摘要翻译：
蛋白质结构预测仍然是计算生物学领域的一个挑战。传统的蛋白质结构预测方法包括基于模板的模型（如同源模型和线程）和从头算。线程算法将查询蛋白质序列作为输入，识别最可能的折叠，最后将查询序列的对齐报告给结构已知的模板作为输出。现有的穿线方法主要利用蛋白质序列谱、溶剂可及性、接触概率等信息，对某些蛋白质进行正确的折叠识别。然而，现有的线程方法对远程同源蛋白的处理效果不佳。如何提高远程同源蛋白的折叠识别率一直是蛋白质结构预测的一个难点。
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英文标题：
《TOPO: Improving remote homologue recognition via identifying common
  protein structure framework》
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作者：
Jianwei Zhu, Haicang Zhang, Chao Wang, Bin Ling, Wei-Mou Zheng, Dongbo
  Bu
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最新提交年份：
2015
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Protein structure prediction remains a challenge in the field of computational biology. Traditional protein structure prediction approaches include template-based modelling (say, homology modelling, and threading), and ab initio. A threading algorithm takes a query protein sequence as input, recognizes the most likely fold, and finally reports the alignments of the query sequence to structure-known templates as output. The existing threading approaches mainly utilizes the information of protein sequence profile, solvent accessibility, contact probability, etc., and correctly recognize folds for some proteins. However, the existing threading approaches show poorly performance for remote homology proteins. How to improve the fold recognition for remote homology proteins remains to be a difficult task for protein structure prediction.
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PDF链接：
https://arxiv.org/pdf/1507.03197