Prashant Ankur Jain

Metabolic pathway analysis and molecular docking analysis for identification of putative drug targets in Toxoplasma gondii: novel approach.

Toxoplasma gondii is an obligate intracellular apicomplexan parasite that can infect a wide range of warm-blooded animals including humans. In humans and other intermediate hosts, toxoplasma develops into chronic infection that cannot be eliminated by host’s immune response or by currently used drugs. In most cases, chronic infections are largely asymptomatic unless the host becomes immune compromised. Thus, toxoplasma is a global health problem and the situation has become more precarious due to the advent of HIV infections and poor toleration of drugs used to treat toxoplasma infection, having severe side effects and also resistance have been developed to the current generation of drugs. The emergence of these drug resistant varieties of T. gondii has led to a search for novel drug targets. We have performed a comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen T. gondii. The enzymes in the unique pathways of T. gondii, which do not show similarity to any protein from the host, represent attractive potential drug targets. We have listed out 11 such potential drug targets which are playing some important work in more than one pathway. Out of these, one important target is Glutamate dehydrogenase enzyme; it plays crucial part in oxidation reduction, metabolic process and amino acid metabolic process. As this is also present in the targets of tropical diseases of TDR (Tropical disease related Drug) target database and no PDB and MODBASE 3D structural model is available, homology models for Glutamate dehydrogenase enzyme were generated using MODELLER9v6. The model was further explored for the molecular dynamics simulation study with GROMACS, virtual screening and docking studies with suitable inhibitors against the NCI diversity subset molecules from ZINC database, by using AutoDock-Vina. The best ten docking solutions were selected (ZINC01690699, ZINC17465979, ZINC17465983, ZINC18141294_03, ZINC05462670, ZINC01572309, ZINC18055497_01, ZINC18141294, ZINC05462674 and ZINC13152284_01). Further the Complexes were analyzed through LIGPLOT. On the basis of Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds, specifically ZINC01690699 (as it has minimum energy score and one of the highest number of interactions with the active site residue), could be promising inhibitors for T. gondii using Glutamate dehydrogenase as Drug target.

Insight into trichomonas vaginalis genome evolution through metabolic pathways comparison

Trichomonas vaginalis causes the trichomoniasis, in women and urethritis and prostate cancer in men. Its genome draft published by TIGR in 2007 presents many unusual genomic and biochemical features like, exceptionally large genome size, the presence of hydrogenosome, gene duplication, lateral gene transfer mechanism and the presence of miRNA. To understand some of genomic features we have performed a comparative analysis of metabolic pathways of the T. vaginalis with other 22 significant common organisms. Enzymes from the biochemical pathways of T. vaginalis and other selected organisms were retrieved from the KEGG metabolic pathway database. The metabolic pathways of T. vaginalis common in other selected organisms were identified. Total 101 enzymes present in different metabolic pathways of T. vaginalis were found to be orthologous by using BLASTP program against the selected organisms. Except two enzymes all identified orthologous enzymes were also identified as paralogous enzymes. Seventy-five of identified enzymes were also identified as essential for the survival of T. vaginalis, while 26 as non-essential. The identified essential enzymes also represent as good candidate for novel drug targets. Interestingly, some of the identified orthologous and paralogous enzymes were found playing significant role in the key metabolic activities while others were found playing active role in the process of pathogenesis. The N-acetylneuraminate lyase was analyzed as the candidate of lateral genes transfer. These findings clearly suggest the active participation of lateral gene transfer and gene duplication during evolution of T. vaginalis from the enteric to the pathogenic urogenital environment.

