A Single Structure-Derived Computational Metric Predicts High-Affinity Antibody Selection Against a Malaria Antigen

Area of Science:

• Immunology
• Computational Biology
• Infectious Disease Research

Background:

• Malaria remains a significant global health burden, necessitating improved passive immunization strategies.
• Developing high-performance antibodies against Plasmodium falciparum circumsporozoite protein (PfCSP) is crucial but challenging.

Purpose of the Study:

• To derive a computational metric for predicting antibody affinity.
• To utilize this metric for rapid exploration of antibody sequence space and generation of high-affinity variants.
• To demonstrate the framework's flexibility with unrelated antibodies.

Main Methods:

• Developed a computational metric based on predicted protein structures to assess antibody affinity.
• Applied the metric to explore a vast sequence space (>3×10^47 variants) of the CIS43 antibody.
• Extended the framework to generate variants for the L9 antibody, targeting PfCSP.

Main Results:

• Successfully derived new, high-affinity CIS43 antibody variants using the computational metric.
• Demonstrated the framework's ability to generate high-affinity variants for an unrelated antibody (L9).
• Showcased the flexibility and efficiency of coupling micro-evolutionary principles with in silico screening.

Conclusions:

• The study presents a novel computational approach for efficient discovery of high-affinity malaria antibodies.
• This method significantly accelerates the development of antibodies for passive immunization against malaria.
• The framework holds promise for generating improved antibody therapies for infectious diseases.