PhD student, National University of Singapore
protein-protein interaction networks
RNA-binding proteins (RBPs) play an important role in post-transcriptional gene regulation (PTGR) that controls the maturation, transport, stability and degradation of RNAs. Discovering novel RBPs and recognizing the RNA-binding potential of known proteins will help increase our understanding of the PTGR system and each protein’s function. There have been many attempts to computationally predict binding of a protein to RNA, using intrinsic protein characteristics such as sequence and structure. However, extrinsic protein characteristics such as the identities of interacting protein neighbors, have not been examined as features useful for predicting RNA binding activity. In this study, we construct a classifier for recognizing RNA binding proteins utilizing both the extrinsic and intrinsic properties of proteins. We recently demonstrated the predictive power of protein-protein interaction (PPI) networks for predicting RNA binding proteins. Here, we extract RNA-binding features from protein sequences with convolution neural networks. Next, a group of accurate RBP classifiers (SONAR+) is obtained by integrating the information of PPI neighbors and sequence features with deep neural network and SVM models. We find SONAR+ outperforms other classifiers and we have experimentally validated several candidate RBPs which were previously uncharacterized for RNA interaction. SONAR+ accurately and efficiently expands the list of RBPs.
Abstract: Hypoplastic left heart syndrome (HLHS) is a fatal congenital heart disease in which the left side of the heart is underdeveloped, impairing the systemic circulation. Underdeveloped left ventricle exerts biomechanical stress on the right ventricle that can progress into heart failure. Genome-wide transcriptome changes have been identified at early stages in the right ventricle (RV) of infants with HLHS, although the molecular mechanisms remain unknown. Here, we demonstrate that the RNA binding protein Rbfox2, which is mutated in HLHS patients, is a contributor to transcriptome changes in HLHS patient RVs. Our results indicate that majority of transcripts differentially expressed in HLHS patient hearts have validated Rbfox2 binding sites. We show that Rbfox2 regulates mRNA levels of targets with 3'UTR binding sites contributing to aberrant gene expression in HLHS patients. Strikingly, the Rbfox2 nonsense mutation identified in HLHS patients truncates the protein, impairs its subcellular distribution and adversely affects its function in RNA metabolism. Overall, our findings uncover a novel role for Rbfox2 in controlling transcriptome in HLHS.
Pub.: 04 Aug '16, Pinned: 08 Jun '17
Abstract: RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.
Pub.: 06 Oct '16, Pinned: 08 Jun '17