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      in-silico characterization of β-(1, 3)-endoglucanase (ENGL1) from Aspergillus fumigatus by homology modeling and docking studies

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          Abstract

          During the past few years a significant rise in aspergillosis caused by filamentous fungus Aspergillus fumigatus has been recorded particularly in immunocompromised patients. At present, there are limited numbers of antifungal agents to combat these infections and the situation has become more complex due to emergence of antifungal resistance and side-effects of antifungal drugs. These situations have increased the demand for novel drug targets. Recent studies have revealed that the β-1,3-endoglucanase (ENGL1) plays an essential role in cell wall remodeling that is absolutely required during growth and morphogenesis of filamentous fungi and thus is a promising target for the development of antifungal agents. Unfortunately no structural information of fungal β- glucanases has yet been available in the Protein Databank (PDB). Therefore in the present study, 3D structure of β-(1,3)- endoglucanase (ENGL1) was modeled by using I-TASSER server and validated with PROCHECK and VERIFY 3D. The best model was selected, energy minimized and used to analyze structure function relationship with substrate β-(1,3)-glucan by C-DOCKER (Accelrys DS 2.0). The results indicated that amino acids (GLU 380, GLN 383, ASP 384, TYR 395, SER 712, and ARG 713) present in β-1,3-endoglucanase receptor are of core importance for binding activities and these residues are having strong hydrogen bond interactions with β-(1,3)-glucan. The predicted model and docking studies permits initial inferences about the unexplored 3D structure of the β-(1,3)-endoglucanase and may be promote in relational designing of molecules for structure-function studies.

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          Most cited references 20

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            Comparative protein modelling by satisfaction of spatial restraints.

             A. Sali,  T L Blundell (1993)
            We describe a comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equivalent residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain C alpha-C alpha distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the molecular pdf such that the model violates the input restraints as little as possible. The molecular pdf is derived as a combination of pdfs restraining individual spatial features of the whole molecule. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modelling of trypsin from two other serine proteinases.
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              A simple method for displaying the hydropathic character of a protein.

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                Author and article information

                Affiliations
                [1 ]Medical Mycology lab, Division of Microbiology, CSIR-Central Drug Research Institute, Sitapur Road, Lucknow-226001, India
                [2 ]Department of Biotechnology, Jamia Hamdard University, New Delhi, India
                Author notes
                [* ]Praveen Kumar Shukla: pk_shukla@ 123456cdri.res.in Phone: 91-522-3290091; Fax: 91-535-2700857
                Journal
                Bioinformation
                Bioinformation
                Bioinformation
                Bioinformation
                Biomedical Informatics
                0973-8894
                0973-2063
                2013
                23 September 2013
                : 9
                : 16
                : 802-807
                3796880
                97320630009802
                10.6026/97320630009802
                © 2013 Biomedical Informatics

                This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.

                Categories
                Hypothesis

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