Unraveling the Catalytic Mechanism of Nitrile Hydratases

To elucidate a detailed catalytic mechanism for nitrile hydratases (NHases), the pH and temperature dependence of the kinetic constants kcat and Km for the cobalt-type NHase from Pseudonocardia thermophila JCM 3095 (PtNHase) were examined. PtNHase was found to exhibit a bell-shaped curve for plots of relative activity versus pH at pH 3.2–11 and was found to display maximal activity between pH 7.2 and 7.8. Fits of these data provided pKES1 and pKES2 values of 5.9 ± 0.1 and 9.2 ± 0.1 (kcat′ = 130 ± 1 s-1), respectively, and pKE1 and pKE2 values of 5.8 ± 0.1 and 9.1 ± 0.1 (kcat′/Km′ = (6.5 ± 0.1) × 103 s-1 mm-1), respectively. Proton inventory studies indicated that two protons are transferred in the rate-limiting step of the reaction at pH 7.6. Because PtNHase is stable at 60 °C, an Arrhenius plot was constructed by plotting ln(kcat) versus 1/T, providing Ea = 23.0 ± 1.2 kJ/mol. The thermal stability of PtNHase also allowed ΔH0 ionization values to be determined, thus helping to identify the ionizing groups exhibiting the pKES1 and pKES2 values. Based on ΔH0ion data, pKES1 is assigned to βTyr68, whereas pKES2 is assigned to βArg52, βArg157, or αSer112 (NHases are α2β2-heterotetramers). A combination of these data with those previously reported for NHases and synthetic model complexes, along with sequence comparisons of both iron- and cobalt-type NHases, allowed a novel catalytic mechanism for NHases to be proposed

Mutation of H63 and its Catalytic Affect on the Methionine Aminopeptidase from \u3cem\u3eEscherichia coli\u3c/em\u3e

In order to gain insight into the mechanistic role of a flexible exterior loop near the active site, made up of Y62, H63, G64, and Y65, that has been proposed to play an important role in substrate binding and recognition in the methionyl aminopeptidase from Escherichia coli (EcMetAP-I), the H63A enzyme was prepared. Mutation of H63 to alanine does not affect the ability of the enzyme to bind divalent metal ions. The specific activity of H63A EcMetAP-I was determined using four different substrates of varying lengths, namely, l-Met-p-NA, MAS, MGMM and MSSHRWDW. For the smallest/shortest substrate (l-Met-p-NA) the specific activity decreased nearly seven fold but as the peptide length increased, the specific activity also increased and became comparable to WT EcMetAP-I. This decrease in specific activity is primarily due to a decrease in the observed kcat values, which decreases nearly sixty-fold for l-Met-p-NA while only a four-fold decrease is observed for the tri- and tetra-peptide substrates. Interestingly, no change in kcat was observed when the octa-peptide MSSHRWDW was used as a substrate. These data suggest that H63 affects the hydrolysis of small peptide substrates whereas large peptides can overcome the observed loss in binding energy, as predicted from Km values, by additional hydrophilic and hydrophobic interactions

Analyzing the Binding of Co(II)-specific Inhibitors to the Methionyl Aminopeptidases from \u3cem\u3eEscherichia coli\u3c/em\u3e and \u3cem\u3ePyrococcus furiosus\u3c/em\u3e

Methionine aminopeptidases (MetAPs) represent a unique class of protease that is capable of the hydrolytic removal of an N-terminal methionine residue from nascent polypeptide chains. MetAPs are physiologically important enzymes; hence, there is considerable interest in developing inhibitors that can be used as antiangiogenic and antimicrobial agents. A detailed kinetic and spectroscopic study has been performed to probe the binding of a triazole-based inhibitor and a bestatin-based inhibitor to both Mn(II)- and Co(II)-loaded type-I (Escherichia coli) and type-II (Pyrococcus furiosus) MetAPs. Both inhibitors were found to be moderate competitive inhibitors. The triazole-type inhibitor was found to interact with both active-site metal ions, while the bestatin-type inhibitor was capable of switching its mode of binding depending on the metal in the active site and the type of MetAP enzyme

Analyzing the Catalytic Role of Asp97 in the Methionine Aminopeptidase from \u3cem\u3eEscherichia coli\u3c/em\u3e

