Gina El Nesr

All Publications

• Learning millisecond protein dynamics from what is missing in NMR spectra. bioRxiv : the preprint server for biology Wayment-Steele, H. K., El Nesr, G., Hettiarachchi, R., Ojoawo, A. M., Kariyawasam, H., Ovchinnikov, S., Kern, D. 2026

  Abstract

  Many proteins' biological functions rely on interconversions between multiple conformations occurring at micro- to millisecond (μs-ms) timescales. A lack of standardized, large-scale experimental data has hindered obtaining a more predictive understanding of these motions. After curating >100 Nuclear Magnetic Resonance (NMR) relaxation datasets, we realized an observable for μs-ms dynamics might be hiding in plain sight. Millisecond dynamics can cause NMR signals to broaden beyond detection, leaving some residues not assigned in the chemical shift datasets of ~10,000 proteins deposited in the Biological Magnetic Resonance Data Bank (BMRB)1. We made the bold assumption that residues missing assignments are exchange-broadened due to μs-ms motions and trained various deep learning models to predict missing assignments. Strikingly, these models also predict exchange measured via NMR relaxation experiments, indicative of μs-ms dynamics. The best of these models, which we named Dyna-1, leverages an intermediate layer of the multimodal language model ESM-32. Notably, dynamics directly linked to biological function, including enzyme catalysis and ligand binding, are particularly well predicted by Dyna-1, which parallels our findings that residues experiencing μs-ms exchange are more conserved. We anticipate the datasets and models presented here will be transformative in unlocking the common language of dynamics and function.

  View details for DOI 10.1101/2025.03.19.642801

  View details for PubMedID 42427544

  View details for PubMedCentralID PMC13345324

• De novo design of RNA pseudoknots with deep learning. bioRxiv : the preprint server for biology Townley, J., Kladwang, W., Baker, D., Blair, H. M., Choe, C. A., Nesr, G. E., Favor, A., Fisker, E., Haack, D. B., He, S., Hingey, J., Huang, P. S., Huang, R., Joshi, C. K., Karagianes, T., Kubaney, A., Liò, P., Mancino, A., Romano, J., Rudolfs, B., Spellmon, N., Toor, N., Wu, V., Yu, Z., Participants, E., Das, R. 2026

  Abstract

  RNA design has been hindered by the limited accuracy of 3D structure prediction. Here, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an Eterna competition involving 57 pseudoknots, generative AI methods matched experienced human designers in solving most blind challenges, evaluated by single-nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryogenic electron microscopy. Unexpectedly, AI-generated molecules with accurate secondary structures formed well-ordered 3D folds stabilized by noncanonical tertiary interactions not modeled during design. Success was guided by a RNet foundation model trained on prior chemical mapping data, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.

  View details for DOI 10.64898/2026.05.21.726960

  View details for PubMedID 42239184

  View details for PubMedCentralID PMC13228335

• Zero-shot design of a de novo metalloenzyme. bioRxiv : the preprint server for biology Nesr, G. E., Dürr, S. L., Mathews, I. I., Wen, Q., Zhao, K., Sarangi, R., Röthlisberger, U., Sunden, F., Huang, P. S. 2026

  Abstract

  The de novo design of enzymes remains a central challenge, requiring consideration of catalytic mechanism and optimization across biochemical and biophysical criteria. To capture these criteria, we draw on principles from evolutionary biology. Here, we present dEVA (design by EVolutionary Algorithm), a multi-objective design framework for structure-based protein design. We apply dEVA to the zero-shot, de novo design of metalloenzymes by optimizing for the coordination sphere of catalytic metals. We fully characterize one of these designs: a bi-zinc metalloenzyme exhibiting promiscuous hydrolytic activity towards both phosphomonoesters and phosphodiesters. This design achieves a catalytic efficiency (kcat/KM) of up to 1500 M-1s-1 and a rate enhancement ((kcat/KM)/kw) of up to 3 × 1013, comparable to characterized natural phosphatases. dEVA offers a general and modular strategy for the programmable design of protein function without dependence on natural templates, predefined motif, or evolutionary information.

