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Quast et al. |

ImmuneBuilder: BCR & TCR

4
3
Protein Design
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Step 1: Upload your data

Data Upload

Drag your file(s) or upload
  • Your file can be in the following formats:csv, txt, fasta, fastq
    • Please provide data paired sequences in alternating format, with a separate label and sequence on each row, e.g. antibody1_H, antibody1_L, antibody2_H, antibody2_L, etc
or
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Step 2: Set Parameters
Antibody Structure Prediction
Step 3: Complete run profile

ImmuneBuilder is a deep learning model suite designed to predict the structure of immune receptor proteins, including antibodies, nanobodies, and T-cell receptors. It offers high accuracy and significant speed improvements over AlphaFold2.

Data should be uploaded with the following format, where paired chains are provided alternating with each other (e.g. H1 then L1 chain then H2 then L2 for antibodies, or A1 then B1 then A2 then B2 for T-Cell Receptors:
>Antibody1
EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYAMSWVRQAPGKGLEWVAVIWWDDQGGMDY
>Antibody1
DIQMTQSPSSLSASVGDRVTITCRASQDVNTAVAWYQQKPGKAPKLLIYGASTRATGIPDRF

Alternatively, paired sequences can be provided on the same line if separated by a "/":

>Antibody1 and 2
EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYAMSWVRQAPGKGLEWVAVIWWDDQGGMDY/DIQMTQSPSSLSASVGDRVTITCRASQDVNTAVAWYQQKPGKAPKLLIYGASTRATGIPDRF

Example use case: Predicting the 3D structure of an antibody to better understand its antigen binding properties, enabling faster development of biotherapeutics.

Technology: ImmuneBuilder consists of specialized models (ABodyBuilder2, NanoBodyBuilder2, TCRBuilder2) for predicting the structure of antibodies, nanobodies, and T-cell receptors. It is significantly faster than AlphaFold2 and includes error estimation for structural predictions.

Limitations: While ImmuneBuilder is faster than AlphaFold2 and shows improved accuracy in certain cases, such as predicting the CDR-H3 loop, its performance may still depend on the quality of input data, and further validation is required for some prediction types.

Parameters: paired amino acids should be provided, such that pairs alternate with each other.

This is the Antibody and T-Cell Receptor structure prediction app. To predict the structure of Nanobodies the following app can be used: https://app.superbio.ai/apps/671222967c065dee4e877feb

Citation:
T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity, Nele P. Quast, Brennan Abanades, Bora Guloglu, Vijaykumar Karuppiah, Stephen Harper, Matthew I. J. Raybould, Charlotte M. Deane, bioRxiv 2024.05.20.594940; doi: https://doi.org/10.1101/2024.05.20.594940
Released:
Oct-16-2024
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