TY - GEN
T1 - Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches
AU - Lee, Jae Hyung
AU - Hamilton, Michael
AU - Gleeson, Colin
AU - Caragea, Cornelia
AU - Zaback, Peter
AU - Sander, Jeffry D.
AU - Li, Xue
AU - Wu, Feihong
AU - Terribilini, Michael
AU - Honavar, Vasant
AU - Dobbs, Drena
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
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M3 - Conference contribution
C2 - 18229711
AN - SCOPUS:40549099084
SN - 9812776087
SN - 9789812776082
T3 - Pacific Symposium on Biocomputing 2008, PSB 2008
SP - 501
EP - 512
BT - Pacific Symposium on Biocomputing 2008, PSB 2008
T2 - 13th Pacific Symposium on Biocomputing, PSB 2008
Y2 - 4 January 2008 through 8 January 2008
ER -