Single-molecule studies of the eukaryotic translation initiation factor 4A: helicase activity, conformational dynamics and function regulation

Thesis (Ph.D.)--Boston UniversityThe PI3K/Akt/mTOR pathway regulates several cellular functions, including cellular proliferation, growth, and survival. The PI3K/Akt/mTOR pathway converges on the eukaryotic translation initiation factor 4F (eIF4F), making it an attractive molecular target for anti-cancer therapies. As a subunit of eIF4F, eIF4A is known to facilitate binding and scanning of the ribosome by unwinding secondary structures in the 5' untranslated region (UTR) of mRNAs. However, the molecular mechanisms of eIF4A activity have remained elusive. Single-molecule Fluorescence Resonance Energy Transfer (sm-FRET) can probe structural changes and interactions of biological systems in real time, which cannot be observed using bulk techniques. First we directly observe and quantify the helicase activity of eIF4A in the presence of the ancillary RNA-binding factor eIF4H using sm-FRET. We show that eIF4H can significantly enhance the helicase activity of eIF4A by strongly binding both to loop structures within the RNA substrate as well as to eIF4A. Electrophoretic mobility shift assay (EMSA) shows that eIF4H binds to the amino-terminal domain (NTD) but not to the carboxyterminal domain (CTD) of eIF4A. In the presence of ATP, the eIF4A/eIF4H complex exhibits rapid and repetitive cycles of unwinding and re-annealing. ATP titration assays suggest that this process consumes a single ATP molecule per cycle. Second, we directly probe the conformational dynamics of eIF4A, in real time, using smFRET. We demonstrate that the eIF4A in the presence of eIF4H can repetitively unwind the RNA hairpin substrate by transitioning between an "open" and a "closed" conformation using the energy from ATP hydrolysis. Upon binding of an RNA hairpin and ATP, which is mediated by eIF4H, eIF4A adopts a closed conformation; after ATP hydrolysis, eIF4A returns to the open conformation and the RNA duplex is completely unwound. Then the eIF4A releases the RNA and the hairpin is quickly reformed. Third, we find that RNA aptamer and the small molecule hippuristanol can inhibit the binding to the RNA substrate or the helicase activity of the eIF4A/eIF4H complex respectively. The RNA aptamer can directly compete with an RNA hairpin for binding to both eIF4A and eIF4H. Hippuristanol inhibits helicase activity by blocking the conformational change of eIF4A

Geometry-Aware Video Quality Assessment for Dynamic Digital Human

Dynamic Digital Humans (DDHs) are 3D digital models that are animated using predefined motions and are inevitably bothered by noise/shift during the generation process and compression distortion during the transmission process, which needs to be perceptually evaluated. Usually, DDHs are displayed as 2D rendered animation videos and it is natural to adapt video quality assessment (VQA) methods to DDH quality assessment (DDH-QA) tasks. However, the VQA methods are highly dependent on viewpoints and less sensitive to geometry-based distortions. Therefore, in this paper, we propose a novel no-reference (NR) geometry-aware video quality assessment method for DDH-QA challenge. Geometry characteristics are described by the statistical parameters estimated from the DDHs' geometry attribute distributions. Spatial and temporal features are acquired from the rendered videos. Finally, all kinds of features are integrated and regressed into quality values. Experimental results show that the proposed method achieves state-of-the-art performance on the DDH-QA database

Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment

Point clouds are widely used in 3D content representation and have various applications in multimedia. However, compression and simplification processes inevitably result in the loss of quality-aware information under storage and bandwidth constraints. Therefore, there is an increasing need for effective methods to quantify the degree of distortion in point clouds. In this paper, we propose simple baselines for projection-based point cloud quality assessment (PCQA) to tackle this challenge. We use multi-projections obtained via a common cube-like projection process from the point clouds for both full-reference (FR) and no-reference (NR) PCQA tasks. Quality-aware features are extracted with popular vision backbones. The FR quality representation is computed as the similarity between the feature maps of reference and distorted projections while the NR quality representation is obtained by simply squeezing the feature maps of distorted projections with average pooling The corresponding quality representations are regressed into visual quality scores by fully-connected layers. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achieving the top spot in four out of the five competition tracks