1、报告一

报告人：清华大学生命学院 龚海鹏 计算生物学教授

题目：Improving the statistical potentials by redesigning the reference state

摘要：Statistical potentials are frequently engaged in the protein structural prediction and protein folding for conformational evaluation. Theoretically, in order to describe the many-body effect, pairwise interaction between two atom groups should be corrected by their relative geometric orientation. The potential functions developed by this means are called orientation-dependent statistical potentials and have exhibited substantially improved performance. However, none of the currently available orientation-dependent statistical potentials employ any reference state, which has been proven to greatly enhance the power of distance-dependent statistical potentials in numerous previous studies. In this work, we designed a reasonable reference state for the orientation-dependent statistical potentials: using the average geometric relationship between atom pairs in known structures by neglecting their residue identities. The statistical potential developed using this reference state (called ORDER_AVE) prevails most available rival potentials in a series of tests on the decoy sets, although the information of side chain atoms (except the β-carbon) is absent in its construction.

2、报告二

报告人：清华大学数学系 丘成栋 教授

题目：Real time virus classification by natural vector method

摘要：The International Committee on Taxonomy of Viruses authorizes and organizes the taxonomic classification of viruses. However, the detailed classifications for all viruses are neither complete nor free from dispute. Using our proposed Natural Vector representation, all referenced viral genomes (including single-segmented and multiple-segmented) in GenBank can be embedded in R^12. Unlike other approaches, this allows us to determine phylogenetic relations for all viruses at any level (e.g., Baltimore class, family, subfamily, genus, and species) in real time. Additionally, the proposed graphical representation for virus phylogeny provides a visualization of the distribution of viruses in R^12. Our approach is successfully used to predict and correct viral classification information, as well as to identify viral origins.