A Deep Dimensionality Reduction method based on Variational Autoencoder for Antibody Complementarity Determining Region Sequences Analysis

Abstract

An essential task in antibody/nanobody therapeutics discovery is to rapidly identify whether an antibody/nanobody has specificity and cross-reactivity to one or multiple tar- gets. Multiple target specificity and cross-reactivity of antibodies can be demonstrated by screening the third Complementarity Determining Region on the heavy chain (CDR-H3) of antibody sequences. However, the existing methods are costly and labor-intensive as repet- itive wet-lab experimentation is required to explore the sequences space. Here, we present a deep learning dimensionality reduction model based on Variational Autoencoder (VAE) and Residual Neural Network (Resnet), which we named VAEResDR. Our VAEResDR can efficiently learn the sequences’ key features while scaling down high-dimensional an- tibody sequences into a two-dimensional visualization representation for coherent analysis and rapid screening. We demonstrate that our VAEResDR can provide a tool to precisely analyze CDR-H3 sequences within the hidden patterns and effectively improve antibody/- nanobody CDR-H3 sequence clustering.