Striking Similarities in Diverse Telomerase Proteins Revealed by Combining Structure Prediction and Machine Learning ApproachesPacific Symposium on Biocomputing
Document TypeConference Proceeding
ConferencePacific Symposium on Biocomputing
Publication VersionPublished Version
Conference TitlePacific Symposium on Biocomputing 2008
Conference DateJanuary 4-8, 2008
AbstractTelomerase 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.
RightsPSB proceedings are published as Open Access chapters by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
Copyright OwnerWorld Scientific
Citation InformationJae-Hyung Lee, Michael Hamilton, Colin Gleeson, Conelia Caragea, et al.. "Striking Similarities in Diverse Telomerase Proteins Revealed by Combining Structure Prediction and Machine Learning Approaches" Kohala Coast, HawaiiPacific Symposium on Biocomputing Vol. 13 (2008) p. 501 - 512
Available at: http://works.bepress.com/drena-dobbs/60/