Prediction of long-range contacts from sequence profile

被引：2
作者：
Chen, Peng [1 ]
Wang, Bing [1 ]
Wong, Hau-San [2 ]
Huang, De-Shuang [1 ]
机构：
[1] Chinese Acad Sci, Hefei Inst Intelligent Machines, Intelligent Computing Lab, Hefei 230031, Anhui, Peoples R China
[2] City Univ Hong Kong, Dept Comp Sci, Virtual Real Lab, Image Comp Grp, Kowloon, Peoples R China
来源：
2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 | 2007年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/IJCNN.2007.4371084
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Theoretic study in this paper shows that we can obtain exact long-range contacts by adopting one classifier if the centers of sequence profiles of residue pairs for long-range contacts and non-long-range contacts are known. The adopted classifier, referred to as multiple conditional probability mass function classifier (MCPMFC), can find an optimized transformation of the variables for each of the classes and therefore resulting in K separate classifiers. As a result, about 44.48% long-range contacts are around at the sequence profile (SP) centre for long-range contacts and about 20.9% long-range contacts are correctly predicted when considering the top L/5 (L is the protein sequence length) predicted contacts and the residue pair with 24 apart. The highest cluster result gives us a clue that SP center should be a sound pathway to investigate contact map in protein structures.
引用
收藏
页码：938 / +
页数：2
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