Identification Of Catalytic Residues Research Articles

Characterization of the active site of a germin like protein 1 as an oxidative stress defense enzyme in plants

Glycosylated proteins like germin-like proteins (GLPs) are incredibly diverse inside the kingdom Plantae, and mostly GLPs exhibit superoxide dismutase (SOD) function. Identification of catalytic residues is important for understanding the mechanism of enzyme-catalyzed reactions. The increased bioactivity of SOD was observed when OsRGLP1 was over-expressed in tobacco. The purpose of the current work was to identify and characterize the active site of OsRGLP1. Bioinformatics tools were used to predict the three-dimensional structure of OsRGLP1 and the shape of residues implicated in the substrate and metal ion binding. The role of predicted active site residues (E116, H109, H111, and H157) in the structure-function relationship in OsRGLP1 was investigated by site-directed mutagenesis where each residue was substituted with glycine. These amino acids are highly conserved among GLP family and structural data have implicated these residues in substrate binding at the active site. Transient transformation of tobacco plants was performed to further study these loss-of-function mutants. To investigate the impact of the mutation on SOD activity, these transgenic plants were employed as a source of mutant and native proteins for SOD activity assays. The SOD assay results revealed a complete loss of activity in all mutants, supporting the crucial role of these residues for metal ion binding in the enzyme active site.

CSmetaPred: a consensus method for prediction of catalytic residues

BackgroundKnowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc.ResultsBoth meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization.ConclusionsThe benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/.

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information.

Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU) for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues) in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues) in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.

A simple extension to the CMASA method for the prediction of catalytic residues in the presence of single point mutations.

The automatic identification of catalytic residues still remains an important challenge in structural bioinformatics. Sequence-based methods are good alternatives when the query shares a high percentage of identity with a well-annotated enzyme. However, when the homology is not apparent, which occurs with many structures from the structural genome initiative, structural information should be exploited. A local structural comparison is preferred to a global structural comparison when predicting functional residues. CMASA is a recently proposed method for predicting catalytic residues based on a local structure comparison. The method achieves high accuracy and a high value for the Matthews correlation coefficient. However, point substitutions or a lack of relevant data strongly affect the performance of the method. In the present study, we propose a simple extension to the CMASA method to overcome this difficulty. Extensive computational experiments are shown as proof of concept instances, as well as for a few real cases. The results show that the extension performs well when the catalytic site contains mutated residues or when some residues are missing. The proposed modification could correctly predict the catalytic residues of a mutant thymidylate synthase, 1EVF. It also successfully predicted the catalytic residues for 3HRC despite the lack of information for a relevant side chain atom in the PDB file.

Identification of Catalytic Residues in ATP-Citrate Lyase

An enzyme in the human body that regulates lipogenesis and cholesterolgenesis is ATP-citrate lyase (ACLY) [1]. ACLY synthesizes acetyl-CoA and oxaloacetate from citrate, CoA and ATP, with citryl-CoA as an intermediate [1]. The product acetyl-CoA is involved in cell growth as well as embryonic and brain development [1]. Studies in mice suggest that ACLY is important during brain development as homozygous Acly knockout mouse embryos died early in development [2]. Knowledge of the reaction mechanism is limited and will be of use in understanding the energy flow in cells. Our current insight into the mechanism by which citryl-CoA is cleaved to form acetyl-CoA and oxaloacetate is based on sequence similarity of ACLY to citrate synthase (CS). Both enzymes have histidine and aspartic acid residues at similar positions in their sequences. We hypothesize that citryl-CoA binds at this site in ACLY and is cleaved into acetyl-CoA and oxaloacetate using these residues. To test this hypothesis, we have mutated these residues to alanine in both human ACLY and ACLY from the bacterium, Chlorobium limicola. Enzymatic activities of the mutant proteins were tested using a coupled-enzyme assay with malate dehydrogenase. The inactive mutants are being used in crystallization trials with substrates, since complexes of the intermediate citryl-CoA can be trapped on the protein. To date, the crystal structure of full-length ACLY has not been published. The structure of only the amino-terminal two-thirds of the human enzyme has been determined [3]; however, the part of the protein that is similar to CS is the carboxy-terminal portion. This work identifies the catalytic residues of ACLY and complements the previous structure determination, increasing our currently limited knowledge about this enzyme.