Virtual screening of AmpC/β‐lactamase as target for antimicrobial resistance in Pseudomonas aeruginosa

AmpC is a group I, class C ‐lactamase present in most Enterobacteriaceae and in Pseudomonas aeruginosa and other nonfermenting gram-negative bacilli. The β‐lactam class of antibiotics is one of the most important structural classes of antibacterial compounds and act by inhibiting the bacterial D ,D - transpeptidases that are responsible for the final step of peptidoglycan cross-linking. Our main aim in the study is to screen possible inhibitors against AmpC / β ‐ lactamase (an enzyme responsible for antimicrobial activity in Pseudomonas aeruginosa), through virtual screening of 1364 NCI (National Cancer Institute) diversity set II compounds. Homology Model of AmpC / β ‐ lactamase was constructed using MODELLER and the Model was validated using PROCHECK and Verify 3D programs to obtain a stable structure, which was further used for virtual screening of NCI (National Cancer Institute) diversity set II compounds through molecular Docking studies using Autodock. The amino acid sequence of the β ‐ lactamase was also subjected to ScanProsite web server to find any pattern present in the sequence. After the prediction of 3-dimensional model of AmpC/ β‐lactamase, the possible Active sites ofβ ‐ lactamase were determined using LIGSITEcsc and CastP web servers simultaneously. The Docked complexes were validated and Enumerated based on the Autodock Scoring function to pick out the best inhibitor based on Autodock energy score. Thus from the entire 1364 NCI diversity set II compounds which were Docked, the best four docking solutions were selected (ZINC12670903, ZINC17465965, ZINC11681166 and ZINC13099024). Further the Complexes were analyzed through LIGPLOT for their interaction for the 4 best docked NCI diversity set II compounds. Thus from the Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds could be promising inhibitors for Pseudomonas aeruginosa using AmpC /β ‐ lactamase as Drug target yet pharmacological studies have to confirm it.

MFPPI – Multi FASTA ProtParam Interface

Physico-chemical properties reflect the functional and structural characteristics of a protein. The comparative study of the physicochemical properties is important to know role of a protein in exploring its molecular evolution. A number of online and offline tools are available for calculating the physico-chemical properties of a single protein sequence. However, a tool is not available for a comparative study with graphical visualization of Multi-FASTA sequences. Hence, we describe the development and utility of MFPPI V.1.0 (a web interface developed in JAVA platform) to input each FASTA sequence from Multi-FASTA file into the ProtParam web server for the calculation of physico-chemical properties. MFPPI V.1.0 calculates different physico-chemical properties for a given set of proteins in a single run and saves the data in the MSExcel sheet. Furthermore, it provides a graphical representation of protein physico-chemical properties for analysis and visualization of data in a user-friendly manner. Therefore, the output from the analysis helps to understand compositional changes and functional relationship in evolution among organisms. We have demonstrated the utility of MFPPI V.1.0 using 17 mtATP6 protein sequences from different mammalian species. It is available for free at http://insilicogenomics.in/mfpcalc/mfppi.html.

Application of Microbes for Recovery of Residual Crude Petroleum

1 Department of Biochemical Engineering, Harcourt Butler Technical University (HBTU), Kanpur208002, India 2 Department of Biotechnology, Naraina Vidya Peeth Engineering and Management Institute, [Affiliated to Dr A P J Abdul Kalam Technical University (AKTU Code-429), Lucknow, Uttar Pradesh, India], Naraina Group of Institution, Gangaganj, Panki, Kanpur, Uttar Pradesh-208020, India 3 Department of Computational Biology and Bioinformatics, Jacob School of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), Allahabad, U.P.-211007, India *Corresponding author

Innovation of System Biological Approach in Computational Drug Discovery

Computational methods like classification and network-based algorithms can be used to understand the mode of action and the efficacy of a given compound and to help elucidating the patho-physiology of a disease. In the pharmacological industry there has already been a shift from symptomatic oriented drugs that can relieve the symptoms but not the cause of the disease to pathology-based drugs whose targets are the genes and proteins involved in the etiology of the disease. Drugs targeting the affected pathway have thus the potential to become therapeutic. A network approach to drug design would examine the effect of drugs in the context of a network of relevant protein regulatory metabolic interactions resulting in the development of a drug that would hit multiple targets selected in such a way as to decrease network integrity and so completely disrupt the functioning of the network. The screening of a compound to quickly identify the proteins it interacts with gives us all the necessary tools to identify and repair the deregulated biological pathway causing the disease.