Natalie Ponomarenko

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About

Natalie Ponomarenko is a pioneering computational immunologist whose work lies at the intersection of structural biology, antibody engineering, and artificial intelligence. Her research focuses on understanding and manipulating the molecular recognition properties of antibodies, with a particular emphasis on developing computational tools for designing and maturing antibody combining sites. In her landmark 2016 study, "Robotic QM/MM-driven maturation of antibody combining sites," Ponomarenko introduced a novel approach that combines quantum mechanics/molecular mechanics (QM/MM) simulations with robotic automation to enhance the affinity and specificity of antibody repertoires. This work, which has garnered 21 citations, represents a significant advance in the field of in vitro antibody selection, offering a powerful method for generating high-affinity scavengers against toxins and other therapeutic targets. Her contributions are particularly notable for bridging the gap between computational chemistry and experimental immunology, enabling more precise and efficient antibody engineering. Ponomarenko's innovative integration of robotic platforms with QM/MM methods has opened new avenues for accelerating the development of therapeutic antibodies, making her a rising leader in the field of computational antibody design.