KNOWLEDGE-BASED PREDICTION OF PROTEIN STRUCTURES
KNOWLEDGE-BASED PREDICTION OF PROTEIN STRUCTURES
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DOI：
10.1016/s0022-5193(05)80253-x
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发表时间：
1990-11-07
影响因子：
2
通讯作者：
SELBIG, J
中科院分区：
文献类型：
--
作者：
KADEN, F;KOCH, I;SELBIG, J
我们提出了一种基于知识的方法，用于在与已知空间结构的蛋白质没有序列同源性的情况下预测蛋白质结构。使用人工智能的方法，我们试图考虑预测过程中的长期相互作用。这不仅允许指定次级结构元素，也允许指定超次级结构元素。具体地，用作预测规则的条件的模式是通过从蛋白质数据库中包含的信息通过学习方法生成的。利用蛋白质结构层次中较高层次上的模式作为约束来减小组合搜索空间。这些模式也可以用来通过交互检索来搜索特定的结构主题。
We propose a knowledge-based approach to the prediction of protein structures in cases where there is no sequence-homology to proteins with known spatial structure. Using methods from Artifical Intelligence we attempt to take into account long-range interactions within the prediction process. This allows not only the assignment of secondary but also of supersecondary structure elements. In particular, the patterns used as conditions of prediction rules are generated by learning methods from information contained in the Protein Data Base. Patterns on higher levels of the protein structure hierarchy are used as constraints to reduce the combinatorial search space. These patterns may also beused to search for specified structure motifs by interactive retrieval.