Why do Sequence Signatures Predict Enzyme Mechanism? Homology versus Chemistry

In-Silico Characterization of Glycosyl Hydrolase Family 1 β-Glucosidase from Trichoderma asperellum UPM1

β-glucosidases (Bgl) are widely utilized for releasing non-reducing terminal glucosyl residues. Nevertheless, feedback inhibition by glucose end product has limited its application. A noticeable exception has been found for β-glucosidases of the glycoside hydrolase (GH) family 1, which exhibit tolerance and even stimulation by glucose. In this study, using local isolate Trichoderma asperellum UPM1, the gene encoding β-glucosidase from GH family 1, hereafter designated as TaBgl2, was isolated and characterized via in-silico analyses. A comparison of enzyme activity was subsequently made by heterologous expression in Escherichia coli BL21(DE3). The presence of N-terminal signature, cis-peptide bonds, conserved active site motifs, non-proline cis peptide bonds, substrate binding, and a lone conserved stabilizing tryptophan (W) residue confirms the identity of Trichoderma sp. GH family 1 β-glucosidase isolated. Glucose tolerance was suggested by the presence of 14 of 22 known consensus residues, along with corresponding residues L167 and P172, crucial in the retention of the active site's narrow cavity. Retention of 40% of relative hydrolytic activity on ρ-nitrophenyl-β-D-glucopyranoside (ρNPG) in a concentration of 0.2 M glucose was comparable to that of GH family 1 β-glucosidase (Cel1A) from Trichoderma reesei. This research thus underlines the potential in the prediction of enzymatic function, and of industrial importance, glucose tolerance of family 1 β-glucosidases following relevant in-silico analyses.

Enzyme function and its evolution

With rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

HIGHLIGHTS

Many CNS targets are being explored for multi-target drug design

New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds

QSAR, virtual screening and docking methods increase the potential of rational drug design

The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D₁-R/D₂-R/5-HT_2A-R/H₃-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.