Discovering Protein-DNA Binding Cores by Aligned Pattern Clustering
Publication in refereed journal


摘要Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and gene regulation. Limited by expensive experiments, it is promising to discover them with variations directly from sequence data. Although existing computational methods have produced satisfactory results, they are one-to-one mappings with no site-specific information on residue/nucleotide variations, where these variations in binding cores may impact binding specificity. This study presents a new representation for modeling binding cores by incorporating variations and an algorithm to discover them from only sequence data. Our algorithm takes protein and DNA sequences from TRANSFAC (a Protein-DNA Binding Database) as input; discovers from both sets of sequences conserved regions in Aligned Pattern Clusters (APCs); associates them as Protein-DNA Co-Occurring APCs; ranks the Protein-DNA Co-Occurring APCs according to their co-occurrence, and among the top ones, finds three-dimensional structures to support each binding core candidate. If successful, candidates are verified as binding cores. Otherwise, homology modeling is applied to their close matches in PDB to attain new chemically feasible binding cores. Our algorithm obtains binding cores with higher precision and much faster runtime ( ≥ 1,600x) than that of its contemporaries, discovering candidates that do not co-occur as one-to-one associated patterns in the raw data.
著者En-Shiun Annie Lee, Ho-Yin Antonio Sze-To, Man Hon Wong, Kwong Sak Leung, Terrence Chi Kong Lau, Andrew K.C. Wong
期刊名稱IEEE/ACM Transactions on Computational Biology and Bioinformatics
頁次254 - 263
關鍵詞Protein-DNA binding, binding cores, aligned pattern cluster, association rule mining

上次更新時間 2020-12-10 於 00:02