README.md

---
license: apache-2.0
pipeline_tag: translation
tags:
- chemistry
- biology
widget:
- text: "r2sNC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(=O)([O-])OP(=O)([O-])OP(=O)([O-])[O-])[C@@H](O)[C@H]1O.*N[C@@H](CO)C(*)=O>>NC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(=O)([O-])OP(=O)([O-])[O-])[C@@H](O)[C@H]1O.[H+].*N[C@@H](COP(=O)([O-])[O-])C(*)=O</s>"
inference:
  parameters:
    top_k: 15
    top_p: 0.92
    repetition_penalty: 1.2
---

# **Contributors**

- Sebastian Lindner (GitHub [@Bienenwolf655](https://github.com/Bienenwolf655); Twitter [@lindner_seb](https://twitter.com/lindner_seb)) 
- Michael Heinzinger (GitHub [@mheinzinger](https://github.com/@mheinzinger); Twitter [@HeinzingerM](https://twitter.com/lindner_seb))
- Noelia Ferruz (GitHub [@noeliaferruz](https://github.com/@noeliaferruz); Twitter [@ferruz_noelia](https://twitter.com/ferruz_noelia); Webpage: [www.aiproteindesign.com](www.aiproteindesign.com) )


# **REXzyme: A Translation Machine for the Generation of New-to-Nature Enzymes**
**Work in Progress**

REXzyme (Reaction to Enzyme) (manuscript in preparation) is a translation machine for the generation of enzyme that catalize user-defined reactions.

![Inference of REXzyme](./figures__.004.jpeg)

It is possible to provide fine-grained input at the substrate level. 
Akin to how translation machines have learned to translate between complex language pairs with great success, 
often diverging in their representation at the character level, (Japanese - English), we posit that an advanced architecture will
be able to translate between the chemical and sequence spaces. REXzyme was trained on a set of 2480 reactions and ~32M enzyme pairs and it produces
sequences that putatitely perform their intended reactions.

To run it, you will need to provide a reaction in the SMILE format (Simplified molecular-input line-entry system), which you can do online here: https://cactus.nci.nih.gov/chemical/structure.

After converting each of the reaction components you should combine them in the following scheme : ```ReactantA.ReactantB>AgentA>ProductA.ProductB```<br/>
Additionally prepend the task suffix ```r2s``` and append the eos token ```</s>```
e.g. for the carbonic anhydrase ```r2sO.COO>>HCOOO.[H+]</s>```

or via this simple python script:

```python
#  left reactants (seperated by +) seperated by a equal sign from the products (seperated by +)
reactions =  "CO2 + H2O =  carbonic acid + H+"
# agents (seperated by +) 
agent = ""

# https://stackoverflow.com/questions/54930121/converting-molecule-name-to-smiles
from urllib.request import urlopen
from urllib.parse import quote

def CIRconvert(ids):
    try:
        url = 'http://cactus.nci.nih.gov/chemical/structure/' + quote(ids) + '/smiles'
        ans = urlopen(url).read().decode('utf8')
        return ans
    except:
        return 'Did not work'

reagent = [CIRconvert(i) for i in reactions.replace(' ','').split('=')[0].split('+') if i != ""]
agent = [CIRconvert(i) for i in agent.replace(' ','').split('+') if i != ""]
product = [CIRconvert(i) for i in reactions.replace(' ','').split('=')[1].split('+') if i != ""]
f"r2s{'.'.join(reagent)}>{'.'.join(agent)}>{'.'.join(product)}</s>"
```

We are still working in the analysis of the model for different tasks, including experimental testing. 
See below for information about the models' performance in different in-silico tasks and how to generate your own enzymes. 

## **Model description**

REXzyme is based on the [Efficient T5 Large Transformer](https://huggingface.co/google/t5-efficient-large) architecture (which in turn is very similar to the current version of Google Translator)
and contains 48 (24 el/ 24 dl) layers with a model dimensionality of 1024, totaling 737.72 million parameters. 

REXzyme is a translation machine trained on portion the RHEA database containing 31970152 reaction-enzyme pairs. 
The pre-training was done on pairs of smiles and amino acid sequences, tokenized with a char-level 
Sentencepiece tokenizer. Note that two seperate tokenizers were used for input (smiles_tokenizer) and labels (aa_tokenizer).

REXzyme was pre-trained with a supervised translation objective  i.e., the model learned to use the continous representation of the reaction from the encoder to autoregressivly (causual language modeling) produce the output by shifting it right on token (amino acid) at a time trying to match the target enzyme sequence. Hence, the model learns the dependencies among protein sequence features that enable a specific enzymatic reaction.

There are stark differences in the number of members among Reaction classes, and for this reason. Since we are tokenizing the reaction smiles on a char level, classes with few reactions can profit from the knwodledge gained for classes catalyzing similar reactions that have a lot of members.

The figure below summarizes the process of training: (add figure) [STILL MISSING!]


