ITsFlexible
A deep learning tool that predicts the conformational flexibility of antibody and T cell receptor (TCR) CDR3 loops, classifying them as 'rigid' or 'flexible'.

ITsFlexible is a deep learning tool based on a graph neural network (GNN) architecture. It was developed to predict the structural flexibility of functionally important antibody and T cell receptor (TCR) complementarity-determining region 3 (CDR3) loops. The model is trained on the ALL-conformations dataset, which was constructed by extracting 1.2 million loop motifs from the Protein Data Bank. ITsFlexible classifies CDR loops as 'rigid' (adopting a single stable state) or 'flexible' (able to adopt multiple conformations) from inputs of antibody or TCR structures. The tool outperforms alternative approaches on crystal structure datasets and successfully generalizes to molecular dynamics simulations.
Estimated resource cost: 0.000248
Categories: Structure Prediction & Folding
Tags: Antibodies Proteins
Key Capabilities
- Powered by a graph neural network (GNN) architecture.
- Classifies antibody and TCR CDR3 loops as conformationally 'rigid' (one conformation) or 'flexible' (multiple conformations).
- Trained on the novel ALL-conformations dataset, comprising 1.2 million loop structures and over 100,000 unique sequences.
- Outperforms biophysical baselines and other zero-shot classifiers (like those based on AF2 pLDDT or MSA subsampling) on crystal structure test sets.
- Successfully generalizes to test sets derived from molecular dynamics (MD) simulations.
- Applicable to antibodies without solved structures; achieves similar performance when using predicted structural models (e.g., from IB or AF2) as input.
Runtime Statistics
| Metric | Value |
|---|---|
| runtime_mean | 9 |
| runtime_median | 9 |
| runtime_std | 0 |
| runtime_90th_percentile | 10 |
| runtime_max | 10 |
Similar Tools
- Immune Builder
- AFcluster
- AlphaFold2
- Boltz-1 (AlphaFold3)
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Estimated Credits: 0.000248
Invite-only, limited-time access. Please contact ztang@getantibody.com.
Invite-only, limited-time access. Please contact ztang@getantibody.com.