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Directed evolution of proteins in sequence space with gradients

Home Page: https://nrel.github.io/EvoProtGrad/

License: BSD 3-Clause "New" or "Revised" License

Jupyter Notebook 6.46% Python 93.54%

directed-evolution huggingface mcmc protein-engineering protein-language-model

evoprotgrad's Issues

Error in demo.ipynb

When running the second cell:

# HuggingFace ESM2 8M model
esm2_expert = evo_prot_grad.get_expert('esm', temperature = 1.0, device = 'cuda')

# Supervised fluorescence regression model
gfp_expert = evo_prot_grad.get_expert(
                        'onehot_downstream_regression',
                        temperature = 1.0,
                        model = AutoModel.from_pretrained('NREL/avGFP-fluorescence-onehot-cnn',trust_remote_code=True),
                        device = 'cuda')

variants, scores = evo_prot_grad.DirectedEvolution(
                        wt_fasta = 'test/gfp.fasta',
                        output = 'all',
                        experts = [esm2_expert, gfp_expert],
                        parallel_chains = 16,
                        n_steps = 1000,              
                        max_mutations = 15,
                        verbose = False
)()

I get the following error:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
File [~/vscode_projects/proteins/EvoProtGrad/evo_prot_grad/__init__.py:54](https://file+.vscode-resource.vscode-cdn.net/Users/amelieschreiber/vscode_projects/proteins/EvoProtGrad/~/vscode_projects/proteins/EvoProtGrad/evo_prot_grad/__init__.py:54), in get_expert(expert_name, temperature, model, tokenizer, device, use_without_wildtype)
     53     expert_mod = importlib.import_module(f"evo_prot_grad.experts.{expert_name}_expert")
---> 54     return expert_mod.build(
     55         temperature = temperature,
     56         model = model,
     57         tokenizer = tokenizer,
     58         device = device,
     59         use_without_wildtype = use_without_wildtype
     60     )
     61 except:

File [~/vscode_projects/proteins/EvoProtGrad/evo_prot_grad/experts/esm_expert.py:65](https://file+.vscode-resource.vscode-cdn.net/Users/amelieschreiber/vscode_projects/proteins/EvoProtGrad/~/vscode_projects/proteins/EvoProtGrad/evo_prot_grad/experts/esm_expert.py:65), in build(**kwargs)
     64 """Builds a Esm2Expert."""
---> 65 return EsmExpert(**kwargs)

File [~/vscode_projects/proteins/EvoProtGrad/evo_prot_grad/experts/esm_expert.py:38](https://file+.vscode-resource.vscode-cdn.net/Users/amelieschreiber/vscode_projects/proteins/EvoProtGrad/~/vscode_projects/proteins/EvoProtGrad/evo_prot_grad/experts/esm_expert.py:38), in EsmExpert.__init__(self, temperature, model, tokenizer, device, use_without_wildtype)
     37     raise ValueError("EsmExpert requires both `model` and `tokenizer` to be specified.")
---> 38 super().__init__(
     39     temperature,
     40     model,
     41     tokenizer.get_vocab(),
     42     device,
     43     use_without_wildtype)
...
     60     )
     61 except:
---> 62     raise ValueError(f"Expert {expert_name} not found in evo_prot_grad.experts.")

ValueError: Expert esm not found in evo_prot_grad.experts.

is it possible to get the importance score of the protein sequence?

I was just wondering is it possible to get the importance score of the protein sequence using EvoProtGrad model? For instance, in https://huggingface.co/datasets/waylandy/phosformer_curated data there are kinase enzymes. Now I want to rank the kinase enzymes based on importance scores.

Furthermore, I found in (https://colab.research.google.com/drive/1e8WjYEbWiikRQg3g4YHQJJcpvTIWVAjp?usp=sharing) that the scores are generated for different variants of a protein sequence. But what is the score of the original protein sequence ? If the score of original sequence can be measured then it can be compared with other variants?

Using Masked Marginal Score for ESM-2 as a Scoring Method

In the paper Language models enable zero-shot prediction of the effects of mutations on protein function the ESM folks introduce the "Masked Marginal Scoring" method to compute effects of mutations on function and show that it performs significantly better than the Log Likelihood Ratio (LLR) method. If I am not mistaken, LLR is used for EvoProtGrad currently. Could the code from the ESM github (where they use ESM-1v) be adapted to ESM-2 and used in EvoProtGrad as a scoring method? In particular, could the masked marginal scoring method found here be modified to work with ESM-2 and used in EvoProtGrad as the scoring method? The masked marginal score is defined as

$$ \sum_{i \in M} \log p(x_i = x_i^{mt} | x_{-M}) - \log p(x_i = x_i^{wt} | x_{-M}) $$

in the paper above, in Appendix A at the bottom of page 18, where $-M$ denotes the sequence with masking at all positions in $M$, where mutations occur. That is they introduce masks at the mutated positions (all at once) and compute the score for a mutation by considering its probability relative to the wildtype amino acid. This might significantly improve the scoring and could be a nice alternative scoring strategy.

Weight of Sequence

Hi,

Is it possible to get the weight of a protein sequence using EvoProtGrad?

nrel / evoprotgrad Goto Github PK

evoprotgrad's Issues

Error in demo.ipynb

is it possible to get the importance score of the protein sequence?

Using Masked Marginal Score for ESM-2 as a Scoring Method

Weight of Sequence

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