We have developed a method for predicting disease prone sites with amino acid sequence based features such as physicochemical properties, conservation scores, secondary structure and peptide motifs to discriminate the disease prone and neutral sites in lung cancer using deep neural network. This method helps to identify crucial sites in lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC).