Update README.md

README.md

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-{}
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+license: mit
+tags:
+  - protein-protein interactions  
+  - paired proteins encoding
+  - protein language model
 ---
+# PLM-interact model
+
+
+PLM-interact： extending protein language models to predict protein-protein interactions 
+The preprint is available at [PLM-interact](https://www.biorxiv.org/content/10.1101/2024.11.05.622169v1) and the code see [github link](https://github.com/liudan111/PLM-interact)
+
+This model is trained on human PPIs from STRING V12. For the PPI preprocessing details, see Methods 
+in the preprint.
+
+## Model description
+        
+PLM-interact, goes beyond a single protein, jointly encoding protein pairs to learn their relationships, 
+analogous to the next-sentence prediction task from natural language processing. This approach provides 
+a significant improvement in performance: Trained on human-human PPIs, PLM-interact predicts mouse, fly, 
+worm, E. coli and yeast PPIs, with 16-28% improvements in AUPR compared with state-of-the-art PPI models. 
+Additionally, it can detect changes that disrupt or cause PPIs and be applied to virus-host PPI prediction.
+
+![PLM-interact](./Figure1_PLM_interact.png)
+        
+### An example to predict interaction probability between proteins 
+        
+```python
+import torch
+import torch.nn as nn
+from transformers import AutoModel,AutoModelForMaskedLM,AutoTokenizer
+import os
+import torch.nn.functional as F
+
+class PLMinteract(nn.Module):
+  def __init__(self,model_name,num_labels,embedding_size): 
+    super(PLMinteract,self).__init__() 
+    self.esm_mask = AutoModelForMaskedLM.from_pretrained(model_name) 
+    self.embedding_size=embedding_size
+    self.classifier = nn.Linear(embedding_size,1) # embedding_size 
+    self.num_labels=num_labels
+
+  def forward_test(self,features):
+    embedding_output = self.esm_mask.base_model(**features, return_dict=True)
+    embedding=embedding_output.last_hidden_state[:,0,:] #cls token
+    embedding = F.relu(embedding)
+    logits = self.classifier(embedding)
+    logits=logits.view(-1)
+    probability = torch.sigmoid(logits)
+    return  probability
+
+# folder_huggingface_download : the download model from huggingface, such as "danliu1226/PLM-interact-650M-humanV11"
+# model_name:  the ESM2 model that PLM-interact trained
+# embedding_size: the embedding size of ESM2  model
+
+folder_huggingface_download='download_huggingface_folder/'
+model_name= 'facebook/esm2_t33_650M_UR50D'
+embedding_size =1280
+
+protein1 ="EGCVSNLMVCNLAYSGKLEELKESILADKSLATRTDQDSRTALHWACSAGHTEIVEFLLQLGVPVNDKDDAGWSPLHIAASAGRDEIVKALLGKGAQVNAVNQNGCTPLHYAASKNRHEIAVMLLEGGANPDAKDHYEATAMHRAAAKGNLKMIHILLYYKASTNIQDTEGNTPLHLACDEERVEEAKLLVSQGASIYIENKEEKTPLQVAKGGLGLILKRMVEG"
+
+protein2= "MGQSQSGGHGPGGGKKDDKDKKKKYEPPVPTRVGKKKKKTKGPDAASKLPLVTPHTQCRLKLLKLERIKDYLLMEEEFIRNQEQMKPLEEKQEEERSKVDDLRGTPMSVGTLEEIIDDNHAIVSTSVGSEHYVSILSFVDKDLLEPGCSVLLNHKVHAVIGVLMDDTDPLVTVMKVEKAPQETYADIGGLDNQIQEIKESVELPLTHPEYYEEMGIKPPKGVILYGPPGTGKTLLAKAVANQTSATFLRVVGSELIQKYLGDGPKLVRELFRVAEEHAPSIVFIDEIDAIGTKRYDSNSGGEREIQRTMLELLNQLDGFDSRGDVKVIMATNRIETLDPALIRPGRIDRKIEFPLPDEKTKKRIFQIHTSRMTLADDVTLDDLIMAKDDLSGADIKAICTEAGLMALRERRMKVTNEDFKKSKENVLYKKQEGTPEGLYL"
+
+DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+tokenizer = AutoTokenizer.from_pretrained(model_name) 
+PLMinter= PLMinteract(model_name, 1, embedding_size)
+load_model = torch.load(f"{folder_huggingface_download}pytorch_model.bin")
+PLMinter.load_state_dict(load_model)
+
+texts=[protein1, protein2]
+tokenized = tokenizer(*texts, padding=True, truncation='longest_first', return_tensors="pt", max_length=1603)       
+tokenized = tokenized.to(DEVICE)
+
+PLMinter.eval()
+PLMinter.to(DEVICE)
+with torch.no_grad():
+    probability = PLMinter.forward_test(tokenized)
+    print(probability.item())
+```
+
+## Training dataset
+This model checkpoint is trained on the benchmarking human PPIs from https://d-script.readthedocs.io/en/stable/data.html
+
 
-This model checkpoint is based on the benchmarking human PPIs from https://d-script.readthedocs.io/en/stable/data.html