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Biology subjects

Akogo, D. A.

Publications and source records attributed to Akogo, D. A..

2 recordsLinked to original sources

Moremi Bio Agent: Application of A Foundation Model and End-to-End Automation in the Design and Validation of Monoclonal Antibodies Targeting Plasmodium falciparum Invasion Complex

Malaria remains a significant global health challenge, with Plasmodium falciparum responsible for the majority of severe cases and fatalities. Targeting the parasites invasion mechanisms offers a promising therapeutic strategy. In this study, we leveraged a novel agentic foundational model, Moremi Bio Agent, to design monoclonal antibodies targeting the AMA1-RON2 complex, a critical component in the parasites invasion of human red blood cells. Using advanced structural modeling, we generated 999 antibodies, which were evaluated for binding affinity, structural integrity, and physicochemical properties. Binding affinity analysis using PRODIGY identified 864 antibodies with successful target interactions, exhibiting binding free energies ({Delta}G) ranging from -116.8 kcal/mol to -5.6 kcal/mol. The strongest candidates demonstrated exceptionally tight binding, with dissociation constants (Kd) in the femtomolar to attomolar range, indicative of highly stable interactions. Additionally, structural validation confirmed that the antibodies were thermodynamically stable with robust fold reliability, essential for functional efficacy. Epitope mapping revealed highly conserved regions within the target complex, enhancing the likelihood of cross-strain efficacy. Glycosylation analysis identified key sites that could improve antibody stability and immune recognition, while BLAST comparison with known therapeutic antibodies demonstrated significant homology, underscoring their potential for clinical development. This study highlights the power of generative AI-driven computational pipelines in antibody discovery, providing a scalable and cost-effective framework for therapeutic development. The findings establish a foundation for experimental validation and optimization, with the potential to advance novel interventions against malaria and other infectious diseases.

synthetic biology↗

FoodEstNet: Estimating True Food Consumption with Machine Learning

We developed a Machine Learning/Artificial Intelligence model that estimates how much of a food type a person truly consumes. People tend to underestimate how much they consume which makes the work of nutritionists and dietitians difficult since they rely on food estimates for food portion size control and nutritional management of diseases. We trained an XGBoost model to estimate how much a patient truly consumes based on Age, Sex, BMI, socioeconomic status and perceived consumption.

physiology↗