Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction

TL;DR

This paper introduces a fast, end-to-end deep learning model for predicting the structure of the antibody CDR H3 loop, matching current accuracy levels but significantly improving speed, and highlights issues with existing benchmarking datasets.

Contribution

The paper presents a novel end-to-end deep learning approach for CDR H3 structure prediction that is faster and maintains accuracy, addressing limitations of current methods.

Findings

Model achieves accuracy comparable to state-of-the-art methods.

The approach is an order of magnitude faster than existing techniques.

Identifies data leakage issues in common benchmarking datasets.

Abstract

Predicting a structure of an antibody from its sequence is important since it allows for a better design process of synthetic antibodies that play a vital role in the health industry. Most of the structure of an antibody is conservative. The most variable and hard-to-predict part is the third complementarity-determining region of the antibody heavy chain (CDR H3). Lately, deep learning has been employed to solve the task of CDR H3 prediction. However, current state-of-the-art methods are not end-to-end, but rather they output inter-residue distances and orientations to the RosettaAntibody package that uses this additional information alongside statistical and physics-based methods to predict the 3D structure. This does not allow a fast screening process and, therefore, inhibits the development of targeted synthetic antibodies. In this work, we present an end-to-end model to predict CDR H3 loop structure, that performs on par with state-of-the-art methods in terms of accuracy but an order of magnitude faster. We also raise an issue with a commonly used RosettaAntibody benchmark that leads to data leaks, i.e., the presence of identical sequences in the train and test datasets.