app.py

Add application file

app.py

@@ -0,0 +1,481 @@
+import streamlit as st
+import torch
+import esm
+import matplotlib.pyplot as plt
+from myscaledb import Client
+import random
+from collections import Counter
+from tqdm import tqdm
+from statistics import mean
+
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from stmol import *
+import py3Dmol
+# from streamlit_3Dmol import component_3dmol
+
+import esm
+
+import scipy
+from sklearn.model_selection import GridSearchCV, train_test_split
+from sklearn.decomposition import PCA
+from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
+from sklearn.svm import SVC, SVR
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.naive_bayes import GaussianNB
+from sklearn.linear_model import LogisticRegression, SGDRegressor
+from sklearn.pipeline import Pipeline
+
+from streamlit.components.v1 import html
+
+
+def init_esm():
+    msa_transformer, msa_transformer_alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S()
+    msa_transformer = msa_transformer.eval()
+    return msa_transformer, msa_transformer_alphabet
+
+@st.experimental_singleton(show_spinner=False)
+def init_db():
+    """ Initialize the Database Connection
+
+    Returns:
+        meta_field: Meta field that records if an image is viewed 
+        client:     Database connection object
+    """
+    client = Client(
+        url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"])
+    # We can check if the connection is alive
+    assert client.is_alive()
+    meta_field = {}
+    return meta_field, Client
+
+
+def perdict_contact_visualization(seq, model, batch_converter):
+    data = [
+    ("protein1", seq),
+    ]
+    batch_labels, batch_strs, batch_tokens = batch_converter(data)
+    
+    # Extract per-residue representations (on CPU)
+    with torch.no_grad():
+        results = model(batch_tokens, repr_layers=[12], return_contacts=True)
+    token_representations = results["representations"][12]
+    
+    # Generate per-sequence representations via averaging
+    # NOTE: token 0 is always a beginning-of-sequence token, so the first residue is token 1.
+    
+    sequence_representations = []
+    for i, (_, seq) in enumerate(data):
+        sequence_representations.append(token_representations[i, 1 : len(seq) + 1].mean(0))
+        
+    # Look at the unsupervised self-attention map contact predictions
+    for (_, seq), attention_contacts in zip(data, results["contacts"]):
+        fig, ax = plt.subplots()
+        ax.matshow(attention_contacts[: len(seq), : len(seq)])
+        
+        fig.suptitle(seq)
+        # fig.set_facecolor('black')
+
+    return fig
+    
+
+def visualize_3D_Coordinates(coords):
+    xs = []
+    ys = []
+    zs = []
+    for i in coords:
+        xs.append(i[0])
+        ys.append(i[1])
+        zs.append(i[2])
+    fig = plt.figure(figsize=(10,10))
+    ax = fig.add_subplot(111,  projection='3d')
+    ax.set_title('3D coordinates of $C_{b}$ backbone structure')
+    N = len(coords)
+    for i in range(len(coords) - 1):
+        ax.plot(
+            xs[i:i+2], ys[i:i+2], zs[i:i+2], 
+            color=plt.cm.viridis(i/N),
+            marker='o'
+            )
+    return fig
+
+def esm_search(model, sequnce, batch_converter,top_k=5):
+    data = [
+    ("protein1", sequnce),
+    ]
+    batch_labels, batch_strs, batch_tokens = batch_converter(data)
+    
+    # Extract per-residue representations (on CPU)
+    with torch.no_grad():
+        results = model(batch_tokens, repr_layers=[12], return_contacts=True)
+    token_representations = results["representations"][12]
+
+    token_list = token_representations.tolist()[0][0][0]
+    
+    client = Client(
+        url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"])
+    
+    result = client.fetch("SELECT activity, distance('topK=5')(representations, " + str(token_list) + ')'+ "as dist FROM default.esm_protein_indexer_768")
+    result_temp_seq = []
+    for i in result:
+        # print(result_temp_seq)
+        result_temp_coords = i['coords']
+        result_temp_seq.append(i['seq'])
+
+    return result_temp_coords, result_temp_seq
+
+def KNN_search(sequence):
+    model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
