Computational learning on specificity-determining residue-nucleotide interactions
Name
Wong-2015-Computational learni.pdf
Size
4.21 MB
Format
Adobe PDF
Checksum (MD5)
6a1b02f3b3e44b4da8f9246a0fd7884a
Author(s)
Wong, Ka-Chun
Li, Yue
Peng, Chengbin
Moses, Alan M.
Zhang, Zhaolei
Date Issued
November 2015
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Citation
Wong, Ka-Chun, Yue Li, Chengbin Peng, Alan M. Moses, and Zhaolei Zhang. “Computational Learning on Specificity-Determining Residue-Nucleotide Interactions.” Nucleic Acids Research (November 2, 2015): gkv1134.
Version
Final published version
Abstract
The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/nar/gkv1134
