revise readme and docs

README.md

@@ -1,5 +1,5 @@
 ---
-title: Evolutionary Scale Prediction Of Atomic Level Protein Structure
+title: Protein Structure Modeling
 emoji: 🌍
 colorFrom: yellow
 colorTo: purple

app.py

@@ -275,7 +275,7 @@ messages = [
 
     We have 120k proteins features stored in our database.
     
-    The app uses the [MyScale](MyScale Database) to store and query protein sequence
+    The app uses MyScale to store and query protein sequence
     using vector search.
     """
 ]
@@ -308,10 +308,14 @@ if 'xq' not in st.session_state:
         st.title("Evolutionary Scale Modeling")
         start = [st.empty(), st.empty(), st.empty(), st.empty(), st.empty(), st.empty(), st.empty()]      
         start[0].info(msg)
-        option = st.selectbox('Application options', ('self-contact prediction', 'search the database', 'activity prediction','PDB viewer'))
+        function_list = ('self-contact prediction',
+                         'search the database for similar proteins',
+                         'activity prediction with similar proteins',
+                         'PDB viewer')
+        option = st.selectbox('Application options', function_list)
 
         st.session_state.db_name_ref = 'default.esm_protein'
-        if option == 'self-contact prediction':
+        if option == function_list[0]:
             sequence = st.text_input('protein sequence', '')
             if st.button('Cas9 Enzyme'):
                 sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
@@ -327,7 +331,7 @@ if 'xq' not in st.session_state:
                 This methodology is based on ICLR 2021 paper, Transformer protein language models are unsupervised structure learners. 
                 (Rao et al. 2020) The MSA Transformer (ESM-MSA-1) takes a multiple sequence alignment (MSA) as input, and uses the tied row self-attention maps in the same way.""")
             st.session_state['xq'] = model
-        elif option == 'search the database':
+        elif option == function_list[1]:
             sequence = st.text_input('protein sequence', '')
             st.write('Try an example:')
             if st.button('Cas9 Enzyme'):
@@ -353,7 +357,7 @@ if 'xq' not in st.session_state:
 
                 start[2] = st.pyplot(visualize_3D_Coordinates(result_temp_coords).figure)
             st.session_state['xq'] = model
-        elif option == 'activity prediction':
+        elif option == function_list[2]:
             st.text('we predict the biological activity of mutations of a protein, using fixed embeddings from ESM.')
             sequence = st.text_input('protein sequence', '')
             st.write('Try an example:')
@@ -362,7 +366,7 @@ if 'xq' not in st.session_state:
             elif st.button('PETase'):
                 sequence = 'MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ'
             
-        elif option == 'PDB viewer':
+        elif option == function_list[3]:
             id_PDB = st.text_input('enter PDB ID', '')
             residues_marker = st.text_input('residues class', '')
             if residues_marker: