diff --git a/bin/mds_structures.py b/bin/mds_structures.py
index 9679d75..a2acd2b 100644
--- a/bin/mds_structures.py
+++ b/bin/mds_structures.py
@@ -69,9 +69,21 @@ def main():
     )
     logging.info(f"Computing TMscore on {len(fnames)} structures")
 
-    pdist_df = get_pairwise_tmscores(fnames, sctm_scores_json=args.sctm)
-    mds = MDS(n_components=2, dissimilarity="precomputed", n_jobs=-1, random_state=SEED)
-    embedding = pd.DataFrame(mds.fit_transform(pdist_df.values), index=pdist_df.index)
+    # in the dissimilarity matrix, larger values should indicate more distant points
+    # therefore we want to do 1 - TMscore (since larger values are closer in TMscore space)
+    pdist_df = 1.0 - get_pairwise_tmscores(fnames, sctm_scores_json=args.sctm)
+    mds = MDS(
+        n_components=2,
+        dissimilarity="precomputed",
+        metric=False,  # TMscores do not respect triangle inequality
+        n_jobs=-1,
+        random_state=SEED,
+    )
+    embedding = pd.DataFrame(
+        mds.fit_transform(pdist_df.values),
+        index=pdist_df.index,
+        columns=["MDS1", "MDS2"],
+    )
 
     format_strings = {
         "Number helices": "{x:.1f}",
@@ -79,17 +91,23 @@ def main():
     # For a variety of coloring keys, compute/read the scores and color scatter
     # plot by the scores.
     for k, v in {
-        "null": None,
         "Max training TM": args.trainingtm,
         "scTM": args.sctm,
-        "length": lambda x: len_pdb_structure(x),
+        "length": len_pdb_structure,
         "Number helices": lambda x: count_structures_in_pdb(x, "psea")[0],
         "Number sheets": lambda x: count_structures_in_pdb(x, "psea")[1],
+        "null": None,
     }.items():
         if v is None or v:
             logging.info(f"Coloring by {k} scores")
+            figsize = (6.4, 4.8)
+            annot_points = False
             if v is None:
                 scores = None
+                figsize = (12.8, 9.6)
+                annot_points = True
+                # If we are doing the null, the plot very big and label each
+                # point with the text id
             elif callable(v):
                 fname_to_key = lambda f: os.path.basename(f).split(".")[0]
                 scores = {
@@ -104,7 +122,7 @@ def main():
             else:
                 raise ValueError(f"Invalid value for {k}: {v}")
 
-            fig, ax = plt.subplots(dpi=300)
+            fig, ax = plt.subplots(figsize=figsize, dpi=300)
             points = ax.scatter(
                 embedding.iloc[:, 0],
                 embedding.iloc[:, 1],
@@ -113,6 +131,13 @@ def main():
                 cmap="RdYlBu",
                 alpha=0.9,
             )
+            if annot_points:
+                for i in range(len(embedding)):
+                    ax.annotate(
+                        embedding.index[i],
+                        (embedding.iloc[i, 0], embedding.iloc[i, 1]),
+                        fontsize=6,
+                    )
             ax.set(
                 xlabel="MDS 1",
                 ylabel="MDS 2",
@@ -130,7 +155,7 @@ def main():
                     fraction=0.08,
                     pad=0.04,
                     location="right",
-                    format=format_strings.get(k, None),
+                    # format=format_strings.get(k, None),
                 )
                 cbar.ax.set_ylabel(k, fontsize=12)