Transcription factors and histone modifications play critical roles in tissue-specific gene expression. Identifying binding sites is key in understanding the regulatory interactions of gene expression. Nave computational approaches uses solely DNA sequence data to construct models known as Position Weight Matrices. However, the various assumptions and the lack of background genomic information leads to a high false positive rate. In an attempt to improve the predictive performance of a PWM, we use a Hidden Markov Model to incorporate chromatin structure, in particular histone modifications. The HMM captures physical interactions between distinct HMs. Indeed, the integration of sequence based PWM models and chromatin modifications improve the predictive ability of the integrative model.