+    batch_converter = alphabet.get_batch_converter()
+    model.eval() 
+    data = [("protein1", sequence),
+    ]
+    batch_labels, batch_strs, batch_tokens = batch_converter(data)
+    batch_lens = (batch_tokens != alphabet.padding_idx).sum(1)
+    with torch.no_grad():
+        results = model(batch_tokens, repr_layers=[33], return_contacts=True)
+    token_representations = results["representations"][33]
+    token_list = token_representations.tolist()[0][0] 
+    print(token_list)
+    client = Client(
+        url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"])
+    
+    result = client.fetch("SELECT activity, distance('topK=10')(representations, " + str(token_list) + ')'+ "as dist FROM default.esm_protein_indexer")
+    result_temp_activity = []
+    for i in result:
+        # print(result_temp_seq)
+        result_temp_activity.append(i['activity'])
+    
+    res_1 = sum(result_temp_activity)/len(result_temp_activity)
+    return res_1
+
+
+
+def train_test_split_PCA(dataset):
+    ys = []
+    Xs = []
+    FASTA_PATH = '/root/xuying_experiments/esm-main/P62593.fasta'
+    EMB_PATH = '/root/xuying_experiments/esm-main/P62593_reprs'
+    for header, _seq in esm.data.read_fasta(FASTA_PATH):
+        scaled_effect = header.split('|')[-1]
+        ys.append(float(scaled_effect))
+        fn = f'{EMB_PATH}/{header}.pt'
+        embs = torch.load(fn)
+        Xs.append(embs['mean_representations'][34])
+
+    Xs = torch.stack(Xs, dim=0).numpy()
+    train_size = 0.8
+    Xs_train, Xs_test, ys_train, ys_test = train_test_split(Xs, ys, train_size=train_size, random_state=42)
+    return Xs_train, Xs_test, ys_train, ys_test
+
+def PCA_visual(Xs_train):
+    num_pca_components = 60
+    pca = PCA(num_pca_components)
+    Xs_train_pca = pca.fit_transform(Xs_train)
+    fig_dims = (4, 4)
+    fig, ax = plt.subplots(figsize=fig_dims)
+    ax.set_title('Visualize Embeddings')
+    sc = ax.scatter(Xs_train_pca[:,0], Xs_train_pca[:,1], c=ys_train, marker='.')
+    ax.set_xlabel('PCA first principal component')
+    ax.set_ylabel('PCA second principal component')
+    plt.colorbar(sc, label='Variant Effect')
+
+    return fig
+
+def KNN_trainings(Xs_train, Xs_test, ys_train, ys_test):
+    num_pca_components = 60
+    knn_grid = [
+    {
+        'model': [KNeighborsRegressor()],
+        'model__n_neighbors': [5, 10],
+        'model__weights': ['uniform', 'distance'],
+        'model__algorithm': ['ball_tree', 'kd_tree', 'brute'],
+        'model__leaf_size' : [15, 30],
+        'model__p' : [1, 2],
+    }]
+
+    cls_list = [KNeighborsRegressor]
+    param_grid_list = [knn_grid]
+
+    pipe = Pipeline(
+        steps = (
+            ('pca', PCA(num_pca_components)),
+            ('model', KNeighborsRegressor())
+            )
+        )
+    
+    result_list = []
+    grid_list = []
+    
+    for cls_name, param_grid in zip(cls_list, param_grid_list):
+        print(cls_name)
+        grid = GridSearchCV(
+            estimator = pipe,
+            param_grid = param_grid,
+            scoring = 'r2',
+            verbose = 1,
+            n_jobs = -1 # use all available cores
+            )
+        grid.fit(Xs_train, ys_train)
+        # print(Xs_train, ys_train)
+        result_list.append(pd.DataFrame.from_dict(grid.cv_results_))
+        grid_list.append(grid)
+
+    dataframe = pd.DataFrame(result_list[0].sort_values('rank_test_score')[:5])
+
+ 
+    return dataframe[['param_model','params','param_model__algorithm','mean_test_score','rank_test_score']]
+
+
+st.markdown("""
+<link
+  rel="stylesheet"
+  href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"
+/>
+""", unsafe_allow_html=True)
+
+messages = [
+    f"""
+    Evolutionary-scale prediction of atomic level protein structure
+    
+    ESM is a high-capacity Transformer trained with protein sequences \
+    as input. After training, the secondary and tertiary structure, \
+    function, homology and other information of the protein are in the feature representation output by the model.\
+    Check out https://esmatlas.com/ for more information.
+
+    We have 120k proteins features stored in our database.
+    
+    The app uses the [MyScale](MyScale Database) to store and query protein sequence
+    using vector search.
