Computational and experimental approaches to decode domain-specific protein-RNA interactions

Computational and experimental approaches to decode domain-specific protein-RNA interactions Project Summary RNA-binding proteins (RBPs) play central roles in post-transcriptional gene regulation by diversifying the types and levels of protein products expressed in specific cellular contexts. This is achieved through interactions of RBPs with specific sequence or structural elements in their target transcripts. Disruption of these regulatory elements accounts for a substantial fraction of human disease associations. However, since most of these elements are embedded in the noncoding genomic regions, they are currently annotated poorly in the human genome. While CLIP-seq and its many variants can map RBP binding footprints on a genome-wide scale, such maps remain sparse in coverage concerning both the number of RBPs and cell types. Predictive computational models can potentially complement experimental data and provide powerful tools to interpret the functional impact of genetic variants, but the success of this approach is still limited. We realize that a major challenge in the precise mapping and prediction of protein-RNA interactions is a critical lack of technologies that can delineate the specificity of individual RNA-binding domains (RBDs) of multi-domain RBPs, which account for about half of all RBPs in humans. Since the current CLIP methods pull down all RNA fragments crosslinked to any RBDs in a mixed population, with each individual RBD recognizing short and degenerate motif sites, we are unable to deconvolute the binding sites of individual RBDs. Such resolution is required to precisely understand the mechanisms conferring the specificity of protein-RNA interactions and interpret the functional significance of genetic variants. In this study, we propose two complementary strategies to overcome the fundamental challenge and develop new methods that will enable one to map domain-specific protein-RNA interactions in the native cellular context, at single-nucleotide resolution, on a genome-wide scale. This project builds on the tight integration of complementary expertise of the Zhang lab in RNA and computational biology and Wang lab in chemical biology. If successful, this study will produce platform technologies that will find impactful applications in studies of gene expression regulation, genotype-phenotype relationships, and development of RNA-based precision genetic medicine. Project Number: 1R01HG014077-01A1 | Fiscal Year: 2026 | NIH Institute/Center: National Human Genome Research Institute (NHGRI) | Principal Investigator: Chaolin Zhang (+1 co-PI) | Institution: COLUMBIA UNIVERSITY HEALTH SCIENCES, NEW YORK, NY | Award Amount: $3,243,342 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 MGG-D (90)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11241762