diff --git a/deepfrier/Predictor.py b/deepfrier/Predictor.py
index 3381d73..83500ad 100644
--- a/deepfrier/Predictor.py
+++ b/deepfrier/Predictor.py
@@ -54,6 +54,8 @@ def __init__(self, model_prefix, gcn=True):
         self.model_prefix = model_prefix
         self.gcn = gcn
         self._load_model()
+        self.prot2goterms = {}
+        self.goidx2chains = {}
 
     def _load_model(self):
         self.model = tf.keras.models.load_model(self.model_prefix + '.hdf5',
@@ -130,6 +132,44 @@ def predict(self, test_prot, cmap_thresh=10.0, chain='query_prot'):
                 self.goidx2chains[idx].add(chain)
                 self.prot2goterms[chain].append((self.goterms[idx], self.gonames[idx], float(y[idx])))
 
+    def predict_with_cmap(self, seqres, cmap, chain):
+        self.Y_hat = np.zeros((1, len(self.goterms)), dtype=float)
+        self.data = {}
+        self.test_prot_list = [chain]
+        if self.gcn:
+            S = seq2onehot(seqres)
+            S = S.reshape(1, *S.shape)
+            A = cmap
+            y = self.model([A, S], training=False).numpy()[:, :, 0].reshape(-1)
+            self.Y_hat[0] = y
+            self.prot2goterms[chain] = []
+            self.data[chain] = [[A, S], seqres]
+            go_idx = np.where((y >= self.thresh) == True)[0]
+            for idx in go_idx:
+                if idx not in self.goidx2chains:
+                    self.goidx2chains[idx] = set()
+                self.goidx2chains[idx].add(chain)
+                self.prot2goterms[chain].append((self.goterms[idx], self.gonames[idx], float(y[idx])))
+
+    def predict_from_sequence(self, sequence, chain):
+        self.test_prot_list = [chain]
+        self.Y_hat = np.zeros((len(self.test_prot_list), len(self.goterms)), dtype=float)
+        self.data = {}
+
+        for i, chain in enumerate(self.test_prot_list):
+            S = seq2onehot(str(sequence))
+            S = S.reshape(1, *S.shape)
+            y = self.model(S, training=False).numpy()[:, :, 0].reshape(-1)
+            self.Y_hat[i] = y
+            self.prot2goterms[chain] = []
+            self.data[chain] = [[S], sequence]
+            go_idx = np.where((y >= self.thresh) == True)[0]
+            for idx in go_idx:
+                if idx not in self.goidx2chains:
+                    self.goidx2chains[idx] = set()
+                self.goidx2chains[idx].add(chain)
+                self.prot2goterms[chain].append((self.goterms[idx], self.gonames[idx], float(y[idx])))
+
     def predict_from_PDB_dir(self, dir_name, cmap_thresh=10.0):
         print ("### Computing predictions from directory with PDB files...")
         pdb_fn_list = glob.glob(dir_name + '/*.pdb*')