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Abstract Molecular recognition of proteins is key to their biological functions and processes such as protein–protein interactions (PPIs). The large binding interface involved and an often relatively flat binding surface make the development of selective protein-binding materials extremely challenging. A general method is reported in this work to construct protein-binding polymeric nanoparticles from cross-linked surfactant micelles. Preparation involves first dynamic covalent chemistry that encodes signature surface lysines on a protein template. A double molecular imprinting procedure fixes the binding groups on the nanoparticle for these lysine groups, meanwhile creating a binding interface complementary to the protein in size, shape, and distribution of acidic groups on the surface. These water-soluble nanoparticles possess excellent specificities for target proteins and sufficient affinities to inhibit natural PPIs such as those between cytochrome c (Cytc) and cytochrome c oxidase (CcO). With the ability to enter cells through a combination of energy-dependent and -independent pathways, they intervene apoptosis by inhibiting the PPI between Cytc and the apoptotic protease activating factor-1 (APAF1). Generality of the preparation and the excellent molecular recognition of the materials have the potential to make them powerful tools to probe protein functions in vitro and in cellulo. 

Researchers and journalists have repeatedly shown that algorithms commonly used in domains such as credit, employment, healthcare, or criminal justice can have discriminatory effects. Some organizations have tried to mitigate these effects by simply removing sensitive features from an algorithm's inputs. In this paper, we explore the limits of this approach using a unique opportunity. In 2019, Facebook agreed to settle a lawsuit by removing certain sensitive features from inputs of an algorithm that identifies users similar to those provided by an advertiser for ad targeting, making both the modified and unmodified versions of the algorithm available to advertisers. We develop methodologies to measure biases along the lines of gender, age, and race in the audiences created by this modified algorithm, relative to the unmodified one. Our results provide experimental proof that merely removing demographic features from a real-world algorithmic system's inputs can fail to prevent biased outputs. As a result, organizations using algorithms to help mediate access to important life opportunities should consider other approaches to mitigating discriminatory effects.