Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+**Github repo: https://github.com/westlake-repl/ProTrek**
+
+## Overview
+ProTrek is a multimodal model that integrates protein sequence, protein structure, and text information for better 
+protein understanding. It adopts contrastive learning to learn the representations of protein sequence and structure. 
+During the pre-training phase, we calculate the InfoNCE loss for each two modalities as [CLIP](https://arxiv.org/abs/2103.00020)
+does.
+
+## Model architecture
+**Protein sequence encoder**:  [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D)
+
+**Protein structure encoder**: foldseek_t30_150M (identical architecture with esm2 except that the vocabulary only contains 3Di tokens)
+
+**Text encoder**:  [BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext)
+
+## Obtain embeddings and calculate similarity score (please clone our repo first)
+```
+import torch
+
+from model.ProtTrek.protrek_trimodal_model import ProTrekTrimodalModel
+from utils.foldseek_util import get_struc_seq
+
+# Load model
+config = {
+    "protein_config": "weights/ProTrek_35M_UniRef50/esm2_t33_650M_UR50D",
+    "text_config": "weights/ProTrek_35M_UniRef50/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
+    "structure_config": "weights/ProTrek_35M_UniRef50/foldseek_t30_150M",
+    "load_protein_pretrained": False,
+    "load_text_pretrained": False,
+    "from_checkpoint": "weights/ProTrek_35M_UniRef50/ProTrek_650M_UniRef50.pt"
+}
+
+device = "cuda"
+model = ProTrekTrimodalModel(**config).eval().to(device)
+
+# Load protein and text
+pdb_path = "example/8ac8.cif"
+seqs = get_struc_seq("bin/foldseek", pdb_path, ["A"])["A"]
+aa_seq = seqs[0]
+foldseek_seq = seqs[1].lower()
+text = "Replication initiator in the monomeric form, and autogenous repressor in the dimeric form."
+
+with torch.no_grad():
+    # Obtain protein sequence embedding
+    seq_embedding = model.get_protein_repr([aa_seq])
+    print("Protein sequence embedding shape:", seq_embedding.shape)
+    
+    # Obtain protein structure embedding
+    struc_embedding = model.get_structure_repr([foldseek_seq])
+    print("Protein structure embedding shape:", struc_embedding.shape)
+    
+    # Obtain text embedding
+    text_embedding = model.get_text_repr([text])
+    print("Text embedding shape:", text_embedding.shape)
+    
+    # Calculate similarity score between protein sequence and structure
+    seq_struc_score = seq_embedding @ struc_embedding.T / model.temperature
+    print("Similarity score between protein sequence and structure:", seq_struc_score.item())
+
+    # Calculate similarity score between protein sequence and text
+    seq_text_score = seq_embedding @ text_embedding.T / model.temperature
+    print("Similarity score between protein sequence and text:", seq_text_score.item())
+    
+    # Calculate similarity score between protein structure and text
+    struc_text_score = struc_embedding @ text_embedding.T / model.temperature
+    print("Similarity score between protein structure and text:", struc_text_score.item())
+```