diff --git a/.conda/arm64/meta.yaml b/.conda/arm64/meta.yaml
index 661783b..4206bdf 100644
--- a/.conda/arm64/meta.yaml
+++ b/.conda/arm64/meta.yaml
@@ -1,4 +1,4 @@
-{% set version = "2.2.0" %}
+{% set version = "2.2.1" %}
 package:
   name: "proteinflow"
   version: {{ version }}
diff --git a/.conda/default/meta.yaml b/.conda/default/meta.yaml
index bb3de45..b125ccd 100644
--- a/.conda/default/meta.yaml
+++ b/.conda/default/meta.yaml
@@ -1,4 +1,4 @@
-{% set version = "2.2.0" %}
+{% set version = "2.2.1" %}
 package:
   name: "proteinflow"
   version: {{ version }}
diff --git a/proteinflow/data/torch.py b/proteinflow/data/torch.py
index ea2dc22..46787cd 100644
--- a/proteinflow/data/torch.py
+++ b/proteinflow/data/torch.py
@@ -4,7 +4,8 @@
 import random
 from collections import defaultdict
 from copy import deepcopy
-from itertools import combinations
+from itertools import combinations, groupby
+from operator import itemgetter
 
 import numpy as np
 import torch
@@ -617,7 +618,6 @@ def _get_masked_sequence(
                 if self.mask_frac is not None:
                     assert self.mask_frac > 0 and self.mask_frac < 1
                     k = int(len(neighbor_indices) * self.mask_frac)
-                    k = max(k, 10)
                 else:
                     up = min(
                         self.upper_limit, int(len(neighbor_indices) * 0.5)
@@ -820,22 +820,45 @@ def _to_pyg_graph(self, data):
             pyg_data[key] = value.unsqueeze(0)
         return pyg_data
 
-    def _get_anchor_ind(self, data):
-        """Get the indices of the anchor residues."""
-        masked_ind = torch.where(data["masked_res"].bool())[0]
-        known_ind = torch.where(data["mask"].bool())[0]
-        start, end = masked_ind[0], masked_ind[-1]
-        start = (
-            known_ind[known_ind < start][-1]
-            if (known_ind < start).sum() > 0
-            else known_ind[0]
-        )
-        end = (
-            known_ind[known_ind > end][0]
-            if (known_ind > end).sum() > 0
-            else known_ind[-1]
-        )
-        return start, end
+    @staticmethod
+    def get_anchor_ind(masked_res, mask):
+        """Get the indices of the anchor residues.
+
+        Anchor residues are defined as the first and last known residues before and
+        after each continuous masked region.
+
+        Parameters
+        ----------
+        masked_res : torch.Tensor
+            A boolean tensor indicating which residues should be predicted
+        mask : torch.Tensor
+            A boolean tensor indicating which residues are known
+
+        Returns
+        -------
+        list
+            A list of indices of the anchor residues
+
+        """
+        anchor_ind = []
+        masked_ind = torch.where(masked_res.bool())[0]
+        known_ind = torch.where(mask.bool())[0]
+        for _, g in groupby(enumerate(masked_ind), lambda x: x[0] - x[1]):
+            group = map(itemgetter(1), g)
+            group = list(map(int, group))
+            start, end = group[0], group[-1]
+            start = (
+                known_ind[known_ind < start][-1]
+                if (known_ind < start).sum() > 0
+                else known_ind[0]
+            )
+            end = (
+                known_ind[known_ind > end][0]
+                if (known_ind > end).sum() > 0
+                else known_ind[-1]
+            )
+            anchor_ind += [start, end]
+        return anchor_ind
 
     def _get_antibody_mask(self, data):
         """Get a mask for the antibody residues."""
@@ -853,11 +876,9 @@ def _patch(self, data):
         """Cut the data around the anchor residues."""
         # adapted from diffab
         pos_alpha = data["X"][:, 2]
-        start, end = self._get_anchor_ind(data)
-        anchor_points = torch.stack([pos_alpha[start], pos_alpha[end]], dim=0)
-        dist_anchor = torch.cdist(pos_alpha, anchor_points[[0]], p=2).min(dim=1)[
-            0
-        ]  # (L, )
+        anchor_ind = self.get_anchor_ind(data["masked_res"], data["mask"])
+        anchor_points = torch.stack([pos_alpha[ind] for ind in anchor_ind], dim=0)
+        dist_anchor = torch.cdist(pos_alpha, anchor_points, p=2).min(dim=1)[0]  # (L, )
         dist_anchor[~data["mask"].bool()] = float("+inf")
         initial_patch_idx = torch.topk(
             dist_anchor,
@@ -867,9 +888,8 @@ def _patch(self, data):
         )[
             1
         ]  # (initial_patch_size, )
-        patch_mask = data["masked_res"].clone()
-        patch_mask[start] = True
-        patch_mask[end] = True
+        patch_mask = data["masked_res"].bool().clone()
+        patch_mask[[int(x) for x in anchor_ind]] = True
         patch_mask[initial_patch_idx] = True
 
         if self.sabdab:
diff --git a/pyproject.toml b/pyproject.toml
index 2edb8d9..ec24b1c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "proteinflow"
-version = "2.2.0"
+version = "2.2.1"
 authors = [
     {name = "Liza Kozlova", email = "liza@adaptyvbio.com"},
     {name = "Arthur Valentin", email = "arthur@adaptyvbio.com"}