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 approximately 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.
authors.affiliation: Bioinformatics & Computational Biology Program, L.H. Baker Center for Bioinformatics & Biological Statistics, Iowa State University, Ames, IA 50010, USA.
authors.forename: Jae-Hyung
authors.initials: JH
authors.lastname: Lee
authors.forename: Michael
authors.initials: M
authors.lastname: Hamilton
authors.forename: Colin
authors.initials: C
authors.lastname: Gleeson
authors.forename: Cornelia
authors.initials: C
authors.lastname: Caragea
authors.forename: Peter
authors.initials: P
authors.lastname: Zaback
authors.forename: Jeffry D
authors.initials: JD
authors.lastname: Sander
authors.forename: Xue
authors.initials: X
authors.lastname: Li
authors.forename: Feihong
authors.initials: F
authors.lastname: Wu
authors.forename: Michael
authors.initials: M
authors.lastname: Terribilini
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
authors.forename: Drena
authors.initials: D
authors.lastname: Dobbs
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D017124:Conserved Sequence; D004247:DNA / Q000737:chemistry / Q000378:metabolism; D030562:Databases, Protein; D006801:Humans; D046911:Macromolecular Substances; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D012313:RNA / Q000737:chemistry / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000201:enzymology / Q000235:genetics; D017386:Sequence Homology, Amino Acid; D019098:Telomerase / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D013768:Tetrahymena / Q000201:enzymology / Q000235:genetics
chemical_list: D046911:Macromolecular Substances; D012313:RNA; D004247:DNA; D019098:Telomerase