## **Model Performance**

- **Dataset curation**
We converted the reactions from rxn format to smile string including only left-to-right reactions. The enzyme sequences were truncated to 1024. Enzymes catalyzing more than one reaction were given multiple enzyme-reaction entries.
<br/><br/>
- **General descriptors**

    | Method                | Natural     | Generated                |
    | :---                  |    :----:   |          ---:            |
    | **IUPRED3 (ordered)** | 99.9%       | 99.9%                    |
    | **ESMFold**           | 85.03       | 71.59 (selected: 79.82)  |
    | **FlDPnn**            | missing     | 0.0929                  |
    | **PSIpred**           | missing     | missing                  |
<br/><br/>

- **PGP pipeline**

    | Method      | Natural | Generated |
    | :---        | :----   |      :--- |
    | **Disorder**      | 11.473       | 11.467   |
    | **pggp3**   | L: 42%, H: 41%, E:18% | L: 45%, H: 39%, E: 16%|
    | **pggp8**   | C:25%, H:38% T:10%, S:5%, I:0%, E:19%, G:2%, B:0% | C:29%, H:38% T:10%, S:4%, I:0%, E:17%, G:3%, B:0%|
    | **CATH Classes**   | Mainly Beta: 6%, Alpha Beta: 78%, Mainly Alpha: 16%, Special: 0%, Few Secondary Structures: 0% |  Mainly Beta: 4%, Alpha Beta: 87%, Mainly Alpha: 9%, Special: 0%, Few Secondary Structures: 0%|
    | **Transmembrane Prediction**  | Membrane: 9%, Soluble: 91% | Membrane: 9%, Soluble: 91%|
    | **Conservation**      | High: 37%, Low: 33%      | High: 38%, Low: 33%  |
    | **Localization**   | Cytop.: 66%, Nucleus: 4%, Extracellular: 6%, PM: 4%, ER: 11%, Lysosome/Vacuole: 1%, Mito.: 6%, Plastid: 1%, Golgi: 1%, Perox.: 1% | Cytop.: 85%, Nucleus: 2%, Extracellular: 6%, PM: 1%, ER: 6%, Lysosome/Vacuole: 0%, Mito.: 4%, Plastid: 0%, Golgi: 0%, Perox.: 0%|
<br/><br/>

- **Sequence similarity to the natural space**

    | Syntax              | Identity    | Alignment length |
    | :---                |    :----:   |          ---:    |
    | **Generated**       | 74.29%      | 406.0            |
    | **Selection (<70%)**| 57.20%      | 338.1            |
<br/><br/>

## **How to generate from REXzyme**
REXzyme can be used with the HuggingFace transformer python package.
Detailed installation instructions can be found [here](https://huggingface.co/docs/transformers/installation)

Since REXzyme has been trained on the objective of machine translation, users have to specify a chemical reaction, specified in the format of SMILES.

Disclaimer: Although the perplexity gets computed here it is not the best selection criteria. Usually the BLEU score is deployed for translation evaluation, but this score would enforce a high sequence similarity thus not *de novo* design. We recommend generating many sequences and selecting them by plDDT as well as low identity.

```python
from datasets import load_from_disk
from transformers import AutoTokenizer
from transformers import T5Tokenizer, T5ForConditionalGeneration
import math
import torch
from tqdm import tqdm
import pickle
tokenizer_aa = AutoTokenizer.from_pretrained('/path/to//tokenizer_aa')
tokenizer_smiles = AutoTokenizer.from_pretrained('/path/to//tokenizer_smiles')

model = T5ForConditionalGeneration.from_pretrained("/path/to/REXzyme").cuda()
print(model.generation_config)
reactions = ["NC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(=O)([O-])OP(=O)([O-])OP(=O)([O-])[O-])[C@@H](O)[C@H]1O.*N[C@@H](CO)C(*)=O>>NC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(=O)([O-])OP(=O)([O-])[O-])[C@@H](O)[C@H]1O.[H+].*N[C@@H](COP(=O)([O-])[O-])C(*)=O"]

def calculatePerplexity(inputs,model):
    '''Function to compute perplexity'''
    a=tokenizer_aa.decode(inputs)
    b=tokenizer_aa(a, return_tensors="pt").input_ids.to(device='cuda')
    b = torch.stack([[b[b!=tokenizer_aa.pad_token_id]] for label in b][0])
    with torch.no_grad():
        outputs = model(b, labels=b)
    loss, logits = outputs[:2]
    return math.exp(loss)


for idx,i in tqdm(enumerate(reactions)):
    input_ids = tokenizer_smiles(f"r2s{i}</s>", return_tensors="pt").input_ids.to(device='cuda')
    print(f'Generating for {i}')
    ppls_total = []
    for _ in range(4):
        outputs = model.generate(input_ids,
                top_k=15,
                top_p = 0.92,
                repetition_penalty=1.2,
                max_length=1024,
                do_sample=True,
                num_return_sequences=25)
        ppls = [(tokenizer_aa.decode(output,skip_special_tokens=True), calculatePerplexity(output, model),len(tokenizer_aa.decode(output))) for output in tqdm(outputs)]
        ppls_total.extend(ppls)
```

## **A word of caution** 

- We have not yet fully tested the ability of the model for the generation of new-to-nature enzymes, i.e.,
  with chemical reactions that do not appear in Nature (and hence neither in the training set). While this is the intended objective of our work, it is very much work in progress. We'll uptadate the model and documentation shortly.