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Abstract Genotype-phenotype relationships are mediated through intricate networks of physical and functional interactions among macromolecules. Knowledge of the interactome is vital to understand and model genetics and cellular biology. Recent advances in accurately predicting tertiary protein structures using artificial intelligence (AI) approaches such as AlphaFold¹have revived the vision that the protein-protein interactome might be fully predictable through computational modeling of quaternary structures. Here we present a comprehensive experimental framework to systematically assess the impact of AI-driven interactome predictions for yeast²and human³. We find that the quality of high-confidence predictions is on par with established experimental approaches. However, in proteome-wide screening, the tested AI approaches underperform in the discovery of strictly novel protein-protein interactions (PPIs) compared to experimental reference interactome maps. In particular, the yeast interactome map described here identifies >40-fold more novel PPIs than its AI counterpart. Strikingly, AlphaFold provides structural models for a substantial number of experimentally identified PPIs missed by the virtual screens. Our results suggest that, at this stage, the main contribution of AI predictions is to provide quaternary structure models for experimentally identified PPIs. 

The global COVID-19 pandemic has raised great public concern about the airborne transmission of viral pathogens. Virus-laden aerosols with small size could be suspended in the air for a long duration and remain infectious. Among a series of measures implemented to mitigate the airborne spread of infectious diseases, filtration by face masks, respirators, and air filters is a potent nonpharmacologic intervention. Compared with conventional air filtration media, nanofibrous membranes fabricated via electrospinning are promising candidates for controlling airborne viruses due to their desired characteristics, i.e., a reduced pore size (submicrometers to several micrometers), a larger specific surface area and porosity, and retained surface and volume charges. So far, a wide variety of electrospun nanofibrous membranes have been developed for aerosol filtration, and they have shown excellent filtration performance. However, current studies using electrospinning for controlling airborne viruses vary significantly in the practice of aerosol filtration tests, including setup configurations and operations. The discrepancy among various studies makes it difficult, if not impossible, to compare filtration performance. Therefore, there is a pressing need to establish a standardized protocol for evaluating the electrospun nanofibrous membranes’ performance for removing viral aerosols. In this perspective, we first reviewed the properties and performance of diverse filter media, including electrospun nanofibrous membranes, for removing viral aerosols. Next, aerosol filtration protocols for electrospun nanofibrous membranes were discussed with respect to the aerosol generation, filtration, collection, and detection. Thereafter, standardizing the aerosol filtration test system for electrospun nanofibrous membranes was proposed. In the end, the future advancement of electrospun nanofibrous membranes for enhanced air filtration was discussed. This perspective provides a comprehensive understanding of status and challenges of electrospinning for air filtration, and it sheds light on future nanomaterial and protocol development for controlling airborne viruses, preventing the spread of infectious diseases, and beyond.