Title :
Mining residue contacts in proteins using local structure predictions
Author :
Zaki, Mohammed J. ; Jin, Shan ; Bystroff, Chris
Author_Institution :
Comput. Sci. Dept., Rensselaer Polytech. Inst., Troy, NY, USA
Abstract :
In this paper we develop data mining techniques to predict 3D contact potentials among protein residues (or amino acids) based on the hierarchical nucleation-propagation model of protein folding. We apply a hybrid approach, using a hidden Markov model to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy results better than those reported previously.
Keywords :
biology computing; data mining; hidden Markov models; molecular biophysics; molecular configurations; physiological models; proteins; HMM; association mining; contact potential discovery; folding initiation site extraction; hidden Markov model; hierarchical nucleation-propagation model; local structure predictions; protein folding; protein residue contact mining; Amino acids; Bioinformatics; Data mining; Databases; Genomics; Hidden Markov models; Pattern analysis; Predictive models; Protein engineering; Sequences;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2003.816916
