Mining p53 binding sites using profile hidden Markov model

Hidden Markov model has been successfully applied to bacterial gene finders and mRNA splicing modeling. Using a set of observing DNA sequences, HMM is derived for homologous search. In this paper we develop profile HMM in detecting p53, a tumor suppressor, binding sites along genes. Without assuming the constant number of nucleotides in p53 binding site, profile HMM and viterbi algorithms are designed to detect the embedded p53 binding sites from the promoter genes chosen from GenBank. The p53 regulated genes containing either single or multiple p53 binding sites distributed as clusters can be identified and classified into 7 functional groups including cell cycle regulation, DNA damage repair, signaling transduction, transcriptional factor, stress response, tumor suppressor, and oncogen.