Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches

Jae Hyung Lee, Michael Hamilton, Colin Gleeson, Cornelia Caragea, Peter Zaback, Jeffry D. Sander, Xue Li, Feihong Wu, Michael Terribilini, Vasant Honavar, Drena Dobbs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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 high-resolution 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.

Original languageEnglish (US)
Title of host publicationPacific Symposium on Biocomputing 2008, PSB 2008
Pages501-512
Number of pages12
StatePublished - 2008
Event13th Pacific Symposium on Biocomputing, PSB 2008 - Kohala Coast, HI, United States
Duration: Jan 4 2008Jan 8 2008

Publication series

NamePacific Symposium on Biocomputing 2008, PSB 2008

Other

Other13th Pacific Symposium on Biocomputing, PSB 2008
CountryUnited States
CityKohala Coast, HI
Period1/4/081/8/08

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Biomedical Engineering
  • Medicine(all)

Fingerprint Dive into the research topics of 'Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches'. Together they form a unique fingerprint.

Cite this