Polycomb group (PcG) proteins have been observed to maintain the pattern of histone by methylation of the histone tail responsible for the gene expression in various cellular processes, of which enhancer of zeste homolog 2 (EZH2) acts as tumor suppressor. Overexpression of EZH2 results in hyper activation found in a variety of cancer. Point mutation on two important residues were induced and the results were compared between the wild type and mutant EZH2. The mutation of Y641 and A677 present in the active region of the protein alters the interaction of the top ranked compound with the newly modeled binding groove of the SET domain, giving a GLIDE score of −12.26 kcal/mol, better than that of the wild type at −11.664 kcal/mol. In depth analysis were carried out for understanding the underlying molecular mechanism using techniques viz. molecular dynamics, principal component analysis, residue interaction network and free energy landscape analysis, which showed that the mutated residues changed the overall conformation of the system along with the residue-residue interaction network. The insight from this study could be of great relevance while designing new compounds for EZH2 enzyme inhibition and the effect of mutation on the overall binding mechanism of the system.

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The response of a grass halophyte Spartina alterniflora at early stages of salt stress was investigated through generation and systematic analysis of expressed sequence tags (ESTs) from both leaf and root tissues. Random EST sequencing produced 1,227 quality ESTs, which were clustered into 127 contigs, and 368 were singletons. Of the 495 unigenes, 27% represented genes for stress response. Comparison of the 368 singletons against the Oryza sativa gene index showed that >85% of these genes had similarity with the rice unigenes. Moreover, the phylogenetic analysis of an EST similar to myo-inositol 1-phosphate synthase of Spartina and some selected grasses and halophytes showed closeness of Spartina with maize and rice. Transcript abundance analysis involving eight known genes of various metabolic pathways and nine transcription factor genes showed temporal and tissue-dependent variation in expression under salinity. Reverse northern analysis of a few selected unknown and ribosomal genes exhibited much higher abundance of transcripts in response to salt stress. The results provide evidence that, in addition to several unknown genes discovered in this study, genes involved in ion transport, osmolyte production, and house-keeping functions may play an important role in the primary responses to salt stress in this grass halophyte.

The significant role of long non-coding RNAs (lncRNAs) in various cellular functions, such as gene imprinting, immune response, embryonic pluripotency, tumorogenesis, and genetic regulations, has been widely studied and reported in recent years. Several experimental and computational methods involving genome-wide search and screenings of ncRNAs are being proposed utilizing sequence features-length, occurrence, and composition of bases with various limitations. The proposed classifier, Deep Neural Network (DNN) is fast and an accurate alternative for the identification of lncRNAs as compared to other existing classifiers. The information content stored in k-mer pattern has been used as a sole feature for the DNN classifier using manually annotated training datasets from LNCipedia and RefSeq database, obtaining accuracy of 98.07 %, sensitivity of 98.98 %, and specificity of 97.19 %, respectively, on test dataset. The k-mer information content generated on the basis of Shannon entropy function has resulted in improved classifier accuracy. This classification framework was also tested on known human genome dataset, and the framework has successfully identified known lncRNAs with 99 % accuracy rate. The said algorithm has been implemented as a web prediction tool, which is available on server interface http:// bioserver.iiita.ac.in/deeplnc.

Finding the relationship between the structure of an odorant molecule and its associated smell has always been an extremely challenging task. The major limitation in establishing the structure−odor relation is the vague and ambiguous nature of the descriptor-labeling, especially when the sources of odorant molecules are different. With the advent of deep networks, data-driven approaches have been substantiated to achieve more accurate linkages between the chemical structure and its smell. In this study, the deep neural network (DNN) with physiochemical properties and molecular fingerprints (PPMF) and the convolution neural network (CNN) with chemical-structure images (IMG) are developed to predict the smells of chemicals using their SMILES notations. A data set of 5185 chemical compounds with 104 smell percepts was used to develop the multilabel prediction models. The accuracies of smell prediction from DNN + PPMF and CNN + IMG (Xception based) were found to be 97.3 and 98.3%, respectively, when applied on an independent test set of chemicals. The deep learning architecture combining both DNN + PPMF and CNN + IMG prediction models is proposed, which classifies smells and may help understand the generic mechanism underlying the relationship between chemical structure and smell perception.

