Package: fair-esm
Evolutionary Scale ModelingThis repository contains code and pre-trained weights forTransformer protein language modelsfrom Facebook AI Research, including our state-of-the-artESM-2andESMFold, as well asMSA Transformer,ESM-1vfor predicting variant effects andESM-IF1for inverse folding.
Transformer protein language models were introduced in the preprint of the paper"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences" (Rives et al., 2019).ESM-2 outperforms all tested single-sequence protein language models across a range of structure prediction tasks.ESMFold harnesses the ESM-2 language model to generate accurate structure predictions end to end directly from the sequence of a protein.Citation@article{lin2022language,title={Language models of protein sequences at the scale of evolution enable accurate structure prediction},author={Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and Smetanin, Nikita and Verkuil, Robert and Kabeli, Ori and Shmueli, Yaniv and dos Santos Costa, Allan and Fazel-Zarandi, Maryam and Sercu, Tom and Candido, Sal and others},journal={bioRxiv},year={2022},publisher={Cold Spring Harbor Laboratory}}@article{rives2021biological,title={Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences},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 others},journal={Proceedings of the National Academy of Sciences},volume={118},number={15},pages={e2016239118},year={2021},publisher={National Acad Sciences}}Table of contentsMain models you should useComparison to related worksUsageQuick StartESMFold Structure PredictionCompute embeddings in bulk from FASTACPU offloading for inference with large modelsZero-shot variant predictionInverse foldingNotebooksAvailable Models and DatasetsPre-trained ModelsESM Structural Split DatasetPre-training Dataset SplitCitationsLicenseWhat's NewNovember 2022: ESMFold - new end-to-end structure prediction model released (seeLin et al. 2022)August 2022: ESM-2 - new SOTA Language Models released (seeLin et al. 2022)April 2022: New inverse folding model ESM-IF1 released, trained on CATH and UniRef50 predicted structures.August 2021: Added flexibility to tokenizer to allow for spaces and special tokens (like<mask>) in sequence.July 2021: New pre-trained model ESM-1v released, trained on UniRef90 (seeMeier et al. 2021).July 2021: New MSA Transformer released, with a minor fix in the row positional embeddings (ESM-MSA-1b).Feb 2021: MSA Transformer added (seeRao et al. 2021). Example usage innotebook.Dec 2020:Self-Attention Contactsfor all pre-trained models (seeRao et al. 2020)Dec 2020: Added new pre-trained modelESM-1b(seeRives et al. 2019Appendix B)Dec 2020:ESM Structural Split Dataset(seeRives et al. 2019Appendix A.10)Main models you should useShorthandesm.pretrained.DatasetDescriptionESM-2esm2_t36_3B_UR50D()esm2_t48_15B_UR50D()UR50 (sample UR90)SOTA general-purpose protein language model. Can be used to predict structure, function and other protein properties directly from individual sequences. Released withLin et al. 2022(Aug 2022 update).ESMFoldesmfold_v1()PDB + UR50End-to-end single sequence 3D structure predictor (Nov 2022 update).ESM-MSA-1besm_msa1b_t12_100M_UR50S()UR50 + MSAMSA Transformer language model. Can be used to extract embeddings from an MSA. Enables SOTA inference of structure. Released withRao et al. 2021(ICML'21 version, June 2021).ESM-1vesm1v_t33_650M_UR90S_1()...esm1v_t33_650M_UR90S_5()UR90Language model specialized for prediction of variant effects. Enables SOTA zero-shot prediction of the functional effects of sequence variations. Same architecture as ESM-1b, but trained on UniRef90. Released withMeier et al. 2021.ESM-IF1esm_if1_gvp4_t16_142M_UR50()CATH + UR50Inverse folding model. Can be used to design sequences for given structures, or to predict functional effects of sequence variation for given structures. Enables SOTA fixed backbone sequence design. Released withHsu et al. 2022.For a complete list of available models, with details and release notes, seePre-trained Models.Comparison to related worksTaskUnsupervised contact predictionStructure PredictionTest setLarge validCASP14CAMEO (Apr-Jun 2022)CASP14CAMEO (Apr-Jun 2022)Gremlin (Potts)39.3TAPE11.2ProtBert-BFD34.1Prot-T5-XL-BFD35.646.162.6Prot-T5-XL-Ur50 (3B)47.949.869.4ESM-133.7ESM-1b41.124.43941.664.5ESM-1v35.3ESM-MSA-1b57.4ESM-2 (8M)15.99.815.736.748.1ESM-2 (35M)28.816.428.441.456.4ESM-2 (150M)42.226.840.149.064.9ESM-2 (700M)50.132.547.651.370.1ESM-2 (3B)52.734.049.952.571.8ESM-2 (15B)54.537.051.755.472.1Comparison to related protein language models on structure prediction tasks.All contact numbers are the top-L,LR precision metric, where long range means sequence separation of at least 24 residuesFor unsupervised contact prediction, a sparse linear combination of the attention heads is used to directly predict protein contacts,
fitted with logistic regression on 20 structures.