Rapid catalytic template searching as an enzyme function prediction procedure.

We present an enzyme protein function identification algorithm, Catalytic Site Identification (CatSId), based on identification of catalytic residues. The method is optimized for highly accurate template identification across a diverse template library and is also very efficient in regards to time and scalability of comparisons. The algorithm matches three-dimensional residue arrangements in a query protein to a library of manually annotated, catalytic residues – The Catalytic Site Atlas (CSA). Two main processes are involved. The first process is a rapid protein-to-template matching algorithm that scales quadratically with target protein size and linearly with template size. The second process incorporates a number of physical descriptors, including binding site predictions, in a logistic scoring procedure to re-score matches found in Process 1. This approach shows very good performance overall, with a Receiver-Operator-Characteristic Area Under Curve (AUC) of 0.971 for the training set evaluated. The procedure is able to process cofactors, ions, nonstandard residues, and point substitutions for residues and ions in a robust and integrated fashion. Sites with only two critical (catalytic) residues are challenging cases, resulting in AUCs of 0.9411 and 0.5413 for the training and test sets, respectively. The remaining sites show excellent performance with AUCs greater than 0.90 for both the training and test data on templates of size greater than two critical (catalytic) residues. The procedure has considerable promise for larger scale searches.

Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction

BackgroundA large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap.ResultsOur results demonstrate that the different methods included in the benchmark in general can be divided into three groups with a limited mutual overlap. One group containing real-value Evolutionary Trace (rvET) methods and conservation, another containing mutual information (MI) methods, and the last containing methods designed explicitly for the identification of specificity determining positions (SDPs): integer-value Evolutionary Trace (ivET), SDPfox, and XDET. In terms of prediction of CR, we find using a proximity score integrating structural information (as the sum of the scores of residues located within a given distance of the residue in question) that only the methods from the first two groups displayed a reliable performance. Next, we investigated to what degree proximity scores for conservation, rvET and cumulative MI (cMI) provide complementary information capable of improving the performance for CR identification. We found that integrating conservation with proximity scores for rvET and cMI achieved the highest performance. The proximity conservation score contained no complementary information when integrated with proximity rvET. Moreover, the signal from rvET provided only a limited gain in predictive performance when integrated with mutual information and conservation proximity scores. Combined, these observations demonstrate that the rvET and cMI scores add complementary information to the prediction system.ConclusionsThis work contributes to the understanding of the different signals of evolution and also shows that it is possible to improve the detection of catalytic residues by integrating structural and higher order sequence evolutionary information with sequence conservation.

Rapid Equilibrium Kinetic Analysis of Arsenite Methylation Catalyzed by Recombinant Human Arsenic (+3 Oxidation State) Methyltransferase (hAS3MT)

In the human body, arsenic is metabolized by methylation. Understanding this process is important and provides insight into the relationship between arsenic and its related diseases. We used the rapid equilibrium kinetic model to study the reaction sequence of arsenite methylation. The results suggest that the mechanism for arsenite methylation is a completely ordered mechanism that is also of general interest in reaction systems with different reductants, such as tris(2-carboxyethyl)phosphine, cysteine, and glutathione. In the reaction, cysteine residues of recombinant human arsenic (+3 oxidation state) methyltransferase (hAS3MT) coordinate with arsenicals and involve the methyl transfer step. S-Adenosyl-l-methionine (AdoMet) is the first-order reactant, which modulates the conformation of hAS3MT to a best matched state by hydrophobic interaction. As the second-order reactant, reductant reduces the disulfide bond, most likely between Cys-250 and another cysteine residue of hAS3MT, and exposes the active site cysteine residues for binding trivalent inorganic arsenic (iAs(3+)) to give monomethylarsonic dicysteine (MADC(3+)). In addition, the reaction can be extended to further methylate MADC(3+) to dimethylarsinic cysteine (DAMC(3+)). In the methylation reaction, the β-pleated sheet content of hAS3MT is increased, and the hydrophobicity of the microenvironment around the active sites is decreased. Similarly, we confirm that both the high β-pleated sheet content of hAS3MT and the high dissociation ability of the enzyme-AdoMet-reductant improve the yield of dimethylated arsenicals.

