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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Anton Bushuiev
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,14 +1,9 @@
 ---
-title: PPIformer CPU
-emoji: 🐢
-colorFrom: blue
-colorTo: blue
+title: PPIformer
+emoji: 🔬
+colorFrom: pink
+colorTo: green
 sdk: gradio
-sdk_version: 5.3.0
 app_file: app.py
-pinned: false
-license: mit
-short_description: Learning to design protein-protein interactions with enhance
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+pinned: true
+---

app.py

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+# print("""
+#  __  __    _    ___ _   _ _____ _____ _   _    _    _   _  ____ _____ 
+# |  \/  |  / \  |_ _| \ | |_   _| ____| \ | |  / \  | \ | |/ ___| ____|
+# | |\/| | / _ \  | ||  \| | | | |  _| |  \| | / _ \ |  \| | |   |  _|  
+# | |  | |/ ___ \ | || |\  | | | | |___| |\  |/ ___ \| |\  | |___| |___ 
+# |_|  |_/_/   \_\___|_| \_| |_| |_____|_| \_/_/   \_\_| \_|\____|_____|
+                                                                      
+#                  ____  ____  _____    _    _  __
+#                 | __ )|  _ \| ____|  / \  | |/ /
+#                 |  _ \| |_) |  _|   / _ \ | ' / 
+#                 | |_) |  _ <| |___ / ___ \| . \ 
+#                 |____/|_| \_\_____/_/   \_\_|\_\
+# """)
+import os
+# os.system("pip uninstall -y gradio")
+# os.system("pip install gradio==3.50.2")
+# os.system("pip uninstall -y spaces")
+# os.system("pip install spaces==0.8")
+os.system("pip uninstall -y torch")
+os.system("pip install torch==2.0.1")
+
+import sys
+import copy
+import random
+import tempfile
+import shutil
+import logging
+from pathlib import Path
+from functools import partial
+
+import spaces
+import gradio as gr
+import torch
+import numpy as np
+import pandas as pd
+from Bio.PDB.Polypeptide import protein_letters_3to1
+from biopandas.pdb import PandasPdb
+from colour import Color
+from colour import RGB_TO_COLOR_NAMES
+
+from mutils.proteins import AMINO_ACID_CODES_1
+from mutils.pdb import download_pdb
+from mutils.mutations import Mutation
+from ppiref.extraction import PPIExtractor
+from ppiref.utils.ppi import PPIPath
+from ppiref.utils.residue import Residue
+from ppiformer.tasks.node import DDGPPIformer
+from ppiformer.utils.api import download_from_zenodo
+from ppiformer.utils.api import predict_ddg as predict_ddg_
+from ppiformer.utils.torch import fill_diagonal
+from ppiformer.definitions import PPIFORMER_WEIGHTS_DIR
+
+
+import pkg_resources
+import sys
+
+def print_package_versions():
+    installed_packages = sorted([f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set])
+    print("Installed packages and their versions:")
+    for package in installed_packages:
+        print(package)
+
+    print("\nPython version:")
+    print(sys.version)
+
+print_package_versions()
+
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[logging.StreamHandler(sys.stdout)]
+)
+
+random.seed(0)
+
+
+@spaces.GPU
+def predict_ddg(models, ppi, muts, return_attn):
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print(f"[INFO] Device on prediction: {device}")
+    models = [model.to(device) for model in models]
+    if return_attn:
+        ddg_pred, attns = predict_ddg_(models, ppi, muts, return_attn=return_attn)
+        return ddg_pred.detach().cpu(), attns.detach().cpu()
+    else:
+        ddg_pred = predict_ddg_(models, ppi, muts, return_attn=return_attn)
+        return ddg_pred.detach().cpu()
+
+
+def process_inputs(inputs, temp_dir):
+    pdb_code, pdb_path, partners, muts, muts_path = inputs
+
+    # Check inputs
+    if not pdb_code and not pdb_path:
+        raise gr.Error("PPI structure not specified.")
+    
+    if pdb_code and pdb_path:
+        gr.Warning("Both PDB code and PDB file specified. Using PDB file.")
+
+    if not partners:
+        raise gr.Error("Partners not specified.")
+
+    if not muts and not muts_path:
+        raise gr.Error("Mutations not specified.")
+    
+    if muts and muts_path:
+        gr.Warning("Both mutations and mutations file specified. Using mutations file.")
