Author

Emily Engelhart

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Synthetic Biology· 2 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:protein structure· 2 Approach:Bayesian inference· 1 Research Focus:database / resource· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Randolph M. Lopez· 5 shared Ryan EmersoniD· 4 shared David A. YoungeriD· 3 shared Kerry L. McGowaniD· 3 shared Aditya A. Agarwal· 2 shared Adrian W. Lange· 2 shared Charles Lin· 2 shared Colleen Shikany· 2 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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5 shared papersSynthetic Biology

Randolph M. Lopez

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

The Synthetic Epitope Atlas: High-Throughput Design and Validation of De Novo Antibody-Antigen Complexes

Nicholas Altieri, Joseph L. Harman, David Noble, Natasha Seelam Murakowska, Alexander Eng, Kerry L. McGowan, Davis Goodnight, Lucian DiPeso, Colleen Shikany, Emily Engelhart, Leah J. Homad, Miranda C. Lahman, Shyam Gandhi, Mackenzie Goodwin, Kendrick Herbst, , Adrian W. Lange

The paper generates and validates synthetic epitope proteins to improve de novo antibody design by combining computational design with high-throughput experimental binding data.

bioRxiv · Apr 19, 2026 · PreprintSynthetic Biology

Effects of protein interface mutations on protein quality and affinity

Jurrian K. de Kanter, Eva Smorodina, Aygul R. Minnegalieva, Marloes Arts, Lasse M. Blaabjerg, Marina Frolenkova, Puneet Rawat, Lina Wolfram, Helena Britze, Yano Wilke, Lucas Matthias Weissenborn, Laurens H. Lindenburg, Emily Engelhart, Kerry L. McGowan, Ryan Emerson, , Victor Greiff

The paper disentangles protein-interaction and protein-quality effects in antibody-antigen binding using deep mutational scanning and evaluates models for their ability to predict these effects.

bioRxiv · Mar 26, 2026 · PreprintMolecular Biology

AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity

Aditya A. Agarwal, James Harrang, David Noble, Kerry L. McGowan, Adrian W. Lange, Emily Engelhart, Miranda C. Lahman, Jeffrey Adamo, Xin Yu, Oliver Serang, Kyle J. Minch, Kimberly Wellman, David A. Younger, Randolph M. Lopez, Ryan Emerson

AlphaBind predicts and optimizes antibody-antigen binding affinity using a domain-specific model trained on quantitative binding data.

mAbs · Jul 22, 2025Peer-reviewedSynthetic Biology

High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2

Christof Angermueller, Zelda Mariet, Benjamin W. Jester, Emily Engelhart, Ryan Emerson, Babak Alipanahi, Charles Lin, Colleen Shikany, Daniel Guion, Joel B. Nelson, Mary Kelley, Margot McMurray, Parker Shaffer, Cameron Cordray, Samer F. Halabiya, , Lucy J. Colwell

This study uses ML to design diverse VHH antibodies that neutralize SARS-CoV-1 and SARS-CoV-2 variants, including Delta and Omicron BA.1.

bioRxiv · Dec 1, 2023 · PreprintBioinformatics

Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries

Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Rafael Jaimes, Emily Engelhart, Randolph M. Lopez, Rajmonda S. Caceres, Tristan Bepler, Matthew Walsh

This paper uses a Bayesian language model to design high-affinity scFv antibody libraries, achieving 28.8-fold better binding than directed evolution.

Nature Communications · Jun 12, 2023Peer-reviewedBioinformatics