Analysis of the Passage Times for Unfolding/Folding of the Adenine Riboswitch Aptamer

Shine-Dalgarno Accessibility Governs Ribosome Binding to the Adenine Riboswitch

Translational riboswitches located in the 5' UTR of the messenger RNA (mRNA) regulate translation through variation of the accessibility of the ribosome binding site (RBS). These are the result of conformational changes in the riboswitch RNA governed by ligand binding. Here, we use a combination of single-molecule colocalization techniques (Single-Molecule Kinetic Analysis of RNA Transient Structure (SiM-KARTS) and Single-Molecule Kinetic Analysis of Ribosome Binding (SiM-KARB)) and microscale thermophoresis (MST) to investigate the adenine-sensing riboswitch in Vibrio vulnificus, focusing on the changes of accessibility between the ligand-free and ligand-bound states. We show that both methods faithfully report on the accessibility of the RBS within the riboswitch and that both methods identify an increase in accessibility upon adenine binding. Expanding on the regulatory context, we show the impact of the ribosomal protein S1 on the unwinding of the RNA secondary structure, thereby favoring ribosome binding even for the apo state. The determined rate constants suggest that binding of the ribosome is faster than the time required to change from the ON state to the OFF state, a prerequisite for efficient regulation decision.

Identification of potential regulatory mechanisms and therapeutic targets for lung cancer

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.Communicated by Ramaswamy H. Sarma.

Microscopic origin of diffusive dynamics in the context of transition path time distributions for protein folding and unfolding

Experimentally measured transition path time distributions are usually analyzed theoretically in terms of a diffusion equation over a free energy barrier. It is though well understood that the free energy profile separating the folded and unfolded states of a protein is characterized as a transition through many stable micro-states which exist between the folded and unfolded states. Why is it then justified to model the transition path dynamics in terms of a diffusion equation, namely the Smoluchowski equation (SE)? In principle, van Kampen has shown that a nearest neighbor Markov chain of thermal jumps between neighboring microstates will lead in a continuum limit to the SE, such that the friction coefficient is proportional to the mean residence time in each micro-state. However, the practical question of how many microstates are needed to justify modeling the transition path dynamics in terms of an SE has not been addressed. This is a central topic of this paper where we compare numerical results for transition paths based on the diffusion equation on the one hand and the nearest neighbor Markov jump model on the other. Comparison of the transition path time distributions shows that one needs at least a few dozen microstates to obtain reasonable agreement between the two approaches. Using the Markov nearest neighbor model one also obtains good agreement with the experimentally measured transition path time distributions for a DNA hairpin and PrP protein. As found previously when using the diffusion equation, the Markov chain model used here also reproduces the experimentally measured long time tail and confirms that the transition path barrier height is ∼3k_BT. This study indicates that in the future, when attempting to model experimentally measured transition path time distributions, one should perhaps prefer a nearest neighbor Markov model which is well defined also for rough energy landscapes. Such studies can also shed light on the minimal number of microstates needed to unravel the experimental data.