+
+    # Prepare PDB input
+    if pdb_path:
+        # convert file name to PPIRef format
+        new_pdb_path = temp_dir / f"pdb/{pdb_path.name.replace('_', '-')}"
+        new_pdb_path.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy(str(pdb_path), str(new_pdb_path))
+        pdb_path = new_pdb_path
+        pdb_path = Path(pdb_path)
+    else:
+        try:
+            pdb_code = pdb_code.strip().lower()
+            pdb_path = temp_dir / f'pdb/{pdb_code}.pdb'
+            download_pdb(pdb_code, path=pdb_path)
+        except:
+            raise gr.Error("PDB download failed.")
+
+    # Parse partners
+    partners = list(map(lambda x: x.strip(), partners.split(',')))
+
+    # Add partners to file name    
+    pdb_path = pdb_path.rename(pdb_path.with_stem(f"{pdb_path.stem}-{'-'.join(partners)}"))
+
+    # Extract PPI into temp dir
+    try:
+        ppi_dir = temp_dir / 'ppi'
+        extractor = PPIExtractor(out_dir=ppi_dir, nest_out_dir=True, join=True, radius=10.0)
+        extractor.extract(pdb_path, partners=partners)
+        ppi_path = PPIPath.construct(ppi_dir, pdb_path.stem, partners)
+    except:
+        raise gr.Error("PPI extraction failed.")
+
+    # Prepare mutations input
+    if muts_path:
+        muts_path = Path(muts_path)
+        muts = muts_path.read_text()
+    
+    # Check mutations
+        
+    # Basic format
+    try:
+        muts = [Mutation.from_str(m) for m in muts.strip().split(';') if m.strip()]
+    except Exception as e:
+        raise gr.Error(f'Mutations parsing failed: {e}')
+    
+    # Partners
+    for mut in muts:
+        for pmut in mut.muts:
+            if pmut.chain not in partners:
+                raise gr.Error(f'Chain of point mutation {pmut} is not in the list of partners {partners}.')
+    
+    # Consistency with provided .pdb
+    muts_on_interface = []
+    for mut in muts:
+        if mut.wt_in_pdb(ppi_path):
+            val = True
+        elif mut.wt_in_pdb(pdb_path):
+            val = False
+        else:
+            raise gr.Error(f'Wild-type of mutation {mut} is not in the provided .pdb file.')
+        muts_on_interface.append(val)
+
+    muts = [str(m) for m in muts]
+
+    return pdb_path, ppi_path, muts, muts_on_interface
+
+
+def plot_3dmol(pdb_path, ppi_path, mut, attn, attn_mut_id=0):
+    # NOTE 3DMol.js adapted from https://huggingface.co/spaces/huhlim/cg2all/blob/main/app.py
+
+    # Read PDB for 3Dmol.js
+    with open(pdb_path, "r") as fp:
+        lines = fp.readlines()
+    mol = ""
+    for l in lines:
+        mol += l
+    mol = mol.replace("OT1", "O  ")
+    mol = mol.replace("OT2", "OXT")
+
+    # Read PPI to customize 3Dmol.js visualization
+    ppi_df = PandasPdb().read_pdb(ppi_path).df['ATOM']
+    ppi_df = ppi_df.groupby(list(Residue._fields)).apply(lambda df: df[df['atom_name'] == 'CA'].iloc[0]).reset_index(drop=True)
+    ppi_df['id'] = ppi_df.apply(lambda row: ':'.join([row['residue_name'], row['chain_id'], str(row['residue_number']), row['insertion']]), axis=1)
+    ppi_df['id'] = ppi_df['id'].apply(lambda x: x[:-1] if x[-1] == ':' else x)
+    muts_id = Mutation.from_str(mut).wt_to_graphein()  # flatten ids of all sp muts 
+    ppi_df['mutated'] = ppi_df.apply(lambda row: row['id'] in muts_id, axis=1)
+
+    # Prepare attention coeffictients per residue (normalized sum of direct attention from mutated residues)
+    attn = torch.nan_to_num(attn, nan=1e-10)
+    attn_sub = attn[:, attn_mut_id, 0, :, 0, :, :, :]  # models, layers, heads, tokens, tokens
+    idx_mutated = torch.from_numpy(ppi_df.index[ppi_df['mutated']].to_numpy())
+    attn_sub = fill_diagonal(attn_sub, 1e-10)
+    attn_mutated = attn_sub[..., idx_mutated, :]
+    attn_mutated.shape
