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
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;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371084