def df_kegg_metadata(self):
        """DataFrame: Pandas DataFrame of KEGG metadata per protein."""
        kegg_pre_df = []
        df_cols = ['gene', 'kegg', 'refseq', 'uniprot', 'num_pdbs', 'pdbs', 'seq_len', 'sequence_file', 'metadata_file']

        for g in self.genes:
            kegg_mappings = g.protein.filter_sequences(KEGGProp)
            for kegg_prop in kegg_mappings:
                kegg_dict = kegg_prop.get_dict(df_format=True, only_attributes=df_cols)
                kegg_dict['gene'] = g.id
                kegg_pre_df.append(kegg_dict)

        # Save a dataframe of the file mapping info
        df = pd.DataFrame.from_records(kegg_pre_df, columns=df_cols).set_index('gene')
        if df.empty:
            log.warning('Empty dataframe')
            return df
        else:
            return ssbio.utils.clean_df(df)