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Subject: Bioinformatics × Author: Manisha × End date: 2022 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Computational modeling and molecular dynamic simulation of pyrophosphatase of rice (Oryza sativa L.)
    (CCSHAU, 2018) Manisha; Sudhir Kumar
    Inorganic pyrophosphatase plays a significant role in various processes in plants. It causes chalkiness and hydrolytic breakdown of ADP-glucose in plastidal compartment. It has significance in lipid metabolism, calcium absorption, DNA synthesis and biochemical transformations. The sequence of inorganic pyrophosphatse was retrieved from NCBI and template was identified using BLASTP. With 84% query coverage and 71% identity 4LUG was selected as template. Modeller 9.19 and RaptorX were used for computational modeling. Predicted models were refined by energy minimization with GROMOS force field from Swiss-pdb Viewer. Minimum energy calculated for Modeller 9.19 and RaptorX predicted models were -2394.489KJ/mol and -7365.312KJ/mol respectively. The structures were assessed by GROMOS, ANOLEA and QMEAN graphs. More favourable region was shown by GROMOS and ANOLEA as compare to QMEAN. WHATIF server programs were used for structures optimization and validation. Bond length Z-score, bond angle Z-score, coarse packing quality and Ramachandran Z-score, were approximately 0.4, 1.2, -0.9 and 0.1 respectively. SAVES server programs score for PROVE, VERIFY3D and ERRAT were approximately 4.2%, 81% and 91% respectively. Ramachandran plot calculated by PROCHECK showed approximately 94% amino acid in core and 6% in allowed region. The models visualization showed coils were dominantly present in both the structures. RMSD for the structures was less than 0.5. Explicit solvent molecular dynamic simulation was done by VMD and NAMD software. The total energy and RMSD graphs calculated after simulation were stable for the structures. Structure superimposition with template showed significant conserved region between template and predicted structures. RMSD calculated after simulation was less than 0.5 Å against for both models template. The model predicted by RaptorX was found better as compared to Modeller 9.19 predicted model.