Identification of Catalytic Residues Using a Novel Feature that Integrates the Microenvironment and Geometrical Location Properties of Residues

Enzymes play a fundamental role in almost all biological processes and identification of catalytic residues is a crucial step for deciphering the biological functions and understanding the underlying catalytic mechanisms. In this work, we developed a novel structural feature called MEDscore to identify catalytic residues, which integrated the microenvironment (ME) and geometrical properties of amino acid residues. Firstly, we converted a residue's ME into a series of spatially neighboring residue pairs, whose likelihood of being located in a catalytic ME was deduced from a benchmark enzyme dataset. We then calculated an ME-based score, termed as MEscore, by summing up the likelihood of all residue pairs. Secondly, we defined a parameter called Dscore to measure the relative distance of a residue to the center of the protein, provided that catalytic residues are typically located in the center of the protein structure. Finally, we defined the MEDscore feature based on an effective nonlinear integration of MEscore and Dscore. When evaluated on a well-prepared benchmark dataset using five-fold cross-validation tests, MEDscore achieved a robust performance in identifying catalytic residues with an AUC1.0 of 0.889. At a ≤10% false positive rate control, MEDscore correctly identified approximately 70% of the catalytic residues. Remarkably, MEDscore achieved a competitive performance compared with the residue conservation score (e.g. CONscore), the most informative singular feature predominantly employed to identify catalytic residues. To the best of our knowledge, MEDscore is the first singular structural feature exhibiting such an advantage. More importantly, we found that MEDscore is complementary with CONscore and a significantly improved performance can be achieved by combining CONscore with MEDscore in a linear manner. As an implementation of this work, MEDscore has been made freely accessible at http://protein.cau.edu.cn/mepi/.

Identification of Catalytic Residues in the As(III)S-Adenosylmethionine Methyltransferase

The enzyme As(III) S-adenosylmethionine methyltransferase (EC 2.1.1.137) (ArsM or AS3MT) is found in members of every kingdom, from bacteria to humans. In these enzymes, there are three conserved cysteine residues at positions 72, 174, and 224 in the CmArsM orthologue from the thermophilic eukaryotic alga Cyanidioschyzon sp. 5508. Substitution of any of the three led to loss of As(III) methylation. In contrast, a C72A mutant still methylated trivalent methylarsenite [MAs(III)]. Protein fluorescence of a single-tryptophan mutant reported binding of As(III) or MAs(III). As(GS)(3) and MAs(GS)(2) bound significantly faster than As(III), suggesting that the glutathionylated arsenicals are preferred substrates for the enzyme. Protein fluorescence also reported binding of Sb(III), and the purified enzyme methylated and volatilized Sb(III). The results suggest that all three cysteine residues are necessary for the first step in the reaction, As(III) methylation, but that only Cys174 and Cys224 are required for the second step, methylation of MAs(III) to dimethylarsenite [DMAs(III)]. The rate-limiting step was identified as the conversion of DMAs(III) to trimethylarsine, and DMAs(III) accumulates as the principal product.

Structure‐based identification of catalytic residues

The identification of catalytic residues is an essential step in functional characterization of enzymes. We present a purely structural approach to this problem, which is motivated by the difficulty of evolution-based methods to annotate structural genomics targets that have few or no homologs in the databases. Our approach combines a state-of-the-art support vector machine (SVM) classifier with novel structural features that augment structural clues by spatial averaging and Z scoring. Special attention is paid to the class imbalance problem that stems from the overwhelming number of non-catalytic residues in enzymes compared to catalytic residues. This problem is tackled by: (1) optimizing the classifier to maximize a performance criterion that considers both Type I and Type II errors in the classification of catalytic and non-catalytic residues; (2) under-sampling non-catalytic residues before SVM training; and (3) during SVM training, penalizing errors in learning catalytic residues more than errors in learning non-catalytic residues. Tested on four enzyme datasets, one specifically designed by us to mimic the structural genomics scenario and three previously evaluated datasets, our structure-based classifier is never inferior to similar structure-based classifiers and comparable to classifiers that use both structural and evolutionary features. In addition to the evaluation of the performance of catalytic residue identification, we also present detailed case studies on three proteins. This analysis suggests that many false positive predictions may correspond to binding sites and other functional residues. A web server that implements the method, our own-designed database, and the source code of the programs are publicly available at http://www.cs.bgu.ac.il/∼meshi/functionPrediction.

Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification

Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.

Cofactor Requirement of HpyAV Restriction Endonuclease

Background Helicobacter pylori is the etiologic agent of common gastritis and a risk factor for gastric cancer. It is also one of the richest sources of Type II restriction-modification (R-M) systems in microorganisms.Principal FindingsWe have cloned, expressed and purified a new restriction endonuclease HpyAV from H. pylori strain 26695. We determined the HpyAV DNA recognition sequence and cleavage site as CCTTC 6/5. In addition, we found that HpyAV has a unique metal ion requirement: its cleavage activity is higher with transition metal ions than in Mg++. The special metal ion requirement of HpyAV can be attributed to the presence of a HNH catalytic site similar to ColE9 nuclease instead of the canonical PD-X-D/EXK catalytic site found in many other REases. Site-directed mutagenesis was carried out to verify the catalytic residues of HpyAV. Mutation of the conserved metal-binding Asn311 and His320 to alanine eliminated cleavage activity. HpyAV variant H295A displayed approximately 1% of wt activity.Conclusions/SignificanceSome HNH-type endonucleases have unique metal ion cofactor requirement for optimal activities. Homology modeling and site-directed mutagenesis confirmed that HpyAV is a member of the HNH nuclease family. The identification of catalytic residues in HpyAV paved the way for further engineering of the metal binding site. A survey of sequenced microbial genomes uncovered 10 putative R-M systems that show high sequence similarity to the HpyAV system, suggesting lateral transfer of a prototypic HpyAV-like R-M system among these microorganisms.

Active site prediction using evolutionary and structural information

Motivation: The identification of catalytic residues is a key step in understanding the function of enzymes. While a variety of computational methods have been developed for this task, accuracies have remained fairly low. The best existing method exploits information from sequence and structure to achieve a precision (the fraction of predicted catalytic residues that are catalytic) of 18.5% at a corresponding recall (the fraction of catalytic residues identified) of 57% on a standard benchmark. Here we present a new method, Discern, which provides a significant improvement over the state-of-the-art through the use of statistical techniques to derive a model with a small set of features that are jointly predictive of enzyme active sites.Results: In cross-validation experiments on two benchmark datasets from the Catalytic Site Atlas and CATRES resources containing a total of 437 manually curated enzymes spanning 487 SCOP families, Discern increases catalytic site recall between 12% and 20% over methods that combine information from both sequence and structure, and by ≥50% over methods that make use of sequence conservation signal only. Controlled experiments show that Discern's improvement in catalytic residue prediction is derived from the combination of three ingredients: the use of the INTREPID phylogenomic method to extract conservation information; the use of 3D structure data, including features computed for residues that are proximal in the structure; and a statistical regularization procedure to prevent overfitting.Contact: kimmen@berkeley.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Graphlet Kernels for Prediction of Functional Residues in Protein Structures

We introduce a novel graph-based kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues. Each vertex in the graph is then represented as a vector of counts of labeled non-isomorphic subgraphs (graphlets), centered on the vertex of interest. A similarity measure between two vertices is expressed as the inner product of their respective count vectors and is used in a supervised learning framework to classify protein residues. We evaluated our method on two function prediction problems: identification of catalytic residues in proteins, which is a well-studied problem suitable for benchmarking, and a much less explored problem of predicting phosphorylation sites in protein structures. The performance of the graphlet kernel approach was then compared against two alternative methods, a sequence-based predictor and our implementation of the FEATURE framework. On both tasks, the graphlet kernel performed favorably; however, the margin of difference was considerably higher on the problem of phosphorylation site prediction. While there is data that phosphorylation sites are preferentially positioned in intrinsically disordered regions, we provide evidence that for the sites that are located in structured regions, neither the surface accessibility alone nor the averaged measures calculated from the residue microenvironments utilized by FEATURE were sufficient to achieve high accuracy. The key benefit of the graphlet representation is its ability to capture neighborhood similarities in protein structures via enumerating the patterns of local connectivity in the corresponding labeled graphs.

