diff --git a/spectrum_fundamentals/metrics/percolator.py b/spectrum_fundamentals/metrics/percolator.py
index 3a71fba..78773ec 100644
--- a/spectrum_fundamentals/metrics/percolator.py
+++ b/spectrum_fundamentals/metrics/percolator.py
@@ -10,6 +10,8 @@
 from scipy import interpolate
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 
+import spectrum_fundamentals.constants as cs
+
 from . import fragments_ratio as fr
 from . import similarity as sim
 from .metric import Metric
@@ -40,6 +42,7 @@ class Percolator(Metric):
     FRAGMENTATION: fragmentation method, e.g. HCD, CID
     RETENTION_TIME: observed retention time
     PREDICTED_RETENTION_TIME: predicted retention time by Prosit
+    PROTEINS
     """
 
     metadata: pd.DataFrame
@@ -272,11 +275,16 @@ def add_percolator_metadata_columns(self):
         self.metrics_val["Label"] = self.target_decoy_labels
         self.metrics_val["ScanNr"] = self.metadata["SCAN_NUMBER"]
         self.metrics_val["filename"] = self.metadata["RAW_FILE"]
+        # self.metrics_val.insert(self.metrics_val.columns.get_loc("filename") + 1, "ExpMass", self.metrics_val.pop("ExpMass"))
         self.metrics_val["Peptide"] = self.metadata["MODIFIED_SEQUENCE"].apply(lambda x: "_." + x + "._")
+        # added a variable for proton mass
+        proton_mass = cs.PARTICLE_MASSES["PROTON"]
+        # added theorictical/expected (mass/charge) column including the charge
 
-        self.metrics_val["Proteins"] = self.metadata[
-            "MODIFIED_SEQUENCE"
-        ]  # we don't need the protein ID to get PSM / peptide results, fill with peptide sequence
+        self.metrics_val["ExpMass"] = (
+            self.metadata["CALCULATED_MASS"] + proton_mass * self.metadata["PRECURSOR_CHARGE"]
+        ) / self.metadata["PRECURSOR_CHARGE"]
+        self.metrics_val["Proteins"] = self.metadata["PROTEINS"]
 
     def apply_lda_and_get_indices_below_fdr(
         self, initial_scoring_feature: str = "spectral_angle", fdr_cutoff: float = 0.01
@@ -371,7 +379,7 @@ def fdrs_to_qvals(fdrs: np.ndarray) -> np.ndarray:
 
     def _reorder_columns_for_percolator(self):
         all_columns = self.metrics_val.columns
-        first_columns = ["SpecId", "Label", "ScanNr", "filename"]
+        first_columns = ["SpecId", "Label", "ScanNr", "filename", "ExpMass"]
         last_columns = ["Peptide", "Proteins"]
         mid_columns = list(set(all_columns) - set(first_columns) - set(last_columns))
         new_columns = first_columns + sorted(mid_columns) + last_columns