An active site aspartate residue, Asp97, in the methionine aminopeptidase (MetAPs) from Escherichia coli (EcMetAP-I) was mutated to alanine, glutamate, and asparagine. Asp97 is the lone carboxylate residue bound to the crystallographically determined second metal-binding site in EcMetAP-I. These mutant EcMetAP-I enzymes have been kinetically and spectroscopically characterized. Inductively coupled plasma–atomic emission spectroscopy analysis revealed that 1.0 ± 0.1 equivalents of cobalt were associated with each of the Asp97-mutated EcMetAP-Is. The effect on activity after altering Asp97 to alanine, glutamate or asparagine is, in general, due to a ∼ 9000-fold decrease in kca towards Met-Gly-Met-Met as compared to the wild-type enzyme. The Co(II) d–d spectra for wild-type, D97E and D97A EcMetAP-I exhibited very little difference in form, in each case, between the monocobalt(II) and dicobalt(II) EcMetAP-I, and only a doubling of intensity was observed upon addition of a second Co(II) ion. In contrast, the electronic absorption spectra of [Co_(D97N EcMetAP-I)] and [CoCo(D97N EcMetAP-I)] were distinct, as were the EPR spectra. On the basis of the observed molar absorptivities, the Co(II) ions binding to the D97E, D97A and D97N EcMetAP-I active sites are pentacoordinate. Combination of these data suggests that mutating the only nonbridging ligand in the second divalent metal-binding site in MetAPs to an alanine, which effectively removes the ability of the enzyme to form a dinuclear site, provides a MetAP enzyme that retains catalytic activity, albeit at extremely low levels. Although mononuclear MetAPs are active, the physiologically relevant form of the enzyme is probably dinuclear, given that the majority of the data reported to date are consistent with weak cooperative binding

The Proteomics of N-terminal Methionine Cleavage

Methionine aminopeptidase (MAP) is a ubiquitous, essential enzyme involved in protein N-terminal methionine excision. According to the generally accepted cleavage rules for MAP, this enzyme cleaves all proteins with small side chains on the residue in the second position (P1′), but many exceptions are known. The substrate specificity of Escherichia coli MAP1 was studied in vitro with a large (\u3e120) coherent array of peptides mimicking the natural substrates and kinetically analyzed in detail. Peptides with Val or Thr at P1′ were much less efficiently cleaved than those with Ala, Cys, Gly, Pro, or Ser in this position. Certain residues at P2′, P3′, and P4′ strongly slowed the reaction, and some proteins with Val and Thr at P1′ could not undergo Met cleavage. These in vitro data were fully consistent with data for 862 E. coli proteins with known N-terminal sequences in vivo. The specificity sites were found to be identical to those for the other type of MAPs, MAP2s, and a dedicated prediction tool for Met cleavage is now available. Taking into account the rules of MAP cleavage and leader peptide removal, the N termini of all proteins were predicted from the annotated genome and compared with data obtained in vivo. This analysis showed that proteins displaying N-Met cleavage are overrepresented in vivo. We conclude that protein secretion involving leader peptide cleavage is more frequent than generally thought

A New Colorimetric Assay for Methionyl Aminopeptidases: Examination of the Binding of a New Class of Pseudopeptide Analog Inhibitors

A direct and convenient spectrophotometric assay has been developed for methionine aminopeptidases (MetAPs). The method employs the hydrolysis of a substrate that is a methionyl analogue of p-nitroaniline (l-Met-p-NA), which releases the chromogenic product p-nitroaniline. This chromogenic product can be monitored continuously using a UV–Vis spectrophotometer set at 405 nm. The assay was tested with the type I MetAP from Escherichia coli (EcMetAP-I) and the type II MetAP from Pyrococcus furiosus (PfMetAP-II). Using l-Met-p-NA, the kinetic constants kcat and Km were determined for EcMetAP-I and PfMetAP-II and were compared with those obtained with a “standard” high-performance liquid chromatography (HPLC) discontinuous assay. The assay has also been used to determine the temperature dependence of the kinetic constant kcat for PfMetAP-II as well as to screen two novel pseudopeptide inhibitors of MetAPs. The results demonstrate that l-Met-p-NA provides a fast, convenient, and effective substrate for both type I and type II MetAPs and that this substrate can be used to quickly screen inhibitors of MetAPs

Kinetic and Spectroscopic Analysis of the Catalytic Role of H79 in the Methionine Aminopeptidase from \u3cem\u3eEscherichia coli\u3c/em\u3e

