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'.
Overview
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.
Run ITsFlexible on Neurosnap
The ITsFlexible online webserver allows anybody with a Neurosnap account to run and access ITsFlexible, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- 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.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
| Statistic | Value |
|---|---|
| Credit Usage Rate | loading... |
| Estimated Total Cost | loading... |
| Runtime Mean | loading... |
| Runtime Median | loading... |
| Runtime Standard Deviation | loading... |
| Runtime 90th Percentile | loading... |
| Runtime Longest | loading... |
API Request
Access ITsFlexible using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
Similar Tools
Explore tools with similar features, categories, and use cases.
Citations
Please cite the original work when using ITsFlexible in publications or research outputs.
|
Spoendlin, F.C., Fernández-Quintero, M.L., Raghavan, S.S.R. et al. Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions. Nat Mach Intell (2025). https://doi.org/10.1038/s42256-025-01131-6 |
|
Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |