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bioRxiv · 10.1101/2024.10.07.617028

Unveiling Novel Hyperglycosylated Analog of Human Erythropoietin: A Comprehensive Computational Exploration

Abstract

IntroductionThe capability of human erythropoietin (hEPO) to treat anemia by stimulating erythropoiesis, has made it become one of the worlds leading biopharmaceuticals. An effective way to improve the efficiency of recombinant hEPO drugs is increasing glycosylation which can increase its serum half-life and thereby its in vivo biological activity. In this context, the objective of the present study was to explore potential glycosylation sites in hEPO to develop new hyperglycosylated analogous drug. Materials and MethodsA rational computational strategy was executed to select compatible region of hEPO for inserting N-linked glycosylation consensus motif (N-X-S/T), thus designing a number of analogs. The 3D models of the analogs were constructed by homology modeling and validated by various in silico tools. The probability of glycosylation was checked and hyperglycosylated models of the selected analogs were developed. Molecular docking and molecular dynamics simulation were performed to select the best analog. ResultsAmong 40 analogs designed in this study, Analog-71.1 showed most promising results. Four possible sites of this analog showed good probability for glycosylation. According to the molecular docking study, the analog obtained high docking score (2313.711) and formed highest number of (25) hydrogen bonds with erythropoietin receptors, which indicated strong and stable interaction with the receptors. Moreover, the steady trajectory of RMSD, Rgyr, RMSF and other findings obtained from MD simulation confirmed the structural stability of the analog. ConclusionsOur overall study has selected Analog-71.1 as a potential candidate to develop new analogous drug with enhanced efficiency, which needs further experimental analyses.

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Thahsin, N., Khan, K. I., Sakib, M. N., Rahman, M. S.. 2024-10-11. Unveiling Novel Hyperglycosylated Analog of Human Erythropoietin: A Comprehensive Computational Exploration. https://doi.org/10.1101/2024.10.07.617028

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