+    attns_per_token = torch.sum(attn_mutated, dim=(0, 1, 2, 3))
+    attns_per_token = (attns_per_token - attns_per_token.min()) / (attns_per_token.max() - attns_per_token.min())
+    attns_per_token += 1e-10
+    ppi_df['attn'] = attns_per_token.numpy()
+
+    chains = ppi_df.sort_values('attn', ascending=False)['chain_id'].unique()
+
+    # Customize 3Dmol.js visualization https://3dmol.csb.pitt.edu/doc/
+    styles = []
+    zoom_atoms = []
+
+    # Cartoon chains
+    preferred_colors = ['LimeGreen', 'HotPink', 'RoyalBlue']
+    all_colors = [c[0] for c in RGB_TO_COLOR_NAMES.values()]
+    all_colors = [c for c in all_colors if c not in preferred_colors + ['Black', 'White']]
+    random.shuffle(all_colors)
+    all_colors = preferred_colors + all_colors
+    all_colors = [Color(c) for c in all_colors]
+    chain_to_color = dict(zip(chains, all_colors))
+    for chain in chains:
+        styles.append([{"chain": chain}, {"cartoon": {"color": chain_to_color[chain].hex_l, "opacity": 0.6}}])
+
+    # Stick PPI and atoms for zoom
+    # TODO Insertions
+    for _, row in ppi_df.iterrows():
+        color = copy.deepcopy(chain_to_color[row['chain_id']])
+        color.saturation = row['attn']
+        color = color.hex_l
+        if row['mutated']:
+            styles.append([
+                {'chain': row['chain_id'], 'resi': str(row['residue_number'])},
+                {'stick': {'color': 'red', 'radius': 0.2, 'opacity': 1.0}}
+            ])
+            zoom_atoms.append(row['atom_number'])
+        else:
+            styles.append([
+                {'chain': row['chain_id'], 'resi': str(row['residue_number'])},
+                {'stick': {'color': color, 'radius': row['attn'] / 5, 'opacity': row['attn']}}
+            ])
+
+    # Convert style dicts to JS lines
+    styles = ''.join(['viewer.addStyle(' + ', '.join([str(s).replace("'", '"') for s in dcts]) + ');\n' for dcts in styles])
+
+    # Convert zoom atoms to 3DMol.js selection and add labels for mutated residues
+    zoom_animation_duration = 500
+    sel = '{\"or\": [' + ', '.join(["{\"serial\": " + str(a) + "}" for a in zoom_atoms]) + ']}'
+    zoom = 'viewer.zoomTo(' + sel + ',' + f'{zoom_animation_duration});'
+    for atom in zoom_atoms:
+        sel = '{\"serial\": ' + str(atom) + '}'
+        row = ppi_df[ppi_df['atom_number'] == atom].iloc[0]
+        label = protein_letters_3to1[row['residue_name']] + row['chain_id'] + str(row['residue_number']) + row['insertion']
+        styles += 'viewer.addLabel(' + f"\"{label}\"," + "{fontSize:16, fontColor:\"red\", backgroundOpacity: 0.0}," + sel + ');\n'
+
+    # Construct 3Dmol.js visualization script embedded in HTML
+    html = (
+        """<!DOCTYPE html>
+        <html>
+        <head>    
+    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+    <style>
+    body{
+        font-family:sans-serif
+    }
+    .mol-container {
+    width: 100%;
+    height: 600px;
+    position: relative;
+    }
+    .mol-container select{
+        background-image:None;
+    }
+    </style>
+     <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js" integrity="sha512-STof4xm1wgkfm7heWqFJVn58Hm3EtS31XFaagaa8VMReCXAkQnJZ+jEy8PCC/iT18dFy95WcExNHFTqLyp72eQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
+    <script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
+    </head>
+    <body>  
+    <div id="container" class="mol-container"></div>
+  
+            <script>
+               let pdb = `"""
+        + mol
+        + """`  
+      
+             $(document).ready(function () {
+                let element = $("#container");
+                let config = { backgroundColor: "white" };
