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

5 Citations (Scopus)

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 - Dec 1 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

Fingerprint

Telomerase
Tetrahymena
Learning systems
Enzymes
Proteins
RNA
Cell Aging
DNA
DNA sequences
Nucleic acids
Chromosomes
Yeast
Ribonucleoproteins
Amino acids
Structural Models
Conservation
Telomere
Aging of materials
Crystal structure
Nucleic Acids

All Science Journal Classification (ASJC) codes

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

Cite this

Lee, J. H., Hamilton, M., Gleeson, C., Caragea, C., Zaback, P., Sander, J. D., ... Dobbs, D. (2008). Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches. In Pacific Symposium on Biocomputing 2008, PSB 2008 (pp. 501-512). (Pacific Symposium on Biocomputing 2008, PSB 2008).
Lee, Jae Hyung ; Hamilton, Michael ; Gleeson, Colin ; Caragea, Cornelia ; Zaback, Peter ; Sander, Jeffry D. ; Li, Xue ; Wu, Feihong ; Terribilini, Michael ; Honavar, Vasant ; Dobbs, Drena. / Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches. Pacific Symposium on Biocomputing 2008, PSB 2008. 2008. pp. 501-512 (Pacific Symposium on Biocomputing 2008, PSB 2008).
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title = "Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches",
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.",
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Lee, JH, Hamilton, M, Gleeson, C, Caragea, C, Zaback, P, Sander, JD, Li, X, Wu, F, Terribilini, M, Honavar, V & Dobbs, D 2008, Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches. in Pacific Symposium on Biocomputing 2008, PSB 2008. Pacific Symposium on Biocomputing 2008, PSB 2008, pp. 501-512, 13th Pacific Symposium on Biocomputing, PSB 2008, Kohala Coast, HI, United States, 1/4/08.

Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches. / Lee, Jae Hyung; Hamilton, Michael; Gleeson, Colin; Caragea, Cornelia; Zaback, Peter; Sander, Jeffry D.; Li, Xue; Wu, Feihong; Terribilini, Michael; Honavar, Vasant; Dobbs, Drena.

Pacific Symposium on Biocomputing 2008, PSB 2008. 2008. p. 501-512 (Pacific Symposium on Biocomputing 2008, PSB 2008).

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

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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

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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

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Lee JH, Hamilton M, Gleeson C, Caragea C, Zaback P, Sander JD et al. Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches. In Pacific Symposium on Biocomputing 2008, PSB 2008. 2008. p. 501-512. (Pacific Symposium on Biocomputing 2008, PSB 2008).