Machine learning has proven useful in analyzing complex biological data and has greatly influenced the course of research in structural biology and precision medicine. Deep neural network models oftentimes fail to predict the structure of complex proteins and are heavily dependent on experimentally determined structures for their training and validation. Single-particle cryogenic electron microscopy (cryoEM) is also advancing the understanding of biology and will be needed to complement these models by continuously supplying high-quality experimentally validated structures for improvements in prediction quality. In this perspective, the significance of structure prediction methods is highlighted, but the authors also ask, what if these programs cannot accurately predict a protein structure important for preventing disease? The role of cryoEM is discussed to help fill the gaps left by artificial intelligence predictive models in resolving targetable proteins and protein complexes that will pave the way for personalized therapeutics.
Keywords: alphaFold; cryoEM; machine Learning; precision medicine.
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