fair-esm 0.3.1

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

Evolutionary Scale Modeling

This repository contains code and pre-trained weights for Transformer protein language models from Facebook AI Research, including our state-of-the-art ESM-1b and MSA Transformer. The models are described in detail in our paper, "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences" (Rives et al., 2019), which first proposed protein language modeling with Transformers.

ESM-1b outperforms all tested single-sequence protein language models across a range of structure prediction tasks. The MSA Transformer (ESM-MSA-1) can improve performance further by leveraging MSA information.

Citation

@article{rives2019biological,
  author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Liu, Jason 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/10.1101/622803v4},
  journal={bioRxiv}
}

Table of contents

• Comparison to related works
• Usage
  • Quick Start
  • Compute embeddings in bulk from FASTA
  • Notebooks
• Benchmarks
  • Comparison on several tasks
• Available Models and Datasets
  • Pre-trained Models
  • ESM Structural Split Dataset
  • Pre-training Dataset Split
• Citations
• License

What's New

• Feb 2021: MSA Transformer added (see Rao et al. 2021). Example usage in notebook.
• Dec 2020: Self-Attention Contacts for all pre-trained models (see Rao et al. 2020)
• Dec 2020: Added new pre-trained model ESM-1b (see Rives et al. 2019 Appendix B)
• Dec 2020: ESM Structural Split Dataset (see Rives et al. 2019 Appendix A.10)

Comparison to related works

Supervised downstreams

Model	Input	Pre-training	Params	SSP	Contact
UniRep	Sequence	UR50*	18M	58.4	21.9
SeqVec	Sequence	UR50*	93M	62.1	29.0
TAPE	Sequence	PFAM*	38M	58.0	23.2
ProtBert-BFD	Sequence	BFD*	420M	70.0	50.3
Prot-T5-XL-BFD	Sequence	BFD*	3B	71.4	55.9
LSTM biLM (S)	Sequence	UR50/S	28M	60.4	24.1
LSTM biLM (L)	Sequence	UR50/S	113M	62.4	27.8
Transformer-6	Sequence	UR50/S	43M	62.0	30.2
Transformer-12	Sequence	UR50/S	85M	65.4	37.7
Transformer-34	Sequence	UR100	670M	64.3	32.7
Transformer-34	Sequence	UR50/S	670M	69.2	50.2
ESM-1b	Sequence	UR50/S	650M	71.6	56.9
ESM-MSA-1	MSA	UR50/S + MSA	100M	72.9	Coming Soon

Comparison to related protein language models. (SSP) Secondary structure Q8 accuracy on CB513, transformer finetuned with convolution + LSTM head. (Contact) Top-L long range contact precision on RaptorX test set, with 32-layer ResNet head. For more details, see Rives et al. 2019.

* Pre-training datasets from related works have differences from ours.

Unsupervised structure prediction

Model	Input	Pre-training	Params	L	L/5
mfDCA	MSA			33.0	54.2
Psicov	MSA			32.6	58.1
Gremlin	MSA			39.3	62.8
TAPE	Sequence	PFAM*	38M	11.2	17.9
ProtBert-BFD	Sequence	BFD*	420M	34.1	57.4
Prot-T5-XL-BFD	Sequence	BFD*	3B	35.6	57.8
Transformer-6	Sequence	UR50/S	43M	13.2	21.5
Transformer-12	Sequence	UR50/S	85M	23.7	39.3
Transformer-34	Sequence	UR50/S	670M	34.7	56.0
ESM-1b	Sequence	UR50/S	650M	41.1	66.1
ESM-MSA-1	MSA	UR50/S + MSA	100M	57.7	83.1

Comparison to related protein language models on unsupervised contact prediction: a sparse linear combination of the attention heads is used to directly predict protein contacts. Average Top-L and Top-L/5 long range contact precision on 14842 test structures, sparse logistic regression trained on 20 structures. Direct coupling analysis methods (Gremlin, mfDCA, Psicov) and ESM-MSA-1 use the trRosetta MSAs, while other methods predict from single sequence. For more details on the method, see Rao et al. 2020.

Usage

Quick Start

As a prerequisite, you must have PyTorch 1.5 or later installed to use this repository.

You can use this one-liner for installation:

$ pip install fair-esm

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

import torch
model, alphabet = torch.hub.load("facebookresearch/esm", "esm1b_t33_650M_UR50S")

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

import torch
import esm

# Load ESM-1b model
model, alphabet = esm.pretrained.esm1b_t33_650M_UR50S()
batch_converter = alphabet.get_batch_converter()

# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
data = [
    ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
    ("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
]
batch_labels, batch_strs, batch_tokens = batch_converter(data)

# Extract per-residue representations (on CPU)
with torch.no_grad():
    results = model(batch_tokens, repr_layers=[33], return_contacts=True)
token_representations = results["representations"][33]

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

# Look at the unsupervised self-attention map contact predictions
import matplotlib.pyplot as plt
for (_, seq), attention_contacts in zip(data, results["contacts"]):
    plt.matshow(attention_contacts[: len(seq), : len(seq)])
    plt.title(seq)
    plt.show()

Compute embeddings in bulk from FASTA

We provide a script that efficiently extracts embeddings in bulk from a FASTA file. A cuda device is optional and will be auto-detected. The following command extracts the final-layer embedding for a FASTA file from the ESM-1b model:

