So far, in order to predict important sites of a protein, many computational methods have been developed. In the era of big-data, it is required for improvements and sophistication of existing methods by integrating sequence data in the structural data. In this paper, we aim at two things: improving sequence-based methods and developing a new method using both sequence and structural data. Therefore, we developed an originally modified evolutionary trace method, in which we defined conservative grades calculated from a given multiple sequence alignment and a proximate grade in order to evaluate predicted active sites from a viewpoint of protein-ion, protein-ligand, protein-nucleic acid, proteinprotein interaction by use of three-dimensional structures. In other words, the proximate grade also can evaluate an amino acid residue. When we applied our method to translation elongation factor Tu/1A proteins, it showed that the conservative grades are evaluated accurately by the proximate grade. Consequently, our idea indicated two advantages. One is that we can take into account various cocrystal structures for evaluation. Another one is that, by calculating the fitness between the given conservative grade and the proximate grade, we can select the best conservative grade.