Evaluation of α-amylase, lipase inhibition and in-vivo pharmacological activities of Eucalyptus camaladulensis Dehnh leaf extract.

In this present study, phytochemical screening, anti-ulcer assay, anti-diarrhea assay, anti-inflammatory assay, analgesic assay, lipase activity assay, amylase activity assay and the anti-bacterial activity of Eucalyptus camaladulensis Dehnh leaf extracted with methanol and 50% ethanol was analyzed for biological significance. Physical characterization of the non-volatile component revealed the higher yield of 16.92% in 50% ethanol expediting the use of 50% ethanol as a better alternative. Further use of crude extract revealed 33.89% (IC50 = 1.44 mg/ml) of α-amylase inhibition by methanol extract and 33.87% (IC50 = 3.21 mg/ml) lipase inhibition by 50% ethanol extract. Furthermore, 44.44% protective ratio towards ulcer was observed with the methanol extract, whereas 54.58% anti-inflammatory activity was shown by the 50% ethanol extract. The effectiveness of the extract was further enhanced by the presence of 62.54% motility and best analgesic property at 180 min of the exposure of the extract orally. The antioxidant activity of crude methanol extract revealed an IC50 value 601.8 µg/ml whereas, ethanol extract showed 1279.58 µg/ml in DPPH assay. Result revealed several health benefits of E. camaldulensis Dehnh leaf.

The effect of ß-glucan and its potential analog on the structure of Dectin-1 receptor.

Dectin-1 is a recently discovered pattern-recognition receptor that plays an important role in antifungal innate immunity, which acts a specific receptor for ß-glucan (BG). The present study, aimed at clarifying effect of BG and a new analog, maltotriose (MT) on Dectin-1 receptor. We implemented molecular docking of MT on Dectin-1 along with model-independent all-atom-molecular dynamics simulations. Simulations were carried out at three levels of complexity: (1) Apo-Dectin-1; (2) BG:Dectin-1; (3) MT:Dectin-1. All three system complexes were undergone stability check before showing a comparative analysis. A characteristic feature, noted for the MT:Dectin-1, is a shifting of loops (loop1 and loop2) orientation towards atoms of MT, a broad interaction suggested a robust and tight binding on comparison with BG:Dectin-1. Free energy estimation corroborated the observation, which furthermore, made a close agreement by revealing contribution of energy components of interacting residues. In addition, cluster analysis of complexes exhibit a smooth continuous transition to a new confirmation, represented by a series of clusters each having a longer lifetime. Principal component analysis revealed a broken pipe at binding site of BG:Dectin-1 during movement of atoms whereas in MT:Dectin-1 exhibited wide band and high amplitude motion of atoms in trajectory, was due to loop orientation toward MT. Observation was further shown by measuring distances and hydrogen binding calculation. Simulations of the BG:Dectin-1 and MT:Dectin-1 complex revealed first time the influence of BG and MT ligands. This study might extend the knowledge of the BG and MT interaction on Dectin-1 and proposed further potential bioassay of MT.

A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition.

The present work employs pseudo amino acid composition (PseAAC) for encoding the protein sequences in their numeric form. Later this will be arranged in the similarity matrix, which serves as input for spectral graph clustering method. Spectral methods are used previously also for clustering of protein sequences, but they uses pair wise alignment scores of protein sequences, in similarity matrix. The alignment score depends on the length of sequences, so clustering short and long sequences together may not good idea. Therefore the idea of introducing PseAAC with spectral clustering algorithm came into scene. We extensively tested our method and compared its performance with other existing machine learning methods. It is consistently observed that, the number of clusters that we obtained for a given set of proteins is close to the number of superfamilies in that set and PseAAC combined with spectral graph clustering shows the best classification results.

Expression of an insecticidal fern protein in cotton protects against whitefly.