+                let viewer = $3Dmol.createViewer(element, config);
+                viewer.addModel(pdb, "pdb");
+                viewer.setStyle({"model": 0}, {"ray_opaque_background": "off"}, {"stick": {"color": "lightgrey", "opacity": 0.5}});
+          """
+        + styles
+        + zoom
+        + """
+                viewer.render();
+              })
+        </script>
+        </body></html>"""
+    )
+
+    return f"""<iframe style="width: 100%; height: 600px" name="result" allow="midi; geolocation; microphone; camera; 
+    display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
+    allow-scripts allow-same-origin allow-popups 
+    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
+    allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>"""
+
+
+def predict(models, temp_dir, *inputs):
+    logging.info('Starting prediction')
+
+    # Process input
+    pdb_path, ppi_path, muts, muts_on_interface = process_inputs(inputs, temp_dir)
+
+    # Create dataframe
+    df = pd.DataFrame({
+        'Mutation': muts,
+        'ddG [kcal/mol]': len(muts) * [np.nan],
+        '10A Interface': muts_on_interface,
+        'Attn Id': len(muts) * [np.nan],
+    })
+
+    # Show warning if some mutations are not on the interface
+    muts_not_on_interface = df[~df['10A Interface']]['Mutation'].tolist()
+    n_muts_not_on_interface = len(muts_not_on_interface)
+    if n_muts_not_on_interface:
+        n_muts_warn = 5
+        muts_not_on_interface = ';'.join(muts_not_on_interface[:n_muts_warn])
+        if n_muts_not_on_interface > n_muts_warn:
+            muts_not_on_interface += f'... (and {n_muts_not_on_interface - n_muts_warn} more)'
+        gr.Warning((
+            f"{muts_not_on_interface} {'is' if n_muts_not_on_interface == 1 else 'are'} not on the interface. "
+            f"The model will predict the effect{'s' if n_muts_not_on_interface > 1 else ''} of "
+            f"mutation{'s' if n_muts_not_on_interface > 1 else ''} on the whole complex. "
+            f"This may lead to less accurate predictions."
+        ))
+
+    logging.info('Inputs processed')
+    
+    # Predict using interface for mutations on the interface and using the whole complex otherwise
+    attn_ppi, attn_pdb = None, None
+    for df_sub, path in [
+        [df[df['10A Interface']], ppi_path],
+        [df[~df['10A Interface']], pdb_path]
+    ]:
+        if not len(df_sub):
+            continue
+
+        # Predict
+        try:
+            ddg, attn = predict_ddg(models, path, df_sub['Mutation'].tolist(), return_attn=True)
+        except Exception as e:
+            print(f"Prediction failed. {str(e)}")
+            raise gr.Error(f"Prediction failed. {str(e)}")
+        ddg = ddg.detach().numpy().tolist()
+
+        logging.info(f'Predictions made for {path}')
+
+        # Update dataframe and attention tensor
+        idx = df_sub.index
+        df.loc[idx, 'ddG [kcal/mol]'] = ddg
+        df.loc[idx, 'Attn Id'] = np.arange(len(idx))
+
+        if path == ppi_path:
+            attn_ppi = attn
+        else:
+            attn_pdb = attn
+    df['Attn Id'] = df['Attn Id'].astype(int)
+
+    # Round ddG values
+    df['ddG [kcal/mol]'] = df['ddG [kcal/mol]'].round(3)
+
+    # Create PPI-specific dropdown
+    dropdown = gr.Dropdown(
+        df['Mutation'].tolist(), value=df['Mutation'].iloc[0],
+        interactive=True,  visible=True, label="Mutation to visualize",
+    )
+
+    # Predefine plot arguments for all dropdown choices
+    dropdown_choices_to_plot_args = {
+        mut: (
+            pdb_path, 
+            ppi_path if df[df['Mutation'] == mut]['10A Interface'].iloc[0] else pdb_path, 
+            mut, 
+            attn_ppi if df[df['Mutation'] == mut]['10A Interface'].iloc[0] else attn_pdb,
