Residues at the Active Site of the Esterase 2 fromAlicyclobacillus acidocaldarius Involved in Substrate Specificity and Catalytic Activity at High Temperature

The recently solved three-dimensional structure of the thermophilic esterase 2 from Alicyclobacillus acidocaldarius allowed us to have a snapshot of an enzyme-sulfonate complex, which mimics the second stage of the catalytic reaction, namely the covalent acyl-enzyme intermediate. The aim of this work was to design, by structure-aided analysis and to generate by site-directed and saturation mutagenesis, EST2 variants with changed substrate specificity in the direction of preference for monoacylesters whose acyl-chain length is greater than eight carbon atoms. Positions 211 and 215 of the polypeptide chain were chosen to introduce mutations. Among five variants with single and double amino acid substitutions, three were obtained, M211S, R215L, and M211S/R215L, that changed the catalytic efficiency profile in the desired direction. Kinetic characterization of mutants and wild type showed that this change was achieved by an increase in k(cat) and a decrease in K(m) values with respect to the parental enzyme. The M211S/R215L specificity constant for p-nitrophenyl decanoate substrate was 6-fold higher than the wild type. However, variants M211T, M211S, and M211V showed strikingly increased activity as well as maximal activity with monoacylesters with four carbon atoms in the acyl chain, compared with the wild type. In the case of mutant M211T, the k(cat) for p-nitrophenyl butanoate was 2.4-fold higher. Overall, depending on the variant and on the substrate, we observed improved catalytic activity at 70 degrees C with respect to the wild type, which was a somewhat unexpected result for an enzyme with already high k(cat) values at high temperature. In addition, variants with altered specificity toward the acyl-chain length were obtained. The results were interpreted in the context of the EST2 three-dimensional structure and a proposed catalytic mechanism in which k(cat), e.g. the limiting step of the reaction, was dependent on the acyl chain length of the ester substrate

A Novel Aspartyl Proteinase from Apocrine Epithelia and Breast Tumors

Abstract GCDFP-15 (gross cysticdisease fluid protein,15 kDa) is a secretory marker of apocrine differentiation in breast carcinoma. In human breast cancer cell lines, gene expression is regulated by hormones, including androgens and prolactin. The protein is also known under different names in different body fluids such as gp17 in seminal plasma. GCDFP-15/gp17 is a ligand of CD4 and is a potent inhibitor of T-cell apoptosis induced by sequential CD4/T-cell receptor triggering. We now report that GCDFP-15/gp17 is a protease exhibiting structural properties relating it to the aspartyl proteinase superfamily. Unexpectedly, GCDFP-15/gp17 appears to be related to the retroviral members rather than to the known cellular members of this class. Site-specific mutagenesis of Asp22 (predicted to be catalytically important for the active site) and pepstatin A inhibition confirmed that the protein is an aspartic-type protease. We also show that, among the substrates tested, GCDFP-15/gp17 is specific for fibronectin. The study of GCDFP-15/gp17-mediated proteolysis may provide a handle to understand phenomena as diverse as mammary tumor progression and fertilization

Machine Learning Methods for Generating High Dimensional Discrete Datasets

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X\u27 that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons

Generating Synthetic Discrete Datasets with Machine Learning

The real data are not always available/accessible/sufficient or in many cases they are incomplete and lacking in semantic content necessary to the definition of optimization processes. In this paper we discuss about the synthetic data generation under two different perspectives. The core common idea is to analyze a limited set of real data to learn the main patterns that characterize them and exploit this knowledge to generate brand new data. The first perspective is constraint-based generation and consists in generating a synthetic dataset satisfying given support constraints on the real frequent patterns. The second one is based on probabilistic generative modeling and considers the synthetic generation as a sampling process from a parametric distribution learned on the real data, typically encoded as a neural network (e.g. Variational Autoencoders, Generative Adversarial Networks)

Probabilistic topic models for sequence data

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The Aes protein and the monomeric alpha-galactosidase from Escherichia coli form a non-covalent complex. Implications for the regulation of carbohydrate metabolism.

