MFB: NSF-BSF: Data-Adaptive and Metamorphosis Machine Learning Architectures for Generative Protein Design of Metal Biosensors

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Dr. Joanna Slusky from the University of Kansas in collaboration with Dr. Margarita Osadchy and Dr. Rachel Kolodny from the University of Haifa, Israel, are designing protein-based biosensors for pollutants that could be used in efforts create cleaner waterways. The technology undergirding the creation of the sensors will include novel machine learning architectures. These new ML architectures could be used to develop biosensors for other applications in biotechnology more broadly. The research will create opportunities for undergraduates from Kansas to learn about how nature controls metal binding, to acquire knowledge and skills that span the intersection of biology and computer science, and to participate in international collaborations.This project involves the use of existing and newly developed machine learning architectures for the generation of new scaffolds for biosensors, particularly metal sensing proteins. Membrane β-barrels are ideal platforms for biosensors for a wide range of analytes because of their shape, variety of radii, and the ability to proscribe chemical moieties on the interior of the barrel. Moreover, because these proteins span membranes, the act of analyte binding causes a measurable change in conductance across the membrane, which can be used as biosensor output. This project thas three ambitious ML objectives: 1) create a fold-specific language model using less training data, 2) generate novel protein sequences with patch-based metamorphosis, locally-aware iterative design, and 3) design and test protein barrel candidates as biosensors for water contaminants. The research pursues a new way to restrict attention for transformer-based language models, a new way to traverse protein sequence space with a metamorphosis based generator, a deeper understanding of membrane β-barrels, newly generated scaffolds for biosensing, a deeper understanding of what makes some protein sites ideal for metal binding, and a design method for proteins that could be used to identify heavy metal contaminants in water. This collaborative US/Israel project is supported by the US National Science Foundation and the Israeli Binational Science Foundation. At the US National Science Foundation, the project is supported by the Division of Chemistry, the Division of Chemical, Bioengineering, Environmental and Transport Systems, the Division of Information and Intelligent Systems, and the Division of Molecular and Cellular Biosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.