Relationships between functional subclasses and information contained in active‐site and ligand‐binding residues in diverse superfamilies

Abstract

To investigate the relationships between functional subclasses and sequence and structural information contained in the active-site and ligand-binding residues (LBRs), we performed a detailed analysis of seven diverse enzyme superfamilies: aldolase class I, TIM-barrel glycosidases, alpha/beta-hydrolases, P-loop containing nucleotide triphosphate hydrolases, collagenase, Zn peptidases, and glutamine phosphoribosylpyrophosphate, subunit 1, domain 1. These homologous superfamilies, as defined in CATH, were selected from the enzyme catalytic-mechanism database. We defined active-site and LBRs based solely on the literature information and complex structures in the Protein Data Bank. From a structure-based multiple sequence alignment for each CATH homologous superfamily, we extracted subsequences consisting of the aligned positions that were used as an active-site or a ligand-binding site by at least one sequence. Using both the subsequences and full-length alignments, we performed cluster analysis with three sequence distance measures. We showed that the cluster analysis using the subsequences was able to detect functional subclasses more accurately than the clustering using the full-length alignments. The subsequences determined by only the literature information and complex structures, thus, had sufficient information to detect the functional subclasses. Detailed examination of the clustering results provided new insights into the mechanism of functional diversification for these superfamilies.

DomSign: a top-down annotation pipeline to enlarge enzyme space in the protein universe

“…This is mainly because the domain architecture is unable to fully encode enzymatic activity, especially substrate specificity [ 38 , 39 ]. Substrate specificity determination is complex [ 40 ], especially for some superfamilies with diverse catalytic functions [ 41 ], and thus much effort has been devoted to this task using pioneering methods such as determining key functional residues in enzymes [ 42 ], key-residue 3D templates [ 43 ] and substrate de novo docking [ 44 ]. Future work will likely include the integration of these methodologies into our pipeline to more precisely predict the substrate specificity–determining fourth EC digit.…”

Toward mechanistic classification of enzyme functions

“…EzCatDB includes examples of both divergently and convergently evolved enzymes, and it has been useful for comparing the catalytic mechanisms of hydrolases and transferases [22] and for comparing active sites and ligand biding residues in functionally diverse superfamilies [39]. Finally, the HCS database [27] classifies active sites based on their catalytic roles and also includes mechanistic information.…”

Ensemble Architecture for Prediction of Enzyme‐ligand Binding Residues Using Evolutionary Information

“…Nevertheless, in these enzyme‐ligand interactions, residues are involved in the partner recognitions, for tethering and conducting of the biochemical process. Identification of these key residues is crucial for analysing molecular interactions and guiding further experimental procedures ,…”

“…Identification of these key residues is crucial for analysing molecular interactions and guiding further experimental procedures. [27,28] In this study, we aimed to understand the ligand binding regions in biochemically diverse enzymes and use the insights to develop a simple and efficient sequence-based de novo approach, which could possibly be suitable for facilitating identification with enhanced precision. Findings of this study could be used eventually in the broader picture of enzyme mechanism or ligand interaction based functional implication, for boosting applications in industry and drug-design.…”