diff --git a/mini3di/__init__.py b/mini3di/__init__.py
index 79966a7..f2d21d7 100644
--- a/mini3di/__init__.py
+++ b/mini3di/__init__.py
@@ -1,10 +1,10 @@
 """A NumPy port of the ``foldseek`` code for encoding structures to 3di.
 """
 
-__all__ = ["Encoder", "FeatureEncoder"]
+__all__ = ["Encoder", "FeatureEncoder", "PartnerIndexEncoder", "VirtualCenterEncoder"]
 __version__ = "0.1.0"
 __author__ = "Martin Larralde <martin.larralde@embl.de>"
 __license__ = "GPLv3"
 __credits__ = "Martin Steinegger and his lab for ``foldseek``."
 
-from .encoder import Encoder, FeatureEncoder
+from .encoder import Encoder, FeatureEncoder, PartnerIndexEncoder, VirtualCenterEncoder
diff --git a/mini3di/encoder.py b/mini3di/encoder.py
index 68783ec..55d15c0 100644
--- a/mini3di/encoder.py
+++ b/mini3di/encoder.py
@@ -71,7 +71,7 @@ def encode_chain(
         if ca_residue:
             residues = [residue for residue in chain.get_residues() if "CA" in residue]
         else:
-            residues = chain.get_residues()
+            residues = list(chain.get_residues())
         # extract atom coordinates
         r = len(residues)
         ca = numpy.array(numpy.nan, dtype=numpy.float32).repeat(3 * r).reshape(r, 3)
@@ -79,7 +79,8 @@ def encode_chain(
         n = ca.copy()
         c = ca.copy()
         for i, residue in enumerate(residues):
-            ca[i, :] = residue["CA"].coord
+            if "CA" in residue:
+                ca[i, :] = residue["CA"].coord
             if "N" in residue:
                 n[i, :] = residue["N"].coord
             if "C" in residue:
@@ -250,12 +251,52 @@ def encode_atoms(
         )
 
 
+class PartnerIndexEncoder(_BaseEncoder["ArrayN[numpy.int64]"]):
+    """An encoder for converting a protein structure to partner indices.
+
+    For each residue, the coordinates of the virtual center are computed
+    from the coordinates of the *Cα*, *Cβ* and *N* atoms. A pairwise
+    distance matrix is then created, and the index of the closest partner
+    residue is extracted for each position.
+
+    """
+
+    def __init__(self) -> None:
+        self.vc_encoder = VirtualCenterEncoder()
+
+    def _find_residue_partners(
+        self,
+        x: ArrayNx3[numpy.floating],
+    ) -> ArrayN[numpy.int64]:
+        # compute pairwise squared distance matrix
+        r = numpy.sum(x * x, axis=-1).reshape(-1, 1)
+        r[0] = r[-1] = numpy.nan
+        D = r - 2 * numpy.ma.dot(x, x.T) + r.T
+        # avoid selecting residue itself as the best
+        D[numpy.diag_indices_from(D)] = numpy.inf
+        # get the closest non-masked residue
+        return numpy.nan_to_num(D, copy=False, nan=numpy.inf).argmin(axis=1)
+
+    def encode_atoms(
+        self,
+        ca: ArrayNx3[numpy.floating],
+        cb: ArrayNx3[numpy.floating],
+        n: ArrayNx3[numpy.floating],
+        c: ArrayNx3[numpy.floating],
+    ) -> ArrayN[numpy.int64]:
+        # encode backbone atoms to virtual center
+        vc = self.vc_encoder.encode_atoms(ca, cb, n, c)
+        # find closest neighbor for each residue
+        return self._find_residue_partners(vc)
+
+
 class FeatureEncoder(_BaseEncoder["ArrayN[numpy.float32]"]):
     """An encoder for converting a protein structure to structural descriptors.
     """
 
     def __init__(self) -> None:
-        self.vc_encoder = VirtualCenterEncoder()
+        self.partner_index_encoder = PartnerIndexEncoder()
+        self.vc_encoder = self.partner_index_encoder.vc_encoder
 
     def _calc_conformation_descriptors(
         self,
@@ -286,19 +327,6 @@ def _calc_conformation_descriptors(
         desc[I, 9] = numpy.copysign(numpy.log(numpy.abs(J - I) + 1), J - I)
         return desc
 
-    def _find_residue_partners(
-        self,
-        x: ArrayNx3[numpy.floating],
-    ) -> ArrayN[numpy.int64]:
-        # compute pairwise squared distance matrix
-        r = numpy.sum(x * x, axis=-1).reshape(-1, 1)
-        r[0] = r[-1] = numpy.nan
-        D = r - 2 * numpy.ma.dot(x, x.T) + r.T
-        # avoid selecting residue itself as the best
-        D[numpy.diag_indices_from(D)] = numpy.inf
-        # get the closest non-masked residue
-        return numpy.nan_to_num(D, copy=False, nan=numpy.inf).argmin(axis=1)
-
     def _create_descriptor_mask(
         self,
         mask: ArrayN[numpy.bool_],
@@ -320,10 +348,10 @@ def encode_atoms(
         n: ArrayNx3[numpy.floating],
         c: ArrayNx3[numpy.floating],
     ) -> ArrayN[numpy.uint8]:
-        # compute the virtual center form the backbone atoms
+        # encode backbone atoms to virtual center
         vc = self.vc_encoder.encode_atoms(ca, cb, n, c)
         # find closest neighbor for each residue
-        partner_index = self._find_residue_partners(vc)
+        partner_index = self.partner_index_encoder._find_residue_partners(vc)
         # build position features from residue angles
         descriptors = self._calc_conformation_descriptors(ca, partner_index)
         # create mask