| Start Date: | 6/8/2016 | Start Time: | 11:00 AM |
| End Date: | 6/8/2016 | End Time: | 1:00 PM |
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Event Description
BIOMED PhD Thesis Defense
Title:
Evolution-Aware Protein Structure Comparison and Applications in Protein-Protein Interaction Prediction
Speaker:
Chunyu Zhao, PhD Candidate, School of Biomedical Engineering, Science and Health Systems
Advisor:
Ahmet Sacan, PhD, Assistant Professor, School of Biomedical Engineering, Science and Health Systems
Abstract:
Comparison of protein structures provide insights into the function and interactions of proteins and enhance our understanding of biomolecular mechanisms driving life and disease. Available protein structure comparison methods are based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins. However, non-geometric features contained in primary sequence and evolutionary history of proteins contain valuable information that can enhance detection of such similarities.
In this study, we introduced a new method to incorporate additional biochemical and evolutionary features of the proteins being compared. We proposed UniScore as a new structure similarity score, which integrates geometric similarity, sequence similarity, and evolutionary profiles of the proteins. We further developed a corresponding UniAlign algorithm for finding structural alignment of proteins with near-optimal UniScore. We evaluated UniAlign in terms of the consistency between the alignments it produces with human-curated alignments, calculated by the fraction of correctly aligned residues. Experimental results show that UniAlign outperforms other structural programs in aligning proteins from the NCBI’s human-curated Conserved Domain Database.
UniAlign's ability in detecting functionally important structural similarities is utilized in an application to discover interactions between HIV-1 ENV protein (gp41 and gp120) and human proteins. Structural compatibility of an HIV-human interaction pairs are evaluated via geometric, biochemical, and evolutionary features and a prediction model is developed using a Support Vector Machine. This provides the first model for prediction of interactions that can also generate a protein-protein 3D complex. The results of the HIV-human interaction study have discovered novel virus-host interactions as well as potential clinical targets for therapeutic intervention. |
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Location:
Monell Chemical Center, Room 114, located at 3508 Market Street. |
Audience:
Undergraduate StudentsGraduate StudentsFacultyStaff |
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