+            df[df['Mutation'] == mut]['Attn Id'].iloc[0]
+        )
+        for mut in df['Mutation']
+    }
+
+    # Create dataframe file
+    path = 'ppiformer_ddg_predictions.csv'
+    if n_muts_not_on_interface:
+        df = df[['Mutation', 'ddG [kcal/mol]', '10A Interface']]
+        df.to_csv(path, index=False)
+        df = gr.Dataframe(
+            value=df,
+            headers=['Mutation', 'ddG [kcal/mol]', '10A Interface'],
+            datatype=['str', 'number', 'bool'],
+            col_count=(3, 'fixed'),
+        )
+    else:
+        df = df[['Mutation', 'ddG [kcal/mol]']]
+        df.to_csv(path, index=False)
+        df = gr.Dataframe(
+            value=df,
+            headers=['Mutation', 'ddG [kcal/mol]'],
+            datatype=['str', 'number'],
+            col_count=(2, 'fixed'),
+        )
+
+    logging.info('Prediction results prepared')
+
+    return df, path, dropdown, dropdown_choices_to_plot_args
+
+
+def update_plot(dropdown, dropdown_choices_to_plot_args):
+    return plot_3dmol(*dropdown_choices_to_plot_args[dropdown])
+
+
+app = gr.Blocks(theme=gr.themes.Default(primary_hue="green", secondary_hue="pink"))
+with app:
+
+    # Input GUI
+    gr.Markdown(value="""
+        # PPIformer Web
+        ### Computational Design of Protein-Protein Interactions
+    """)
+    gr.Image("assets/readme-dimer-close-up.png")
+    gr.Markdown(value="""
+        [PPIformer](https://github.com/anton-bushuiev/PPIformer/tree/main) is a state-of-the-art predictor of the effects of mutations 
+        on protein-protein interactions (PPIs), as quantified by the binding free energy changes (ddG). PPIformer was shown to successfully 
+        identify known favourable mutations of the [staphylokinase thrombolytics](https://pubmed.ncbi.nlm.nih.gov/10942387/) 
+        and a [human antibody](https://www.pnas.org/doi/10.1073/pnas.2122954119) against the SARS-CoV-2 spike protein. The model was pre-trained 
+        on the [PPIRef](https://github.com/anton-bushuiev/PPIRef) 
+        dataset via a coarse-grained structural masked modeling and fine-tuned on the [SKEMPI v2.0](https://life.bsc.es/pid/skempi2) dataset via log odds. 
+        Please see more details in [our ICLR 2024 paper](https://arxiv.org/abs/2310.18515).
+                
+        **Inputs.** To use PPIformer on your data, please specify the PPI structure (PDB code or .pdb file), interacting proteins of interest 
+        (chain codes in the file) and mutations (semicolon-separated list or file with mutations in the 
+        [standard format](https://foldxsuite.crg.eu/parameter/mutant-file): wild-type residue, chain, residue number, mutant residue). 
+        For inspiration, you can use one of the examples below: click on one of the rows to pre-fill the inputs. After specifying the inputs, 
+        press the button to predict the effects of mutations on the PPI. Currently the model runs on CPU, so the predictions may take a few minutes.
+                
+        **Outputs.** After making a prediction with the model, you will see binding free energy changes for each mutation (ddG values in kcal/mol). 
+        A more negative value indicates an improvement in affinity, whereas a more positive value means a reduction in affinity. 
+        Below you will also see a 3D visualization of the PPI with wild types of mutated residues highlighted in red. The visualization additionally shows
+        the attention coefficients of the model for the nearest neighboring residues, which quantifies the contribution of the residues 
+        to the predicted ddG value. The brighter and thicker a residue is, the more attention the model paid to it.
