ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation

Abstract

Motivation: Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen-binding function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures.

Results: In this work, we present ABlooper, an end-to-end equivariant deep learning-based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49 Å, which drops to 2.05 Å when considering only its 75% most confident predictions.

Availability and implementation: https://github.com/oxpig/ABlooper.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Flowchart showing how E(n)-EGNN is used to predict CDR loops in ABlooper. The input geometry for each CDR loop is generated by aligning its residues between their anchors, while the node features are extracted from the loop sequence. Atom coordinates are then iteratively updated using a four-layer E(n)-EGNN resulting in a predicted set of conformations for each CDR

Fig. 2.

(A) CDR-H3 loop RMSD between final averaged prediction and crystal structure compared with average RMSD between the five ABlooper predictions for both the Rosetta Antibody Benchmark and the SAbDab Latest Structures set. (B) An example of a poorly predicted CDR-H3 loop. All five predictions are given in grey, with the final averaged prediction in blue and the crystal structure in green. The predictions from the five networks are very different, indicating an incorrect final prediction. (C) Example of correctly predicted CDR loops. All five predictions are similar, indicating a high confidence prediction. Colours are the same as in (B). (D) Effect of removing structures with a high CDR-H3 inter-prediction RMSD on the averaged RMSD for the set. The number of structures remaining after each quality cut-off is shown as a percentage. Data shown for the RAB and the SLS sets