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The Locus PgaABCD of Acinetobacter junii Putatively Responsible for Poly-β-(1,6)-N-Acetylglucosamine Biosynthesis Might Be Related to Biofilm Formation: A Computational Analysis 被引量:1
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作者 Bipransh Kumar Tiwary Arvind Kumar +3 位作者 Ravi kant Pathak Nishtha Pandey krishna kant Yadav Ranadhir Chakraborty 《Advances in Microbiology》 2016年第3期222-232,共11页
Poly-β-(1,6)-N-acetylglucosamine (PNAG), the chief mediator of intercellular adhesion in many bacteria, plays an important role in biofilm formation. The pgaABCD locus was recognized from the whole genome sequence of... Poly-β-(1,6)-N-acetylglucosamine (PNAG), the chief mediator of intercellular adhesion in many bacteria, plays an important role in biofilm formation. The pgaABCD locus was recognized from the whole genome sequence of A. junii SH205. The enzyme glycosyltransferase, PgaC, catalyzes the production of PNAG with N-acetyl-D-glucosamine monomer. In this study, the possibility of PNAG biosynthesis in A. junii SH205 with its own PgaC was explored with the aid of bioinformatics. Multiple alignments of PgaC sequences of different bacteria were used to identify conserved amino acid residues that might be critical for the functioning of the protein. Three-dimensional model of A. junii SH205 PgaC was generated for spatial visualization of amino acid residues. The analyses have shown that the protein PgaC has five conserved amino acids, Asp<sup>140</sup>, Asp<sup>233</sup>, Gln<sup>269</sup>, Arg<sup>272</sup> and Trp<sup>273</sup>, critical for the activity of enzyme. Interaction of UDP-N-acetylglucosamine within the conserved pocket of glycosyltransferase was explored from molecular docking studies. 展开更多
关键词 UDP-N-ACETYLGLUCOSAMINE Glycosyl Transferase Homology Modeling Molecular Docking
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Application of Artificial Neural Network for Analysis of Triangular Plate with Hole Considering Different Geometrical and Loading Parameters
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作者 Saket Rusia krishna kant Pathak 《Open Journal of Civil Engineering》 2016年第1期31-41,共11页
In this study, Artificial Neural Network has been employed for analysis of triangular plate with different geometrical and loading parameters. Plates, having different sizes of concentric holes are analyzed. Finite el... In this study, Artificial Neural Network has been employed for analysis of triangular plate with different geometrical and loading parameters. Plates, having different sizes of concentric holes are analyzed. Finite element analysis for 81 cases is carried out using ANSYS Workbench 15.0 software. Using these data of FEM analysis an Artificial Neural Network has been trained. The successfully trained network is further used for analysis of four new cases which are also validated by using ANSYS Workbench 15.0 software. 展开更多
关键词 Artificial Neural Networks Finite Element Analysis Triangular Plate ANSYS
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