Biology subjects

Niesen, M. J.

Publications and source records attributed to Niesen, M. J..

2 recordsLinked to original sources

bioRxiv2023

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

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 models 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 neutralizing activity for this prospective dataset, with an R2 of 0.77. Expanding our methodology to include all available data from NCBI virus and NCATS OpenData Portal up to date, we assessed predicted changes in activity for current COVID-19 monoclonal antibodies and vaccines against newly identified SARS-CoV-2 lineages. Our predictions suggest that current therapeutic and vaccine-induced antibodies will have significantly reduced activity against newer XBB descendants, notably EG.5, FL.1.5.1, and XBB.1.16. Using the model, we were able to primarily attribute the observed predicted loss in activity to the F456L spike mutation found in EG.5 and FL.1.5.1 sequences. Conversely, mRNA-bivalent vaccines are predicted to be less susceptible to the recent BA.2.86 variant compared to new XBB descendants. These findings align closely with recent research, underscoring the potential of deep learning in shaping therapeutic and vaccine strategies for emerging viral variants.

immunology↗

bioRxiv2020

Force transduction creates long-ranged coupling in ribosomes stalled by arrest peptides

Force-sensitive arrest peptides regulate protein biosynthesis by stalling the ribosome as they are translated. Synthesis can be resumed when the nascent arrest peptide experiences a pulling force of sufficient magnitude to break the stall. Efficient stalling is dependent on the specific identity of a large number of amino acids, including amino acids which are tens of angstroms away from the peptidyl transferase center (PTC). The mechanism of force-induced restart and the role of these essential amino acids far from the PTC is currently unknown. We use hundreds of independent molecular dynamics trajectories spanning over 120 s in combination with kinetic analysis to characterize the barriers along the force-induced restarting pathway for the arrest peptide SecM. We find that the essential amino acids far from the PTC play a major role in controlling the transduction of applied force. In successive states along the stall-breaking pathway, the applied force propagates up the nascent chain until it reaches the C-terminus of SecM and the PTC, inducing conformational changes that allow for restart of translation. A similar mechanism of force propagation through multiple states is observed in the VemP stall-breaking pathway, but secondary structure in VemP allows for heterogeneity in the order of transitions through intermediate states. Results from both arrest peptides explain how residues that are tens of angstroms away from the catalytic center of the ribosome impact stalling efficiency by mediating the response to an applied force and shielding the amino acids responsible for maintaining the stalled state of the PTC. Significance StatementAs nascent proteins are synthesized by the ribosome, their interactions with the environment can create pulling forces on the nascent protein that can be transmitted to the ribosomes catalytic center. These forces can affect the rate and even the outcome of translation. We use simulations to characterize the pathway of force transduction along arrest peptides and discover how secondary structure in the nascent protein and its interactions with the ribosome exit tunnel impede force propagation. This explains how amino acids in arrest peptides that are tens of angstroms away from the ribosomes catalytic center contribute to stalling, and, more broadly, suggests how structural features in the nascent protein dictate the ribosomes ability to functionally respond to its environment.

biophysics↗