Computational learning on specificity-determining residue-nucleotide interactions.
Bottom Line: 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.
Affiliation: Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong email@example.com.Show MeSH
Mentions: Based on the CISBP data and PDB data, we can train and test models for learning and predicting the specificity-determining residue–nucleotide interactions for each DBD family. The overall approach is summarized in Figure 1, which can be divided into two phases: training and testing.
Affiliation: Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong firstname.lastname@example.org.