ImmuneBuilder 0.0.3

Set of functions to predict the structure of immune receptor proteins

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ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins

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Abstract

Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding what antigen they bind. Here, we present ImmuneBuilder, a set of deep learning models trained to accurately predict the structure of antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) and T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state of the art accuracy while being much faster than AlphaFold2. For example, on a benchmark of 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.81Å, a 0.09Å improvement over AlphaFold-Multimer, while being over a hundred times faster. Similar results are also achieved for nanobodies (NanoBodyBuilder2 predicts CDR-H3 loops with an average RMSD of 2.89Å, a 0.55Å improvement over AlphaFold2) and TCRs. By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction.

Install

To install, download from github:

$ git clone https://github.com/brennanaba/ImmuneBuilder.git
$ pip install ImmuneBuilder/

This package requires PyTorch. If you do not already have PyTorch installed, you can do so following these instructions.

It also requires OpenMM and pdbfixer for the refinement step. OpenMM and pdbfixer can be installed via conda using:

$ conda install -c conda-forge openmm pdbfixer

It also uses anarci for trimming and numbering sequences. We recommend installing ANARCI from here, but it can also be installed using (maintained by a third party):

$ conda install -c bioconda anarci

Usage

To test the method out without installing it you can try this Google Colab

Antibody structure prediction

To predict an antibody structure using the python API you can do the following.

from ImmuneBuilder import ABodyBuilder2
predictor = ABodyBuilder2()

output_file = "my_antibody.pdb"
sequences = {
  'H': 'EVQLVESGGGVVQPGGSLRLSCAASGFTFNSYGMHWVRQAPGKGLEWVAFIRYDGGNKYYADSVKGRFTISRDNSKNTLYLQMKSLRAEDTAVYYCANLKDSRYSGSYYDYWGQGTLVTVS',
  'L': 'VIWMTQSPSSLSASVGDRVTITCQASQDIRFYLNWYQQKPGKAPKLLISDASNMETGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQYDNLPFTFGPGTKVDFK'}

antibody = predictor.predict(sequences)
antibody.save(output_file)

ABodyBuilder2 can also be used via de command line. To do this you can use:

ABodyBuilder2 --fasta_file my_antibody.fasta -v

You can get information about different options by using:

ABodyBuilder2 --help

I would recommend using the python API if you intend to predict many structures as you only have to load the models once.

Happy antibodies!!

Nanobody structure prediction

The python API for nanobodies is quite similar than for antibodies.

from ImmuneBuilder import NanoBodyBuilder2
predictor = NanoBodyBuilder2()

output_file = "my_nanobody.pdb"
sequence = {'H': 'QVQLVESGGGLVQPGESLRLSCAASGSIFGIYAVHWFRMAPGKEREFTAGFGSHGSTNYAASVKGRFTMSRDNAKNTTYLQMNSLKPADTAVYYCHALIKNELGFLDYWGPGTQVTVSS'}

nanobody = predictor.predict(sequence)
nanobody.save(output_file)

And it can also be used from the command line:

NanoBodyBuilder2 --fasta_file my_nanobody.fasta -v

TCR structure prediction

It is all pretty much the same for TCRs

from ImmuneBuilder import TCRBuilder2
predictor = TCRBuilder2()

output_file = "my_tcr.pdb"
sequences = {
"A": "AQSVTQLGSHVSVSEGALVLLRCNYSSSVPPYLFWYVQYPNQGLQLLLKYTSAATLVKGINGFEAEFKKSETSFHLTKPSAHMSDAAEYFCAVSEQDDKIIFGKGTRLHILP",
"B": "ADVTQTPRNRITKTGKRIMLECSQTKGHDRMYWYRQDPGLGLRLIYYSFDVKDINKGEISDGYSVSRQAQAKFSLSLESAIPNQTALYFCATSDESYGYTFGSGTRLTVV"}

tcr = predictor.predict(sequences)
tcr.save(output_file)

And it can also be used from the command line:

TCRBuilder2 --fasta_file my_tcr.fasta -v