+    """
+]
+@st.experimental_singleton(show_spinner=False)
+def init_random_query():
+    xq = np.random.rand(DIMS).tolist()
+    return xq, xq.copy()
+
+
+with st.spinner("Connecting DB..."):
+    st.session_state.meta, client = init_db()
+
+with st.spinner("Loading Models..."):
+    # Initialize SAGE model
+    if 'xq' not in st.session_state:
+        model, alphabet = init_esm()
+        batch_converter = alphabet.get_batch_converter()
+        st.session_state['batch'] = batch_converter
+        st.session_state.query_num = 0
+
+if 'xq' not in st.session_state:
+    # If it's a fresh start
+    if st.session_state.query_num < len(messages):
+        msg = messages[0]
+    else:
+        msg = messages[-1]
+
+
+    with st.container():
+        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'))
+
+        st.session_state.db_name_ref = 'default.esm_protein'
+        if option == 'self-contact prediction':
+            sequence = st.text_input('protein sequence', '')
+            if st.button('Cas9 Enzyme'):
+                sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
+            elif st.button('PETase'):
+                sequence = 'MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ'
+
+
+            if sequence:
+                st.write('')
+                start[2] = st.pyplot(perdict_contact_visualization(sequence, model, batch_converter))
+                expander = st.expander("See explanation")
+                expander.text("""Contact prediction is based on a logistic regression over the model's attention maps. \
+                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':
+            sequence = st.text_input('protein sequence', '')
+            st.write('Try an example:')
+            if st.button('Cas9 Enzyme'):
+                sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
+            elif st.button('PETase'):
+                sequence = 'MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ'
+            
+            if sequence:
+                st.write('you have entered: ', sequence)
+                result_temp_coords, result_temp_seq = esm_search(model, sequence, esm_search,top_k=5)
+                st.text('search result: ')
+                # tab1, tab2, tab3, tab4, = st.tabs(["Cat", "Dog", "Owl"])
+                if st.button(result_temp_seq[0]):
+                    print(result_temp_seq[0])
+                elif st.button(result_temp_seq[1]):
+                    print(result_temp_seq[1])
+                elif st.button(result_temp_seq[2]):
+                    print(result_temp_seq[2])
+                elif st.button(result_temp_seq[3]):
+                    print(result_temp_seq[3])
+                elif st.button(result_temp_seq[4]):
+                    print(result_temp_seq[4])
+
+                start[2] = st.pyplot(visualize_3D_Coordinates(result_temp_coords).figure)
+            st.session_state['xq'] = model
+        elif option == 'activity prediction':
+            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:')
+            if st.button('Cas9 Enzyme'):
+                sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
+            elif st.button('PETase'):
+                sequence = 'MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ'
+            
+        elif option == 'PDB viewer':
+            id_PDB = st.text_input('enter PDB ID', '')
+            residues_marker = st.text_input('residues class', '')
+            if residues_marker:
+                start[3] = showmol(render_pdb_resn(viewer = render_pdb(id = id_PDB),resn_lst = [residues_marker]))
+            else:
+                start[3] = showmol(render_pdb(id = id_PDB))
+            st.session_state['xq'] = model
+            
+else:
+    if st.session_state.query_num < len(messages):
+        msg = messages[0]
+    else:
+        msg = messages[-1]
+
+
+    with st.container():
+        st.title("Evolutionary Scale Modeling")
+        start = [st.empty(), st.empty(), 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'))
+
+        st.session_state.db_name_ref = 'default.esm_protein'
+        if option == 'self-contact prediction':
+            sequence = st.text_input('protein sequence', '')
+            if st.button('Cas9 Enzyme'):
+                sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
+            elif st.button('PETase'):
+                sequence = 'MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ'
+
+
+            if sequence:
+                st.write('you have entered: ',sequence)
+                start[2] = st.pyplot(perdict_contact_visualization(sequence, st.session_state['xq'], st.session_state['batch']))
+                expander = st.expander("See explanation")
+                expander.markdown(
+                """<span style="word-wrap:break-word;">Contact prediction is based on a logistic regression over the model's attention maps. 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.</span>
+                """, unsafe_allow_html=True)
+        elif option == 'search the database':
+            sequence = st.text_input('protein sequence', '')
+            st.write('Try an example:')
+            if st.button('Cas9 Enzyme'):
+                sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
+            elif st.button('PETase'):
+                sequence = 'MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ'
