Computational Identification of Repeat-Containing Proteins and Systems
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 a...
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Published in | QRB discovery Vol. 1; p. e10 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
England
Cambridge University Press
2020
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Han Altae-Tran and Linyi Gao contributed equally to this work. |
ISSN: | 2633-2892 2633-2892 |
DOI: | 10.1017/qrd.2020.14 |