Systematic Characterization of Proteins Binding to RNA G-quadruplex Structures

Abstract

G-quadruplexes (G4s) are highly stable nucleic acid secondary structures formed by guanine-rich sequences and are increasingly recognized as key regulatory elements in cellular biology. These structures participate in numerous essential processes, including transcription, translation, genome stability, and signal transduction. Although DNA G4s (dG4s) have been intensively investigated, RNA G4s (rG4s) remain comparatively less explored, largely due to the ongoing challenge of identifying their interacting partners, rG4-binding proteins (rG4BPs). In this thesis, we propose a comprehensive framework integrating biochemical and computational strategies to identify rG4BPs, decipher their binding specificities, and explore their biological functions.

First, we developed High-Throughput RNA G-quadruplex Systematic Evolution of Ligands by Exponential Enrichment (HTRG4-SELEX), a high-throughput screening platform leveraging K⁺/Li⁺ ion contrast to modulate rG4 folding in a diverse RNA library. This optimized design enabled the identification of 92 high-affinity rG4BPs, which were further validated using orthogonal methods. Many of these rG4BPs supplement existing datasets and are complementary to those identified by alternative approaches, thereby expanding the known repertoire of rG4 interactors.

Second, we systematically characterized rG4-protein binding features, revealing that rG4BPs exhibit preferences for specific structural variants, such as those with medium-to-long loops and three to four G-quartet layers. Motif analysis identified known domains like RRM and RGG alongside novel motifs validated via mutagenesis. Building on these insights, we developed DeepScan-rG4BP, a convolutional neural network with high predictive accuracy for distinguishing rG4BPs. Its application to the proteomes of ten representative species revealed thousands of candidate rG4BPs, several of which were validated experimentally, highlighting the model’s generalizability.

Finally, we show that TIA1 associates with stress granules through interactions with rG4 elements, providing additional mechanistic insight into its role in stress granule (SG) assembly and liquid-liquid phase separation (LLPS). Systematic analyses identify membraneless organelles as a predominant localization of rG4BPs from HTRG4-SELEX, suggesting rG4-mediated protein recruitment as a contributing mechanism of phase-separated compartments.

In summary, this work establishes a systematic framework for identifying and characterizing rG4-binding proteins, integrates quantitative binding rules with predictive modeling, and further elucidates rG4 elements as a mechanistic component by which diverse proteins participate in LLPS.

Date of Award	8 May 2026
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Jian YAN (Supervisor)