title: Clustering of domains of functionally related enzymes in the interaction database PRECISE by the generation of primary sequence patterns.
abstract: The PRECISE database was developed by our laboratory to allow for the systematic study of the ligand interactions common to a set of functionally related enzymes, where an interaction site is defined broadly as any residue(s) that interact with a ligand. During the construction of PRECISE, enzyme chains are extracted from the protein data bank (PDB) and clustered according to functional homology as defined by the enzyme commission (EC) nomenclature system. A sequence representative is chosen from each cluster based on the criterion set forth by the non-redundant PDB set, and pair-wise alignments of each cluster member to the representative are performed. Atom-based residue-ligand interactions are calculated for each cluster member, and the summation of ligand interactions for all cluster members at each aligned position is determined. Although we were able to successfully align most clusters using a simple dynamic programming algorithm, several cluster created exhibited poor pair-wise alignments of each cluster member to its sequence representative. We hypothesized that the observed alignment problems were, in most cases, due to the incorrect separation and alignment of different domains in multi-domain proteins, a mistake that frequently causes error proliferation in functional annotation. Here we present the results of generating primary sequence patterns for each poorly aligned cluster in PRECISE to assess the extent to which multi-domain proteins that are incorrectly aligned contributes to poor pair-wise alignments of each cluster member to its representative. This requires the use of an iterative locally optimal pair-wise alignment algorithm to build a hierarchical similarity-based sequence pattern for a set of functionally related enzymes. Our results show that poor alignments in PRECISE are caused most frequently by the misalignment of multi-domain proteins, and that the generation of primary sequence patterns for the assignment of sequence family membership yields better alignments for the functionally related enzyme clusters in PRECISE than our original alignment algorithm.
authors.affiliation: Graduate Program in Bioinformatics and Systems Biology, Boston University, Boston, MA 02215, USA.
authors.forename: Melissa R
authors.initials: MR
authors.lastname: Landon
authors.forename: David R
authors.initials: DR
authors.lastname: Lancia
authors.forename: Karl H
authors.initials: KH
authors.lastname: Clodfelter
authors.forename: Sandor
authors.initials: S
authors.lastname: Vajda
mesh_terms: D000595:Amino Acid Sequence; D016000:Cluster Analysis; D017124:Conserved Sequence; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry* / Q000378:metabolism*; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D017386:Sequence Homology, Amino Acid; D013329:Structure-Activity Relationship
chemical_list: D004798:Enzymes