Sequence, Structure, and Functional Space of Drosophila De Novo Proteins

Middendorf, Lasse; Iyengar Ravi, Bharat; Eicholt, Lars A.

Forschungsartikel (Zeitschrift) | Peer reviewed

Zusammenfassung

De novo proteins emerge from formerly noncoding regions of genomic DNA. While a few de novo proteins have been shown to perform essential functions in different organisms, it is experimentally laborious to discover the structure and the function of de novo proteins, especially at a large scale. To help address this problem, we used machine learning based computational tools to predict the structure and the function of ∼1,500 Drosophila de novo proteins, thereby providing a dataset for a focused experimental validation.

Details zur Publikation

FachzeitschriftGenome Biology and Evolution
Jahrgang / Bandnr. / Volume16
Ausgabe / Heftnr. / Issue8
Artikelnummerevae176
StatusVeröffentlicht
Veröffentlichungsjahr2024 (30.08.2024)
Sprache, in der die Publikation verfasst istEnglisch
DOI10.1093/gbe/evae176
Link zum Volltexthttps://academic.oup.com/gbe/article/16/8/evae176/7745839?searchresult=1
Stichwörterde novo proteins, protein function, structural comparison, protein structure, structure predictions, sequence space

Autor*innen der Universität Münster

Eicholt, Lars Albert
Arbeitsgruppe Bioinformatik (Prof. Bornberg-Bauer)
Ravi, Bharat
Arbeitsgruppe Bioinformatik (Prof. Bornberg-Bauer)