For more details on the method, seeRao et al. 2020.For structure prediction, an AlphaFold2 structure module is trained directly from the frozen language model embeddings.
For more details on the method, seeLin et al. 2022.Direct coupling analysis methods (Gremlin, mfDCA, Psicov) and ESM-MSA-1 use thetrRosetta MSAs, while other methods predict from single sequence.UsageQuick StartAs a prerequisite, you must have PyTorch installed to use this repository.You can use this one-liner for installation, using the latest release of esm:pipinstallfair-esm# latest release, OR:pipinstallgit+https://github.com/facebookresearch/esm.git# bleeding edge, current repo main branchTo use the ESMFold model, make sure you start from an environment with python <= 3.9 and pytorch installed.
Then add the[esmfold]option to your pip install, which will install the dependencies for OpenFold
automatically. Openfold installation requiresnvcc.pipinstallfair-esm[esmfold]# OpenFold and its remaining dependencypipinstall'dllogger @ git+https://github.com/NVIDIA/dllogger.git'pipinstall'openfold @ git+https://github.com/aqlaboratory/openfold.git@4b41059694619831a7db195b7e0988fc4ff3a307'NOTE: If openfold installation fails, please double check thatnvccis available and that a cuda-compatable version of PyTorch has been installed.Alternatively, we provide theesmfoldconda environment, which can be built viaconda env create -f environment.yml.We also support PyTorch Hub, which removes the need to clone and/or install this repository yourself:importtorchmodel,alphabet=torch.hub.load("facebookresearch/esm:main","esm2_t33_650M_UR50D")After pip install, you can load and use a pretrained model as follows:importtorchimportesm# Load ESM-2 modelmodel,alphabet=esm.pretrained.esm2_t33_650M_UR50D()batch_converter=alphabet.get_batch_converter()model.eval()# disables dropout for deterministic results# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)data=[("protein1","MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),("protein2","KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),("protein2 with mask","KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),("protein3","K A <mask> I S Q"),]batch_labels,batch_strs,batch_tokens=batch_converter(data)# Extract per-residue representations (on CPU)withtorch.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=[]fori,(_,seq)inenumerate(data):sequence_representations.append(token_representations[i,1:len(seq)+1].mean(0))# Look at the unsupervised self-attention map contact predictionsimportmatplotlib.pyplotaspltfor(_,seq),attention_contactsinzip(data,results["contacts"]):plt.matshow(attention_contacts[:len(seq),:len(seq)])plt.title(seq)plt.show()ESMFold Structure PedictionAfter installing with the[esmfold]option, you can use the ESMFold structure prediction model as follows:importtorchimportesmmodel=esm.pretrained.esmfold_v1()model=model.eval().cuda()# Optionally, uncomment to set a chunk size for axial attention. This can help reduce memory.# Lower sizes will have lower memory requirements at the cost of increased speed.# model.set_chunk_size(128)sequence="MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"# Multimer prediction can be done with chains separated by ':'withtorch.no_grad():output=model.infer_pdb(sequence)withopen("result.pdb","w")asf:f.write(output)importbiotite.structure.ioasbsiostruct=bsio.load_structure("result.pdb",extra_fields=["b_factor"])print(struct.b_factor.mean())# this will be the pLDDT# 88.3Besidesesm.pretrained.esmfold_v1()which is the best performing model we recommend using, we
also provideesm.pretrained.esmfold_v0()which was used for the experiments inLin et al. 2022.We also provide a script (scripts/esmfold_inference.py) that efficiently predicts structures in bulk from a FASTA file using ESMFold. This can be run withpythonscripts/esmfold_inference.py\-i<inputfilewithmultiplesequences>\-o<pathtooutputdirectory>\--max-tokens-per-batch<int,default:1024>\--num-recycles<int,default:4>\--cpu-only<booleanflag>--cpu-offload<booleanflag>The script will make one prediction for every sequence in the fasta file. Multimers can be predicted and should be entered in the fasta file as a single sequence, with chains seprated by a ":" character.By default, predictions will be batched together so that shorter sequences are predicted simultaneously. This can be disabled by setting--max-tokens-per-batch=0. Batching can significantly improve prediction speed on shorter sequences.The--cpu-offloadflag can be useful for making predictions on longer sequences. It will attempt to offload some parameters to the CPU RAM, rather than storing on GPU.Compute embeddings in bulk from FASTAWe 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-2 model:pythonscripts/extract.pyesm2_t33_650M_UR50Dexamples/data/some_proteins.fasta\examples/data/some_proteins_emb_esm2--repr_layers03233--includemeanper_tokDirectorysome_proteins_emb_esm2/now contains one.ptfile per FASTA sequence; usetorch.load()to load them.scripts/extract.pyhas flags that determine what's included in the.ptfile:--repr-layers(default: final only) selects which layers to include embeddings from.