Immune Builder API
The Immune Builder API provides deep learning-based structure prediction for immune receptor proteins including antibodies, nanobodies, and T-cell receptors (TCRs). This API is based on the ImmuneBuilder project, which uses state-of-the-art deep learning models to predict protein structures with high accuracy.
Overview
ImmuneBuilder generates structures with state-of-the-art accuracy while being much faster than AlphaFold2. The API supports three main modes:
- Antibody mode: Predicts antibody structures using ABodyBuilder3
- Nanobody mode: Predicts nanobody structures using NanoBodyBuilder2
- TCR mode: Predicts T-cell receptor structures using TCRBuilder2+
Command Line Interface
Examples
Predict antibody structure
lev engine submit immune-builder \
--mode antibody \
--heavy-chain heavy.fasta \
--light-chain light.fasta
Predict antibody structure with specific model type
lev engine submit immune-builder \
--mode antibody \
--heavy-chain heavy.fasta \
--light-chain light.fasta \
--model-type language
Predict TCR structure
lev engine submit immune-builder \
--mode tcr \
--alpha-fasta alpha.fasta \
--beta-fasta beta.fasta
Predict nanobody structure
lev engine submit immune-builder \
--mode nanobody \
--heavy-chain heavy.fasta
Flags
--mode
(str) (Required)- The modeling mode to use
- Options:
antibody
: Predict antibody structures (requires heavy and light chain FASTA files)tcr
: Predict T-cell receptor structures (requires alpha and beta chain FASTA files)nanobody
: Predict nanobody structures (requires heavy chain FASTA file only)
--heavy-chain
(str) (Conditional)- Path to the heavy chain FASTA file
- Required for
antibody
andnanobody
modes
--light-chain
(str) (Conditional)- Path to the light chain FASTA file
- Required for
antibody
mode only
--alpha-fasta
(str) (Conditional)- Path to the alpha chain FASTA file
- Required for
tcr
mode only
--beta-fasta
(str) (Conditional)- Path to the beta chain FASTA file
- Required for
tcr
mode only
--model-type
(str) (Default:plddt
)- Model type for antibody mode
- Options:
plddt
: Use pLDDT-based modellanguage
: Use language model-based approach
- Only applicable for
antibody
mode
Python Interface
Examples
Predict antibody structure:
from engine import EngineClient
client = EngineClient()
client.authorize()
job_id = client.submit_immune_builder(
mode="antibody",
heavy_fasta="heavy.fasta",
light_fasta="light.fasta"
)
Predict antibody structure with specific model type:
job_id = client.submit_immune_builder(
mode="antibody",
heavy_fasta="heavy.fasta",
light_fasta="light.fasta",
model_type="language"
)
Predict TCR structure:
job_id = client.submit_immune_builder(
mode="tcr",
alpha_fasta="alpha.fasta",
beta_fasta="beta.fasta"
)
Predict nanobody structure:
job_id = client.submit_immune_builder(
mode="nanobody",
heavy_fasta="heavy.fasta"
)
Outputs
ImmuneBuilder_output.pdb
(PDB file)- The predicted 3D structure in PDB format
Performance and Accuracy
Based on the ImmuneBuilder repository, the models achieve the following performance:
- ABodyBuilder2: CDR-H3 loops with RMSD of 2.81Å (0.09Å improvement over AlphaFold-Multimer)
- ABodyBuilder3: CDR-H3 loops with RMSD of 2.42Å (0.12Å improvement over AbodyBuilder2 on a different dataset)
- NanoBodyBuilder2: CDR-H3 loops with RMSD of 2.89Å (0.55Å improvement over AlphaFold2)
- TCRBuilder2+: State-of-the-art TCR structure prediction
All models are significantly faster than AlphaFold2 while maintaining high accuracy.
Processing Time
Typical processing times vary by mode and sequence length, but are generally much faster than AlphaFold2.