Title :
Mining residue contacts in proteins using local structure predictions
Author :
Zaki, Mohammed J ; Shan Jin ; Bystroff, Christopher
Author_Institution :
Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY
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 (HMM) 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; contact potential; data mining; hidden Markov models; molecular biophysics; molecular configurations; nucleation; physics computing; potential energy functions; proteins; 3D contact potential prediction; accuracy; amino acids; association mining; data mining; folding initiation sites; hidden Markov model; hierarchical nucleation-propagation model; local structure predictions; protein folding; protein residue contacts; Amino acids; Computational biology; Computer science; Data mining; Databases; Hidden Markov models; Libraries; Peptides; Protein engineering; Sequences;
Conference_Titel :
Bio-Informatics and Biomedical Engineering, 2000. Proceedings. IEEE International Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7695-0862-6
DOI :
10.1109/BIBE.2000.889604
