ThreaDNA: predicting DNA mechanics’ contribution to sequence selectivity of proteins along whole genomes | Zendy

Jasmin Cevost | Zendy; Cédric Vaillant | Zendy; Sam Meyer | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

ThreaDNA: predicting DNA mechanics’ contribution to sequence selectivity of proteins along whole genomes

Author(s) -

Jasmin Cevost,

Cédric Vaillant,

Sam Meyer

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx634

Subject(s) - executable , computer science , software , python (programming language) , genome , computational biology , dna , graphical user interface , computational science , algorithm , data mining , biology , genetics , programming language , gene

Many DNA-binding proteins recognize their target sequences indirectly, by sensing DNA's response to mechanical distortion. ThreaDNA estimates this response based on high-resolution structures of the protein-DNA complex of interest. Implementing an efficient nanoscale modeling of DNA deformations involving essentially no adjustable parameters, it returns the profile of deformation energy along whole genomes, at base-pair resolution, within minutes on usual laptop/desktop computers. Our predictions can also be easily combined with estimations of direct selectivity through a generalized form of position-weight-matrices. The formalism of ThreaDNA is accessible to a wide audience.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research