--includespecifies what embeddings to save. You can use the following:per_tokincludes the full sequence, with an embedding per amino acid (seq_len x hidden_dim).meanincludes the embeddings averaged over the full sequence, per layer.bosincludes the embeddings from the beginning-of-sequence token.
(NOTE: Don't use with the pre-trained models - we trained without bos-token supervision)CPU offloading for inference with large modelsIf you want to load very large models like 15B and/or do inference on long sequences on your machine, regular GPU inference may lead to OOM errors.
We show how to load the model with Fairscale'sFully Sharded Data Parallel (FSDP)and
use its CPU offloading feature.
This allows to do inference of large models on a single GPU.
Please check outexamples/esm2_infer_fairscale_fsdp_cpu_offloading.pyfor more details.Zero-shot variant predictionSee "examples/variant-prediction/" for code and pre-trained weights for the ESM-1v models described inLanguage models enable zero-shot prediction of the effects of mutations on protein function. (Meier et al. 2021).Note that ESM-2 could be used for variant prediction as well, and is expected to have similar performance to ESM-1v.Inverse foldingSee "examples/inverse_folding/" for detailed user guide. The ESM-IF1 model is described asGVPTransformerinLearning inverse folding from millions of predicted structures. (Hsu et al. 2022).We also provide a colab notebook for the sequence design and sequence scoring functionalities.The ESM-IF1 inverse folding model is built for predicting protein sequences
from their backbone atom coordinates. We provide scripts here 1) to sample sequence
designs for a given structure and 2) to score sequences for a given structure.Trained with 12M protein structures predicted by AlphaFold2, the ESM-IF1
model consists of invariant geometric input processing layers followed by a
sequence-to-sequence transformer, and achieves 51% native sequence recovery on
structurally held-out backbones with 72% recovery for buried residues.
The model is also trained with span masking to tolerate missing backbone
coordinates and therefore can predict sequences for partially masked structures.Sample sequence designs for a given structureThe environment setup is described inthis subsection of examples/inverse_folding.To sample sequences for a given structure in PDB or mmCIF format, use thesample_sequences.pyscript. The input file can have either.pdbor.cifas suffix.For example, to sample 3 sequence designs for the golgi casein kinase structure
(PDB5YH2;PDB Molecule of the Month
from January 2022), we can run the following
command from the esm root directory:python examples/inverse_folding/sample_sequences.py examples/inverse_folding/data/5YH2.pdb \
  --chain C --temperature 1 --num-samples 3 --outpath examples/inverse_folding/output/sampled_sequences.fastaThe sampled sequences will be saved in a fasta format to the specified output file.The temperature parameter controls the sharpness of the probability
distribution for sequence sampling. Higher sampling temperatures yield more
diverse sequences but likely with lower native sequence recovery.
The default sampling temperature is 1. To optimize for native sequence
recovery, we recommend sampling with low temperature such as 1e-6.Scoring sequencesTo score the conditional log-likelihoods for sequences conditioned on a given
structure, use thescore_log_likelihoods.pyscript.For example, to score the sequences inexamples/inverse_folding/data/5YH2_mutated_seqs.fastaaccording to the structure inexamples/inverse_folding/data/5YH2.pdb, we can run
the following command from the esm root directory:python examples/inverse_folding/score_log_likelihoods.py examples/inverse_folding/data/5YH2.pdb \
  examples/inverse_folding/data/5YH2_mutated_seqs.fasta --chain C \
  --outpath examples/inverse_folding/output/5YH2_mutated_seqs_scores.csvThe conditional log-likelihoods are saved in a csv format in the specified output path.