$ python extract.py esm1b_t33_650M_UR50S examples/some_proteins.fasta my_reprs/ \
    --repr_layers 0 32 33 --include mean per_tok

Directory 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)

Notebooks

Variant prediction - using the embeddings

To help you get started with using the embeddings, this jupyter notebook tutorial shows how to train a variant predictor using embeddings from ESM-1. 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:

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

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

Unsupervised contact prediction

This jupyter notebook tutorial demonstrates contact prediction with both the ESM-1b and MSA Transformer (ESM-MSA-1) models. Contact prediction is based on a logistic regression over the model's attention maps. This methodology is based on our ICLR 2021 paper, Transformer protein language models are unsupervised structure learners. (Rao et al. 2020) The MSA Transformer (ESM-MSA-1) takes a multiple sequence alignment (MSA) as input, and uses the tied row self-attention maps in the same way. See MSA Transformer. (Rao et al. 2021).

ESMStructuralSplitDataset and self-attention contact prediction

And this jupyter notebook tutorial shows how to load and index the ESMStructuralSplitDataset, and computes the self-attention map unsupervised contact predictions using ESM-1b.

Available Models and Datasets

Pre-trained Models

Shorthand	Full Name	#layers	#params	Dataset	Embedding Dim	Model URL (automatically downloaded to ~/.cache/torch/hub/checkpoints)
ESM-MSA-1	esm_msa1_t12_100M_UR50S	12	100M	UR50/S	768	https://dl.fbaipublicfiles.com/fair-esm/models/esm_msa1_t12_100M_UR50S.pt
ESM-1b	esm1b_t33_650M_UR50S	33	650M	UR50/S	1280	https://dl.fbaipublicfiles.com/fair-esm/models/esm1b_t33_650M_UR50S.pt
ESM-1	esm1_t34_670M_UR50S	34	670M	UR50/S	1280	https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR50S.pt
	esm1_t34_670M_UR50D	34	670M	UR50/D	1280	https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR50D.pt
	esm1_t34_670M_UR100	34	670M	UR100	1280	https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR100.pt
	esm1_t12_85M_UR50S	12	85M	UR50/S	768	https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t12_85M_UR50S.pt
	esm1_t6_43M_UR50S	6	43M	UR50/S	768	https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t6_43M_UR50S.pt

ESM Structural Split Dataset

This is a five-fold cross validation dataset of protein domain structures that can be used to measure generalization of representations across different levels of structural dissimilarity. The dataset implements structural holdouts at the family, superfamily, and fold level. The SCOPe database is used to classify domains. Independently for each level of structural hold-out, the domains are split into 5 equal sets, i.e. five sets of folds, superfamilies, or families. This ensures that for each of the five partitions, structures having the same classification do not appear in both the train and test sets. For a given classification level each structure appears in a test set once, so that in the cross validation experiment each of the structures will be evaluated exactly once.

The dataset provides 3d coordinates, distance maps, and secondary structure labels. For further details on the construction of the dataset see Rives et al. 2019 Appendix A.10.

This jupyter notebook tutorial shows how to load and index the ESMStructuralSplitDataset.

ESMStructuralSplitDataset, upon initializing, will download splits and pkl. We also provide msas for each of the domains. The data can be directly downloaded below.

Name	Description	URL
splits	train/valid splits	https://dl.fbaipublicfiles.com/fair-esm/structural-data/splits.tar.gz
pkl	pkl objects containing sequence, SSP labels, distance map, and 3d coordinates	https://dl.fbaipublicfiles.com/fair-esm/structural-data/pkl.tar.gz
msas	a3m files containing MSA for each domain	https://dl.fbaipublicfiles.com/fair-esm/structural-data/msas.tar.gz

Pre-training Dataset Split

The split files establishing which UniRef50 clusters were used as held-out evaluation set for pre-training in Rives et al. 2019 and Rao et al. 2021 can be found here:

• UniRef50 IDs of evaluation set: 3.016 M clusters
• UniRef100 IDs of evaluation set: 13.745 M proteins, expanding the same UniRef50 clusters.

These files only contain only the UniRef50 IDs and UniRef100 IDs corresponding to the UniRef database, 2018-03 release which is released by the UniProt Consortium under a Creative Commons Attribution (CC BY 4.0) License.

Citations

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

@article{rives2019biological,
  author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Liu, Jason 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/10.1101/622803v4},
  journal={bioRxiv}
}

For the self-attention contact prediction, see the following paper (biorxiv preprint):

@article{rao2020transformer,
  author = {Rao, Roshan M and Meier, Joshua and Sercu, Tom and Ovchinnikov, Sergey and Rives, Alexander},
  title={Transformer protein language models are unsupervised structure learners},
  year={2020},
  doi={10.1101/2020.12.15.422761},
  url={https://www.biorxiv.org/content/10.1101/2020.12.15.422761v1},
  journal={bioRxiv}
}

For the MSA Transformer, see the following paper (biorxiv preprint):

@article{rao2021msa,
  author = {Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier, Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom and Rives, Alexander},
  title={MSA Transformer},
  year={2021},
  doi={10.1101/2021.02.12.430858},
  url={https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1},
  journal={bioRxiv}
}

Much of this code builds on the fairseq sequence modeling framework. We use fairseq internally for our protein language modeling research. We highly recommend trying it out if you'd like to pre-train protein language models from scratch.

License

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