diff --git a/MindSPONGE/applications/model_cards/MEGAProtein.md b/MindSPONGE/applications/model_cards/MEGAProtein.md
index dd00ed33eb0f287bda059f5fede62a55abdff962..1d9e74e7ca62714f3ffa374548f5ff315eb01bab 100644
--- a/MindSPONGE/applications/model_cards/MEGAProtein.md
+++ b/MindSPONGE/applications/model_cards/MEGAProtein.md
@@ -102,8 +102,8 @@ msa_feature['decoy_aatype'] = np.pad(aatype, (0, 256 - aatype.shape[0]))
 msa_feature['decoy_atom_positions'] = np.pad(final_atom_positions, ((0, 256 - final_atom_positions.shape[0]), (0, 0), (0, 0)))
 msa_feature['decoy_atom_mask'] = np.pad(final_atom_mask, ((0, 256 - final_atom_mask.shape[0]), (0, 0)))
 
-res = protein_assessment.predict(msa_feature)
-print("score is:", np.mean(res))
+res = protein_assessment.model.predict(msa_feature)
+print("score is:", np.mean(res[:msa_feature['num_residues']]))
 ```
 
 ### 使用场景
diff --git a/tests/st/mindsponge/test_megaprotein/test_megaprotein.py b/tests/st/mindsponge/test_megaprotein/test_megaprotein.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8bce8d5f4a1e3567212a43b5aa5ddbe89bc35fa
--- /dev/null
+++ b/tests/st/mindsponge/test_megaprotein/test_megaprotein.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Test MEGAFoldProtein examples."""
+import numpy as np
+import mindspore as ms
+from mindsponge import PipeLine
+
+ms.set_context(mode=ms.GRAPH_MODE)
+
+# MEGA-EvoGen推理获取蛋白质生成MSA后的特征
+fasta = "GYDKDLCEWSMTADQTEVETQIEADIMNIVKRDRPEMKAEVQKQLKSGGVMQYNYVLYCDKNFNNKNIIAEVVGE"
+msa_generator = PipeLine(name="MEGAEvoGen")
+msa_generator.set_device_id(0)
+msa_generator.initialize(key="evogen_predict_256")
+msa_generator.model.from_pretrained()
+msa_feature = msa_generator.predict(fasta)
+
+# MEGA-Fold推理获取蛋白质结构信息
+fold_prediction = PipeLine(name="MEGAFold")
+fold_prediction.set_device_id(0)
+fold_prediction.initialize(key="predict_256")
+fold_prediction.model.from_pretrained()
+final_atom_positions, final_atom_mask, aatype, _, _ = fold_prediction.model.predict(msa_feature)
+
+# MEGA-Assessment对蛋白质结构进行评价
+protein_assessment = PipeLine(name="MEGAAssessment")
+protein_assessment.set_device_id(0)
+protein_assessment.initialize("predict_256")
+protein_assessment.model.from_pretrained()
+msa_feature['decoy_aatype'] = np.pad(aatype, (0, 256 - aatype.shape[0]))
+msa_feature['decoy_atom_positions'] = np.pad(final_atom_positions,
+                                             ((0, 256 - final_atom_positions.shape[0]), (0, 0), (0, 0)))
+msa_feature['decoy_atom_mask'] = np.pad(final_atom_mask, ((0, 256 - final_atom_mask.shape[0]), (0, 0)))
+
+res = protein_assessment.model.predict(msa_feature)
+print("score is:", np.mean(res[:msa_feature['num_residues']]))