fair-esm 0.1.1

Evolutionary Scale Modeling (esm): Pretrained language models for proteins. From Facebook AI Research.

====================================================== Evolutionary Scale Modeling (ESM)

Pretrained language models for proteins

This repository contains a PyTorch implementation of and pre-trained weights for the transformer protein language models in "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences" (Rives et al., 2019)_ from Facebook AI Research:

.. code-block:: bibtex

@article{rives2019biological,
  author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Guo, Demi and Ott, Myle and Zitnick, C. Lawrence and Ma, Jerry and Fergus, Rob},
  title={Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences},
  year={2019},
  doi={10.1101/622803},
  url={https://www.biorxiv.org/content/10.1101/622803v3},
  journal={bioRxiv}
}

.. _"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences" (Rives et al., 2019): https://doi.org/10.1101/622803

Quickstart

As a prerequisite, you must have PyTorch 1.5 or later installed to use this repository. A cuda device is optional and will be auto-detected.

You can either work in the root of this repository, or use this one-liner for installation:

.. code-block:: bash

$ pip install git+https://github.com/facebookresearch/esm.git

Then, you can load and use a pretrained model as follows:

.. code-block:: python

import torch
import esm

# Load 34 layer model
model, alphabet = esm.pretrained.esm1_t34_670M_UR50S()
batch_converter = alphabet.get_batch_converter()

# Prepare data (two protein sequences)
data = [("protein1", "MYLYQKIKN"), ("protein2", "MNAKYD")]
batch_labels, batch_strs, batch_tokens = batch_converter(data)

# Extract per-residue embeddings (on CPU)
with torch.no_grad():
    results = model(batch_tokens, repr_layers=[34])
token_embeddings = results["representations"][34]

# Generate per-sequence embeddings via averaging
# NOTE: token 0 is always a beginning-of-sequence token, so the first residue is token 1.
sequence_embeddings = []
for i, (_, seq) in enumerate(data):
    sequence_embeddings.append(token_embeddings[i, 1:len(seq) + 1].mean(0))

We also support PyTorch Hub, which removes the need to clone and/or install this repository yourself:

.. code-block:: python

import torch

model, alphabet = torch.hub.load("facebookresearch/esm", "esm1_t34_670M_UR50S")

FASTA embedding extractor

For your convenience, we have provided a script that efficiently extracts embeddings in bulk from a FASTA file:

.. code-block:: bash

# Extract final-layer embedding for a FASTA file from a 34-layer model
$ python extract.py esm1_t34_670M_UR50S examples/some_proteins.fasta my_reprs/ \
    --repr_layers 0 32 34 --include mean per_tok



# my_reprs/ now contains one ".pt" file per FASTA sequence; use torch.load() to load them
# extract.py has flags that determine what's included in the ".pt" file:
# --repr-layers (default: final only) selects which layers to include embeddings from.
# --include specifies what embeddings to save. You can use the following:
# * per_tok includes the full sequence, with an embedding per amino acid (seq_len x hidden_dim).
# * mean includes the embeddings averaged over the full sequence, per layer.
# * bos includes the embeddings from the beginning-of-sequence token. 
#    (NOTE: Don't use with the pre-trained models - we trained without bos-token supervision)

Tutorial

|ImageLink|_

.. |ImageLink| image:: https://colab.research.google.com/assets/colab-badge.svg .. _ImageLink: https://colab.research.google.com/github/facebookresearch/esm/blob/master/examples/variant_prediction.ipynb

To help you get started, we provide a jupyter notebook tutorial__ demonstrating how to train a variant predictor using embeddings from ESM. You can adopt a similar protocol to train a model for any downstream task, even with limited data. First you can obtain the embeddings for examples/P62593.fasta either by downloading the precomputed__ embeddings as instructed in the notebook or by running the following:

.. code-block:: bash

# Obtain the embeddings
$ python extract.py esm1_t34_670M_UR50S examples/P62593.fasta examples/P62593_reprs/ \
    --repr_layers 34 --include mean

__ examples/variant_prediction.ipynb __ https://dl.fbaipublicfiles.com/fair-esm/examples/P62593_reprs.tar.gz

Then, follow the remaining instructions in the tutorial. You can also run the tutorial in a colab notebook__.

__ https://colab.research.google.com/github/facebookresearch/esm/blob/master/examples/variant_prediction.ipynb

Available models

The following table lists the pretrained models available for use. See also Table 1 in the paper_.

