Browsing by Author "Khalid, Zoya"
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Item A comprehensive study on identifying the structural and functional SNPs of human neuronal membrane glycoprotein M6A (GPM6A)(TAYLOR \& FRANCIS INC, 2021-01-01) Khalid, Zoya; Sezerman, Osman UgurGlycoprotein M6A, a stress related gene, plays an important role in synapse and filopodia formation. Filopodia formation is vital for development, immunity, angiogenesis, wound healing and metastasis. In this study, structural and functional analysis of high-risk SNPs associated with Glycoprotein M6-A were evaluated using six different bioinformatics tools. Results classified T210I, T134I, Y153H, I215T, F156L, T160I, I226T, R247W, R178C, W159R, N157S and P151L as deleterious mutants that are crucial for the structure and function of the protein causing malfunction of M6-a and ultimately leads to disease development. The three-dimensional structure of wild-type M6-a and mutant M6-a were also predicted. Furthermore, the effects of high risk substitutions were also analyzed with interaction with valproic acid. Based on structural models obtained, the binding pocket of ligand bound glycoprotein M6-A structure showed few core interacting residues which are different in the mutant models. Among all substitutions, F156L showed complete loss of binding pocket when interacting with valproic acid as compared to the wild type model. Up to the best of our knowledge this is the first comprehensive study where GPM6A mutations were analyzed. The mechanism of action of GPM6A is still not fully defined which limits the understanding of functional details encoding M6-A. Our results may help enlighten some molecular aspects underlying glycoprotein M6-A. Communicated by Ramaswamy H. SarmaItem ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2017-01-01) Khalid, Zoya; Sezerman, Osman UgurExtracting useful knowledge from an unstructured textual data is a challenging task for biologists, since biomedical literature is growing exponentially on a daily basis. Building an automated method for such tasks is gaining much attention of researchers. ZK DrugResist is an online tool that automatically extracts mutations and expression changes associated with drug resistance from PubMed. In this study we have extended our tool to include semantic relations extracted from biomedical text covering drug resistance and established a server including both of these features. Our system was tested for three relations, Resistance (R), Intermediate (I) and Susceptible (S) by applying hybrid feature set. From the last few decades the focus has changed to hybrid approaches as it provides better results. In our case this approach combines rule-based methods with machine learning techniques. The results showed 97.67\% accuracy with 96\% precision, recall and F-measure. The results have outperformed the previously existing relation extraction systems thus can facilitate computational analysis of drug resistance against complex diseases and further can be implemented on other areas of biomedicine. (C) 2017 Elsevier Inc. All rights reserved.