Machine learning predicts nucleosome binding modes of transcription factors

• Main Authors: Cui, F. (Author), Kishan, K.C (Author), Li, R. (Author), Subramanya, S.K (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: amino acid sequence; Amino Acid Sequence; Binary classifiers; Binding energy; binding site; Binding sites; Binding Sites; Classification (of information); Comprehensive analysis; Computational predictions; genetics; Interaction modes; machine learning; Machine learning; Machine Learning; metabolism; Multi label classification; Nested cross validations; nucleosome; Nucleosome binding modes; Nucleosomes; Off-the-shelf machine; protein binding; Protein Binding; Protein sequences; transcription factor; Transcription factors; Transcription Factors
• Online Access: View Fulltext in Publisher

 Background: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding, dyad binding, groove binding, and gyre spanning. However, there are substantial experimental challenges in measuring nucleosome binding modes for thousands of TFs in different species. Results: We present a computational prediction of the binding modes based on TF protein sequences. With a nested cross-validation procedure, our model outperforms several fine-tuned off-the-shelf machine learning (ML) methods in the multi-label classification task. Our binary classifier for the EB mode performs better than these ML methods with the area under precision-recall curve achieving 75%. The end preference of most TFs is consistent with low nucleosome occupancy around their binding site in GM12878 cells. The nucleosome occupancy data is used as an alternative dataset to confirm the superiority of our EB classifier. Conclusions: We develop the first ML-based approach for efficient and comprehensive analysis of nucleosome binding modes of TFs. © 2021, The Author(s).