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      Pharmacoinformatic Approach to Explore the Antidote Potential of Phytochemicals on Bungarotoxin from Indian Krait, Bungarus caeruleus

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          Abstract

          Venomous reptiles especially serpents are well known for their adverse effects after accidental conflicts with humans. Upon biting humans these serpents transmit arrays of detrimental toxins with diverse physiological activities that may either lead to minor symptoms such as dermatitis and allergic response or highly severe symptoms such as blood coagulation, disseminated intravascular coagulation, tissue injury, and hemorrhage. Other complications like respiratory arrest and necrosis may also occur. Bungarotoxins are a group of closely related neurotoxic proteins derived from the venom of kraits ( Bungarus caeruleus) one of the six most poisonous snakes in India whose bite causes respiratory paralysis and mortality without showing any local symptoms. In the current study, by employing various pharmacoinformatic approaches, we have explored the antidote properties of 849 bioactive phytochemicals from 82 medicinal plants which have already shown antidote properties against various venomous toxins. These herbal compounds were taken and pharmacoinformatic approaches such as ADMET, docking and molecular dynamics were employed. The three-dimensional modelling approach provides structural insights on the interaction between bungarotoxin and phytochemicals. In silico simulations proved to be an effective analytical tools to investigate the toxin–ligand interaction, correlating with the affinity of binding. By analyzing the results from the present study, we proposed nine bioactive phytochemical compounds which are, 2-dodecanol, 7-hydroxycadalene, indole-3-(4'-oxo)butyric acid, nerolidol-2, trans-nerolidol, eugenol, benzene propanoic acid, 2-methyl-1-undecanol, germacren-4-ol can be used as antidotes for bungarotoxin.

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          Protein structure prediction and structural genomics.

          Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.
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            Comparative protein structure modeling using Modeller.

            Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.
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              The Universal Protein Resource (UniProt)

              The ability to store and interconnect all available information on proteins is crucial to modern biological research. Accordingly, the Universal Protein Resource (UniProt) plays an increasingly important role by providing a stable, comprehensive, freely accessible central resource on protein sequences and functional annotation. UniProt is produced by the UniProt Consortium, formed in 2002 by the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt web site and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of three major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase and the UniProt Reference Clusters. An additional component consisting of metagenomic and environmental sequences has recently been added to UniProt to ensure availability of such sequences in a timely fashion. UniProt is updated and distributed on a bi-weekly basis and can be accessed online for searches or download at .
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                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                31 October 2018
                2018
                31 October 2018
                : 16
                : 450-461
                Affiliations
                [a ]Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau
                [b ]Department of Physics, Sathyabama Institute of Science and Technology, Deemed to be University, Chennai 600119, India
                Author notes
                [* ]Corresponding author. hfkwok@ 123456umac.mo
                [1]

                The first two authors contributed equally to this work.

                Article
                S2001-0370(18)30087-4
                10.1016/j.csbj.2018.10.005
                6231056
                bb2e1a6b-aa8f-4d58-a783-efbaae95ae6a
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 12 August 2018
                : 8 October 2018
                : 12 October 2018
                Categories
                Research Article

                toxins,bungarotoxin,molecular docking,pharmacokinetic profiling,drug design,molecular dynamics

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