Nikhil Sharma

Carboxylesterases: Sources, Characterizationand Broader Applications

Carboxylesterases (CEs) are a group of versatile lipolytic enzymes capable of catalyzing the hydrolysis of esters into acid and alcohol molecules. These enzymes are extensively used in diverse xenobiotic and endobiotic degradations, biocatalysis, and drug metabolism. The present review article focuses on structure, function and major applications of CEs mainly sourced from bacteria and archaea. The CEs are divided in different families (15 families) depending upon their source, biochemical properties, common pentapeptide motif (GXSXG) with catalytic Ser, an entirely different GDSL motif, (family II)/ SXXK motif (family VIII), position of catalytic triad and their protein forms. CEs find diverse applications in degradation of xenobiotic compounds, biocatalysis, biotransformation of compounds such as cholesterol, synthesis of optically active compounds, food industry, anticancer therapeutics, drug and prodrugs like aspirin, delapril etc.

Mining of Microbial Genomes for the Novel Sources of Nitrilases

Next-generation DNA sequencing (NGS) has made it feasible to sequence large number of microbial genomes and advancements in computational biology have opened enormous opportunities to mine genome sequence data for novel genes and enzymes or their sources. In the present communication in silico mining of microbial genomes has been carried out to find novel sources of nitrilases. The sequences selected were analyzed for homology and considered for designing motifs. The manually designed motifs based on amino acid sequences of nitrilases were used to screen 2000 microbial genomes (translated to proteomes). This resulted in identification of one hundred thirty-eight putative/hypothetical sequences which could potentially code for nitrilase activity. In vitro validation of nine predicted sources of nitrilases was done for nitrile/cyanide hydrolyzing activity. Out of nine predicted nitrilases, Gluconacetobacter diazotrophicus, Sphingopyxis alaskensis, Saccharomonospora viridis, and Shimwellia blattae were specific for aliphatic nitriles, whereas nitrilases from Geodermatophilus obscurus, Nocardiopsis dassonvillei, Runella slithyformis, and Streptomyces albus possessed activity for aromatic nitriles. Flavobacterium indicum was specific towards potassium cyanide (KCN) which revealed the presence of nitrilase homolog, that is, cyanide dihydratase with no activity for either aliphatic, aromatic, or aryl nitriles. The present study reports the novel sources of nitrilases and cyanide dihydratase which were not reported hitherto by in silico or in vitro studies.

In Silico Analysis of β-Galactosidases Primary and Secondary Structure in relation to Temperature Adaptation.

β-D-Galactosidases (EC 3.2.1.23) hydrolyze the terminal nonreducing β-D-galactose residues in β-D-galactosides and are ubiquitously present in all life forms including extremophiles. Eighteen microbial β-galactosidase protein sequences, six each from psychrophilic, mesophilic, and thermophilic microbes, were analyzed. Primary structure reveals alanine, glycine, serine, and arginine to be higher in psychrophilic β-galactosidases whereas valine, glutamine, glutamic acid, phenylalanine, threonine, and tyrosine are found to be statistically preferred by thermophilic β-galactosidases. Cold active β-galactosidase has a strong preference towards tiny and small amino acids, whereas high temperature inhabitants had higher content of basic and aromatic amino acids. Thermophilic β-galactosidases have higher percentage of α-helix region responsible for temperature tolerance while cold loving β-galactosidases had higher percentage of sheet and coil region. Secondary structure analysis revealed that charged and aromatic amino acids were significant for sheet region of thermophiles. Alanine was found to be significant and high in the helix region of psychrophiles and valine counters in thermophilic β-galactosidase. Coil region of cold active β-galactosidase has higher content of tiny amino acids which explains their high catalytic efficiency over their counterparts from thermal habitat. The present study has revealed the preference or prevalence of certain amino acids in primary and secondary structure of psychrophilic, mesophilic, and thermophilic β-galactosidase.