Whitefly (Bemisia tabaci) damages field crops by sucking sap and transmitting viral diseases. None of the insecticidal proteins used in genetically modified (GM) crop plants to date are effective against whitefly. We report the identification of a protein (Tma12) from an edible fern, Tectaria macrodonta (Fee) C. Chr., that is insecticidal to whitefly (median lethal concentration = 1.49 µg/ml in in vitro feeding assays) and interferes with its life cycle at sublethal doses. Transgenic cotton lines that express Tma12 at ∼0.01% of total soluble leaf protein were resistant to whitefly infestation in contained field trials, with no detectable yield penalty. The transgenic cotton lines were also protected from whitefly-borne cotton leaf curl viral disease. Rats fed Tma12 showed no detectable histological or biochemical changes, and this, together with the predicted absence of allergenic domains in Tma12, indicates that Tma12 might be well suited for deployment in GM crops to control whitefly and the viruses it carries.

In-silico screening of Schistosoma mansoni Sirtuin1 inhibitors for prioritization of drug candidates.

Schistosomiasis is a common, neglected parasitic disease caused by Schistosoma mansoni. Availability of two specific drug oxamniquine and praziquintel for treatment of the disease is a major concern. Recently NAD+ dependent lysine deacetylases have been identified as new drug targets in pathogens. Sirtuins are NAD+ dependent lysine deacetylases that are involved in a wide variety of vital cellular processes. Amongst them, members of sirtuin's class1 proteins are considered to be main target of the drugs. Sirtinol and Salermide are two known inhibitors of Schistosoma mansoni Class1sirtuin which is a protein with unknown 3-D structure. Here, we investigate molecular insights of interaction between modeled sirtuin1 structure and it's inhibitors, that were derivatives of Sirtinol and Salermide, to prioritize them for their binding affinities with target. A detailed examination of absorption, distribution, metabolism and toxicity of these inhibitors has also been included in the study. Finally we found two derivatives of Sirtinol to be most appropriate drug candidates for Schistosomiasis.

In-silico analysis of Sirt2 from Schistosoma mansoni: structures, conformations, and interactions with inhibitors.

Sirtuins are NAD+-dependent lysine deacetylases member of the class III HDAC family. These are demonstrated to be therapeutic targets in parasitic diseases like schistosomiasis. Observations suggested that sirtuin enzyme is necessary for the functionality of fe/male reproductive system, due to which SmSirt2 is treated as a potential therapeutic target. There are no structural and molecular features of SmSirt2 have been reported yet. In this study, homology modeling has been used to determine the three-dimensional features of the SmSITRT2. Further, structure validation has been performed by energy minimization and Ramachandran plot. Validated structures are further subjected to molecular docking and virtual screening to find the best lead molecules for downstream analysis. Ten lead molecules were selected while comparing virtual screening of hSirt2 and SmSirt2 both. These leads are further compared with AKG2 which is known inhibitor of hSirt2 (-8.8 kcal/mol). Out of selected 10 leads, four of them (ZINC23995485 (-9.5 kcal/mol), ZINC53298162 (-9.4 kcal/mol), ZINC70927268 (-10.0 kcal/mol), ZINC89878705 (-11.2 kcal/mol)) have shown better interaction with SmSirt2, in which ZINC89878705 (-11.2 kcal/mol) shows a more compact packing as compared to AKG2 and rest of ligands. These molecules could be further subject to in vitro study and model of SmSirt2 has been proposed for further structure-based drug design projects concerning sirtuins from Schistosoma mansoni.

Protein sequences insight into heavy metal tolerance in Cronobacter sakazakii BAA-894 encoded by plasmid pESA3.

The recently annotated genome of the bacterium Cronobacter sakazakii BAA-894 suggests that the organism has the ability to bind heavy metals. This study demonstrates heavy metal tolerance in C. sakazakii, in which proteins with the heavy metal interaction were recognized by computational and experimental study. As the result, approximately one-fourth of proteins encoded on the plasmid pESA3 are proposed to have potential interaction with heavy metals. Interaction between heavy metals and predicted proteins was further corroborated using protein crystal structures from protein data bank database and comparison of metal-binding ligands. In addition, a phylogenetic study was undertaken for the toxic heavy metals, arsenic, cadmium, lead and mercury, which generated relatedness clustering for lead, cadmium and arsenic. Laboratory studies confirmed the organism's tolerance to tellurite, copper and silver. These experimental and computational study data extend our understanding of the genes encoding for proteins of this important neonatal pathogen and provide further insights into the genotypes associated with features that can contribute to its persistence in the environment. The information will be of value for future environmental protection from heavy toxic metals.