  View details for DOI 10.64898/2026.04.23.720277

  View details for PubMedID 42079180

  View details for PubMedCentralID PMC13131597

• Bispecific antibodies targeting the N-terminal and receptor binding domains potently neutralize SARS-CoV-2 variants of concern. Science translational medicine Rubio, A. A., Baharani, V. A., Dadonaite, B., Parada, M., Abernathy, M. E., Wang, Z., Lee, Y. E., Eso, M. R., Phung, J., Ramos, I., Chen, T., El Nesr, G., Bloom, J. D., Bieniasz, P. D., Nussenzweig, M. C., Barnes, C. O. 2025; 17 (788): eadq5720

  Abstract

  The ongoing emergence of severe acute respiratory syndrome coronavirus 2 (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 amino-terminal domain (NTD)- and receptor binding domain (RBD)-specific monoclonal antibodies previously isolated from coronavirus disease 2019 (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-electron microscopy structural analysis revealing recognition of a distinct NTD epitope outside of the site i antigenic supersite. Given C1596's favorable binding profile, we designed a series of bispecific antibodies (bsAbs), termed CoV2-biRNs, that featured both NTD and RBD specificities. Two of the C1596-inclusive bsAbs, CoV2-biRN5 and CoV2-biRN7, retained potent in vitro neutralization activity against all Omicron variants tested, including XBB.1.5, BA.2.86, and JN.1, contrasting the diminished potency of parental antibodies delivered as monotherapies or as a cocktail. Furthermore, prophylactic delivery of CoV2-biRN5 reduced the viral load within the lungs of K18-hACE2 mice after challenge with SARS-CoV-2 XBB.1.5. In conclusion, NTD-RBD bsAbs offer promising potential for the design of resilient, next-generation antibody therapeutics against SARS-CoV-2 VOCs.

  View details for DOI 10.1126/scitranslmed.adq5720

  View details for PubMedID 40043139

• Targeting peptide antigens using a multiallelic MHC I-binding system. Nature biotechnology Du, H., Mallik, L., Hwang, D., Sun, Y., Kaku, C., Hoces, D., Sun, S. M., Ghinnagow, R., Carro, S. D., Phan, H. A., Gupta, S., Blackson, W., Lee, H., Choe, C. A., Dersh, D., Liu, J., Bell, B., Yang, H., Papadaki, G. F., Young, M. C., Zhou, E., El Nesr, G., Goli, K. D., Eisenlohr, L. C., Minn, A. J., Hernandez-Lopez, R. A., Jardine, J. G., Sgourakis, N. G., Huang, P. S. 2024

  Abstract

  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. We demonstrate rapid screening of TRACeR-I against a panel of disease-relevant HLAs with peptides derived from human viruses (human immunodeficiency virus, Epstein-Barr virus and severe acute respiratory syndrome coronavirus 2), and oncoproteins (Kirsten rat sarcoma virus, paired-like homeobox 2b and New York esophageal squamous cell carcinoma 1). TRACeR-based bispecific T cell engagers and chimeric antigen receptor T cells exhibit on-target killing of tumor cells with high efficacy in the low nanomolar range. Our platform empowers the development of broadly applicable MHC I-targeting molecules for research, diagnostic and therapeutic applications.

  View details for DOI 10.1038/s41587-024-02505-8

  View details for PubMedID 39672954

  View details for PubMedCentralID 8363505

• An all-atom protein generative model. Proceedings of the National Academy of Sciences of the United States of America Chu, A. E., Kim, J., Cheng, L., El Nesr, G., Xu, M., Shuai, R. W., Huang, P. S. 2024; 121 (27): e2311500121

  Abstract

  Proteins 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 represents all sidechain states at once as a "superposition" state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.

  View details for DOI 10.1073/pnas.2311500121

  View details for PubMedID 38916999

• Singular value decomposition of protein sequences as a method to visualize sequence and residue space PROTEIN SCIENCE Baxter-Koenigs, A. R., El Nesr, G., Barrick, D. 2022; 31 (10): e4422

  Abstract

  Singular value decomposition (SVD) of multiple sequence alignments (MSAs) is an important and rigorous method to identify subgroups of sequences within the MSA, and to extract consensus and covariance sequence features that define the alignment and distinguish the subgroups. This information can be correlated to structure, function, stability, and taxonomy. However, the mathematics of SVD is unfamiliar to many in the field of protein science. Here, we attempt to present an intuitive yet comprehensive description of SVD analysis of MSAs. We begin by describing the underlying mathematics of SVD in a way that is both rigorous and accessible. Next, we use SVD to analyze sequences generated with a simplified model in which the extent of sequence conservation and covariance between different positions is controlled, to show how conservation and covariance produce features in the decomposed coordinate system. We then use SVD to analyze alignments of two protein families, the homeodomain and the Ras superfamilies. Both families show clear evidence of sequence clustering when projected into singular value space. We use k-means clustering to group MSA sequences into specific clusters, show how the residues that distinguish these clusters can be identified, and show how these clusters can be related to taxonomy and function. We end by providing a description a set of Python scripts that can be used for SVD analysis of MSAs, displaying results, and identifying and analyzing sequence clusters. These scripts are freely available on GitHub.

  View details for DOI 10.1002/pro.4422

  View details for Web of Science ID 000859977800001

  View details for PubMedID 36173173

  View details for PubMedCentralID PMC9514065