ProtGPT2

ProtGPT2

ProtGPT2 (peer-reviewed paper) is a language model that speaks the protein language and can be used for de novo protein design and engineering. ProtGPT2 generated sequences conserve natural proteins' critical features (amino acid propensities, secondary structural content, and globularity) while exploring unseen regions of the protein space.

Model description

ProtGPT2 is based on the GPT2 Transformer architecture and contains 36 layers with a model dimensionality of 1280, totalling 738 million parameters.

ProtGPT2 is a decoder-only transformer model pre-trained on the protein space, database UniRef50 (version 2021_04). The pre-training was done on the raw sequences without FASTA headers. Details of training and datasets can be found here: https://huggingface.co/datasets/nferruz/UR50_2021_04

ProtGPT2 was trained in a self-supervised fashion, i.e., the raw sequence data was used during training without including the annotation of sequences. In particular, ProtGPT2 was trained using a causal modelling objective, in which the model is trained to predict the next token (or, in this case, oligomer) in the sequence. By doing so, the model learns an internal representation of proteins and is able to speak the protein language.

How to use ProtGPT2

ProtGPT2 can be used with the HuggingFace transformer python package. Detailed installation instructions can be found here: https://huggingface.co/docs/transformers/installation

Since ProtGPT2 has been trained on the classical language model objective, it excels at generating protein sequences. It can be used to generate sequences in a zero-shot fashion or to generate sequences of a particular type after finetuning on a user-defined dataset.

Example 1: Generating de novo proteins in a zero-shot fashion

In the example below, ProtGPT2 generates sequences that follow the amino acid 'M'. Any other amino acid, oligomer, fragment, or protein of choice can be selected instead. The model will generate the most probable sequences that follow the input. Alternatively, the input field can also be left empty and it will choose the starting tokens.

