Computational Identification of Repeat-Containing Proteins and Systems

Han Altae-Tran; Linyi Gao; Jonathan Strecker; Rhiannon K. Macrae; Feng Zhang

doi:10.1017/qrd.2020.14

QRB Discovery (Jan 2020)

Computational Identification of Repeat-Containing Proteins and Systems

Han Altae-Tran,
Linyi Gao,
Jonathan Strecker,
Rhiannon K. Macrae,
Feng Zhang

Affiliations

Han Altae-Tran: ORCiD; Broad Institute of MIT and Harvard Cambridge, Cambridge, MA 02142, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Linyi Gao: ORCiD; Broad Institute of MIT and Harvard Cambridge, Cambridge, MA 02142, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Jonathan Strecker: ORCiD; Broad Institute of MIT and Harvard Cambridge, Cambridge, MA 02142, USA
Rhiannon K. Macrae: ORCiD; Broad Institute of MIT and Harvard Cambridge, Cambridge, MA 02142, USA McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Feng Zhang: ORCiD; Broad Institute of MIT and Harvard Cambridge, Cambridge, MA 02142, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Howard Hughes Medical Institute, Cambridge, MA 02139, USA

DOI: https://doi.org/10.1017/qrd.2020.14
Journal volume & issue: Vol. 1

Abstract

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Repetitive sequence elements in proteins and nucleic acids are often signatures of adaptive or reprogrammable systems in nature. Known examples of these systems, such as transcriptional activator-like effectors (TALE) and CRISPR, have been harnessed as powerful molecular tools with a wide range of applications including genome editing. The continued expansion of genomic sequence databases raises the possibility of prospectively identifying new such systems by computational mining. By leveraging sequence repeats as an organizing principle, here we develop a systematic genome mining approach to explore new types of naturally adaptive systems, five of which are discussed in greater detail. These results highlight the existence of a diverse range of intriguing systems in nature that remain to be explored and also provide a framework for future discovery efforts.

Published in QRB Discovery

ISSN: 2633-2892 (Online)
Publisher: Cambridge University Press
Country of publisher: United Kingdom
LCC subjects: Technology: Chemical technology: Biotechnology; Science: Biology (General)
Website: https://www.cambridge.org/core/journals/qrb-discovery

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