De novo design of modulators for protein-nucleic acid interactions

Protein–nucleic acid (NA) interactions are fundamental to many cellular processes, and their disruption can lead to diseases such as autoimmune disorders and cancer. However, targeting NA-binding proteins with small molecules is difficult due to the “undruggable” nature of their interfaces. Recent advancements in computational protein design and deep learning have created new opportunities for the development of protein-based therapeutics. However, current methods struggle to design proteins that can effectively modulate or mimic the intricate protein-NA interactions directly. While some NA-aware models have been developed, they often underperform due to our limited understanding of protein-NA interactions and the scarcity of diverse structural training data. To overcome these challenges, I propose to pioneer the design of NA-mimicking proteins (NAMPs) capable of directly modulating protein-NA interactions with high specificity and adaptable functionality. I will develop computational methods that incorporate the structural and chemical features of NAs, and create NAMPs tailored to engage therapeutically relevant NA-binding proteins and regulate their activity. In addition, I will develop approaches to expand the structural data on protein-NA complexes using accelerated structure determination methods to refine NA-aware models. This will enable the design of NA-binding proteins with sequence- and shape-specific recognition. Lastly, to create molecular machines with complex functionalities, I will develop new methodologies for the de novo design of multi-domain proteins capable of adopting multiple, defined conformations—long considered a “holy grail” in protein design. By integrating these approaches, I aim to overcome the current limitations in studying and designing protein-NA interactions, paving the way for the creation of sophisticated molecular tools with vast applications in synthetic biology, biotechnology, and medicine.