Aes, a 36-kDa acetylesterase fromEscherichia coli, belongs to the hormone-sensitive lipase family, and it is involved in the regulation of MalT, the transcriptional activator of the maltose regulon. The activity of MalT is depressed through a direct protein-protein interaction with Aes. Although the effect is clear-cut, the meaning of this interaction and the conditions that trigger it still remain elusive. To perform a comparative thermodynamic study between the mesophilic Aes protein and two homologous thermostable enzymes, Aes was overexpressed in E. coli and purified. At the last step of the purification procedure the enzyme was eluted from a Mono Q HR 5/5 column as a major form migrating, anomalously, at 56 kDa on a calibrated Superdex 75 column. A minor peak that contains the Aes protein and a polypeptide of 50 kDa was also detected. By a combined analysis of size-exclusion chromatography and surface-enhanced laser desorption ionization-time of flight mass spectrometry, it was possible to demonstrate the presence in this peak of a stable 87-kDa complex, containing the Aes protein itself and the 50-kDa polypeptide in a 1:1 ratio. The homodimeric molecular species of Aes and of the 50-kDa polypeptide were also detected. The esterase activity associated with the 87-kDa complex, when assayed with p-nitrophenyl butanoate as substrate, proved 6-fold higher than the activity of the major Aes form of 56 kDa. Amino-terminal sequencing highlighted that the 50-kDa partner of Aes in the complex was the α-galactosidase from E. coli. TheE. coli cells harboring plasmid pT7-SCII-aesand, therefore, expressing Aes were hampered in their growth on a minimal medium containing raffinose as a sole carbon source. Because α-galactosidase is involved in the metabolism of raffinose, the above findings suggest a potential role of Aes in the regulation of carbohydrate metabolism in E. coli

Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study

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SSo

Previously, we reported from the Sulfolobus solfataricus open reading frame (ORF) SSO2517 the cloning, overexpression and characterization of an esterase belonging to the hormone-sensitive lipase (HSL) family and apparently having a deletion at the N-terminus, which we named SsoNΔ. Searching the recently reported Sulfolobus acidocaldarius genome by sequence alignment, using SSO2517 as a query, allowed identity of a putative esterase (ORF SAC1105) sharing high sequence similarity (82%) with SSO2517. This esterase displays an N-terminus and total length similar to other known esterases of the HSL family. Analysis of the upstream DNA sequence of SS02517 revealed the possibility of expressing a longer version of the protein with an extended N-terminus; however, no clear translation signal consistent with a longer protein version was detected. This new version of SSO2517 was cloned, over-expressed, purified and characterized. The resulting protein, named SsoNΔlong, was 15-fold more active with the substrate p-nitrophenyl hexanoate than SsoNΔ. Furthermore, SsoNΔlong and SsoNΔ displayed different substrate specificities for triacylglycerols. These results and the phylogenetic relationship between S. solfataricus and S. acidocaldarius suggest a common origin of SSO2517 and SAC1105 from an ancestral gene, followed by divergent evolution. Alternatively, a yet-to-be discovered mechanism of translation that directs the expression of SsoNΔlong under specific metabolic conditions could be hypothesized

Redox stress proteins are involved in adaptation response of the hyperthermoacidophilic archaeon Sulfolobus solfataricus to nickel challenge

<p>Abstract</p> <p>Background</p> <p>Exposure to nickel (Ni) and its chemical derivatives has been associated with severe health effects in human. On the contrary, poor knowledge has been acquired on target physiological processes or molecular mechanisms of this metal in model organisms, including Bacteria and Archaea. In this study, we describe an analysis focused at identifying proteins involved in the recovery of the archaeon <it>Sulfolobus solfataricus </it>strain MT4 from Ni-induced stress.</p> <p>Results</p> <p>To this purpose, <it>Sulfolobus solfataricus </it>was grown in the presence of the highest nickel sulphate concentration still allowing cells to survive; crude extracts from treated and untreated cells were compared at the proteome level by using a bi-dimensional chromatography approach. We identified several proteins specifically repressed or induced as result of Ni treatment. Observed up-regulated proteins were largely endowed with the ability to trigger recovery from oxidative and osmotic stress in other biological systems. It is noteworthy that most of the proteins induced following Ni treatment perform similar functions and a few have eukaryal homologue counterparts.</p> <p>Conclusion</p> <p>These findings suggest a series of preferential gene expression pathways activated in adaptation response to metal challenge.</p

Modeling Events and Interactions through Temporal Processes -- A Survey

In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.Comment: Image replacement