Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana

• Published in: Nature communications 2021-11, Vol.12 (1), p.6549-6549, Article 6549
• Main Authors: Sielemann, Janik, Wulf, Donat, Schmidt, Romy, Bräutigam, Andrea
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2114; Amino acid sequence; Arabidopsis - genetics; Arabidopsis - metabolism; Arabidopsis thaliana; Binding Sites; Computational Biology; Deoxyribonucleic acid; DNA; DNA - genetics; DNA - metabolism; Gene expression; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Nucleotide sequence; Protein Binding; Regulatory sequences; Science; Science (multidisciplinary); Transcription factors; Transcription Factors - genetics; Transcription Factors - metabolism
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-26819-2

Understanding gene expression will require understanding where regulatory factors bind genomic DNA. The frequently used sequence-based motifs of protein-DNA binding are not predictive, since a genome contains many more binding sites than are actually bound and transcription factors of the same family share similar DNA-binding motifs. Traditionally, these motifs only depict sequence but neglect DNA shape. Since shape may contribute non-linearly and combinational to binding, machine learning approaches ought to be able to better predict transcription factor binding. Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differential binding by transcription factor family members which share the same binding motif. We observed that DNA shape features were individually weighted for each transcription factor, even if they shared the same binding sequence. Methods to predict transcription factor binding sites typically focus on sequence motifs without considering DNA shape. Here the authors use a random forest machine learning approach that incorporates DNA shape and improves binding site prediction for Arabidopsis thaliana transcription factors.