Local DNA melting is integral to fundamental processes such as replication or transcription. In vivo, these two processes do not occur on molecules free in solution but, instead, involve DNA molecules which are organized into DNA/proteins complexes. Atomic force microscopy imaging offers a possibility to look at individual molecules. It allowed us to follow the progress of local denaturation in liquid, but with the added constraints of DNA lying on a surface. We present a kinetic analysis of the mapping of the temperature-driven melting seen at a replication origin (Schizosaccharomyces pombe ars1). The results indicate an expected base composition dependency, but also a strong extremity effect. Noteworthy, a "structural" effect is clearly occurring - which is shown by the greater susceptibility of the strongly curved region present in the sequence to unwind. DNA melting, at this place, is seen to occur after an increase in the curvature amplitude and a simultaneous shift of the nucleotide sequence positioned at the apex. Because this may determine the position of the Replication Initiation (R.I.) site, the result suggests that eukaryotic replication origins, although described as possessing no consensus sequences, may well have their mechanics sustained by the properties of common structural features. Our analysis may, therefore, provide new information that will give genuine insights on how DNA molecules behave when organized into primosomes, replisomes, promoter initiation complexes, etc. and thus, be essential to better understanding the way genes function.
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http://dx.doi.org/10.1016/j.biochi.2007.02.007 | DOI Listing |
Nucleic Acids Res
January 2025
Department of Physiology and Biophysics, Virginia Commonwealth University, School of Medicine, Richmond, VA 23298, United States.
The Rep68 protein from Adeno-Associated Virus (AAV) is a multifunctional SF3 helicase that performs most of the DNA transactions necessary for the viral life cycle. During AAV DNA replication, Rep68 assembles at the origin of replication, catalyzing the DNA melting and nicking reactions during the hairpin rolling replication process to complete the second-strand synthesis of the AAV genome. We report the cryo-electron microscopy structures of Rep68 bound to the adeno-associated virus integration site 1 in different nucleotide-bound states.
View Article and Find Full Text PDFJ Funct Biomater
January 2025
Department of Chemistry, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy.
Behav Brain Res
March 2025
Department of Psychological and Brain Sciences, Indiana University, USA. Electronic address:
Exposure to extreme stress can negatively impact behavior and lead to prolonged fear sensitization. These processes can be studied in the lab using stress-enhanced fear learning (SEFL), where prior exposure to inescapable stress exacerbates later contextual fear conditioning. A common method to reduce conditional fear is through extinction, where a conditional stimulus once paired with an unconditional (US; e.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
National Center for International Research on Green Optoelectronics, Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
Ice melting, a common yet complex phenomenon, remains incompletely understood. While theoretical studies suggest that preexisting defects in ice generate "off-lattice" water molecules, triggering bulk ice melting, direct experimental evidence of their form has been lacking as the transparent and transient nature of ice poses significant challenges for observation with current techniques. Here, we introduce an ice-melting-induced lyophilization (IMIL) technique that employs graphene-based nanoprobes to replicate and track liquid evolution within melting bulk ice.
View Article and Find Full Text PDFJ Chem Phys
January 2025
School of Chemistry, Beihang University, Beijing 100191, China.
Dynamic density functional theory (DDFT) is a fruitful approach for modeling polymer dynamics, benefiting from its multiscale and hybrid nature. However, the Onsager coefficient, the only free parameter in DDFT, is primarily derived empirically, limiting the accuracy and broad application of DDFT. Herein, we propose a machine learning-based, bottom-up workflow to directly extract the Onsager coefficient from molecular simulations, circumventing partly heuristic assumptions in traditional approaches.
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