Technical university register data analysis using GIS methods

katedra: KGE; přílohy: 2 tabulky, 7 map, 1 CD; rozsah: 77 s., 9 s. přílohThe aim of the Diploma thesis is to analyse the data which are saved in the register of the Technical university in Liberec with methods of GIS. Spatiality is verified by the data of the faculty of education of the TUL from 2004 to 2008. The result is to find the procedure for the repeated application of analyses, which are effective on the other files of data and its other benefits. The rusults are visualised in a form of the thematic map.Předmětem této diplomové práce je analyzovat data uložená v matrice TUL metodami GIS. Na datech FP TUL ze let 2004-2008 je ověřována prostorovost informací v matrice. Dílčím cílem je nalézt postup pro opakovanou aplikaci analýz na další soubory dat a její další využití. Výsledky jsou vizualizovány formou tématických map

Transhalogenation Catalysed by Haloalkane Dehalogenases Engineered to Stop Natural Pathway at Intermediate

Haloalkane dehalogenases (HLDs) are alpha/beta-hydrolases that convert halogenated compounds to their corresponding alcohols. The overall kinetic mechanism proceeds via four steps: (i) binding of halogenated substrate, (ii) bimolecular nucleophilic substitution (S(N)2) leading to the cleavage of a carbon-halogen bond and the formation of an alkyl-enzyme intermediate, (iii) nucleophilic addition of a water molecule resulting in the hydrolysis of the intermediate to the corresponding alcohol and (iv) release of the reaction products - an alcohol, a halide ion and a proton. Although, the overall reaction has been reported as irreversible, several kinetic evidences from previous studies suggest the reversibility of the first S(N)2 chemical step. To study this phenomenon, we have engineered HLDs to stop the catalytic cycle at the stage of the alkyl-enzyme intermediate. The ability of the intermediate to exchange halides was confirmed by a stopped-flow fluorescence binding analysis. Finally, the transhalogenation reaction was confirmed with several HLDs and 2,3-dichloropropene in the presence of a high concentration of iodide. The formation of the transhalogenation product 3-iodo-2-chloropropene catalysed by five mutant HLDs was identified by gas chromatography coupled with mass spectrometry. Hereby we demonstrated the reversibility of the cleavage of the carbon-halogen bond by HLDs resulting in a transhalogenation. After optimization, the transhalogenation reaction can possibly find its use in biocatalytic applications. Enabling this reaction by strategically engineering the enzyme to stop at an intermediate in the catalytic cycle that is synthetically more useful than the product of the natural pathway is a novel concept

Machine Learning in Enzyme Engineering

Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts

Engineering enzyme access tunnels

Enzymes are efficient and specific catalysts for many essential reactions in biotechnological and pharmaceutical industries. Many times, the natural enzymes do not display the catalytic efficiency, stability or specificity required for these industrial processes. The current enzyme engineering methods offer solutions to this problem, but they mainly target the buried active site where the chemical reaction takes place. Despite being many times ignored, the tunnels and channels connecting the environment with the active site are equally important for the catalytic properties of enzymes. Changes in the enzymatic tunnels and channels affect enzyme activity, specificity, promiscuity, enantioselectivity and stability. This review provides an overview of the emerging field of enzyme access tunnel engineering with case studies describing design of all the aforementioned properties. The software tools for the analysis of geometry and function of the enzymatic tunnels and channels and for the rational design of tunnel modifications will also be discussed. The combination of new software tools and enzyme engineering strategies will provide enzymes with access tunnels and channels specifically tailored for individual industrial processes

EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities

Millions of protein sequences are being discovered at an incredible pace, representing an inexhaustible source of biocatalysts. Despite genomic databases growing exponentially, classical biochemical characterization techniques are time-demanding, cost-ineffective and low-throughput. Therefore, computational methods are being developed to explore the unmapped sequence space efficiently. Selection of putative enzymes for biochemical characterization based on rational and robust analysis of all available sequences remains an unsolved problem. To address this challenge, we have developed EnzymeMiner-a web server for automated screening and annotation of diverse family members that enables selection of hits for wet-lab experiments. EnzymeMiner prioritizes sequences that are more likely to preserve the catalytic activity and are heterologously expressible in a soluble form in Escherichia coli. The solubility prediction employs the in-house SoluProt predictor developed using machine learning. EnzymeMiner reduces the time devoted to data gathering, multi-step analysis, sequence prioritization and selection from days to hours. The successful use case for the haloalkane dehalogenase family is described in a comprehensive tutorial available on the EnzymeMiner web page

FireProt: web server for automated design of thermostable proteins

There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot.Web of Science45W1W399W39

Reconstruction of Mycobacterial Dehalogenase Rv2579 by Cumulative Mutagenesis of Haloalkane Dehalogenase LinB

The homology model of protein Rv2579 from Mycobacterium tuberculosis H37Rv was compared with the crystal structure of haloalkane dehalogenase LinB from Sphingomonas paucimobilis UT26, and this analysis revealed that 6 of 19 amino acid residues which form an active site and entrance tunnel are different in LinB and Rv2579. To characterize the effect of replacement of these six amino acid residues, mutations were introduced cumulatively into the six amino acid residues of LinB. The sixfold mutant, which was supposed to have the active site of Rv2579, exhibited haloalkane dehalogenase activity with the haloalkanes tested, confirming that Rv2579 is a member of the haloalkane dehalogenase protein family

Degradation of β-Hexachlorocyclohexane by Haloalkane Dehalogenase LinB from Sphingomonas paucimobilis UT26

β-Hexachlorocyclohexane (β-HCH) is the most recalcitrant among the α-, β-, γ-, and δ-isomers of HCH and causes serious environmental pollution problems. We demonstrate here that the haloalkane dehalogenase LinB, reported earlier to mediate the second step in the degradation of γ-HCH in Sphingomonas paucimobilis UT26, metabolizes β-HCH to produce 2,3,4,5,6-pentachlorocyclohexanol