+-----------+---------------------+---------+---------+---------+---------------+----------------+-----------------------------------------------------------------------+ | Shorthand | Full Name | #layers | #params | Dataset | Embedding Dim | Perplexity/ECE | Model URL | +-----------+---------------------+---------+---------+---------+---------------+----------------+-----------------------------------------------------------------------+ | ESM1-main | esm1_t34_670M_UR50S | 34 | 670M | UR50/S | 1280 | 8.54 | https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR50S.pt | +-----------+---------------------+---------+---------+---------+---------------+----------------+-----------------------------------------------------------------------+ | | esm1_t34_670M_UR50D | 34 | 670M | UR50/D | 1280 | 8.46 | https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR50D.pt | +-----------+---------------------+---------+---------+---------+---------------+----------------+-----------------------------------------------------------------------+ | | esm1_t34_670M_UR100 | 34 | 670M | UR100 | 1280 | 10.32 | https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR100.pt | +-----------+---------------------+---------+---------+---------+---------------+----------------+-----------------------------------------------------------------------+ | | esm1_t12_85M_UR50S | 12 | 85M | UR50/S | 768 | 10.45 | https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t12_85M_UR50S.pt | +-----------+---------------------+---------+---------+---------+---------------+----------------+-----------------------------------------------------------------------+ | | esm1_t6_43M_UR50S | 6 | 43M | UR50/S | 768 | 11.79 | https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t6_43M_UR50S.pt | +-----------+---------------------+---------+---------+---------+---------------+----------------+-----------------------------------------------------------------------+

Comparison to related work

This table compares to related pre-training methods, and corresponds to Table 8 in the paper_. The last 3 columns are the major benchmark results:

• RH: Remote Homology at the fold level, using Hit-10 metric on SCOP.
• SSP: Secondary structure Q8 accuracy on CB513.
• Contact: Top-L long range contact precision on RaptorX test set from Wang et al. (2017)_.

.. _the paper: https://doi.org/10.1101/622803

+----------------+--------------+--------+------+------+---------+ | Model | Pre-training | Params | RH | SSP | Contact | +----------------+--------------+--------+------+------+---------+ | UniRep_ | | 18M | .527 | 58.4 | 21.9 | +----------------+--------------+--------+------+------+---------+ | SeqVec_ | | 93M | .545 | 62.1 | 29.0 | +----------------+--------------+--------+------+------+---------+ | TAPE_ | | 38M | .581 | 58.0 | 23.2 | +----------------+--------------+--------+------+------+---------+ | LSTM biLM (S) | UR50/S | 28M | .558 | 60.4 | 24.1 | +----------------+--------------+--------+------+------+---------+ | LSTM biLM (L) | UR50/S | 113M | .574 | 62.4 | 27.8 | +----------------+--------------+--------+------+------+---------+ | Transformer-6 | UR50/S | 43M | .653 | 62.0 | 30.2 | +----------------+--------------+--------+------+------+---------+ | Transformer-12 | UR50/S | 85M | .639 | 65.4 | 37.7 | +----------------+--------------+--------+------+------+---------+ | Transformer-34 | UR100 | 670M | .599 | 64.3 | 32.7 | +----------------+--------------+--------+------+------+---------+ | Transformer-34 | UR50/S | 670M | .639 | 69.2 | 50.2 | +----------------+--------------+--------+------+------+---------+ .. _Wang et al. (2017): https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005324 .. _UniRep: https://www.nature.com/articles/s41592-019-0598-1 .. _SeqVec: https://github.com/rostlab/SeqVec

Performance on TAPE benchmark

We evaluated our best performing model on the TAPE_ benchmark (Rao, et al. 2019), finding that our neural embeddings perform similarly to or better than alignment-based methods.

.. _TAPE: https://github.com/songlab-cal/tape

+--------------------+------+------+-----------------+--------------+-----------+-------------+ | Model | SS3 | SS8 | Remote homology | Fluorescence | Stability | Contact | +--------------------+------+------+-----------------+--------------+-----------+-------------+ | ESM (best neural) | 0.82 | 0.67 | 0.33 | 0.68 | 0.71 | (0.61)* | +--------------------+------+------+-----------------+--------------+-----------+-------------+ | TAPE (best neural) | 0.75 | 0.59 | 0.26 | 0.68 | 0.73 | 0.4 | +--------------------+------+------+-----------------+--------------+-----------+-------------+ | TAPE (alignment) | 0.8 | 0.63 | 0.09 | N/A | N/A | 0.64 | +--------------------+------+------+-----------------+--------------+-----------+-------------+ * Not comparable: ESM (bests neural) uses a linear projection on the features (the contact head available in the PyTorch version of TAPE), but the results from the TAPE paper use a ResNet head. See the previous table for a rigorous comparison of ESM and TAPE in a fair benchmarking setup.

Reference

If you find the model useful in your research, we ask that you cite the following paper:

.. code-block:: bibtex

@article{rives2019biological,
  author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Guo, Demi and Ott, Myle and Zitnick, C. Lawrence and Ma, Jerry and Fergus, Rob},
  title={Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences},
  year={2019},
  doi={10.1101/622803},
  url={https://www.biorxiv.org/content/10.1101/622803v3},
  journal={bioRxiv}
}

Additionally, much of this code hails from the excellent fairseq_ sequence modeling framework; we have released this standalone model to facilitate more lightweight and flexible usage. We encourage those who wish to pretrain protein language models from scratch to use fairseq.

.. _fairseq: https://github.com/pytorch/fairseq

License

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.