Improve text, add logos

app.py

@@ -370,21 +370,31 @@ app = gr.Blocks(theme=gr.themes.Default(primary_hue="green", secondary_hue="pink
 with app:
 
     # Input GUI
-    gr.Markdown(value="# PPIformer Web")
+    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 energy changes (ddG). 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 [SKEMPI v2.0](https://life.bsc.es/pid/skempi2) via log odds. 
-        PPIformer was shown to successfully identify known favorable mutations of the [staphylokinase thrombolytic](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. Please see more details in [our paper](https://arxiv.org/abs/2310.18515).
+        [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).
                 
-        To use PPIformer on your data, please specify the PPI structure (PDB code or 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)). 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 prediction may take a few minutes.
+        **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.
                 
-        After making a prediction with the model, you will see binding free energy changes (ddG values) for each mutation and a 3D visualization of the PPI with 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 brighted and thicker a reisudes is, the more attention the model paid to it.
+        **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):
@@ -393,7 +403,7 @@ with app:
             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")
+            pdb_path = gr.File(file_count="single", label="Or .pdb file instead of PDB code")
 
         with gr.Column():
             gr.Markdown("## Mutations")
@@ -425,6 +435,27 @@ with app:
     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 to 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 [anton.bushuiev@cvut.cz](mailto:anton.bushuiev@cvut.cz).
+    """)
+    gr.Image("assets/logos.png")
+
     # Download weights from Zenodo
     download_weights()