To gain insight into the role of the strictly conserved histidine residue, H79, in the reaction mechanism of the methionyl aminopeptidase from Escherichia coli (EcMetAP-I), the H79A mutated enzyme was prepared. Co(II)-loaded H79A exhibits an overall \u3e7000-fold decrease in specific activity. The almost complete loss of activity is primarily due to a \u3e6000-fold decrease in kcat. Interestingly, the Km value obtained for Co(II)-loaded H79A was approximately half the value observed for wild-type (WT) EcMetAP-I. Consequently, kcat/Km values decreased only 3000-fold. On the other hand, the observed specific activity of Mn(II)-loaded H79A EcMetAP-I decreased by ∼2.6-fold while kcat decreased by ∼3.5-fold. The observed Km value for Mn(II)-loaded H79A EcMetAP-I was ∼1.4-fold larger than that observed for WT EcMetAP-I, resulting in a kcat/Km value that is lower by ∼3.4-fold. Metal binding, UV−vis, and EPR data indicate that the active site is unperturbed by mutation of H79, as suggested by X-ray crystallographic data. Kinetic isotope data indicate that H79 does not transfer a proton to the newly forming amine since a single proton is transferred in the transition state for both the WT and H79A EcMetAP-I enzymes. Therefore, H79 functions to position the substrate by hydrogen bonding to either the amine group of the peptide linkage or a backbone carbonyl group. Together, these data provide new insight into the catalytic mechanism of EcMetAP-I

DNA sequence variation and haplotype structure of the ICAM1 and TNF genes in 12 ethnic groups of India reveal patterns of importance in designing association studies

We have examined the patterns of DNA sequence variation in and around the genes coding for ICAM1 and TNF, which play functional and correlated roles in inflammatory processes and immune cell responses, in 12 diverse ethnic groups of India. We aimed to (a) quantify the nature and extent of the variation, and (b) analyse the observed patterns of variation in relation to population history and ethnic background. At the ICAM1 and TNF loci, respectively, the total numbers of SNPs that were detected were 28 and 12. Many of these SNPs are not shared across ethnic groups and are unreported in the dbSNP or TSC databases, including two fairly common non-synonymous SNPs at positions 13487 and 13542 in the ICAM1 gene. Conversely, the TNF-376A SNP that is reported to be associated with susceptibility to malaria was not found in our study populations, even though some of the populations inhabit malaria endemic areas. Wide between-population variation in the frequencies of shared SNPs and coefficients of linkage disequilibrium have been observed. These findings have profound implications in case-control association studies

MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method

BACKGROUND: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. METHODOLOGY/PRINCIPAL FINDING: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. CONCLUSIONS/SIGNIFICANCE: MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm

SFSSClass: an integrated approach for miRNA based tumor classification

Background: MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification. Results: In this article, a novel classification technique called SFSSClass is developed that judiciously integrates a biclustering technique SAMBA for simultaneous feature (miRNA) and sample (tissue) selection (SFSS), a cancer-miRNA network that we have developed by mining the literature of experimentally verified cancer-miRNA relationships and a classifier uncorrelated shrunken centroid (USC). SFSSClass is used for classifying multiple classes of tumors and cancer cell lines. In a part of the investigation, poorly differentiated tumors (PDT) having non diagnostic histological appearance are classified while training on more differentiated tumor (MDT) samples. The proposed method is found to outperform the best known accuracy in the literature on the experimental data sets. For example, while the best accuracy reported in the literature for classifying PDT samples is similar to 76.5%, the accuracy of SFSSClass is found to be similar to 82.3%. The advantage of incorporating biclustering integrated with the cancer-miRNA network is evident from the consistently better performance of SFSSClass (integration of SAMBA, cancer-miRNA network and USC) over USC (eg., similar to 70.5% for SFSSClass versus similar to 58.8% in classifying a set of 17 MDT samples from 9 tumor types, similar to 91.7% for SFSSClass versus similar to 75% in classifying 12 cell lines from 6 tumor types and similar to 382.3% for SFSSClass versus similar to 41.2% in classifying 17 PDT samples from 11 tumor types). Conclusion: In this article, we develop the SFSSClass algorithm which judiciously integrates a biclustering technique for simultaneous feature (miRNA) and sample (tissue) selection, the cancer-miRNA network and a classifier. The novel integration of experimental knowledge with computational tools efficiently selects relevant features that have high intra-class and low interclass similarity. The performance of the SFSSClass is found to be significantly improved with respect to the other existing approaches