Publications by authors named "Gina El Nesr"
Identifying highly specific T cell receptors (TCRs) or antibodies against epitopic peptides presented by class I major histocompatibility complex (MHC I) proteins remains a bottleneck in the development of targeted therapeutics. Here, we introduce targeted recognition of antigen-MHC complex reporter for MHC I (TRACeR-I), a generalizable platform for targeting peptides on polymorphic HLA-A*, HLA-B* and HLA-C* allotypes while overcoming the cross-reactivity challenges of TCRs. Our TRACeR-MHC I co-crystal structure reveals a unique antigen recognition mechanism, with TRACeR forming extensive contacts across the entire peptide length to confer single-residue specificity at the accessible positions.
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- Proteins perform their functions through chemical interactions, making it essential to model these interactions for protein design, especially focusing on sidechains.
- The authors introduce Protpardelle, an all-atom diffusion model that represents all sidechain states as a "superposition" state, allowing for efficient sample generation of protein structures.
- This model effectively combines structure and sequence design, producing high-quality proteins that mimic the properties of natural proteins, and has potential applications in designing proteins without relying on traditional backbone and rotamer frameworks.
The ongoing emergence of SARS-CoV-2 variants of concern (VOCs) that reduce the effectiveness of antibody therapeutics necessitates development of next-generation antibody modalities that are resilient to viral evolution. Here, we characterized N-terminal domain (NTD) and receptor binding domain (RBD)-specific monoclonal antibodies previously isolated from COVID-19 convalescent donors for their activity against emergent SARS-CoV-2 VOCs. Among these, the NTD-specific antibody C1596 displayed the greatest breadth of binding to VOCs, with cryo-EM structural analysis revealing recognition of a distinct NTD epitope outside of the site i antigenic supersite.
View Article and Find Full Text PDFProteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which instantiates a "superposition" over the possible sidechain states, and collapses it to conduct reverse diffusion for sample generation.
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- SVD is a powerful method for analyzing multiple sequence alignments (MSAs) that helps identify sequence subgroups and extract important features related to structure and function.
- SVD can be made more accessible by explaining its mathematics intuitively, as demonstrated through a simplified model that shows how sequence conservation and covariance affect alignment features.
- The study applies SVD to two protein families, revealing sequence clustering and providing Python scripts for users to conduct their own SVD analyses on MSAs, which are available for free on GitHub.