DocumentCode :
1943327
Title :
Prediction of Long-range Contacts from Sequence Profile
Author :
Chen, Peng ; Wang, Bing ; Wong, Hau-San ; Huang, De-Shuang
Author_Institution :
Chinese Acad. of Sci., Hefei
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
938
Lastpage :
943
Abstract :
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.
Keywords :
pattern classification; probability; proteins; long-range contact prediction; multiple conditional probability mass function classifier; protein structures; sequence profile; Accuracy; Amino acids; Computational modeling; Computer simulation; Genetic programming; Hidden Markov models; Neural networks; Predictive models; Protein sequence; Stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371084
Filename :
4371084
Link To Document :
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