In Silico identification of catalytic residues in azobenzene reductase from bacillus subtilis and its docking studies with azo dyes

Prediction of catalytic residues of an enzyme molecule is of great importance for a range of applications including molecular docking, drug design, structural identification and comparison of binding sites. Over the last decades, many studies have been conducted to identify the enzyme catalytic site. But, the catalytic residues of the azobenzene reductase from bacillus subtilis are still unknown. Investigation shows that under anaerobic conditions, azo dyes can be reduced by this enzyme and other environmental microorganisms to colorless amines, which may be toxic, mutagenic, and carcinogenic to humans and animals. To assess and estimate the toxicity, it is essential to identify the catalytic residues of this enzyme. The computational methods developed that address this issue are few. In this approach, we identify the catalytic residues of azobenzene reductase from bacillus subtilis, which were then analyzed in terms of properties including function, conservation, hydrogen bonding, B-factor, solvent accessibility, and flexibility. The results indicate that, Lys (83) and Tyr (74) play an important role as catalytic site residues in the azobenzene reductase from bacillus subtilis. It is hoped that this information will provide a better understanding of the molecular mechanisms involved in catalysis and a heuristic basis for predicting the catalytic residues in enzymes of unknown function. In this study, our approach mainly looks for a better understanding of the biodegradation of the Sudan I, Sudan II, Sudan III and Sudan IV dyes mediated by azobenzene reductase from bacillus subtilis. Further more, the catalytic site residues information is essential for understanding and altering substrate specificity and for the design of enzyme inhibitors.

Bioinformatics of the glycoside hydrolase family 57 and identification of catalytic residues in amylopullulanase from Thermococcus hydrothermalis.

Fifty-nine amino acid sequences belonging to family 57 (GH-57) of the glycoside hydrolases were collected using the CAZy server, Pfam database and blast tools. Owing to the sequence heterogeneity of the GH-57 members, sequence alignments were performed using mainly manual methods. Likewise, five conserved regions were identified, which are postulated to be GH-57 consensus motifs. In the 659 amino acid-long 4-alpha-glucanotransferase from Thermococcus litoralis, these motifs correspond to 13_HQP (region I), 76_GQLEIV (region II), 120_WLTERV (region III), 212_HDDGEKFGVW (region IV), and 350_AQCNDAYWH (region V). The third and fourth conserved regions contain the catalytic nucleophile and the proton donor, respectively. Based on our sequence alignment, residues Glu291 and Asp394 were proposed as the nucleophile and proton donor, respectively, in a GH-57 amylopullulanase from Thermococcus hydrothermalis. To validate this prediction, site-directed mutagenesis was performed. The results of this work reveal that both residues are critical for the pullulanolytic and amylolytic activities of the amylopullulanase. Therefore, these data support the prediction and strongly suggest that the bifunctionality of the amylopullulanase is determined by a single catalytic centre. Despite this positive validation, our alignment also reveals that certain GH-57 members do not possess the Glu and Asp corresponding to the predicted GH-57 catalytic residues. However, the sequences concerned by this anomaly encode putative proteins for which no biochemical or enzymatic data are yet available. Finally, the evolutionary trees generated for GH-57 reveal that the entire family can be divided into several subfamilies that may reflect the different enzyme specificities.

Replacement of the catalytic nucleophile cysteine-296 by serine in class II polyhydroxyalkanoate synthase from Pseudomonas aeruginosa-mediated synthesis of a new polyester: identification of catalytic residues.

