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Understanding How HIV-1 Tat Variation influences Transactivation Potential

Friday, January 31, 2020

11:00 AM-1:00 PM

BIOMED PhD Research Proposal

Understanding How HIV-1 Tat Variation influences Transactivation Potential

Robert William Link, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Brian Wigdahl, PhD
Professor and Chair
Department of Microbiology and Immunology
Drexel University College of Medicine

Will Dampier, PhD
Assistant Professor
Department of Microbiology and Immunology
Drexel University College of Medicine

Human immunodeficiency virus type 1 (HIV-1) infection is still a major public health threat due to the formation of a latent reservoir that cannot be removed by conventional antiretroviral therapy. Infected cells can reverse latency and replicate if HIV-1 expresses its Tat protein and Tat transactivates the HIV-1 5’ long terminal repeat (LTR), a region that acts as the viral promoter. However, Tat cannot transactivate the LTR alone. It must: (1) bind with host Cyclin T1 (CycT1) and with HIV-1’s transactivation response element (TAR) RNA, and (2) be localized within the nucleus. HIV-1’s wide genetic diversity adds an additional layer of complexity to this problem.

While it is known how Tat traditionally interacts with TAR and CycT1, it is unknown how variation within and outside of these regions influences transactivation ability. However, testing how all variants behave in vitro would be infeasible and traditional machine learning prediction algorithms do not have enough data to make adequate predictions. These issues can be circumvented by using deep learning (DL) models, which have previously shown to achieve state-of-the-art performance using only raw sequence data and from training on more generalized datasets. More importantly, these models can derive important features from the raw data to make these predictions.

As DL models become more prevalent, investigators are increasingly capable of extracting what the model has learned; allowing the potential to learn novel information from big data that would otherwise be too complex to recognize. Given this, we hypothesize that deep learning can be used to understand how Tat variation influences its transactivation potential. This can be achieved by using DL models designed to predict protein-protein interactions, RNA-protein interactions, and protein localizations and refining them using HIV-1 specific data. This will allow us to investigate how variation within Tat influences its ability to interact with CycT1/TAR and to determine its localization profile. This knowledge can then be applied to engineer Tat variants designed to maximize the likelihood of exhibiting specific properties and observe how these alterations change Tat’s transactivation ability. This research will provide a better understanding of how variation influences Tat’s behavior and contribute to improved HIV-1 therapeutic developments.

Contact Information

Ken Barbee

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