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README.md

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+
 ---
-library_name: transformers
-tags: []
+tags:
+- LoRA
+- protein language model
+datasets:
+- SoftDis
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-
-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
+# LoRA-DR-suite
 
-## Technical Specifications [optional]
+## Model details
 
-### Model Architecture and Objective
+LoRA-DR-suite is a family of models for the identification of disordered regions (DR) in proteins, built upon state-of-the-art Protein Language Models (PLMs) trained on protein sequences only. They leverage Low-Rank Adaptation (LoRA) fine-tuning for binary classification of intrinsic and soft disorder.  
+Intrinsically-disordered residues are experimentally detected through circular dichroism and X-ray cristallography, while soft disorder is characterized by high B-factor, or intermittently
+missing residues across different X-ray crystal structures of the same sequence.
 
-[More Information Needed]
+Models for intrinsic disorder are trained on DisProt 7.0 data only (DisProt7 suffix) or on additional data from the first and second edition of the Critical Assesment of Intrinsic Disorder (CAID), indicated with the ID suffix. 
 
-### Compute Infrastructure
+Models for soft disorder classification are trained instead on the SoftDis dataset, derived from an extensive analysis of clusters of alternative structures for the same protein
+sequence in the Protein Data Bank (PDB). For each position in the represantitive sequence of each cluster, it provides the frequency of closely-related homologs for which the corresponding residue is higly flexible or missing.
 
-[More Information Needed]
 
-#### Hardware
+## Model checkpoints
 
-[More Information Needed]
+We provide different model checkpoints, based on training data and pre-trained PLM.
 
-#### Software
+| Checkpoint name | Training dataset | Pre-trained checkpoint |  
+|-----------------|------------------|------------------------|
+| [esm2_650M-LoRA-DisProt7](https://huggingface.co/CQSB/esm2_650M-LoRA-DisProt7) | DisProt 7.0 | [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) |
+| [esm2_35M-LoRA-DisProt7](https://huggingface.co/CQSB/esm2_35M-LoRA-DisProt7) | DisProt 7.0 | [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) |
+| [Ankh-LoRA-DisProt7](https://huggingface.co/CQSB/Ankh-LoRA-DisProt7) | DisProt 7.0 | [ankh-large](https://huggingface.co/ElnaggarLab/ankh-large) |
+| [PortT5-LoRA-DisProt7](https://huggingface.co/CQSB/ProtT5-LoRA-DisProt7) | DisProt 7.0 | [prot_t5_xl_uniref5](Rostlab/prot_t5_xl_uniref50) |
+| [esm2_650M-LoRA-ID](https://huggingface.co/CQSB/esm2_650M-LoRA-ID) | Intrinsic dis.* | [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) |
+| [esm2_35M-LoRA-ID](https://huggingface.co/CQSB/esm2_35M-LoRA-ID) | Intrinsic dis.* | [esm2_t12_35M_UR50D](https://huggingface.co/facebook//esm2_t12_35M_UR50D) |
+| [Ankh-LoRA-ID](https://huggingface.co/CQSB/Ankh-LoRA-ID) | Intrinsic dis.* | [ankh-large](https://huggingface.co/ElnaggarLab/ankh-large) |
+| [PortT5-LoRA-ID](https://huggingface.co/CQSB/ProtT5-LoRA-ID) | Intrinsic dis.* | [prot_t5_xl_uniref5](Rostlab/prot_t5_xl_uniref50) |
+| [esm2_650M-LoRA-SD](https://huggingface.co/CQSB/esm2_650M-LoRA-SD) | SoftDis | [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) |
+| [esm2_35M-LoRA-SD](https://huggingface.co/CQSB/esm2_35M-LoRA-SD) | SoftDis | [esm2_t12_35M_UR50D](https://huggingface.co/facebook//esm2_t12_35M_UR50D) |
+| [Ankh-LoRA-SD](https://huggingface.co/CQSB/Ankh-LoRA-SD) | SoftDis | [ankh-large](https://huggingface.co/ElnaggarLab/ankh-large) |
+| [PortT5-LoRA-SD](https://huggingface.co/CQSB/ProtT5-LoRA-SD) | SoftDis | [prot_t5_xl_uniref5](Rostlab/prot_t5_xl_uniref50) |
 
-[More Information Needed]
+\* DisProt7, CAID1 and CAID2 data
 
-## Citation [optional]
+## Intended uses & limitations
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+The models are intended to be used for classification of different disorder types.  
+Models for intrinsic disorder trained on DisProt 7.0 were evaluated on CAID1 and CAID2 challenge, but we suggest to use "ID" models for classification of new sequences, as they show better generalization.
 
-**BibTeX:**
+In addition to its relation to flexibility and assembly pathways, soft disorder can be used to infer confidence score for structure prediciton tools, as we found high negative Spearman correlation between soft disorder probabilities and pLDDT from AlphaFold2 predicitons.
 
-[More Information Needed]
+### Model usage
 
-**APA:**
+All models can be loaded as PyTorch Modules, together with their associated tokenizer, with the following code:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
 
-## Glossary [optional]
+model_id = "CQSB/esm2_35M-LoRA-ID-DisProt7"  # model_id for selected model
+model = AutoModelForTokenClassification.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Once the model is loadded, disorder profile for all residues in a sequence can be obtained as follow:
 
-[More Information Needed]
+```python
+import torch
+import torch.nn.functional as F
 
-## More Information [optional]
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
 
-[More Information Needed]
+# example sequence (DP02948)
+sequence = "METLTLLWRASSSCLLVVLSHSLLRLLGVRCLEKSGAQPNKLFRPPCCQKGPSFARHSRCVYYTQSRE"
 
-## Model Card Authors [optional]
+# each pre-trained model adds its own special tokens to the tokenized sequence,  
+# special_tokens_mask allows to deal with them (padding included, for batched inputs)
+# without changing the code
+inputs = tokenizer([sequence], return_tensors="pt", return_special_tokens_mask=True)
+input_ids = inputs['input_ids'].to(device)
+attention_mask = inputs['attention_mask'].to(device)
+special_tokens_mask = inputs['special_tokens_mask'].bool()
 
-[More Information Needed]
+# extract predicted disorder probability
+with torch.inference_mode():
+    output = model(input_ids=input_ids,attention_mask=attention_mask).squeeze(0).cpu()
+disorder_proba = F.softmax(output[~special_tokens_mask], dim=-1)[:, 1]
+```
 
-## Model Card Contact
+## How to cite
 
-[More Information Needed]
+Coming soon...