Biology subjects

Suratekar, R.

Publications and source records attributed to Suratekar, R..

3 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↗

bioRxiv2021

High diversity in Delta variant across countries revealed via genome-wide analysis of SARS-CoV-2 beyond the Spike protein

The highly contagious Delta variant of SARS-CoV-2 has emerged as the new dominant global strain, and reports of reduced effectiveness of COVID-19 vaccines against the Delta variant are highly concerning. While there has been extensive focus on understanding the amino acid mutations in the Delta variant s Spike protein, the mutational landscape of the rest of the SARS-CoV-2 proteome (25 proteins) remains poorly understood. To this end, we performed a systematic analysis of mutations in all the SARS-CoV-2 proteins from nearly 2 million SARS-CoV-2 genomes from 176 countries/territories. Six highly-prevalent missense mutations in the viral life cycle-associated Membrane (I82T), Nucleocapsid (R203M, D377Y), NS3 (S26L), and NS7a (V82A, T120I) proteins are almost exclusive to the Delta variant compared to other variants of concern (mean prevalence across genomes: Delta = 99.74%, Alpha = 0.06%, Beta = 0.09%, Gamma = 0.22%). Furthermore, we find that the Delta variant harbors a more diverse repertoire of mutations across countries compared to the previously dominant Alpha variant (cosine similarity: meanAlpha = 0.94, S.D.Alpha = 0.05; meanDelta = 0.86, S.D.Delta = 0.1; Cohen s dAlpha-Delta = 1.17, p-value < 0.001). Overall, our study underscores the high diversity of the Delta variant between countries and identifies a list of targetable amino acid mutations in the Delta variant s proteome for probing the mechanistic basis of pathogenic features such as high viral loads, high transmissibility, and reduced susceptibility against neutralization by vaccines.

genomics↗

bioRxiv2017

Evidence of sinks and sources in the PLC activated PIP2 cycle

In many eukaryotic signalling cascades, receptor mediated phospholipase C (PLC) activity triggers phosphatidylinositol 4,5 bisphosphate (PIP2) hydrolysis leading to information transfer in cells. Coupled with PLC activation is a sequence of reactions spread across multiple compartments by which PIP2 is resynthesized, a process essential to maintain PIP2 levels and support sustained PLC signalling. The biochemical strategies to co-ordinate these reactions and support PIP2 levels have remained poorly understood. In particular, the question of whether the PIP2 cycle is a closed cycle with no net addition or loss of metabolites has not been addressed. Using mathematical modelling approaches, we find that a closed PIP2 cycle cannot explain experimentally observed changes in the metabolic intermediates when changing enzyme activities in the PIP2 cycle. Thus, we propose that the PIP2 cycle likely includes at least one metabolic source and one sink whose net activity results in the experimentally observed regulation of this key signalling pathway.

biophysics↗