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

• URL: http://arxiv.org/abs/2111.10656v1
• Date: Sat, 20 Nov 2021 18:55:09 GMT
• Title: Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction
• Authors: Natalia Zenkova, Ekaterina Sedykh, Tatiana Shugaeva, Vladislav Strashko, Timofei Ermak, Aleksei Shpilman
• Abstract summary: We present an end-to-end model to predict CDR H3 loop structure, that performs on par with state-of-the-art methods. We also raise an issue with a commonly used RosettaAntibody benchmark that leads to data leaks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 {\it 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.

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