update description to give warning about tt3d behavior

app.py

@@ -22,29 +22,36 @@ title = "D-SCRIPT: Predicting Protein-Protein Interactions"
 description = """
 """
 
-article = """
+# article = """
+
+# <hr>
+
+# <img style="margin-left:auto; margin-right:auto" src="https://raw.githubusercontent.com/samsledje/D-SCRIPT/main/docs/source/img/dscript_architecture.png" alt="D-SCRIPT architecture" width="70%"/>
 
-<hr>
+# <hr>
 
-<img style="margin-left:auto; margin-right:auto" src="https://raw.githubusercontent.com/samsledje/D-SCRIPT/main/docs/source/img/dscript_architecture.png" alt="D-SCRIPT architecture" width="70%"/>
+# D-SCRIPT is a deep learning method for predicting a physical interaction between two proteins given just their sequences.
+# It generalizes well to new species and is robust to limitations in training data size. Its design reflects the intuition that for two proteins to physically interact, 
+# a subset of amino acids from each protein should be in contact with the other. The intermediate stages of D-SCRIPT directly implement this intuition, with the penultimate stage 
+# in D-SCRIPT being a rough estimate of the inter-protein contact map of the protein dimer. This structurally-motivated design enhances the interpretability of the results and, 
+# since structure is more conserved evolutionarily than sequence, improves generalizability across species.
 
-<hr>
+# <hr>
 
-D-SCRIPT is a deep learning method for predicting a physical interaction between two proteins given just their sequences.
-It generalizes well to new species and is robust to limitations in training data size. Its design reflects the intuition that for two proteins to physically interact, 
-a subset of amino acids from each protein should be in contact with the other. The intermediate stages of D-SCRIPT directly implement this intuition, with the penultimate stage 
-in D-SCRIPT being a rough estimate of the inter-protein contact map of the protein dimer. This structurally-motivated design enhances the interpretability of the results and, 
-since structure is more conserved evolutionarily than sequence, improves generalizability across species.
+# Computational methods to predict protein-protein interaction (PPI) typically segregate into sequence-based "bottom-up" methods that infer properties from the characteristics of the 
+# individual protein sequences, or global "top-down" methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate 
+# top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. Topsy-Turvy builds upon D-SCRIPT by synthesizing both views in a sequence-based, 
+# multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by 
+# incorporating patterns from both global and molecular-level views of protein interaction. In a cross-species context, we show it achieves state-of-the-art performance, offering the 
+# ability to perform genome-scale, interpretable PPI prediction for non-model organisms with no existing experimental PPI data.
 
-<hr>
 
-Computational methods to predict protein-protein interaction (PPI) typically segregate into sequence-based "bottom-up" methods that infer properties from the characteristics of the 
-individual protein sequences, or global "top-down" methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate 
-top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. Topsy-Turvy builds upon D-SCRIPT by synthesizing both views in a sequence-based, 
-multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by 
-incorporating patterns from both global and molecular-level views of protein interaction. In a cross-species context, we show it achieves state-of-the-art performance, offering the 
-ability to perform genome-scale, interpretable PPI prediction for non-model organisms with no existing experimental PPI data.
+# """
+
+article = """
 
+Note that running here with the "TT3D" model does not run structure prediction on the sequences, but rather uses the [ProstT5](https://github.com/mheinzinger/ProstT5) language model to
+translate amino acid to 3di sequences. This is much faster than running structure prediction, but the results may not be as accurate.
 
 """
 
@@ -168,7 +175,7 @@ demo = gr.Interface(
     ],
     # title = title,
     # description = description,
-    # article = article,
+    article = article,
     theme = theme,
 )