Multivariate PLS Modeling of Apicomplexan FabD-Ligand Interaction Space for Mapping Target-Specific Chemical Space and Pharmacophore Fingerprints

Biomolecular recognition underlying drug-target interactions is determined by both binding affinity and specificity. Whilst, quantification of binding efficacy is possible, determining specificity remains a challenge, as it requires affinity data for multiple targets with the same ligand dataset. Thus, understanding the interaction space by mapping the target space to model its complementary chemical space through computational techniques are desirable. In this study, active site architecture of FabD drug target in two apicomplexan parasites viz. Plasmodium falciparum (PfFabD) and Toxoplasma gondii (TgFabD) is explored, followed by consensus docking calculations and identification of fifteen best hit compounds, most of which are found to be derivatives of natural products. Subsequently, machine learning techniques were applied on molecular descriptors of six FabD homologs and sixty ligands to induce distinct multivariate partial-least square models. The biological space of FabD mapped by the various chemical entities explain their interaction space in general. It also highlights the selective variations in FabD of apicomplexan parasites with that of the host. Furthermore, chemometric models revealed the principal chemical scaffolds in PfFabD and TgFabD as pyrrolidines and imidazoles, respectively, which render target specificity and improve binding affinity in combination with other functional descriptors conducive for the design and optimization of the leads.

Studies of Solids and Surfaces by Auger Electron Spectroscopy

Although the applications of Auger electron spectroscopy in surface analysis have by far outweighed its use as a tool to investigate electron states of solids and surfaces, there are a variety of situations where Auger spectroscopy provides unique information. Apart from the chemical shifts, Auger intensities are useful in determining the number of d-electron states in transition metal systems. Auger spectroscopy is a good probe to investigate the surface oxidation of metals. In addition to the intra-atomic Auger transitions, inter-atomic transitions observed in oxides and other systems reveal the nature of electron states of surfaces. Charge-transfer and hybridization effects in alloys are also usefully studied by Auger spectroscopy. Auger electron spectroscopy has not been a popular technique to investigate adsorption of molecules on surfaces, but the technique is useful to obtain fingerprints of surface species.

Feature extraction for fingerprint classification

This paper presents two algorithms for smoothing and feature extraction for fingerprint classification. Deutsch's(2) Thinning algorithm (rectangular array) is used for thinning the digitized fingerprint (binary version). A simple algorithm is also suggested for classifying the fingerprints. Experimental results obtained using such algorithms are presented.

Molecular Characterization of Sclerospora graminicola, the Incitant of Pearl Millet Downy Mildew Using ISSR Markers

The DNA polymorphism among 22 isolates of Sclerospora graminicola, the causal agent of downy mildew disease of pearl millet was assessed using 20 inter simple sequence repeats (ISSR) primers. The objective of the study was to examine the effectiveness of using ISSR markers for unravelling the extent and pattern of genetic diversity in 22 S. graminicola isolates collected from different host cultivars in different states of India. The 19 functional ISSR primers generated 410 polymorphic bands and revealed 89% polymorphism and were able to distinguish all the 22 isolates. Polymorphic bands used to construct an unweighted pair group method of averages (UPGMA) dendrogram based on Jaccard's co-efficient of similarity and principal coordinate analysis resulted in the formation of four major clusters of 22 isolates. The standardized Nei genetic distance among the 22 isolates ranged from 0.0050 to 0.0206. The UPGMA clustering using the standardized genetic distance matrix resulted in the identification of four clusters of the 22 isolates with bootstrap values ranging from 15 to 100. The 3D-scale data supported the UPGMA results, which resulted into four clusters amounting to 70% variation among each other. However, comparing the two methods show that sub clustering by dendrogram and multi dimensional scaling plot is slightly different. All the S. graminicola isolates had distinct ISSR genotypes and cluster analysis origin. The results of ISSR fingerprints revealed significant level of genetic diversity among the isolates and that ISSR markers could be a powerful tool for fingerprinting and diversity analysis in fungal pathogens.