Ebola virus is a single-stranded, negative-sense RNA virus that causes severe hemorrhagic fever in humans and non-human primates. This virus is unreceptive to a large portion of the known antiviral drugs, and there is no valid treatment as on date for disease created by this pathogen. Looking into its ability to create a pandemic scenario across globe, there is an utmost need for new drugs and therapy to combat this life-threatening infection. The current study deals with the evaluation of the inhibitory activity of flavonoids against the four selected Ebola virus receptor proteins, using in silico studies. The viral proteins VP40, VP35, VP30 and VP24 were docked with small molecules obtained from flavonoid class and its derivatives and evaluated on the basis of energetics, stereochemical considerations and pharmacokinetic properties to identify potential lead compounds. The results showed that both top-ranking screened flavonoids, i.e., Gossypetin and Taxifolin, showed better docking scores and binding energies in all the EBOV receptors when compared to those of the reported compound. All the screened flavonoids have known antiviral activity, acceptable pharmacokinetic properties and are being used on human and thus can be taken as anti-Ebola therapy without the time lag for clinical trial.

Olfaction, the sense of smell detects and discriminate odors as well as social cues which influence our innate responses. The olfactory system in human beings is found to be weak as compared to other animals; however, it seems to be very precise. It can detect and discriminate millions of chemical moieties (odorants) even in minuscule quantities. The process initiates with the binding of odorants to specialized olfactory receptors, encoded by a large family of Olfactory Receptor (OR) genes belonging to the G-protein-coupled receptor superfamily. Stimulation of ORs converts the chemical information encoded in the odorants, into respective neuronal action-potentials which causes depolarization of olfactory sensory neurons. The olfactory bulb relays this signal to different parts of the brain for processing. Odors are encrypted using a combinatorial approach to detect a variety of chemicals and encode their unique identity. The discovery of functional OR genes and proteins provided an important information to decipher the genomic, structural and functional basis of olfaction. ORs constitute 17 gene families, out of which 4 families were reported to contain more than hundred members each. The olfactory machinery is not limited to GPCRs; a number of non- GPCRs is also employed to detect chemosensory stimuli. The article provides detailed information about such olfaction machinery, structures, transduction mechanism, theories of odor perception, and challenges in the olfaction research. It covers the structural, functional and computational studies carried out in the olfaction research in the recent past.

ADP-glucose pyrophosphorylase (AGPase) is a heterotetrameric enzyme with two large subunits (LS) and two small subunits (SS). It plays a critical role in starch biosynthesis. We are reporting here detailed structure, function and evolution of the genes encoding the LS and the SS among monocots and dicots. “True” orthologs of maize Sh2 (AGPase LS) and Bt2 (AGPase SS) were identified in seven other monocots and three dicots; structure of the enzyme at protein level was also studied. Novel findings of the current study include the following: (i) at the DNA level, the genes controlling the SS are more conserved than those controlling the LS; the variation in both is mainly due to intron number, intron length and intron phase distribution; (ii) at protein level, the SS genes are more conserved relative to those for LS; (iii) “QTCL” motif present in SS showed evolutionary differences in AGPase belonging to wheat 7BS, T. urartu, rice and sorghum, while “LGGG” motif in LS was present in all species except T. urartu and chickpea; SS provides thermostability to AGPase, while LS is involved in regulation of AGPase activity; (iv) heterotetrameric structure of AGPase was predicted and analyzed in real time environment through molecular dynamics simulation for all the species; (v) several cis-acting regulatory elements were identified in the AGPase promoters with their possible role in regulating spatial and temporal expression (endosperm and leaf tissue) and also the expression, in response to abiotic stresses; and (vi) expression analysis revealed downregulation of both subunits under conditions of heat and drought stress. The results of the present study have allowed better understanding of structure and evolution of the genes and the encoded proteins and provided clues for exploitation of variability in these genes for engineering thermostable AGPase.