Uncovering the flexibility of CDR loops in antibodies and TCRs through large-scale molecular dynamics
Cagiada, M.; Spoendlin, F. C.; Ifashe, K.; Deane, C. M.
Show abstract
Antibody structures are composed of framework regions that adopt a conserved fold and complementarity determining regions (CDR) loops which are far more variable. Flexibility of CDR loops has been linked to key properties such as affinity and specificity. However, owing to the scarcity of available data it has not been possible to study the functional implications of their dynamics in detail. To overcome these data limitations, we introduce CALVADOS 3-Fv, a customised set-up of the residue-based CALVADOS 3 model, utilizing restraints and parameterization tailored for immune receptor simulations. CALVADOS 3-Fv reproduces ensemble metrics in all atom simulations and experimental data with high accuracy. Having validated our protocol, we created FlAbDab and FTCRDab, two databases containing simulations of more than 150,000 antibodies and T-cell receptors. The databases are released open source to enable the study of CDR dynamics and as a large data source for training machine learning models.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Transient exposure of a buried phosphorylation site in an autoinhibited protein 95%
- Protein dynamics enables phosphorylation of buried residues in Cdk2/Cyclin A-bound p27 95%
- ATOMDANCE: kernel-based denoising and allosteric resonance analysis for functional and evolutionary comparisons of protein dynamics 95%
Similar papers in this journal
- A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases 95%
- Enriching stabilizing mutations through automated analysis of molecular dynamics simulations using BoostMut 95%
- De novo protein design by inversion of the AlphaFold structure prediction network 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.