Microbial Degradation of Cyanides and Nitriles

Cyanide and nitriles are produced by a wide range of microorganisms and plants as part of their normal metabolism. These are ubiquitous at low levels in soil and water including surface and ground water. Cyanide is used in metallurgical operations in mining of gold and chemical synthesis and nitriles are also extensively used in organic synthesis and as agrochemicals. The sources of cyanide and nitrile contamination of environment include emissions from iron and steel production, coal combustion, petroleum refineries, solid waste incinerators, combustion of nitriles, use of agrochemicals, chemical industries, vehicle exhausts and cigarette smoke. The industrial and anthropological activities have resulted in contamination of soil, water and air with toxic levels of nitriles and cyanide in the environment. In some situations nitrile and cyanide pollution becomes a threat to animal and human beings. A large number of microorganisms have been reported to the degrade nitriles and cyanide to corresponding non-toxic acids or amides. Some microbes have cyanide hydratase or dihydratase enzymes to convert cyanide to formamide or formic acid while others are endowed with nitrilase, nitrile hydratase and amidase systems which transform nitriles to acids and amides. In this chapter we will discuss the sources and extent of cyanide and nitrile contamination of soil, water and air and the potential application of nitrile or cyanide metabolizing organisms in the bioremediation of contaminated habitats will be discussed.

Classifying nitrilases as aliphatic and aromatic using machine learning technique

ProCos (Protein Composition Server, script version), one of the machine learning techniques, was used to classify nitrilases as aliphatic and aromatic nitrilases. Some important feature vectors were used to train the algorithm, which included pseudo-amino acid composition (PAAC) and five-factor solution score (5FSS). This clearly differentiated into two groups of nitrilases, i.e., aliphatic and aromatic, achieving maximum sensitivity of 100.00%, specificity of 90.00%, accuracy of 95.00% and Mathew Correlation Coefficient (MCC) of about 0.90 for the pseudo-amino acid composition. On the other hand, five-factor solution score achieved a sensitivity of 96.00%, specificity of 84.00%, accuracy of 90.00% and Mathew Correlation Coefficient (MCC) of about 0.81. The total count of aliphatic amino acids, Ala (A), Gly (G), Leu (L), Ile (I), Val (V), Met (M) and Pro (P), was found to be higher, i.e., 42.7 in case of aliphatic nitrilases, whereas it was 40.1 in aromatic nitrilases. On the other hand, aromatic amino acids, Tyr (Y), Trp (W), His (H) and Phe (F) number, were found to be higher, i.e., 12.7 in aromatic nitrilases as compared to aliphatic nitrilases which was 10.7. This approach will help in predicting a nitrilase as aromatic or aliphatic nitrilase based on its amino acid sequence. Access to the scripts can be done logging onto GitHub using keyword ‘Nitrilase’ or ‘https://github.com/rover2380/Nitrilase.git’.

