星期五),13:00-14:30
地点:生命科学学院邓祐才报告厅
主持人:苏晓东教授
摘要:
First, I will discuss our recent progresses on the protein structure prediction using the methodology of global optimization as illustrated in the CASP11/12 competitions held in 2014/2016. We will demonstrate that this method can be applied to difficult MR (molecular replacement) targets to determine X-ray crystallography structures of proteins and protein complexes, which could not be solved using conventional MR methods. We will also discuss the possible application of our method to the high throughput NMR structure determination of large proteins (over 20 kDa) and membrane proteins.First, I will discuss our recent progresses on the protein structure prediction using the global optimization method of Conformational Space Annealing (CSA) as illustrated in the CASP11/12 competitions held in 2014/2016. We will demonstrate that this method can be applied to difficult MR (molecular replacement) targets to determine X-ray crystallography structures of proteins and protein complexes, which could not be solved using conventional MR methods. We will also discuss the possible application of our method to the high throughput NMR structure determination of large proteins (over 20 kDa) and membrane proteins.
If time is allowed, I will also discuss the optimization issue in the study of machine learning (ML). A preliminary study indicates that proper application of CSA to ML can provide a solution to the overtraining problem in ML. I will share the progress of our attempt to build our own AlphaGo in this respect.
欢迎各位老师同学积极参加!
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