Microarray data analysis, structure prediction and in silico docking of drugs for inhibiting the over expression of High Mobility Group A1 in human malignant neoplasias
Abstract
The High Mobility Group A1 (HMGA1) gene over expression has been widely observed in various types of cancers. The raw data for microarray data analysis was obtained from the dataset record GDS3525. The SOM and K-means of the Genesis led to the identification of two clusters (each consisting of 30 genes) bearing HMGA1 gene. This on further analysis resulted into identification of 14 similar genes by Easy M-A. The evolutionary similarity of HMGA1 and GORASP2 is clearly observed in the Phylogenetic Tree. Due to the absence of precise structures, the homology modeling was done by using EasyModeller and the resulting models of proteins HMGA1 and GORASP2 were validated by Ramachandran plot. These models were further put to loop optimization by Modloop and the output models were assessed by Ramachandran plot (Rampage) and through SAVS (Procheck). The molecular docking was done by using Autodock, this resulted in two ligands, DB11641 (Vinflunine) and DB12674 (Lurbinectedin), showing potential for the effective treatment of various types of cancers characterized by the over expression of HMGA1 and GORASP2.
Keywords:
High Mobility Group A Proteins; homology modeling; microarray data analysis; molecular dockingDOI
https://doi.org/10.25004/IJPSDR.2020.120504References
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