The class II PHA (polyhydroxyalkanoate) synthases [PHA(MCL) synthases (medium-chain-length PHA synthases)] are mainly found in pseudomonads and catalyse synthesis of PHA(MCL)s using CoA thioesters of medium-chain-length 3-hydroxy fatty acids (C6-C14) as a substrate. Only recently PHA(MCL) synthases from Pseudomonas oleovorans and Pseudomonas aeruginosa were purified and in vitro activity was achieved. A threading model of the P. aeruginosa PHA(MCL) synthase PhaC1 was developed based on the homology to the epoxide hydrolase (1ek1) from mouse which belongs to the alpha/beta-hydrolase superfamily. The putative catalytic residues Cys-296, Asp-452, His-453 and His-480 were replaced by site-specific mutagenesis. In contrast to class I and III PHA synthases, the replacement of His-480, which aligns with the conserved base catalyst of the alpha/beta-hydrolases, with Gln did not affect in vivo enzyme activity and only slightly in vitro enzyme activity. The second conserved histidine His-453 was then replaced by Gln, and the modified enzyme showed only 24% of wild-type in vivo activity, which indicated that His-453 might functionally replace His-480 in class II PHA synthases. Replacement of the postulated catalytic nucleophile Cys-296 by Ser only reduced in vivo enzyme activity to 30% of wild-type enzyme activity and drastically changed substrate specificity. Moreover, the C296S mutation turned the enzyme sensitive towards PMSF inhibition. The replacement of Asp-452 by Asn, which is supposed to be required as general base catalyst for elongation reaction, did abolish enzyme activity as was found for the respective amino acid residue of class I and III enzymes. In the threading model residues Cys-296, Asp-452, His-453 and His-480 reside in the core structure with the putative catalytic nucleophile Cys-296 localized at the highly conserved gamma-turns of the alpha/beta-hydrolases. Inhibitor studies indicated that catalytic histidines reside in the active site. The conserved residue Trp-398 was replaced by Phe and Ala, respectively, which caused inactivation of the enzyme indicating an essential role of this residue. In the threading model this residue was found to be surface-exposed. No evidence for post-translational modification by 4-phosphopantetheine was obtained. Overall, these data suggested that in class II PHA synthases the conserved histidine which was found as general base catalyst in the catalytic triad of enzymes related to the alpha/beta-hydrolase superfamily, was functionally replaced by His-453 which is conserved among all PHA synthases.

Biochemical characterization and identification of catalytic residues in alpha-glucuronidase from Bacillus stearothermophilus T-6.

Alpha-D-glucuronidases cleave the alpha-1,2-glycosidic bond of the 4-O-methyl-D-glucuronic acid side chain of xylan, as a part of an array of xylan hydrolyzing enzymes. The alpha-D-glucuronidase from Bacillus stearothermophilus T-6 was overexpressed in Escherichia coli using the T7 polymerase expression system. The purification procedure included two steps, heat treatment and gel filtration chromatography, and provided over 0.3 g of pure enzyme from 1 L of overnight culture. Based on gel filtration, the native protein is comprised of two identical subunits. Kinetic constants with aldotetraouronic acid as a substrate, at 55 degrees C, were a Km of 0.2 mM, and a specific activity of 42 U x mg(-1) (kcat = 54.9 s(-1)). The enzyme was most active at 65 degrees C, pH 5.5-6.0, in a 10-min assay, and retained 100% of its activity following incubation at 70 degrees C for 20 min. Based on differential scanning calorimetry, the protein denatured at 73.4 degrees C. Truncated forms of the enzyme, lacking either 126 amino acids from its N-terminus or 81 amino acids from its C-terminus, exhibited low residual activity, indicating that the catalytic site is located in the central region of the protein. To identify the potential catalytic residues, site-directed mutagenesis was applied on highly conserved acidic amino acids in the central region. The replacements Glu392-->Cys and Asp364-->Ala resulted in a decrease in activity of about five orders of magnitude, suggesting that these residues are the catalytic pair.

Identification of Catalytic Residues in Glyoxal Oxidase by Targeted Mutagenesis

Glyoxal oxidase is a copper metalloenzyme produced by the wood-rot fungus Phanerochaete chrysosporium as an essential component of its extracellular lignin degradation pathways. Previous spectroscopic studies on glyoxal oxidase have demonstrated that it contains a free radical-coupled copper active site remarkably similar to that found in another fungal metalloenzyme, galactose oxidase. Alignment of primary structures has allowed four catalytic residues of glyoxal oxidase to be targeted for site-directed mutagenesis in the recombinant protein. Three glyoxal oxidase mutants have been heterologously expressed in both a filamentous fungus (Aspergillus nidulans) and in a methylotrophic yeast (Pichia pastoris), the latter expression system producing as much as 2 g of protein per liter of culture medium under conditions of high density methanol-induced fermentation. Biochemical and spectroscopic characterization of the mutant enzymes supports structural correlations between galactose oxidase and glyoxal oxidase, clearly identifying the catalytically important residues in glyoxal oxidase and demonstrating the functions of each of these residues.