Transcript poster

CSBSI 2007
Bioinformatics and Computational Biology Program
Department of Genetics, Development, and Cell Biology
Department of Computer Science
Generating Models as a Platform for Comparing Functional and Structural Elements of Telomerase
Colin Gleeson, Michael Hamilton, Jae-Hyung Lee, Cornelia Caragea, Vasant Honavar, Drena Dobbs
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 and 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.
Ref: Lee, Hamilton, Gleeson et al. PSB 08, submitted
DOMAIN STRUCTURE OF TELOMERASE
REVERSE TRANSCRIPTASE
1 – Comparison of TEN Domain Sequences
from Human, Yeast and Tetrahymena
The multiple sequence alignment in the TEN domain displayed
below suggests a significant evolutionary divergence in the
primary sequence of telomerase across species. Pairwise
sequence identity between hTERT and tTERT is <20%. This
led us to attempt a multi-template approach to homology
modeling the TEN domain of hTERT.
4 – Comparison of TEN Domain Structures
1) Using the structure of tTERT (PDB 2B2A) as a template,
homology modeling was performed on hTERT (ii) and sTERT
(S. cerevisiae telomerase) (iv).
2) However, incorporating additional templates provided an
hTERT model (iii) that is energetically more favorable than
the single template model (0.944 vs. 1.332 Anolea score,
E/kT).
2 – Homology Modelling of the TEN Domain
1) Homology models were
constructed using Modeller by
first aligning hTERT
N-terminal sequence against
several templates.
2) The selected templates
provided spatial constraints.
3) The hTERT N-terminus
sequence was fitted to the
template and adjusted to
satisfy constraints.
Mapped functional domains and conserved motifs of TERT are shown
above shaded boxes representing clusters of predicted DNA and RNA
interface residues. Predicted interface residues are indicated by a +
below the amino acid sequence. Boxed regions correspond to
experimentally validated interface residues. (Ref. 3, 4, 5, 6, 7)
DNA
Lee et al., PSB 2008
5 – Evaluation of Models
RNA
•After generation of the hTERT TEN domain structural
model, energy minimization was performed using gradient
descent on the GROMOS force field.
•To assess the quality of the model, Anolea energy
statistics of the hTERT model were compared against
models generated from random sequences.
•The hTERT model has a much more favorable energy
state over the randomized models.
3 – Template Usage in Homology Modelling
1IMHC: Tonicity-responsive enhancer binding protein-DNA complex
1JFIB: Negative Cofactor 2-TATA box binding protein-DNA
2DYRM: bovine heart cytochrome C oxidase
1B1UA: inhibitor of Trypsin and Alpha-Amylase from Ragi seeds
2I7RA: Glyoxalase-like protein
2B2AA: N-terminal domain of tTERT
ClustalX Alignment of Template Sequences
Terriblini et al., RNA 2006
Conclusions
http://www.salilab.org/modeller/manual/node11.html#fig:feature
A) TERT contains four functional regions: the essential N-terminal
(TEN) domain, an RNA-binding domain (TRBD), reverse
transcriptase (RT), and a C-terminal extension (TEC).
B) A cartoon illustrating TERT domain organization, along with the
RNA template (TER). The TEN domain is the recently
determined Tetrahymena structure (PDB ID: 2B2A), and the RT
domain is from HIV-RT (PDB ID: 3HVT). The template is bound
by the RT domain and the 5’ flanking region is bound by TRBD.
The 3’ flanking region is illustrated as binding to the TEN
domain, although anchoring of this end of the template is not
well understood. The interaction between TEC and TEN is
similarly speculative. (Figure modeled after Collins, 2006)
7 – Predicted vs. Verified DNA and RNA
Binding Sites in hTERT and tTERT
6 – Mapping of Predicted and Verified Nucleic
Acid Binding Sites on hTERT TEN Domain
Views of predicted nucleic acid interaction sites with
experimentally verified sites of interaction on the hTERT
structural model are displayed below.
Yellow : Verified Residues
Blue : Predicted Residues
Green : Overlapping Residues
DNA
•Availability of an experimentally determined structure for
the TEN Domain of tTERT provided an impetus to
generate a structural model for human TEN.
•Generating a human TEN model from multiple templates
results in a more stable structure than that obtained using
only tTERT as a template.
•Human TEN models analyzed in tandem with functional
predictions provide valuable insight into the nature of a
complex, clinically important protein.
•Inferences drawn from analysis can fuel future
computational and wet lab investigations of telomerase.
References
RNA
Acknowledgements: This research was supported in part by grants from the National Institutes of Health (GM 066387) and NIH-NSF BBSI (0608769).
1) N. Eswar, M. A. Mari-Renom, B. Webb et al..
2) Terribilini, M. Lee, J-H. Yan, C.
3) S. A. Jacobs, E. R. Podell, and T. R. Cech,
4) T. J. Moriarty, S. Huard, S. Dupuis et al.,
5) Moriarty, R. J. Ward, M. A. Taboski et al.,
6) Wyatt, D. A. Lobb, and T. L. Beattie,
7) F. Melo and E. Feytmans,
8) Melo, F., Devos, D. et al.,
Current Protocols in Bioinformatics, Supplement 15, 5.6.1-5.6.30, 200.
RNA 12:1450-1462 (2006)
Nat. Struct. Mol. Biol. 13, 218 (2006)
Mol. Cell. Biol. 22, 1253(2002) T. J.
Mol. Biol. Cell. 16, 3152 (2005) H. D.
Mol. Cell. Biol. 27, 3226 (2007)
J. Mol. Biol. 277, 1141 (1998)
Intelligent Systems for Molecular Biology 97, 110-113 (1997)
GFST 2007