| 报告题目： |
FALCON---Constructing effective energy function using reverse sampling and predicting protein structure using deep learning |
| 报告人： |
卜东波 教授 |
| 报告人单位： |
中国科学院计算技术研究所 |
| 报告时间： |
1月16号（星期三）10:00-12:00 |
| 报告地点： |
科技楼北410 |
| 报告摘要： |
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The ab initio approaches to protein structure prediction usually employ the Monte Carlo technique to search the structural conformation that has the lowest energy. However, the widely-used energy functions are usually ineffective for conformation search. How to construct an effective energy function remains a challenging task. Here, we present a framework to construct effective energy functions for protein structure prediction. Unlike existing energy functions only requiring the native structure to be the lowest one, we attempt to maximize the attraction-basin where the native structure lies in the energy landscape. The underlying rationale is that each energy function determines a specific energy landscape together with a native attraction-basin, and the larger the attraction-basin is, the more likely for the Monte Carlo search procedure to find the native structure. We also present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. |
| 报告人简介： |
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Dr. Dongbo Bu received his Ph. D. from ICT, CAS at 2001. Currently he is focusing on algorithm design and bioinformatics, especially on protein structure prediction, genome assembly, and glycan identification. He has developed several software suites, including FALCON software for protein structure prediction, GIPS for glycan identification, and SIGA for genome assembly. |
