diff --git a/src/geckopy/adapter/params.py b/src/geckopy/adapter/params.py
index b709593..ea2597a 100644
--- a/src/geckopy/adapter/params.py
+++ b/src/geckopy/adapter/params.py
@@ -195,8 +195,9 @@ class OkpParams(BaseModel):
     method: str = Field(
         default="CataPro",
         description=(
-            "Predictor method: CataPro, CatPred, DLKcat, EITLEM, "
-            "KinForm-H, KinForm-L, UniKP (see GET /api/v1/methods/)."
+            "Predictor method: CataPro, CatPred, CatRange, DLKcat, EITLEM, "
+            "KinForm-H, KinForm-L, OmniESI, OmniESI-O2DENet, TurNuP, UniKP "
+            "(see GET /api/v1/methods/)."
         ),
     )
     handle_long_sequences: str = Field(
diff --git a/src/geckopy/gather_kcats/open_kinetics_predictor/parse_output.py b/src/geckopy/gather_kcats/open_kinetics_predictor/parse_output.py
index 55b8945..e9366c1 100644
--- a/src/geckopy/gather_kcats/open_kinetics_predictor/parse_output.py
+++ b/src/geckopy/gather_kcats/open_kinetics_predictor/parse_output.py
@@ -24,6 +24,34 @@
 
 logger = logging.getLogger(__name__)
 
+_KCAT_COLUMN_SUFFIX = "kcat (1/s)"
+_RANGE_RE = re.compile(
+    r"^\s*([0-9.eE+-]+)\s+to\s+([0-9.eE+-]+)\s*(?:s\^-1)?\s*$"
+)
+
+
+def _kcat_value(text: str) -> float | None:
+    """A prediction as a number in 1/s.
+
+    Plain numbers are returned as they are. CatRange predicts a class,
+    which OKP returns as its range (``"10 to 100 s^-1"``); it becomes the
+    geometric midpoint, or half the upper bound for the class starting at
+    0, the values OKP returned for these classes before. Anything else is
+    None.
+    """
+    try:
+        return float(text)
+    except (TypeError, ValueError):
+        pass
+    match = _RANGE_RE.match(text or "")
+    if match is None:
+        return None
+    try:
+        low, high = float(match.group(1)), float(match.group(2))
+    except ValueError:
+        return None
+    return (low * high) ** 0.5 if low > 0 else high / 2
+
 _OUTPUT_COLUMNS = [
     "rxn_id", "source", "eccode", "substrates", "genes",
     "kcat", "wildcard_level", "origin",
@@ -44,9 +72,10 @@ def parse_okp_output(
         ``ec.rxn_enz_mat`` and per-metabolite SMILES populated.
     result
         Either the CSV text or a path to the result file. Expected
-        columns include ``kcat (1/s)``, ``Source kcat``,
-        ``Protein Sequence`` and ``Substrate`` (extra similarity
-        columns are ignored).
+        columns include ``kcat (1/s)`` (CatRange: ``Predicted kcat
+        range: kcat (1/s)``, a number or a class range such as
+        ``10 to 100 s^-1``), ``Source kcat``, ``Protein Sequence`` and
+        ``Substrate`` (extra similarity columns are ignored).
 
     Returns
     -------
@@ -71,15 +100,18 @@ def parse_okp_output(
     df = pd.read_csv(io.StringIO(text), dtype=str, keep_default_na=False)
     df.columns = [c.strip() for c in df.columns]
 
-    required = ["kcat (1/s)", "Source kcat", "Protein Sequence", "Substrate"]
+    kcat_columns = [c for c in df.columns if c.endswith(_KCAT_COLUMN_SUFFIX)]
+    required = ["Source kcat", "Protein Sequence", "Substrate"]
     missing = [c for c in required if c not in df.columns]
+    if len(kcat_columns) != 1:
+        missing.insert(0, _KCAT_COLUMN_SUFFIX)
     if missing:
         raise ValueError(
             f"OKP result is missing expected column(s): {missing}. "
             f"Found: {list(df.columns)}"
         )
 
-    numeric_kcat = pd.to_numeric(df["kcat (1/s)"], errors="coerce")
+    numeric_kcat = pd.to_numeric(df[kcat_columns[0]].map(_kcat_value), errors="coerce")
     valid_mask = numeric_kcat.notna()
     if not valid_mask.any():
         raise ValueError(
diff --git a/tests/test_open_kinetics_predictor.py b/tests/test_open_kinetics_predictor.py
index a34fd14..cefacdf 100644
--- a/tests/test_open_kinetics_predictor.py
+++ b/tests/test_open_kinetics_predictor.py
@@ -216,6 +216,37 @@ def test_parse_no_numeric_kcat_raises():
         parse_okp_output(_ec_model(), csv_text)
 
 
+def test_parse_catrange_class_ranges():
+    """CatRange's column holds class ranges; each becomes its geometric
+    midpoint, as OKP returned it before."""
+    csv_text = (
+        "Predicted kcat range: kcat (1/s),Source kcat,Extra Info kcat,Protein Sequence,Substrate\n"
+        "10 to 100 s^-1,Prediction from CatRange,Predicted range: 10 to 100 s^-1,S1,CCO\n"
+    )
+    df = parse_okp_output(_ec_model(), csv_text)
+    assert not df.empty
+    assert all(df["source"] == "OKP-CatRange")
+    assert df["kcat"].iloc[0] == pytest.approx(10 ** 1.5)
+
+
+@pytest.mark.parametrize("text, expected", [
+    ("12.5", 12.5),
+    ("10 to 100 s^-1", 10 ** 1.5),
+    ("0 to 1e-08 s^-1", 5e-9),
+    ("1000 to 1e+08 s^-1", 10 ** 5.5),
+    ("", None),
+    ("NA", None),
+])
+def test_kcat_value(text, expected):
+    from geckopy.gather_kcats.open_kinetics_predictor.parse_output import _kcat_value
+
+    value = _kcat_value(text)
+    if expected is None:
+        assert value is None or value != value
+    else:
+        assert value == pytest.approx(expected)
+
+
 def test_parse_missing_column_raises():
     csv_text = "kcat (1/s),Protein Sequence\n1.0,S1\n"
     with pytest.raises(ValueError, match="missing expected column"):