This pipeline implemented in Python allows you to perform two different analyses of the last hidden state from ESM-2 model results: Correlation and DimensionalityReduction. To run it you just need to define a config.json that contains all the necessary parameters.

1.1. Clone the repository

git clone https://github.com/sarata00/esm_analyzer.git

1.2. Create a conda environment and install all the dependencies

This file contains all the variables used in our analyses. There are common arguments in both type of analysis:

Then, for Correlation analysis we have some specific arguments:

Finally, for the Dimensionality reduction analysis we have just one argument:

Take into account that in the data folder there is no tensor file, due to storage limits. So, you will need to create it using embedding_generators functions.

Let me give you an example!

The first step is to generate the embedding tensors. For that, you can use both scripts: embedding_generators/embedding_generator_esm2 and embedding_generators/tensor_generator_hugging_face depending if you want to use ESM-2 or HugginFace configuration, respectively.

# Using ESM-2 modules
python embedding_generators/embedding_generator_esm2 -i mutant_library.fasta -m "esm2_t36_3B_UR50D" -o path/to/output -norm True
        # - norm = True in case you want to normalize the embedding dimensions

# Using HuggingFace modules
python embedding_generators/embedding_generator_hugging_face -i mutant_library.fasta -m "facebook/esm2_t36_3B_UR50D" -o path/to/output

The following step is the analysis of the results. These embeddings (normalized or not) can be processed and analyzed by using analysis module. Finally, the results are stored in results folder.

Notice that the "path/to/output" has to be the same path than the "path_to_tensor" in the json file!

In the repository folder:

python scripts/analysis -c config/config_corr_1.json

As a result, we obtain three dataframes:

• df_correlation: where we will find the correlation analysis between the experimental data ("fitness" column) and the processed model data (according to the type of analysis - global or positional- and distance - euclidean or cosine.)
• df_meanPos_corr: correlation analysis results by mean Position analysis. This means that the results show the mean distance of the variant embeddings at a given position to the wildtype embedding.
• df_model_exp: where we merge both experimental and model data.

In the repository folder:

python scripts/analysis -c config/config_dr_1.json

As a result, we will obtain a figure (in svg format) of the analysis plot.

1. Merge both embedding_generator and analyzer.
2. Add logits analysis