The Geometry of Interactions between Catalytic Residues and their Substrates

β-Glucosidase on clay minerals: Structure and function in the synthesis of octyl glucoside

β-Glucosidase is a biological macromolecule that catalyzes the hydrolysis of various glycosides and oligosaccharides. It may also be used to catalyze the synthesis of glycosides under suitable conditions. Carrier-bound β-glucosidase can enhance the enzymatic activity in the synthesis of glycosides in organic solvent solutions, although the molecular mechanism regulating activity is yet unknown. This study investigated the impact of utilizing montmorillonite (Mmt), attapulgite (Attp), and kaolinite (Kao) as carriers on the activity of β-glucosidase from Prunus dulcis (PdBg). When Attp was used as carriers, the molecular dynamic (MD) simulations found the distance between pNPG and the active site residues E183 and E387 was minimally impacted by the adsorptions, hence PdBg maintained about 81.3 ± 0.89 % of its native activity. Out of the three clay minerals, the relative activity of PdBg loaded on Mmt was the lowest because of the highest electrostatic energy. The substrate channel of PdBg on Kao is directed towards the surface, limiting the accessibility of substrates. Secondary structure and conformation studies revealed that the conformational stability of PdBg in solvent solutions was enhanced by coupling to Attp. Unlike dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF) and 1,2-dimethoxyethane (DME), tert-butanol (t-BA) did not penetrate into the active site of PdBg interfering with its binding to the substrate. The maximum yield of n-octyl-β-glucoside (OGP) synthesis catalyzed by Attp-immobilized PdBg reached 48.3 %.

An Overview of the Bacterial Carbonic Anhydrases

Bacteria encode carbonic anhydrases (CAs, EC 4.2.1.1) belonging to three different genetic families, the α-, β-, and γ-classes. By equilibrating CO₂ and bicarbonate, these metalloenzymes interfere with pH regulation and other crucial physiological processes of these organisms. The detailed investigations of many such enzymes from pathogenic and non-pathogenic bacteria afford the opportunity to design both novel therapeutic agents, as well as biomimetic processes, for example, for CO₂ capture. Investigation of bacterial CA inhibitors and activators may be relevant for finding antibiotics with a new mechanism of action.

Structure Determination of Mycobacterium tuberculosis Serine Protease Hip1 (Rv2224c)

The Mycobacterium tuberculosis (Mtb) serine protease Hip1 (hydrolase important for pathogenesis; Rv2224c) promotes tuberculosis (TB) pathogenesis by impairing host immune responses through proteolysis of a protein substrate, Mtb GroEL2. The cell surface localization of Hip1 and its immunomodulatory functions make Hip1 a good drug target for new adjunctive immune therapies for TB. Here, we report the crystal structure of Hip1 to a resolution of 2.6 Å and the kinetic studies of the enzyme against model substrates and the protein GroEL2. The structure shows a two-domain protein, one of which contains the catalytic residues that are the signature of a serine protease. Surprisingly, a threonine is located within the active site close enough to hydrogen bond with the catalytic residues Asp463 and His490. Mutation of this residue, Thr466, to alanine established its importance for function. Our studies provide insights into the structure of a member of a novel family of proteases. Knowledge of the Hip1 structure will aid in designing inhibitors that could block Hip1 activity.

An overview of the alpha-, beta- and gamma-carbonic anhydrases from Bacteria: can bacterial carbonic anhydrases shed new light on evolution of bacteria?

Carbonic anhydrases (CAs, EC 4.2.1.1) are metalloenzymes which catalyze a simple but physiologically crucial reaction in all life Domains, the carbon dioxide hydration to bicarbonate and protons: CO2 + H2O ⇔ HCO3(-)+ H(+). These enzymes are involved in many physiologic processes, such as photosynthesis, respiration, CO2 transport, as well as metabolism of xenobiotics. Five different, genetically distinct CA families are known to date: the α-, β-, γ-, δ- and ζ-CAs. α-, β- and δ-CAs use Zn(II) ions at the active site, the γ-CAs are probably Fe(II) enzymes (but they are active also with bound Zn(II) or Co(II) ions), whereas the ζ-class uses Cd(II) or Zn(II) to perform the physiologic reaction catalysis. Bacteria encode for enzymes belonging to the α-, β-, and γ-CA classes. They contain zinc ion (Zn(2+)) in their active site, coordinated by three histidine residues and a water molecule/hydroxide ion (in the α and γ) or by two cysteine and one histidine residues (in the β class), with the fourth ligand being a water molecule/hydroxide ion. Here we propose that bacterial CAs can be used as markers for understanding the evolution and genetic variability of the Gram-positive and Gram-negative bacteria. We addressed several questions such as: (1) why are α-CAs present only in the genome of Gram-negative bacteria; (2) why are α-CAs not present in all Gram-negative bacteria; (3) why do Bacteria show an intricate pattern of CA gene expression; (4) what are the physiologic roles of such diverse CAs in these prokaryotes. We proposed possible answers to the previous questions. Moreover, we speculated on the evolution of the CA classes (α, β and γ) identified in the Gram-negative and -positive bacteria. Our main hypothesis is that from the ancestral Ur-CA, the γ-class arose first, followed by the β-class; the α-class CAs came last it is found only in the Gram-negative bacteria.

