Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria
NIAID - National Institute of Allergy and Infectious Diseases
About This Grant
Project Summary - Current treatments for Pv are limited, highlighting the need for new therapies targeting both liver and blood stages. Relapsing parasites account for up to 80% of infections and disease, establishing a global reservoir that is difficult to eliminate with current treatments. Blocking liver-stage infections is the most effective strategy to prevent dormant parasites. A human monoclonal antibody (humAb826827) targets Pv apical membrane antigen1 (AMA1), blocking both liver and blood stage Pv infections. Large language models and binder designs will be used to develop novel human monoclonal antibodies targeting Pv invasion ligands, which will be tested in vitro with Pv clinical isolates. This strategy adapts methods used in enhancing anti- EGFR mAb, Cetuximab, for cancer therapy. Three approaches will be used to design mAbs: enhancing existing mAbs, designing new mAbs, and optimizing current mAbs. Enhanced and novel mAbs will be tested using a collaborative network in Cambodia. The computational approach avoids the bottleneck of isolating PBMCs from Pv-infected individuals. Developing effective mAb candidates requires optimization across multiple dimensions, including specific target binding, conformational stability, scalable production, and an acceptable immunogenicity profile. Aim 1 focuses on improving existing and developing new mAbs based on 826827 and 864865. Computational development of 1000 mAb scaffolds per target epitope will be done using RFDiffusion, PyRosetta, and MPNN. Optimal mAbs will be selected based on production efficiency, competition with mAb826827, and blocking capability. Aim 2 focuses on developing new mAbs recognizing PvCSP VK210 and VK247. Computational approaches from Aim 1 will be applied to PvCSP, using blocking murine mAbs to guide the generation of new mAbs. Optimal mAbs will be selected based on production efficiency, competition with murine 2F2, and blocking capability. The project aims to establish which target proteins are functionally relevant for blocking Pv growth and determine the best therapeutic mAbs, either alone or in combination.
Grant Summary
Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria is a NIAID - National Institute of Allergy and Infectious Diseases grant providing up to $283K for university, nonprofit, healthcare org. Applications are due 2028-03-31 (open). Check eligibility and apply with FindGrants.
Focus Areas
Eligibility
How to Apply
Up to $283K
2028-03-31
- 1Confirm your organization is eligible for Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria from NIAID - National Institute of Allergy and Infectious Diseases, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIAID - National Institute of Allergy and Infectious Diseases before the deadline.
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Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria: Frequently Asked Questions
Who is eligible for the Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria?
Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria is offered by NIAID - National Institute of Allergy and Infectious Diseases and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria provide?
Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria provides up to $283K per award from NIAID - National Institute of Allergy and Infectious Diseases. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria deadline?
Applications for Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria are due 2028-03-31 (open). Because deadlines can change, verify the date with the funder, NIAID - National Institute of Allergy and Infectious Diseases, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria?
To apply for Leveraging Large Language Models for the Design of Monoclonal Antibodies Against Malaria, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIAID - National Institute of Allergy and Infectious Diseases.