Biology subjects

Zemla, A.

Publications and source records attributed to Zemla, A..

5 recordsLinked to original sources

bioRxiv2026

A Selenium-Deficient Mouse Model of Mouse-Adapted SARS-CoV-2 Demonstrates Variant Emergence Observed in SARS-CoV-2 Pandemic Variants

Host selenium deficiency has been shown to generate novel genetic variants in RNA viruses. With the predicted rise of selenium deficiency globally, we sought to determine if host selenium deficiency can be a predictive factor for RNA virus variant emergence. We utilized a selenium-deficient BALB/c mouse model to investigate how host selenium status influences the emergence of viral variants in mouse-adapted SARS-CoV-2. Mice were maintained on control or selenium-deficient diets and subjected to sequential rounds of diet-matched viral passage to generate diet-specific virus populations. Deep sequencing of passaged viral populations revealed that selenium-deficient passage drove a marked increase in inter-host genomic heterogeneity and produced a distinct mutational profile relative to control passage. Eighteen mutations were identified as unique to selenium-deficient passage, including variants previously observed during natural human SARS-CoV-2 evolution. These mutations were largely maintained at sub-consensus frequencies, indicating that selenium deficiency can expand the viral quasispecies landscape that enrichs reservoirs of adaptive potential. To determine how this altered mutant spectrum affected pathogenesis, we challenged normal diet-fed adult and aged BALB/c mice with control- or selenium-deficient-passaged virus. Although overt differences in weight loss, survival, and viral burden were generally modest, selenium-deficient-passaged virus induced pronounced increases in antiviral cytokine expression. Together, these findings identify host selenium deficiency as a driver of RNA virus population diversification and show that nutritionally stressed animal models can reproducibly generate mutations observed in nature.

microbiology↗

bioRxiv2023

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-based Featurization Method

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multi-target interactions are a first step in finding an effective therapeutic; undesirable off-target interactions are a first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets, and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featurized by the ligands which bind to their best co-complex template matches. The simplicity and interpretability of this approach provides a granular characterization of the human proteome at the protein pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7,000 compounds.

synthetic biology↗

bioRxiv2022

PDBspheres - a method for finding 3D similarities in local regions in proteins

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions ("spheres") adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA structure alignment algorithm as the main engine for detecting structure similarities between the protein of interest and template spheres from the library, which currently contains more than 2 million spheres. To assess confidence in structural matches an all-atom-based similarity metric takes sidechain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres - a strictly structure-based method - performs on a curated dataset of 2,528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4,876 structures in the "refined set" of the PDBbind 2019 dataset.

bioinformatics↗

bioRxiv2021

SARS-COV-2 Omicron variant predicted to exhibit higher affinity to ACE-2 receptor and lower affinity to a larger range of neutralizing antibodies, using a rapid assessment platform

Rapid assessment of whether a pandemic pathogen may have increased transmissibility or be capable of evading existing vaccines and therapeutics is critical to mounting an effective public health response. Over the period of seven days, we utilized rapid computational prediction methods to evaluate potential public health implications of the emerging SARS-CoV-2 Omicron variant. Specifically, we modeled the structure of the Omicron variant, examined its interface with human angiotensin converting enzyme 2 (ACE-2) and evaluated the change in binding affinity between Omicron, ACE-2 and publicly known neutralizing antibodies. We also compared the Omicron variant to known Variants of Concern (VoC). Seven of the 15 Omicron mutations occurring in the spike protein receptor binding domain (RBD) occur at the ACE-2 cell receptor interface, and therefore may play a critical role in enhancing binding to ACE-2. Our estimates of Omicron RBD-ACE-2 binding affinities indicate that at least two of RBD mutations, Q493R and N501Y, contribute to enhanced ACE-2 binding, nearly doubling delta-delta-G (ddG) free energies calculated for other VoCs. Binding affinity estimates also were calculated for 55 known neutralizing SARS-CoV-2 antibodies. Analysis of the results showed that Omicron substantially degrades binding for more than half of these neutralizing SARS-CoV-2 antibodies, and for roughly 10 times as many of the antibodies than the currently dominant Delta variant. This early study lends support to use of rapid computational risk assessments to inform public health decision-making while awaiting detailed experimental characterization and confirmation.

bioinformatics↗

bioRxiv2020

Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing

Rapidly responding to novel pathogens, such as SARS-CoV-2, represents an extremely challenging and complex endeavor. Numerous promising therapeutic and vaccine research efforts to mitigate the catastrophic effects of COVID-19 pandemic are underway, yet an efficacious countermeasure is still not available. To support these global research efforts, we have used a novel computational pipeline combining machine learning, bioinformatics, and supercomputing to predict antibody structures capable of targeting the SARS-CoV-2 receptor binding domain (RBD). In 22 days, using just the SARS-CoV-2 sequence and previously published neutralizing antibody structures for SARS-CoV-1, we generated 20 initial antibody sequences predicted to target the SARS-CoV-2 RBD. As a first step in this process, we predicted (and publicly released) structures of the SARS-CoV-2 spike protein using homology-based structural modeling. The predicted structures proved to be accurate within the targeted RBD region when compared to experimentally derived structures published weeks later. Next we used our in silico design platform to iteratively propose mutations to SARS-CoV-1 neutralizing antibodies (known not to bind SARS-Cov-2) to enable and optimize binding within the RBD of SARS-CoV-2. Starting from a calculated baseline free energy of -48.1 kcal/mol ({+/-} 8.3), our 20 selected first round antibody structures are predicted to have improved interaction with the SARS-CoV-2 RBD with free energies as low as -82.0 kcal/mole. The baseline SARS-CoV-1 antibody in complex with the SARS-CoV-1 RBD has a calculated interaction energy of -52.2 kcal/mole and neutralizes the virus by preventing it from binding and entering the human ACE2 receptor. These results suggest that our predicted antibody mutants may bind the SARS-CoV-2 RBD and potentially neutralize the virus. Additionally, our selected antibody mutants score well according to multiple antibody developability metrics. These antibody designs are being expressed and experimentally tested for binding to COVID-19 viral proteins, which will provide invaluable feedback to further improve the machine learning-driven designs. This technical report is a high-level description of that effort; the Supplementary Materials includes the homology-based structural models we developed and 178,856 in silico free energy calculations for 89,263 mutant antibodies derived from known SARS-CoV-1 neutralizing antibodies.

bioinformatics↗