Molecular docking and molecular dynamics study on SmHDAC1 to identify potential lead compounds against Schistosomiasis.

Schistosomiasis, a disease caused by helminth parasites of genus Schistosoma. Its treatment intensively depends on single drug, praziquantel which increases the risk of development of drug-resistant parasite. Inhibitors of human HDAC are profoundly reported as novel anti-cancer drugs and used as new anit-parasitic agents. Schistosoma monsoni class I HDACs are expressed in all stages of life cycle and indicating that this enzyme is most likely a major target for the designing specific inhibitors. In order to find novel target for the treatment of Schistosomiasis, three dimensional structure of SmHDAC1 was generated, using homology modelling. Features of the generated structure, was then deduced with respect to conformation of peptide backbone, local compatibility of the generated structure in terms of energy and molecular dynamics study. Considering these features of the generated structure, we selected all the class 1 inhibitors reported so far, which showed interactions with HDACs. Virtual screening was done using reported inhibitors (70) and using SmHDAC1 and HsHDAC1 as the targets. On the basis of binding affinity and IC50 value, 24th ligand was selected for the molecular docking purpose. In this study, out of all the reported inhibitors, 24th inhibitor (N,8-dihydroxy-8-(naphthalen-2-yl) octanamide zinc id- ZINC13474421) showed better binding with SmHDAC1 (-8.1 kcal/mol) as compared to HsHDAC1 (-6.4 kcal/mol) in terms of binding energy and supported by IC50 value. This paper throws light on the reliable model for further structure based drug designing, concerning SmHDAC1 of S. mansoni. Molecular docking studies highlighted advantages of comparative in silico interaction studies of SmHDAC1 and HsHDAC1. N,8-dihydroxy-8-(naphthalen-2-yl) octanamide can further use for the clinical trial.

Phylogenetic analysis of gammaproteobacterial arsenate reductase proteins specific to Enterobacteriaceae family, signifying arsenic toxicity.

This study focuses on the phylogenetic analysis of all the ArsC protein sequences, obtained from similarity search against Gammaproteobacteria, and also studies the role of Gammaproteobacterial family in arsenic toxicity. The ars gene provides arsenic tolerance for microbial cell system and encodes for an arsenate reductase (ArsC), which is essential for arsenate resistance that converts arsenate into arsenite. Phylogenetic analysis offers an opportunity to understand the evolutionary relationship between organisms of interest. The phylogenetic experiment was set up for all possible ArsC sequences in class Gammaproteobacteria. The results suggested a wide similarity between ArsC sequences in the species of Enterobacteriaceae family rather than other families in Gammaproteobacteria. The three evolutionary clades revealed a role of Enterobacteriaceae species, which has the capability to code ArsC protein. Further phylogenetic analysis of ArsC crystal structure sequences has also shown the separate cluster of Enterobacter species. The overall phylogeny of the ArsC protein sequences suggests the species of Enterobacteriaceae family express more among all family of Gammaproteobacteria. This study could be advantageous to emphasize the importance of Enterobacteriaceae in arsenic toxicity.

Computational protein structure modeling and analysis of UV-B stress protein in Synechocystis PCC 6803.

This study focuses on Ultra Violet stress (UVS) gene product which is a UV stress induced protein from cyanobacteria, Synechocystis PCC 6803. Three dimensional structural modeling of target UVS protein was carried out by homology modeling method. 3F2I pdb from Nostoc sp. PCC 7120 was selected as a suitable template protein structure. Ultimately, the detection of active binding regions was carried out for characterization of functional sites in modeled UV-B stress protein. The top five probable ligand binding sites were predicted and the common binding residues between target and template protein was analyzed. It has been validated for the first time that modeled UVS protein structure from Synechocystis PCC 6803 was structurally and functionally similar to well characterized UVS protein of another cyanobacterial species, Nostoc sp PCC 7120 because of having same structural motif and fold with similar protein topology and function. Investigations revealed that UVS protein from Synechocystis sp. might play significant role during ultraviolet resistance. Thus, it could be a potential biological source for remediation for UV induced stress.