Local network patterns in protein-protein interfaces

Protein-protein interfaces hold the key to understanding protein-protein interactions. In this paper we investigated local interaction network patterns beyond pair-wise contact sites by considering interfaces as contact networks among residues. A contact site was defined as any residue on the surface of one protein which was in contact with a residue on the surface of another protein. We labeled the sub-graphs of these contact networks by their amino acid types. The observed distributions of these labeled sub-graphs were compared with the corresponding background distributions and the results suggested that there were preferred chemical patterns of closely packed residues at the interface. These preferred patterns point to biological constraints on physical proximity between those residues on one protein which were involved in binding to residues which were close on the interacting partner. Interaction interfaces were far from random and contain information beyond pairs and triangles. To illustrate the possible application of the local network patterns observed, we introduced a signature method, called iScore, based on these local patterns to assess interface predictions. On our data sets iScore achieved 83.6% specificity with 82% sensitivity.

Is EC class predictable from reaction mechanism?

BACKGROUND

We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used.

RESULTS

The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones.

CONCLUSIONS

Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways.The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.

Characterizing the complexity of enzymes on the basis of their mechanisms and structures with a bio-computational analysis

Enzymes are basically composed of 20 naturally occurring amino acids, yet they catalyse a dizzying array of chemical reactions, with regiospecificity and stereospecificity and under physiological conditions. In this review, we attempt to gain some understanding of these complex proteins, from the chemical versatility of the catalytic toolkit, including the use of cofactors (both metal ions and organic molecules), to the complex mapping of reactions to proteins (which is rarely one-to-one), and finally the structural complexity of enzymes and their active sites, often involving multidomain or multisubunit assemblies. This work highlights how the enzymes that we see today reflect millions of years of evolution, involving de novo design followed by exquisite regulation and modulation to create optimal fitness for life.

Charting the chemical space of target sites: insights into the binding modes of amine and amidine groups

Nowadays there is growing awareness that the translation of the increasing number of lead compounds into clinical candidates is still a slow and often inefficient process. In order to facilitate the lead optimization procedure, due consideration must be given to the use of the right bioisosteric replacements. Very recently, we reported that exploring a chemical space of binding sites is a more effective strategy for studying the bioisosteric relationships existing among functional groups. As a continuation of our work in this field, we report herein the construction of a chemical space covered by binding sites of small molecules containing diverse amine and amidine groups. The analysis of the differences in some properties of the binding sites of these functional groups allow for gaining insights into the binding modes of positively charged groups. In addition, this study pinpoints that different types of interactions and bioisosteric relationships exist among primary, secondary, tertiary, quaternary amine, and amidine moieties.

SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations. Examples of reaction centre detection in Metabolite are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10(-1) to 10(-5) and their order of magnitude reflected the known and experimentally observed metabolites. SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure-activity and physiochemical information.

Testing geometrical discrimination within an enzyme active site: constrained hydrogen bonding in the ketosteroid isomerase oxyanion hole

Enzymes are classically proposed to accelerate reactions by binding substrates within active-site environments that are structurally preorganized to optimize binding interactions with reaction transition states rather than ground states. This is a remarkably formidable task considering the limited 0.1-1 A scale of most substrate rearrangements. The flexibility of active-site functional groups along the coordinate of substrate rearrangement, the distance scale on which enzymes can distinguish structural rearrangement, and the energetic significance of discrimination on that scale remain open questions that are fundamental to a basic physical understanding of enzyme active sites and catalysis. We bring together 1.2-1.5 A resolution X-ray crystallography, (1)H and (19)F NMR spectroscopy, quantum mechanical calculations, and transition-state analogue binding measurements to test the distance scale on which noncovalent forces can constrain the structural relaxation or translation of side chains and ligands along a specific coordinate and the energetic consequences of such geometric constraints within the active site of bacterial ketosteroid isomerase (KSI). Our results strongly suggest that packing and binding interactions within the KSI active site can constrain local side-chain reorientation and prevent hydrogen bond shortening by 0.1 A or less. Further, this constraint has substantial energetic effects on ligand binding and stabilization of negative charge within the oxyanion hole. These results provide evidence that subtle geometric effects, indistinguishable in most X-ray crystallographic structures, can have significant energetic consequences and highlight the importance of using synergistic experimental approaches to dissect enzyme function.

The chemistry of protein catalysis

We report, for the first time, on the statistics of chemical mechanisms and amino acid residue functions that occur in enzyme reaction sequences using the MACiE database of 202 distinct enzyme reaction mechanisms as a knowledge base. MACiE currently holds representatives from each Enzyme Commission sub-subclass where there is an available crystal structure and sufficient evidence in the primary literature for a mechanism. Each catalytic step of every reaction sequence in MACiE is fully annotated, so that it includes the function of the catalytic residues involved in the reaction and the chemical mechanisms by which substrates are transformed into products. We show that the most catalytic amino acid residues are histidine, cysteine and aspartate, which are also the residues whose side-chains are more likely to serve as reactants, and that have the greatest versatility of function. We show that electrophilic reactions in enzymes are very rare, and the majority of enzyme reactions rely upon nucleophilic and general acid/base chemistry. However, although rare, radical (homolytic) reactions are much more common than electrophilic reactions. Thus, the majority of amino acid residues perform stabilisation roles (as spectators) or proton shuttling roles (as reactants). The analysis presented provides a better understanding of the mechanisms of enzyme catalysis and may act as an initial step in the validation and prediction of mechanism in an enzyme active site.