Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions

Abstract

Many proteins are highly flexible and their ability to adapt their shape can be fundamental to their functional properties. For example, the flexibility of antibody complementarity-determining region (CDR) loops influences binding affinity and specificity, making it a key factor in understanding and designing antigen interactions. With methods such as AlphaFold, it is possible to computationally predict a single, static protein structure with high accuracy. However, the reliable prediction of structural flexibility has not yet been achieved. A major factor limiting such predictions is the scarcity of suitable training data. Here we focus on predicting the structural flexibility of functionally important antibody and T cell receptor CDR3 loops. To this end, we constructed ALL-conformations by extracting CDR3s and CDR3-like loop motifs from all structures deposited in the Protein Data Bank. This dataset comprises 1.2 million loop structures representing more than 100,000 unique sequences and captures all experimentally observed conformations of these motifs. Using this dataset, we develop ITsFlexible, a deep learning tool with graph neural network architecture. We trained the model to binary classify CDR loops as 'rigid' or 'flexible' from inputs of antibody structures. ITsFlexible outperforms all alternative approaches on our crystal structure datasets and successfully generalizes to molecular dynamics simulations. We also used ITsFlexible to predict the flexibility of three CDRH3 loops with no solved structures and experimentally determined their conformations using cryogenic electron microscopy.

Keywords: Biologics; Structural biology.

Fig. 1. Overview of ALL-conformations and ITsFlexible.

a, ALL-conformations is a dataset that contains the crystal structures of antibody CDR3s, TCR CDR3s and CDR-like loop motifs across all proteins. The dataset captures all the observed conformational states of such loops. b, Loops are labelled as either flexible (if they are observed in more than one conformation) or rigid (if evidence suggests that they adopt a single conformation). We define a conformation by structural similarity and use an RMSD of 1.25 Å as a threshold to separate states. c, Flowchart detailing the ITsFlexible method predicting the conformational flexibility of CDR loops. The structure and sequence of a loop (cyan) and its context (grey) are extracted from a PDB file, and a graph representation is generated. A graph neural network (GNN) classifies loops as conformationally flexible or rigid.

Fig. 2. ITsFlexible performance evaluated on the PDB test set.

a,b, Classification on the PDB test set containing 2,845 loop motifs is evaluated with metrics of PR AUC (a) and ROC AUC (b). ITsFlexible performance (light grey) is compared with random classification (red), three biophysical baselines (green), an AF2 pLDDT-based model (blue) and two ITsFlexible versions with input ablations (dark grey). Exact PR AUC and ROC AUC values are presented in Supplementary Tables 9 and 10.

Fig. 3. ITsFlexible performance evaluated on the CDR test sets.

a,b, Classification on the four test sets is evaluated with metrics of PR AUC (a) and ROC AUC (b). ITsFlexible performance from inputs of crystal structures, IB and AF2 models (light grey) is compared with random classification (red), three biophysical baselines (green) and three zero-shot models based on the outputs of protein structure prediction tools (blue). Exact PR AUC and ROC AUC values are presented in Supplementary Tables 9 and 10. c,d, Example of antibodies predicted to be flexible and rigid by ITsFlexible. c, Overlay of six structures of the same antibody Fv with CDRH3 predicted to be flexible. Crystal structures indicated that the CDRH3 (highlighted in colour) adopts three different conformations (red, blue and green). d, Overlay of 22 structures of the same antibody Fv with CDRH3 predicted to be rigid. The CDRH3 (red) occupies the same conformations in all 22 structures.

Fig. 4. ITsFlexible performance evaluated on the MD test set containing 19 antibodies.

a,b, Classification of CDRH3s and CDRL3s is evaluated with metrics of PR AUC (a) and ROC AUC (b). A prediction was made for each representative structure extracted from MD and classification performed based on the maximum ITsFlexible score observed across the ensemble. Exact PR AUC and ROC AUC values and performance based on the mean ITsFlexible score observed across the ensemble are presented in Supplementary Table 17.

Fig. 5. Case-study antibodies selected for cryo-EM experiments.

High-resolution structure of the three antibodies in complex with influenza H1N1 HA were solved. a,d,g, Top and side views of the cryo-EM map of antibodies (heavy chain, dark colour; light chain, bright colour) in complex with the antigen (grey). In each structure, three symmetrically arranged copies of the antibody were captured. Antibody 1 (9N5Y; a) binds to the HA stem and antibodies 2 (9N5Z; d) and 3 (g) to the HA head. b,e,h, Cartoon representation of antibody–antigen binding interfaces of antibodies 9N5Y (b), 9N5Z (e) and 3 (h). CDRH3s are shown in different shades of colour and binding interactions are highlighted in stick representation. c,f,i, Summary tables showing the CDRH3 length and sequence, sequence identity to the closest example in the ITsFlexible training set, the ITsFlexible prediction score (a higher value indicates a higher likelihood of being flexible), a binary prediction of loop flexibility based on the ITsFlexible score and the flexibility determined with the cryo-EM experiments for antibodies 9N5Y (c), 9N5Z (f) and 3 (i). Additional metadata for the three antibodies are shown in Supplementary Table 21.