Discovering Protein-DNA Binding Cores by Aligned Pattern Clustering

Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and gene regulation. Variations (or mutations) in binding cores are ubiquitous and have different levels of effects on the binding specificity. To alleviate expensive experiments, we have developed a new method to discover directly from sequence data binding cores and study the effect due to variations. Although existing computational methods have produced satisfactory TF-TFBS binding cores, they are only one-to-one mappings with no site-specific information on residue/nucleotide variations; and also are largely overlapped. In this study, we propose a new representation for modeling TF-TFBS binding with variants known as TF-TFBS Co-Supportive Aligned Pattern Clusters (APCs), which are more compact, with more details for site-specific variants, and biologically more intuitive for analysis. To achieve this task, we have also developed an algorithm to discover TF-TFBS Co-Supportive APCs to capture binding cores at a higher precision with much faster runtime (≥1600X) comparing to other methods. The variants in TF-TFBS Co-Supportive APCs are also statistically analyzed and demonstrated that they can assist homology modeling to synthesize new biological knowledge.