# Research Project 4: Mining human antibody responses to inform vaccine and therapeutic design > **NIH NIH U19** · ALBERT EINSTEIN COLLEGE OF MEDICINE · 2024 · $2,202,045 ## Abstract Abstract: This proposal embodies the development of an advanced platform for rapid identification, characterization, and optimization of monoclonal antibodies (mAbs) to effectively combat nairoviruses, hantaviruses, and paramyxoviruses. Our work is centered on comprehensive immune profiling of naïve and convalescent donors, identifying and characterizing mAbs throughout disease progression. To identify mAbs, we employ multivalent memory B cell sorting, Ig-Seq proteomics, naive B cell sorting, and antibody engineering using eukaryotic display technologies. Our proposal includes a comprehensive study of immune responses to catalog epitopes and immunodominance hierarchies, which will be instrumental in improving our understanding of establishment and maintenance of prototypical protective humoral immunity to these viruses. This critical new information will enable the design of next-generation vaccine candidates for these virus families. The second aim focuses on the understanding of antibody-antigen interactions at the molecular level. This aim incorporates high-throughput epitope mapping using state-of-the-art mammalian display technology, performed using deep mutational scanning and epitope shuffling. In addition, we will explore potential viral escape mechanisms to inform the design of broadly protective antibody cocktails. By combining cryo-electron microscopy, viral mutational escape through forward genetics, and deep mutational scanning, we plan to visualize the escape maps and understand how these viruses may evolve over time to avoid neutralizing antibodies elicited by vaccines. The third aim is directed at optimizing mAbs for effective development and enhancing their protective capabilities. Advanced machine learning algorithms and antibody engineering techniques will be used for creating mAbs that are efficient to produce, stable, highly-affinity, and broadly protective. The algorithms will be trained to predict and address potential sequence-based manufacturing and developability issues to optimize mAb sequences for efficient expression and stability during production, purification, and storage. This aim includes engineering bispecific mAbs and mAb candidates for serum stability, development of stable cell lines during hit identification and optimization, and evaluation of virus neutralization and protective potential in relevant animal models. The interplay of these three aims will facilitate a novel platform for rapid generation of high- affinity, broadly protective, manufacturable, and serum-stable mAbs against prototypical viruses. This holistic approach is expected to improve our ability to respond rapidly and effectively to emerging biological threats, provide crucial information to the design of efficacious vaccines in projects 2 and 3, and refine a platform for countermeasures development. Our collaborative research team is comprised of experienced scientists with expertise in immunology, virology, antibody/protein engineering, a... ## Key facts - **NIH application ID:** 10863601 - **Project number:** 1U19AI181977-01 - **Recipient organization:** ALBERT EINSTEIN COLLEGE OF MEDICINE - **Principal Investigator:** Jimmy D Gollihar - **Activity code:** U19 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $2,202,045 - **Award type:** 1 - **Project period:** 2024-09-01 → 2029-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10863601 ## Citation > US National Institutes of Health, RePORTER application 10863601, Research Project 4: Mining human antibody responses to inform vaccine and therapeutic design (1U19AI181977-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10863601. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*