Deep neural network-based prediction of RNA aptamers targeting E6 protein of high-risk human papilloma virus
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Abstract
Background: The Human papilloma virus is the primary cause of cervical cancer. The virus integrates with the human genome to produce the E6 oncoprotein. Therefore, the E6 oncoprotein is a crucial molecular target for cancer progression or treatment. The development of aptamers is beneficial for interacting with the target protein and serves as a new strategy for detection or delivery systems.
Objectives: We aim to explore the candidate aptamer sequence against E6 oncoprotein using a computational-based method.
Materials and methods: This study designed the candidate aptamer against the target protein based on computational approaches using the AptaTrans pipeline. After obtaining the candidate aptamer sequences, the minimum free energy was calculated using the RNAfold web server. The tertiary structure was then generated using RNAComposer. Next, the molecular docking score was acquired from the GRAMM web server.
Results: The aptamer sequences with the best stability, as indicated by minimum free energy (MFE), are Sq3_16E6, Sq3_Actn, and Sq3_18E6, respectively. The aptamer sequences of Sq3_16E6 and Sq2_18E6 showed potential interactions with 8GCR and 6SJV, respectively.
Conclusion: Sq3_16E6 and Sq2_18E6 are appropriate for the development of the detection of the E6 protein in cervical swabs. Further investigation should be performed.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Personal views expressed by the contributors in their articles are not necessarily those of the Journal of Associated Medical Sciences, Faculty of Associated Medical Sciences, Chiang Mai University.
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