Striking Similarities in Diverse Telomerase Proteins Revealed by Combining Structure Prediction and Machine Learning Approaches

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2008-01-01
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Lee, Jae-Hyung
Hamilton, Michael
Gleeson, Colin
Caragea, Conelia
Zaback, Peter
Sander, Jeffry
Li, Xue
Wu, Feihong
Terribilini, Michael
Honavar, Vasant
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Dobbs, Drena
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Genetics, Development and Cell Biology

The Department of Genetics, Development, and Cell Biology seeks to teach subcellular and cellular processes, genome dynamics, cell structure and function, and molecular mechanisms of development, in so doing offering a Major in Biology and a Major in Genetics.

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The Department of Genetics, Development, and Cell Biology was founded in 2005.

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Bioinformatics and Computational Biology
The Bioinformatics and Computational Biology (BCB) Program at Iowa State University is an interdepartmental graduate major offering outstanding opportunities for graduate study toward the Ph.D. degree in Bioinformatics and Computational Biology. The BCB program involves more than 80 nationally and internationally known faculty—biologists, computer scientists, mathematicians, statisticians, and physicists—who participate in a wide range of collaborative projects.
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Telomerase is a ribonucleoprotein enzyme that adds telomeric DNA repeat sequences to the ends of linear chromosomes. The enzyme plays pivotal roles in cellular senescence and aging, and because it provides a telomere maintenance mechanism for ~90% of human cancers, it is a promising target for cancer therapy. Despite its importance, a highresolution structure of the telomerase enzyme has been elusive, although a crystal structure of an N-terminal domain (TEN) of the telomerase reverse transcriptase subunit (TERT) from Tetrahymena has been reported. In this study, we used a comparative strategy, in which sequence-based machine learning approaches were integrated with computational structural modeling, to explore the potential conservation of structural and functional features of TERT in phylogenetically diverse species. We generated structural models of the N-terminal domains from human and yeast TERT using a combination of threading and homology modeling with the Tetrahymena TEN structure as a template. Comparative analysis of predicted and experimentally verified DNA and RNA binding residues, in the context of these structures, revealed significant similarities in nucleic acid binding surfaces of Tetrahymena and human TEN domains. In addition, the combined evidence from machine learning and structural modeling identified several specific amino acids that are likely to play a role in binding DNA or RNA, but for which no experimental evidence is currently available.

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This is a proceeding from Pacific Symposium on Biocomputing 13 (2008): 501. Posted with permission.

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Tue Jan 01 00:00:00 UTC 2008