The output values are the average log-likelihoods averaged over all amino acids in a sequence.For more information, see "./examples/inverse_folding/" for detailed user guide.NotebooksInverse folding - predicting or scoring sequences based on backbone structuresThe ESM-IF1 inverse folding model predicts protein sequences from their backbone atom coordinates, trained with 12M protein structures predicted by AlphaFold2.
This notetook guide you through examples of sampling sequences, calculating conditional log-likelihoods, and extracting encoder output as structure representation.Supervised variant prediction - training a classifier on the embeddingsTo help you get started with using the embeddings, thisjupyter notebook tutorialshows how to train a supervised 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 forexamples/data/P62593.fastaeither bydownloading the precomputedembeddings
as instructed in the notebook or by running the following:# Obtain the embeddingspythonscripts/extract.pyesm1v_t33_650M_UR90S_1examples/data/P62593.fasta\examples/data/P62593_emb_esm1v--repr_layers33--includemeanThen, follow the remaining instructions in the tutorial. You can also run the tutorial in acolab notebook.Note, alternatively usethe newer instructions for zero-shot variant prediction,
which predicts mutational effects without any supervised training.Unsupervised contact predictionThisjupyter notebook tutorialdemonstrates contact prediction with both the ESM-2 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.
SeeMSA Transformer. (Rao et al. 2021).To get unsupervised attention-based contacts, callmodel.predict_contacts(tokens)ormodel(tokens, return_contacts=True).ESMStructuralSplitDataset and self-attention contact predictionAnd thisjupyter notebook tutorialshows how to load and index theESMStructuralSplitDataset,
and computes the self-attention map unsupervised contact predictions using ESM-2.Available Models and DatasetsPre-trained ModelsShorthandesm.pretrained.#layers#paramsDatasetEmbedding DimModel URL (automatically downloaded to~/.cache/torch/hub/checkpoints)ESM-2esm2_t48_15B_UR50D4815BUR50/D 2021_045120https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t48_15B_UR50D.ptesm2_t36_3B_UR50D363BUR50/D 2021_042560https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t36_3B_UR50D.ptesm2_t33_650M_UR50D33650MUR50/D 2021_041280https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t33_650M_UR50D.ptesm2_t30_150M_UR50D30150MUR50/D 2021_04640https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t30_150M_UR50D.ptesm2_t12_35M_UR50D1235MUR50/D 2021_04480https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t12_35M_UR50D.ptesm2_t6_8M_UR50D68MUR50/D 2021_04320https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t6_8M_UR50D.ptESM-IF1esm_if1_gvp4_t16_142M_UR5020124MCATH 4.3 + predicted structures for UR50512https://dl.fbaipublicfiles.com/fair-esm/models/esm_if1_gvp4_t16_142M_UR50.ptESM-1vesm1v_t33_650M_UR90S_[1-5]33650MUR90/S 2020_031280https://dl.fbaipublicfiles.com/fair-esm/models/esm1v_t33_650M_UR90S_1.ptESM-MSA-1besm_msa1b_t12_100M_UR50S12100MUR50/S + MSA 2018_03768https://dl.fbaipublicfiles.com/fair-esm/models/esm_msa1b_t12_100M_UR50S.ptESM-MSA-1esm_msa1_t12_100M_UR50S12100MUR50/S + MSA 2018_03768https://dl.fbaipublicfiles.com/fair-esm/models/esm_msa1_t12_100M_UR50S.ptESM-1besm1b_t33_650M_UR50S33650MUR50/S 2018_031280https://dl.fbaipublicfiles.com/fair-esm/models/esm1b_t33_650M_UR50S.ptESM-1esm1_t34_670M_UR50S34670MUR50/S 2018_031280https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR50S.ptesm1_t34_670M_UR50D34670MUR50/D 2018_031280https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR50D.ptesm1_t34_670M_UR10034670MUR100 2018_031280https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t34_670M_UR100.ptesm1_t12_85M_UR50S1285MUR50/S 2018_03768https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t12_85M_UR50S.ptesm1_t6_43M_UR50S643MUR50/S 2018_03768https://dl.fbaipublicfiles.com/fair-esm/models/esm1_t6_43M_UR50S.ptHere is a chronological list of the released models and the paper they were introduced in:ShorthandRelease NotesESM-1Released with Rives et al. 2019 (Aug 2020 update).ESM-1bReleased with Rives et al. 2019 (Dec 2020 update). See Appendix B.ESM-MSA-1Released with Rao et al. 2021 (Preprint v1).ESM-MSA-1bReleased with Rao et al. 2021 (ICML'21 version, June 2021).ESM-1vReleased with Meier et al. 2021.ESM-IF1Released with Hsu et al. 2022.ESM-2Released with Lin et al. 2022.ESM Structural Split DatasetThis 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
seeRives et al. 2019Appendix A.10.Thisjupyter notebook tutorialshows how to load and index theESMStructuralSplitDataset.ESMStructuralSplitDataset, upon initializing, will downloadsplitsandpkl.