Motif?s of aromatic and aliphatic nitrilases

Pathway analysis is an important approach to reveal the biological meaning of the multidimensional transcriptomics and proteomics data sets. Here, we will present the comparison of the capabilities and will demonstrate the advantage of combining several pathway analysis tools, including MetaCore (GeneGO, Thomson Reuters), Pathway Studio (Ariadne Genomics, Elsevier), GeneXplain platform (GeneXplain, GmbH) and PathOlogist (Greenblum et al) based on three proteomics and two transcriptomics case studies. The discussed proteomics case studies are: (i) label-free quantitative LC-MS proteomics study of Alzheimer’s disease and normally aged human brains, (ii) the iTRAQ-labeled LC-MS/MS study of the dynamics of human plasma proteome during leptin replacement therapy in genetically based leptin deficiency, (iii) the spectral count proteomics of the adipose tissue dynamics in leptin replacement. The discussed transcriptomics studies are: (i) the peripheral blood gene expression analysis in intestinal transplantation in adult human patients, (ii) the peripheral blood gene expression analysis in intestinal transplantation in model animals (syngeneic and allogeneic rats) without immunosuppressant treatment. BiographyI recent years, pattern recognition has been applied to solve multiplicity of problems in several fields on computer science and technology. One example it is the design of pharmaceuticals in silico, or sequence analysis of proteins for identification or discovering of new biological targets. For example, this approach has been used in the prediction and design of new antimicrobial peptides, because these compounds could be used as an alternative to conventional antibiotics. For this reason, in this work is proposed to use machine learning tools such as support vector machines (SVM) linked to models of Quantitative StructureActivity Relationships (QSAR), in order to carry out pattern recognition and create algorithm allowing identification of antibacterial activity of synthetic peptides. In this study, we worked with a set of 2288 peptide sequences with and without antimicrobial activity (1144 by group), codifying the structural information of every sequence (charge, molecular weight, isoelectric point, hydrofobicity, size, secondary structure, twist tendency, and in vitro and in vivo aggregation). We developed a classifier in cascade, conformed by two SVMs. In the first one, peptides with and without antimicrobial activity are classified, filtering peptides with antimicrobial activity. In the second one, it is determined if these selected antimicrobial peptides are or not antibacterial ones. Our classification model showed an estimated precision of 80%, which allowed, based on structural descriptors and codification of sequences, to correlate peptide sequences with antibacterial activity by means of learning machines.A common approach to identify the peptides in tandem mass spectrometry (MS/MS) experiments is to use database search software that compares the MS/MS spectra against theoretical spectra derived from a database of known and putative peptide sequences. These software packages use the identification of multiple peptides resulting from artificially induced cleavage for robust identification of the precursor protein. In addition, longer peptides are likely to result in more informative spectra and more unambiguous identification. This situation does not apply to the detection of individual and typically short neuropeptides that result from the natural cleavage of prohormone proteins. The performance of three open-source database search software to identify neuropeptides was studied and characterized. A collection of 7850 known and predicted rat peptides from prohormone cleavage by protein convertases was compiled. The peptide sequences were used to simulate 23550 ideal, uniform MS/MS spectra without post-translational modifications under a range of conditions including neutral mass loss, charge state and missing ions. The simulated spectra were searched across a database that included all known and predicted peptides. Across threshold P-values, Crux correctly matched all the simulated spectra to the corresponding peptides in the database meanwhile OMSSA and X!Tandem failed to correctly recognize a few spectra. Small peptides less than 10 amino acids in length were difficult to match at stringent significance levels. At a threshold P-value < 0.01, more than 64% and 75% of the 6and 7-residue peptides were correctly identified by Crux. At a threshold P-value < 0.01, Crux, OMSSA, and X!Tandem correctly detected 98.3%, 99.9%, and 97.4% of the peptides respectively. Successful identification of short neuropeptides requires tailored detection criteria based on peptide size rather than a single global threshold.S biology encompasses the system level understanding of the interactions of the biological molecules. It takes up new approaches to integrate the data within a molecular network of genes and proteins and other biomolecules like DNA and RNA to reveal their function and behavior which makes up a living organism. A biological network is a graphical representation of such genes, proteins, molecules and individual entities as nodes and their interactions as edges. Mainly these are protein interaction networks, metabolic networks, gene regulatory networks, and signal transduction networks. Recent advances in bioinformatics results in many efficient algorithms to analyze common pathways in these networks, which will contribute a lot to their understanding. Shortest path plays a great importance in network biology and it is the area of research for centuries in graph theory itself. Here, on the basis of the shortest pathway concept, we have developed a Cytoscape plugin called IMPROT which analyzes the biological network to find probable important molecules using modified shortest path algorithm. It gives a rank to proteins as IMPROT score using which the top ranked ones from the whole network can be studied further for therapeutic drug targeting. The improt scores were analyzed and verified with literature and the application of the plugin was bench marked with respect to the whole genome networks of Mycoplasma genitalium, Escherichia coli and Helicobacter pylori. The plugin can be used in future research in drug target identification and the future prospects of research in system biology can be envisioned.P transcriptional regulation is vital for controlling gene expression in trypanosomatids [1, 2]. This control is ruled by mRNA features that contribute to RNA maturation, stability and translation [1, 3] and results from specific RNA/protein interactions [4]. Mapping efforts for identifying RNA cis-elements in Leishmania and trypanosomes have shown that regulatory elements are mainly located on the 3 ́ non-translated regions (UTRs) and, although, the 5 ́ UTR is also involved, it plays a secondary role [1, 5]. Identification and characterization of RNA binding proteins could lead to the discovery of new targets for drug design as well as to a greater understanding of posttranscriptional mechanisms in trypanosomatids. Consequently, our rationale was to study the RNA/protein interactions involved in the expression of the heat shock protein-70 (HSP70), an important virulence factor of Leishmania [6, 7]. Using the HSP70 5 ́ and 3 ́ UTRs as baits, a search of L. braziliensis HSP70 trans-acting factors was performed through RNA/protein pull down strategy followed by two dimensional gel electrophoresis and mass spectrometry identification of protein spots. Proteins related to RNA metabolism and translation process as elongation factor EF-1α, elongation factor 2, a conserved hypothetical protein (with some sequence similarity with the translation initiation factor 3 subunit K), putative eukaryotic initiation factor 4α, putative eukaryotic translation initiation factor 5, and putative poly(A)-binding protein 2 and 3 were identified. Moonlight proteins, unrelated with posttranscriptional control, as glycolytic enzymes were also found. Future experiments will confirm the involvement of these proteins in HSP70 mRNA expression and reveal the importance of these molecules for parasite survival.