Publications by authors named "Ankur Annapareddy"

Pandemic countermeasures require the rapid design of antigens for vaccines, profiling patient antibody responses, assessing antigen structure-function landscapes, and the surveillance of emerging viral lineages. Cell surface display of a viral antigen or its subdomains can facilitate these goals by coupling the phenotypes of protein variants to their DNA sequence. Screening surface-displayed proteins via flow cytometry also eliminates time-consuming protein purification steps.

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The worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the repeated emergence of variants of concern. For the Omicron variant, sub-lineages BA.1 and BA.

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Article Synopsis
  • * A new platform called Spike Display has been developed to quickly analyze variations in spike proteins from over 200 SARS-CoV-2 variants in terms of their functionality, including how well they bind to receptors and how they're recognized by antibodies.
  • * The study identified critical mutations in variants that affect spike protein expression and their ability to evade neutralizing antibodies, suggesting that Spike Display could enhance the development of better vaccines and therapies against SARS-CoV-2 and similar viruses.
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The ongoing evolution of SARS-CoV-2 into more easily transmissible and infectious variants has sparked concern over the continued effectiveness of existing therapeutic antibodies and vaccines. Hence, together with increased genomic surveillance, methods to rapidly develop and assess effective interventions are critically needed. Here we report the discovery of SARS-CoV-2 neutralizing antibodies isolated from COVID-19 patients using a high-throughput platform.

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Despite the promise of deep learning accelerated protein engineering, examples of such improved proteins are scarce. Here we report that a 3D convolutional neural network trained to associate amino acids with neighboring chemical microenvironments can guide identification of novel gain-of-function mutations that are not predicted by energetics-based approaches. Amalgamation of these mutations improved protein function across three diverse proteins by at least 5-fold.

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