Deorphanization of Malonyl CoA:ACP Transacylase Drug Target in Plasmodium falciparum (PfFabD) Using Bacterial Antagonists: A Piggyback' Approach for Antimalarial Drug Discovery

Quest for new drug targets in Plasmodium sp. has underscored malonyl CoA:ACP transacylase (PfFabD) of fatty acid biosynthetic pathway in apicoplast. In this study, a piggyback approach was employed for the receptor deorphanization using inhibitors of bacterial FabD enzymes. Due to the lack of crystal structure, theoretical model was constructed using the structural details of homologous enzymes. Sequence and structure analysis has localized the presence of two conserved pentapeptide motifs: GQGXG and GXSXG and five key invariant residues viz., Gln109, Ser193, Arg218, His305 and Gln354 characteristic of FabD enzyme. Active site mapping of PfFabD using substrate molecules has disclosed the spatial arrangement of key residues in the cavity. As structurally similar molecules exhibit similar biological activities, signature pharmacophore fingerprints of FabD antagonists were generated using 0D-3D descriptors for molecular similarity-based cluster analysis and to correlate with their binding profiles. It was observed that antagonists showing good geometrical fitness score were grouped in cluster-1, whereas those exhibiting high binding affinities in cluster-2. This study proves important to shed light on the active site environment to reveal the hotspot for binding with higher affinity and to narrow down the virtual screening process by searching for close neighbors of the active compounds.

High-temperature kinetics of the reaction between CN and hydrocarbons using a novel high-enthalpy flow tube

More than 70 molecules of varied nature have been identified in the envelopes of carbon-rich stars through their spectral fingerprints in the microwave or far infrared regions. Many of them are carbon chain molecules and radicals, and a significant number are unique to the circumstellar medium. The determination of relevant laboratory kinetics data is critical to keep up with the development of the high spectral and spatial resolution observations and of the refinement of chemical models. Neutralneutral reactions of the CN radical with unsaturated hydrocarbons could be a dominant route in the formation of cyanopolyynes, even at low temperatures and deserve a detailed laboratory investigation. The approach we have developed aims to bridge the temperature gap between resistively heated flow tubes and shock tubes. The present kinetic measurements are obtained using a new reactor combining a high-enthalpy source with a flow tube and a pulsed laser photolysislaser-induced fluorescence system to probe the undergoing chemical reactions. The high-enthalpy flow tube has been used to measure the rate constant of the reaction of the CN radical with propane (C3H8), propene (C3H6), allene (C3H4), 1,3-butadiene (1,3-C4H6), and 1-butyne (C4H6) over a temperature range extending from 300 to 1200 K. All studied reactions of CN with unsaturated hydrocarbons are rapid, with rate coefficients greater than 10-10 cm3 center dot molecule-1 center dot s-1 and exhibit slight negative temperature dependence above room temperature. (c) 2012 Wiley Periodicals, Inc. Int J Chem Kinet 44: 753766, 2012

Phylogenetic relationships and classification of thiolases and thiolase-like proteins of Mycobacterium tuberculosis and Mycobacterium smegmatis

Thiolases are enzymes involved in lipid metabolism. Thiolases remove the acetyl-CoA moiety from 3-ketoacyl-CoAs in the degradative reaction. They can also catalyze the reverse Claisen condensation reaction, which is the first step of biosynthetic processes such as the biosynthesis of sterols and ketone bodies. In human, six distinct thiolases have been identified. Each of these thiolases is different from the other with respect to sequence, oligomeric state, substrate specificity and subcellular localization. Four sequence fingerprints, identifying catalytic loops of thiolases, have been described. In this study genome searches of two mycobacterial species (Mycobacterium tuberculosis and Mycobacterium smegmatis), were carried out, using the six human thiolase sequences as queries. Eight and thirteen different thiolase sequences were identified in M. tuberculosis and M. smegmatis, respectively. In addition, thiolase-like proteins (one encoded in the Mtb and two in the Msm genome) were found. The purpose of this study is to classify these mostly uncharacterized thiolases and thiolase-like proteins. Several other sequences obtained by searches of genome databases of bacteria, mammals and the parasitic protist family of the Trypanosomatidae were included in the analysis. Thiolase-like proteins were also found in the trypanosomatid genomes, but not in those of mammals. In order to study the phylogenetic relationships at a high confidence level, additional thiolase sequences were included such that a total of 130 thiolases and thiolase-like protein sequences were used for the multiple sequence alignment. The resulting phylogenetic tree identifies 12 classes of sequences, each possessing a characteristic set of sequence fingerprints for the catalytic loops. From this analysis it is now possible to assign the mycobacterial thiolases to corresponding homologues in other kingdoms of life. The results of this bioinformatics analysis also show interesting differences between the distributions of M. tuberculosis and M. smegmatis thiolases over the 12 different classes. (C) 2014 Elsevier Ltd. All rights reserved.

On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.

FIVDL: Fingerprint Image Verification using Dictionary Learning

Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.