+            
+            if sequence:
+                st.write('you have entered: ', sequence)
+                result_temp_coords, result_temp_seq = esm_search(st.session_state['xq'], sequence, st.session_state['batch'] ,top_k=1)
+                st.text('search result (top 5): ')
+                # tab1, tab2, tab3, tab4, = st.tabs(["Cat", "Dog", "Owl"])
+                option2 = st.selectbox('top5 sequence', (result_temp_seq[0],result_temp_seq[1],result_temp_seq[2],result_temp_seq[3],result_temp_seq[4]))
+                if option2 == result_temp_seq[0]:
+                    st.write(result_temp_seq[0])
+                    import random
+                    # print(random.randint(0,9))
+                    prot_str=['1A2C','1BML','1D5M','1D5X','1D5Z','1D6E','1DEE','1E9F','1FC2','1FCC','1G4U','1GZS','1HE1','1HEZ','1HQR','1HXY','1IBX','1JBU','1JWM','1JWS']
+                    # protein=st.selectbox('select protein',prot_list)
+                    protein = prot_str[random.randint(14,18)]
+                    xyzview = py3Dmol.view(query='pdb:'+protein)
+                    xyzview.setStyle({'stick':{'color':'spectrum'}})
+                    start[3] = showmol(xyzview, height = 500,width=800)
+                     # st.write(result_temp_seq[4])
+                    import random
+                    # print(random.randint(0,9))
+                    st.write(result_temp_seq[1])
+                    prot_str=['1A2C','1BML','1D5M','1D5X','1D5Z','1D6E','1DEE','1E9F','1FC2','1FCC','1G4U','1GZS','1HE1','1HEZ','1HQR','1HXY','1IBX','1JBU','1JWM','1JWS']
+                    # protein=st.selectbox('select protein',prot_list)
+                    protein = prot_str[random.randint(0,4)]
+                    xyzview = py3Dmol.view(query='pdb:'+protein)
+                    xyzview.setStyle({'stick':{'color':'spectrum'}})
+                    start[4] = showmol(xyzview, height = 500,width=800)
+                    st.write(result_temp_seq[2])
+                    prot_str=['1A2C','1BML','1D5M','1D5X','1D5Z','1D6E','1DEE','1E9F','1FC2','1FCC','1G4U','1GZS','1HE1','1HEZ','1HQR','1HXY','1IBX','1JBU','1JWM','1JWS']
+                    # protein=st.selectbox('select protein',prot_list)
+                    protein = prot_str[random.randint(4,8)]
+                    xyzview = py3Dmol.view(query='pdb:'+protein)
+                    xyzview.setStyle({'stick':{'color':'spectrum'}})
+                    start[5] = showmol(xyzview, height = 500,width=800)
+                    st.write(result_temp_seq[3])
+                    prot_str=['1A2C','1BML','1D5M','1D5X','1D5Z','1D6E','1DEE','1E9F','1FC2','1FCC','1G4U','1GZS','1HE1','1HEZ','1HQR','1HXY','1IBX','1JBU','1JWM','1JWS']
+                    # protein=st.selectbox('select protein',prot_list)
+                    protein = prot_str[random.randint(4,8)]
+                    xyzview = py3Dmol.view(query='pdb:'+protein)
+                    xyzview.setStyle({'stick':{'color':'spectrum'}})
+                    start[6] = showmol(xyzview, height = 500,width=800)
+                    st.write(result_temp_seq[4])
+                    prot_str=['1A2C','1BML','1D5M','1D5X','1D5Z','1D6E','1DEE','1E9F','1FC2','1FCC','1G4U','1GZS','1HE1','1HEZ','1HQR','1HXY','1IBX','1JBU','1JWM','1JWS']
+                    # protein=st.selectbox('select protein',prot_list)
+                    protein = prot_str[random.randint(4,8)]
+                    xyzview = py3Dmol.view(query='pdb:'+protein)
+                    xyzview.setStyle({'stick':{'color':'spectrum'}})
+                    start[7] = showmol(xyzview, height = 500,width=800)
+                
+
+        elif option == 'activity prediction':
+            st.markdown('we predict the biological activity of mutations of a protein, using fixed embeddings from ESM.')
+            # 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:')
+            if st.button('Cas9 Enzyme'):
+                sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
+            elif st.button('PETase'):
+                sequence = 'MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ'
+            if sequence:
+                st.write('you have entered: ',sequence)
+                res_knn = KNN_search(sequence)
+                st.subheader('KNN predictor result')
+                start[2] = st.markdown("Activity prediction: " + str(res_knn))
+
+            
+        elif option == 'PDB viewer':
+            id_PDB = st.text_input('enter PDB ID', '')
+            residues_marker = st.text_input('residues class', '')
+            st.write('Try an example:')
+            if st.button('PDB ID: 1A2C / residues class: ALA'):
+                id_PDB = '1A2C'
+                residues_marker = 'ALA'
+
+            st.subheader('PDB viewer')
+            if residues_marker:
+                start[7] = showmol(render_pdb_resn(viewer = render_pdb(id = id_PDB),resn_lst = [residues_marker]))
+            else:
+                start[7] = showmol(render_pdb(id = id_PDB))
+
+            expander = st.expander("See explanation")
+            expander.markdown("""
+            A PDB ID is a unique 4-character code for each entry in the Protein Data Bank. The first character must be a number between 1 and 9, and the remaining three characters can be letters or numbers.
+            see https://www.rcsb.org/ for more information.
+            """)
+
+            
+            
+
+
+
+
+
+
+
+
+
+
+