>>> from transformers import pipeline
>>> protgpt2 = pipeline('text-generation', model="nferruz/ProtGPT2")
# length is expressed in tokens, where each token has an average length of 4 amino acids.
>>> sequences = protgpt2("", max_length=100, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=10, eos_token_id=0)
>>> for seq in sequences:
        print(seq):
 {'generated_text': 'MINDLLDISRIISGKMTLDRAEVNLTAIARQVVEEQRQAAEAKSIQLLCSTPDTNHYVFG\nDFDRLKQTLWNLLSNAVKFTPSGGTVELELGYNAEGMEVYVKDSGIGIDPAFLPYVFDRF\nRQSDAADSRNYGGLGLGLAIVKHLLDLHEGNVSAQSEGFGKGATFTVLLPLKPLKRELAA\nVNRHTAVQQSAPLNDNLAGMKILIVEDRPDTNEMVSYILEEAGAIVETAESGAAALTSLK\nSYSPDLVLSDIGMPMMDGYEMIEYIREWKTTKGG'}
{'generated_text': 'MQGDSSISSSNRMFT\nLCKPLTVANETSTLSTTRNSKSNKRVSKQRVNLAESPERNAPSPASIKTNETEEFSTIKT\nTNNEVLGYEPNYVSYDFVPMEKCNLCNENCSIELASLNEETFVKKTICCHECRKKAIENA\nENNNTKGSAVSNNSVTSSSGRKKIIVSGSQILRNLDSLTSSKSNISTLLNPNHLAKLAKN\nGNLSSLSSLQSSASSISKSSSTSSTPTTSPKVSSPTNSPSSSPINSPTP'}
{'generated_text': 'M\nSTHVSLENTLASLQATFFSLEARHTALETQLLSTRTELAATKQELVRVQAEISRADAQAQ\nDLKAQILTLKEKADQAEVEAAAATQRAEESQAALEAQTAELAQLRLEKQAPQHVAEEGDP\nQPAAPTTQAQSPVTSAAAAASSAASAEPSKPELTFPAYTKRKPPTITHAPKAPTKVALNP\nSTLSTSGSGGGAKADPTPTTPVPSSSAGLIPKALRLPPPVTPAASGAKPAPSARSKLRGP\nDAPLSPSTQS'}
{'generated_text': 'MVLLSTGPLPILFLGPSLAELNQKYQVVSDTLLRFTNTV\nTFNTLKFLGSDS\n'}
{'generated_text': 'M\nNNDEQPFIMSTSGYAGNTTSSMNSTSDFNTNNKSNTWSNRFSNFIAYFSGVGWFIGAISV\nIFFIIYVIVFLSRKTKPSGQKQYSRTERNNRDVDSIKRANYYG\n'}
{'generated_text': 'M\nEAVYSFTITETGTGTVEVTPLDRTISGADIVYPPDTACVPLTVQPVINANGTWTLGSGCT\nGHFSVDTTGHVNCLTGGFGAAGVHTVIYTVETPYSGNSFAVIDVNVTEPSGPGDGGNGNG\nDRGDGPDNGGGNNPGPDPDPSTPPPPGDCSSPLPVVCSDRDCADFDTQAQVQIYLDRYGG\nTCDLDGNHDGTPCENLPNNSGGQSSDSGNGGGNPGTGSTHQVVTGDCLWNIASRNNGQGG\nQAWPALLAANNESITNP'}
{'generated_text': 'M\nGLTTSGGARGFCSLAVLQELVPRPELLFVIDRAFHSGKHAVDMQVVDQEGLGDGVATLLY\nAHQGLYTCLLQAEARLLGREWAAVPALEPNFMESPLIALPRQLLEGLEQNILSAYGSEWS\nQDVAEPQGDTPAALLATALGLHEPQQVAQRRRQLFEAAEAALQAIRASA\n'}
{'generated_text': 'M\nGAAGYTGSLILAALKQNPDIAVYALNRNDEKLKDVCGQYSNLKGQVCDLSNESQVEALLS\nGPRKTVVNLVGPYSFYGSRVLNACIEANCHYIDLTGEVYWIPQMIKQYHHKAVQSGARIV\nPAVGFDSTPAELGSFFAYQQCREKLKKAHLKIKAYTGQSGGASGGTILTMIQHGIENGKI\nLREIRSMANPREPQSDFKHYKEKTFQDGSASFWGVPFVMKGINTPVVQRSASLLKKLYQP\nFDYKQCFSFSTLLNSLFSYIFNAI'}
{'generated_text': 'M\nKFPSLLLDSYLLVFFIFCSLGLYFSPKEFLSKSYTLLTFFGSLLFIVLVAFPYQSAISAS\nKYYYFPFPIQFFDIGLAENKSNFVTSTTILIFCFILFKRQKYISLLLLTVVLIPIISKGN\nYLFIILILNLAVYFFLFKKLYKKGFCISLFLVFSCIFIFIVSKIMYSSGIEGIYKELIFT\nGDNDGRFLIIKSFLEYWKDNLFFGLGPSSVNLFSGAVSGSFHNTYFFIFFQSGILGAFIF\nLLPFVYFFISFFKDNSSFMKLF'}
{'generated_text': 'M\nRRAVGNADLGMEAARYEPSGAYQASEGDGAHGKPHSLPFVALERWQQLGPEERTLAEAVR\nAVLASGQYLLGEAVRRFETAVAAWLGVPFALGVASGTAALTLALRAYGVGPGDEVIVPAI\nTFIATSNAITAAGARPVLVDIDPSTWNMSVASLAARLTPKTKAILAVHLWGQPVDMHPLL\nDIAAQANLAVIEDCAQALGASIAGTKVGTFGDAAAFSFYPTKNMTTGEGGMLVTNARDLA\nQAARMLRSHGQDPPTAYMHSQVGFN'}

Example 2: Finetuning on a set of user-defined sequences

This alternative option to the zero-shot generation permits introducing direction in the generation process. User-defined training and validation files containing the sequences of interest are provided to the model. After a short update of the model's weights, ProtGPT2 will generate sequences that follow the input properties.

To create the validation and training file, it is necessary to (1) substitute the FASTA headers for each sequence with the