We also providemsasfor each of the domains. The data can be directly downloaded below.NameDescriptionURLsplitstrain/valid splitshttps://dl.fbaipublicfiles.com/fair-esm/structural-data/splits.tar.gzpklpkl objects containing sequence, SSP labels, distance map, and 3d coordinateshttps://dl.fbaipublicfiles.com/fair-esm/structural-data/pkl.tar.gzmsasa3m files containing MSA for each domainhttps://dl.fbaipublicfiles.com/fair-esm/structural-data/msas.tar.gzPre-training Dataset SplitThe split files establishing which UniRef50 clusters were used as held-out evaluation set for pre-training
inRives et al. 2019andRao et al. 2021can be found here:UniRef50 IDs of evaluation set: 3.016 M clustersUniRef100 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 theUniRef database, 2018-03 releasewhich is released by the UniProt Consortium under aCreative Commons Attribution (CC BY 4.0) License.CitationsIf you find the models useful in your research, we ask that you cite the relevant 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:@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:@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}}For variant prediction using ESM-1v:@article{meier2021language,author={Meier, Joshua and Rao, Roshan and Verkuil, Robert and Liu, Jason and Sercu, Tom and Rives, Alexander},title={Language models enable zero-shot prediction of the effects of mutations on protein function},year={2021},doi={10.1101/2021.07.09.450648},url={https://www.biorxiv.org/content/10.1101/2021.07.09.450648v1},journal={bioRxiv}}For inverse folding using ESM-IF1:@article{hsu2022learning,author={Hsu, Chloe and Verkuil, Robert and Liu, Jason and Lin, Zeming and Hie, Brian and Sercu, Tom and Lerer, Adam and Rives, Alexander},title={Learning inverse folding from millions of predicted structures},year={2022},doi={10.1101/2022.04.10.487779},url={https://www.biorxiv.org/content/early/2022/04/10/2022.04.10.487779},journal={bioRxiv}}For the ESM-2 language model and ESMFold:@article{lin2022language,title={Language models of protein sequences at the scale of evolution enable accurate structure prediction},author={Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and Smetanin, Nikita and dos Santos Costa, Allan and Fazel-Zarandi, Maryam and Sercu, Tom and Candido, Sal and others},journal={bioRxiv},year={2022},publisher={Cold Spring Harbor Laboratory}}Much of this code builds on thefairseqsequence 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.Additionally, if you would like to use the variant prediction benchmark from Meier et al. (2021), we provide a bibtex file with citations for all data in./examples/variant-prediction/mutation_data.bib. You can cite each paper individually, or add all citations in bulk using the LaTeX command:\nocite{wrenbeck2017deep,klesmith2015comprehensive,haddox2018mapping,romero2015dissecting,firnberg2014comprehensive,deng2012deep,stiffler2015evolvability,jacquier2013capturing,findlay2018comprehensive,mclaughlin2012spatial,kitzman2015massively,doud2016accurate,pokusaeva2019experimental,mishra2016systematic,kelsic2016rna,melnikov2014comprehensive,brenan2016phenotypic,rockah2015systematic,wu2015functional,aakre2015evolving,qi2014quantitative,matreyek2018multiplex,bandaru2017deconstruction,roscoe2013analyses,roscoe2014systematic,mavor2016determination,chan2017correlation,melamed2013deep,starita2013activity,araya2012fundamental}LicenseThis source code is licensed under the MIT license found in theLICENSEfile
in the root directory of this source tree.