+    """)
+
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            gr.Markdown("## PPI structure")
+            with gr.Row(equal_height=True):
+                pdb_code = gr.Textbox(placeholder="1BUI", label="PDB code", info="Protein Data Bank identifier for the structure (https://www.rcsb.org/)")
+                partners = gr.Textbox(placeholder="A,B,C", label="Partners", info="Protein chain identifiers in the PDB file forming the PPI interface (two or more)")
+            pdb_path = gr.File(file_count="single", label="Or .pdb file instead of PDB code (your structure will only be used for this prediction and not stored anywhere)")
+
+        with gr.Column():
+            gr.Markdown("## Mutations")
+            muts = gr.Textbox(placeholder="SC16A;FC47A;SC16A,FC47A", label="List of (multi-point) mutations", info="SC16A;FC47A;SC16A,FC47A for three mutations: serine to alanine at position 16 in chain C, phenylalanine to alanine at position 47 in chain C, and their double-point combination")
+            muts_path = gr.File(file_count="single", label="Or file with mutations")
+
+    examples = gr.Examples(
+        examples=[
+            ["1BUI", "A,B,C", "SC16A,FC47A;SC16A;FC47A"],
+            ["3QIB", "A,B,P,C,D", "YP7F,TP12S;YP7F;TP12S"],
+            ["1KNE", "A,P", ';'.join([f"TP6{a}" for a in AMINO_ACID_CODES_1])]
+        ],
+        inputs=[pdb_code, partners, muts],
+        label="Examples (click on a line to pre-fill the inputs)",
+        cache_examples=False
+    )
+
+    # Predict GUI
+    predict_button = gr.Button(value="Predict effects of mutations on PPI", variant="primary")
+
+    # Output GUI
+    gr.Markdown("## Predictions")
+    df_file = gr.File(label="Download predictions as .csv", interactive=False, visible=True)
+    df = gr.Dataframe(
+        headers=["Mutation", "ddG [kcal/mol]"],
+        datatype=["str", "number"],
+        col_count=(2, "fixed"),
+    )
+    dropdown = gr.Dropdown(interactive=True, visible=False)
+    dropdown_choices_to_plot_args = gr.State([])
+    plot = gr.HTML()
+
+    # Bottom info box
+    gr.Markdown(value="""
+        <br/>
+        
+        ## About this web
+                
+        **Use cases**. The predictor can be used in: (i) Drug Discovery for the development of novel drugs and vaccines for various diseases such as cancer, 
+        neurodegenerative disorders, and infectious diseases, (ii) Biotechnological Applications to develop new biocatalysts for biofuels, 
+        industrial chemicals, and pharmaceuticals (iii) Therapeutic Protein Design to develop therapeutic proteins with enhanced stability, 
+        specificity, and efficacy, and (iv) Mechanistic Studies to gain insights into fundamental biological processes, such as signal transduction, 
+        gene regulation, and immune response.
+                
+        **Acknowledgement**. Please, use the following citation to acknowledge the use of our service. The web server is provided free of charge for non-commercial use.
+        > Bushuiev, Anton, Roman Bushuiev, Petr Kouba, Anatolii Filkin, Marketa Gabrielova, Michal Gabriel, Jiri Sedlar, Tomas Pluskal, Jiri Damborsky, Stanislav Mazurenko, Josef Sivic. 
+        > "Learning to design protein-protein interactions with enhanced generalization". The Twelfth International Conference on Learning Representations (ICLR 2024). 
+        > [https://arxiv.org/abs/2310.18515](https://arxiv.org/abs/2310.18515).
+                
+        **Contact**. Please share your feedback or report any bugs through [GitHub Issues](https://github.com/anton-bushuiev/PPIformer/issues/new), or feel free to contact us directly at [[email protected]](mailto:[email protected]).
+    """)
+    gr.Image("assets/logos.png")
+
+    # Download weights from Zenodo
+    download_from_zenodo('weights.zip')
+
+    # Set device
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print(f"[INFO] Device on start: {device}")
+
+    # Load models
+    models = [
+        DDGPPIformer.load_from_checkpoint(
+            PPIFORMER_WEIGHTS_DIR / f'ddg_regression/{i}.ckpt',
+            map_location=torch.device('cpu')
+        ).eval()
+        for i in range(3)
+    ]
+    models = [model.to(device) for model in models]
+
+    # Create temporary directory for storing downloaded PDBs and extracted PPIs
+    temp_dir_obj = tempfile.TemporaryDirectory()
+    temp_dir = Path(temp_dir_obj.name)
+
+    # Main logic
+    inputs = [pdb_code, pdb_path, partners, muts, muts_path]
+    outputs = [df, df_file, dropdown, dropdown_choices_to_plot_args]
+    predict = partial(predict, models, temp_dir)
+    predict_button.click(predict, inputs=inputs, outputs=outputs)
+
+    # Update plot on dropdown change
+    dropdown.change(update_plot, inputs=[dropdown, dropdown_choices_to_plot_args], outputs=[plot])
+
+app.launch(allowed_paths=['./assets'])

requirements.txt

@@ -0,0 +1,7 @@
+ppiformer @ git+https://github.com/anton-bushuiev/ppiformer.git@main
+# gradio==3.50.2
+# spaces
+# typing_extensions==4.7.1
+# gradio[oauth]==5.3.0
+# uvicorn>=0.14.0
+# spaces==0.30.4