Deep learning-assisted tools to understand the structural biology of the synapse

The function of our brain is the result of the balanced interplay between billions of neurons forming a network of enormous complexity. However, the neurons themselves are also immensely complex entities, with many specialized macromolecular structures orchestrating signal processing and propagation. The postsynaptic density is an elaborate network of interconnected proteins, a dynamic yet highly organized molecular assembly beneath the dendritic membrane, and plays a pivotal role in learning, memory formation, and the development of a number of cognitive disorders. In this review, we argue that with the recent blooming of AI-assisted computational tools in structural biology, we might be able to get closer to understanding the molecular-level mechanistic aspects of this machinery. Nevertheless, we have to use these methods with caution as they are not yet capable of solving all the questions that arise for such a complex macromolecular system. First, we focus on the unique features of the postsynaptic protein network, highlighting those that pose particular challenges for such a modeling task, and put these in the light of the currently available deep learning-based approaches. We highlight the aspects that need specific attention and the areas where future developments could facilitate the detailed description of neural function at the molecular level.