Computational identification and analysis of arsenate reductase protein in Cronobacter sakazakii ATCC BAA-894 suggests potential microorganism for reducing arsenate.

This study focuses a bioinformatics-based prediction of arsC gene product arsenate reductase (ArsC) protein in Cronobacter sakazakii BAA-894 strain. A protein structure-based study encloses three-dimensional structural modeling of target ArsC protein, was carried out by homology modeling method. Ultimately, the detection of active binding regions was carried out for characterization of functional sites in protein. The ten probable ligand binding sites were predicted for target protein structure and highlighted the common binding residues between target and template protein. It has been first time identified that modeled ArsC protein structure in C. sakazakii was structurally and functionally similar to well-characterized ArsC protein of Escherichia coli because of having same structural motifs and fold with similar protein topology and function. Investigation revealed that ArsC from C. sakazakii can play significant role during arsenic resistance and potential microorganism for bioremediation of arsenic toxicity.

Elman RNN based classification of proteins sequences on account of their mutual information.

In the present work we have employed the method of estimating residue correlation within the protein sequences, by using the mutual information (MI) of adjacent residues, based on structural and solvent accessibility properties of amino acids. The long range correlation between nonadjacent residues is improved by constructing a mutual information vector (MIV) for a single protein sequence, like this each protein sequence is associated with its corresponding MIVs. These MIVs are given to Elman RNN to obtain the classification of protein sequences. The modeling power of MIV was shown to be significantly better, giving a new approach towards alignment free classification of protein sequences. We also conclude that sequence structural and solvent accessible property based MIVs are better predictor.

A graph-based clustering method applied to protein sequences.

The number of amino acid sequences is increasing very rapidly in the protein databases like Swiss-Prot, Uniprot, PIR and others, but the structure of only some amino acid sequences are found in the Protein Data Bank. Thus, an important problem in genomics is automatically clustering homologous protein sequences when only sequence information is available. Here, we use graph theoretic techniques for clustering amino acid sequences. A similarity graph is defined and clusters in that graph correspond to connected subgraphs. Cluster analysis seeks grouping of amino acid sequences into subsets based on distance or similarity score between pairs of sequences. Our goal is to find disjoint subsets, called clusters, such that two criteria are satisfied: homogeneity: sequences in the same cluster are highly similar to each other; and separation: sequences in different clusters have low similarity to each other. We tested our method on several subsets of SCOP (Structural Classification of proteins) database, a gold standard for protein structure classification. The results show that for a given set of proteins the number of clusters we obtained is close to the superfamilies in that set; there are fewer singeltons; and the method correctly groups most remote homologs.

Hidden markov model for the prediction of transmembrane proteins using MATLAB.

UNLABELLED: Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. AVAILABILITY: The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.

Efficient production of gossypol from hairy root cultures of cotton (Gossypium hirsutum L.).

A protocol for induction and establishment of Agrobacterium rhizogenes mediated hairy root culture of Gossypium hirsutum was developed through infection with the A4 strain and co-cultivation on hormone-free semi-solid MS medium with B5 vitamins. It resulted in the emergence of hairy roots from the leaf explants, 21 days after infection. The transformation of hairy roots was established by PCR amplification of rol B and rol C genes of the Ri plasmid. All root lines expressed gossypol, although distinct inter-clonal quantitative variations were noticed. Five independent hairy root lines were studied for their growth kinetics as well as gossypol production. The yield potentials of one of them superseded others, as well as the non-transformed, in-vitro grown control roots. The content of gossypol in hairy roots reached a level of 2.43 mg/g DW. It was 4.5 times higher than in vitro and 1.47 times higher than in vivo grown roots of G. hirsutum. Selection of high gossypol producing hairy root lines of G. hirsutum can provide an alternative source for large-scale production of gossypol.