A deep learning framework for predicting the neutralizing activity of COVID-19 therapeutics and vaccines against evolving SARS-CoV-2 variants

作者：Robert P. Matson, Işın Y. Comba, Eli Silvert, Michiel J.M. Niesen, Karthik Murugadoss, Dhruti Padwardhan, Rohit Suratekar, Elizabeth-Grace Goel, Brittany J. Poelaert, Kanny K. Wan, Kyle R. Brimacombe, AJ Venkatakrishnan, Venky Soundararajan · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2023 · DOI：10.1101/2023.10.24.563847 · 研究领域：SARS-CoV-2 and COVID-19 Research、vaccines and immunoinformatics approaches、Monoclonal and Polyclonal Antibodies Research

Abstract Understanding how viral variants evade neutralization is crucial for improving antibody-based treatments, especially with rapidly evolving viruses like SARS-CoV-2. Yet, conventional assays are limited in the face of rapid viral evolution, relying on a narrow set of viral isolates, and falling short in capturing the full spectrum of variants. To address this, we have developed a deep learning approach to predict changes in neutralizing antibody activity of COVID-19 therapeutics and vaccines against emerging viral variants. First, we trained a variational autoencoder (VAE) using all 67,885 unique SARS-CoV-2 spike protein sequences from the NCBI virus (up to October 31, 2022) database to encode spike protein variants into a latent space. Using this VAE and a curated dataset of 7,069 in vitro assay data points from the NCATS OpenData Portal, we trained a neural network regression model to predict fold changes in neutralizing activity of 40 COVID-19 therapeutics and vaccines against spike protein sequence variants, relative to their neutralizing activity against the ancestral strain (Wuhan-Hu-1). Our model also employs Bayesian inference to quantify prediction uncertainty, providing more nuanced and informative estimates. To validate the model’s predictive capacity, we assessed its performance on a test set of in vitro assay data collected up to eight months after the data included in the model training (N = 980). The model accurately predicted fold changes in neutralizin...