Progress in Biophysics & Molecular Biology最新文献

RNA-small molecule interactions are fundamental to cellular regulation and have emerged as highly attractive therapeutic targets. Despite their potential, discovering RNA-binding small molecules remains challenging due to RNA's intrinsic structural flexibility, transient and context-dependent binding pockets, and the limited availability of high-resolution complex structures. Computational prediction approaches have evolved from early statistical models relying on handcrafted descriptors to advanced machine and deep learning frameworks that integrate sequence, structural, energetic, and topological information. More recently, large language models have enabled the capture of long-range sequence dependencies and contextual patterns, complementing structure-based encoders for multimodal modeling of RNA- ligand interactions. In this review, we summarize the principles and current state of computational strategies for RNA-ligand binding site prediction, highlighting methodological evolution, multimodal feature integration, and persisting challenges, and we discuss emerging directions toward accurate, generalizable, and interpretable predictions to accelerate rational RNA-targeted drug discovery.

About 10-15% of all instances of breast cancer are triple-negative breast cancer (TNBC). TNBCs are not responsive to hormonal or anti-HER2 therapies because they lack estrogen and progesterone receptors and have low HER2 levels. TNBC is a highly aggressive subtype of breast cancer and has a prognosis often worse than that of other subtypes. Usually, chemotherapy and surgery are combined since this is a very efficient way to remove tumors. Chemotherapy medications that effectively remove cancer cells may adversely affect healthy cells and have severe repercussions, which can impair patients' psychological well-being and quality of life (QOL). To minimize adverse effects, improve patient quality of life, and maintain therapeutic efficacy, a more targeted therapy approach for TNBC should be explored. Magnetic hyperthermia (MHT) is a passive-targeting, minimally invasive treatment for TNBC that minimizes the requirement for other severe, well-established therapies having both short- and long-term toxicities for patients. MHT involves heating magnetic nanoparticles (MNPs) in an alternating magnetic field (AMF) to heat local tissues/cells without killing normal epithelial cells, as they are more temperature-resistant than tumor cells. Additionally, MNPs can bind chemotherapeutics, nucleic acids, synthetic antibodies, or radionuclide compounds, a strategy considered for drug delivery. This review summarizes the implications and current treatment options for TNBC, highlighting the use of MNPs for MHT as a potential treatment strategy.

Shear stress, a stress that acts co-planar with the cross-section of a system, profoundly influences cellular behavior and function. Understanding how cells respond to shear stress is critical for advancing research in vascular biology, tissue engineering, and cancer metastasis. On the other hand, Electric Cell-Substrate Impedance Sensing (ECIS) is a powerful tool for real-time, label-free monitoring of cellular behavior. This review examines the application of combining ECIS and flow systems to study, in real-time, the effects of shear stress on cell monolayers, such as the impact on barrier function. It highlights its advantages, the various experimental setups, and key experimental findings.