Title :
Identifying protein binding functionality of protein family sequences by aligned pattern clusters
Author :
Lee, En-Shiun Annie ; Wong, Andrew K. C.
Author_Institution :
Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada
Abstract :
A basic task in protein analysis is to discover a set of sequence patterns that reflect the function of a protein family. This set of sequence patterns contains non-exact significant residue associations. Currently, the existing combinatorial methods are computationally expensive and probabilistic methods require richer representation of the amino acid associations. To undertake this task, we create a synthesized pattern representation called an Aligned Pattern (AP) Cluster that identifies the residue associations in the binding segment and the site variations in the aligned residues. In this paper, our algorithm identifies the binding segments for two protein families: the Cytochrome Complex and the Ubiquitin protein families. For each of the experiments, the AP Clusters obtained correspond to protein binding segments including a few beyond those identified by the other protein databases, PROSITE and pFam. Furthermore, the columns of aligned sites that exist only as a single value in the AP Clusters also corresponds to the binding residues. Additional information retained by the AP Clusters can reveal the amino acid residues of interest, thus averting time-consuming simulations and experimentation.
Keywords :
biology computing; biomembranes; molecular biophysics; molecular configurations; pattern clustering; probability; proteins; PROSITE protein databases; aligned pattern cluster; aligned pattern clusters; amino acid associations; binding segment; combinatorial methods; cytochrome complex; nonexact significant residue associations; pFam protein databases; pattern representation; probabilistic methods; protein binding functionality; protein family sequences; time-consuming simulations; ubiquitin protein families; Amino acids; Clustering algorithms; Conferences; Hidden Markov models; Probabilistic logic; Protein engineering; Proteins; Aligned Pattern Cluster; Hierarchical Clustering; Pattern; Protein Function Prediction;
Conference_Titel :
Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on
Conference_Location :
Philadelphia, PA
Print_ISBN :
978-1-4673-2746-6
Electronic_ISBN :
978-1-4673-2744-2
DOI :
10.1109/BIBMW.2012.6470286