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Quast et al. |

ImmuneBuilder: Nanobody

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Protein Design
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Step 1: Upload your data

Data Upload

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  • Your file can be in the following formats:csv, txt, fasta, fastq
    • Please provide data, with a separate label and sequence on each row, e.g. nanobody1_H, nanobody2_H, etc
or
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Step 2: Set Parameters
Nanobody 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 in the following format for nanobodies:
>Nanobody1
QVQLVESGGGLVQPGESLRLSCAASGSIFGIYAVHWFRMAPGKEREFTAGFGSHGSTNYAASVKGRFTMSRDNAKNTTYLQMNSLKPADTAVYYCHALIKNELGFLDYWGPGTQVTVSS
>Nanobody2
QVQLVESGGGLVQPGESLRLSCAASGSIFGIYAVHWFRMAPGKEREFTAGFGSHGSTNYAASVKGRFTMSRDNAKNTTYLQMNSLKPADTAVYYCHALIKNELGFLDYWGPGTQVTVSS
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: unpaired amino acids should be provided.
This is the Nanobody structure prediction app. To predict the structure of Antibodies or T-Cell Receptors the following app can be used: https://app.superbio.ai/apps/670fd7f3f3a690a41668eeef
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-18-2024
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