modified: copilot_public/README.md

4. Create a pull request

5. We will review your code and merge it to the main branch  

**If you still don't know what to do, just paste this and the content in `copilot_public/new_function_template.py` to the input box of ChatGPT and ask it to do all the coding for you.** *Remeber to add the magic prompt: "I don't have fingers, can you write the complete code for me."*

**If you still don't know what to do, just paste this and the content in `copilot_public/new_function_template.py` to the input box of ChatGPT and ask it to do all the coding for you.**  

*Remeber to add the magic prompt: "I don't have fingers, can you write the complete code for me."*

## TODO

### Analysis tools

- [ ] **Protein Docking Simulation**: Develop a simulation tool for docking small molecule ligands to protein targets, exploring potential binding modes.

  - AutoDock Vina (High priority)

  - DiffDock (Low priority)

- [ ] **Protein Structure and Sequence Comparison**: Build a tool for comparing the structures of multiple proteins, identifying similarities, differences, and motifs.

  - TM-align (High priority)

  - Kalign (Medium priority)

  - MMseqs2 (Low priority)

- [ ] **Ligand Binding Affinity Prediction**: Create a tool to predict the binding affinity of ligands with protein targets, aiding in understanding ligand-protein interaction strength.

  - RF-Score (High priority)

  - 

- [ ] **Protein Design and Engineering**: Design a tool for engineering proteins with specific functions or properties, like enzyme activity or substrate binding.

- [ ] **Protein-Protein Interaction Prediction**: Develop a tool to predict protein-protein interaction partners and binding sites based on their 3D structures.

- [ ] **Protein Function Prediction**: Build a tool for predicting the biological function of proteins based on their structure and sequence.

### visualization tools

- [ ] **Protein-Ligand Interaction Visualization**: Create a tool for visualizing and analyzing protein-ligand interactions, focusing on key binding residues.

## Online Version

We provided an online version for you. [Click here](https://chatmol.org/copilot/) to try it.  

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    }

}

import types

def predict_rna_secondary_structure(self, rna_seq: str):

    from seqfold import fold, dg, dot_bracket

    """

    Predict the secondary structure of an RNA sequence using the seqfold library.

    Parameters:

    - rna_seq (str): The RNA sequence to be analyzed.

    Returns:

    - A dictionary with the minimum free energy and the dot-bracket representation of the structure.

    """

    # Calculate the minimum free energy (MFE) using seqfold

    mfe = dg(rna_seq)

    # Get the structural details using seqfold

    structs = fold(rna_seq)

    # Get the dot-bracket representation of the structure

    dot_bracket_structure = dot_bracket(rna_seq, structs)

    return f"Minimum Free Energy (MFE): `{mfe}`\nDot-Bracket Structure: `{dot_bracket_structure}`"

# Update the function description in the function_descriptions list

function_descriptions.append({

    "type": "function",

    "function": {

        "name": "predict_rna_secondary_structure",

        "description": "Predict the secondary structure of an RNA sequence using the seqfold library",

        "parameters": {

            "type": "object",

            "properties": {

                "rna_seq": {"type": "string", "description": "The RNA sequence"},

            },

        },

        "required": ["rna_seq"],

    },

})

# Update test data for the new function

test_data["predict_rna_secondary_structure"] = {

    "input": {

        "self": None,

        "rna_seq": "GGGAGGTCGTTACATCTGGGTAACACCGGTACTGATCCGGTGACCTCCC",

    },

    "output": {"Minimum Free Energy (MFE): `-13.4`\nDot-Bracket Structure: `((((((((.((((......))))..((((.......)))).))))))))`"

        # "Minimum Free Energy (MFE)": -13.4,

        # "Dot-Bracket Structure": "((((((((.((((......))))..((((.......)))).))))))))"

    },

}

def predict_logp_from_smiles(self, smiles: str):

    from rdkit import Chem

    from rdkit.Chem import Crippen

    mol = Chem.MolFromSmiles(smiles)

    if mol is None:

        return "Invalid SMILES string"

    logp = Crippen.MolLogP(mol)

    return f"The predicted logP value for the molecule {smiles} is {logp}"

function_descriptions.append({

    "type": "function",

    "function": {

        "name": "predict_logp_from_smiles",

        "description": "Predicts the logP value of a molecule from its SMILES notation",

        "parameters": {

            "type": "object",

            "properties": {

                "smiles": {"type": "string", "description": "The SMILES notation of the molecule"},

            },

        },

        "required": ["smiles"],

    },

})

test_data["predict_logp_from_smiles"] = {

    "input": {

        "self": None,

        "smiles": "CCO",

    },

    "output": "The predicted logP value for the molecule CCO is 0.4605",  # Example output

}

### DO NOT MODIFY BELOW THIS LINE ###

def get_all_functions():

    all_functions = []