Exploring and characterizing RNA-binding proteins with selective N6-methyladenosine recognition

Inside each cell, some RNA molecules are marked by a chemical tag known as m6A. This tag acts like a sticky note, guiding the cell on how to use the RNA. Special proteins, called \"readers\" interact with and interpret these sticky notes. These readers are crucial for health, and their malfunction can lead to cancer. However, many of these readers remain undiscovered due to the technical challenges in detecting them. Cancer cells often become resistant to treatments because they excel at repairing damaged DNA. Recent findings suggest that m6A-modified RNAs help cancer cells repair this damage, leading to treatment failure. While we know of a few reader proteins involved, ourcurrent detection methods are limited, akin to searching for specific individuals in a crowded stadium using a dim flashlight, likely missing many key players. Our project aims to uncover these hidden readers and characterize them.Our project will introduce a powerful new method called PRICE, which functions like a high-speed searchlight, enabling the rapid testing of hundreds of proteins to identify those attracted to the m6A sticky notes. Initial tests with this method have been successful, identifying 52 readers, including 35 previously unknown ones, such as a protein called HNRNPK. The project has three main objectives: (1) The Big Search: Using PRICE technology, we will screen 500 human proteins to significantly expand the list of known m6A readers. (2) The Deep Dive: We will study the newly identified reader, HNRNPK, to understand its role in helping cancer cells repair DNA and resist treatment. We will test whether inhibiting HNRNPK can make cancer cells more susceptible to existing drugs. (3) The AI Predictor: We will employ Artificial Intelligence to analyze our data and develop a computer model that predicts other potential but untested m6A readers in the human body, guiding future discoveries beyond our project.By achieving these goals, we will enhance our understanding of cellular processes. The readers, like HNRNPK, could serve as targets for new drugs. A medication that blocks a specific reader could impair a cancer cell's repair mechanisms, making treatments like chemotherapy more effective. Our PRICE and AI technology will become invaluable resources for scientists globally, accelerating discoveries not only in cancer but also in other diseases influenced by this hidden RNA language. In essence, we are piecing together a cellular puzzle to develop smarter, more effective means to characterize epitranscriptomic regulators.