We present a systematic study of the long-timescale dynamics of the Drew-Dickerson dodecamer (DDD: d(CGCGAATTGCGC)2) a prototypical B-DNA duplex. Using our newly parameterized PARMBSC1 force field, we describe the conformational landscape of DDD in a variety of ionic environments from minimal salt to 2 M Na(+)Cl(-) or K(+)Cl(-) The sensitivity of the simulations to the use of different solvent and ion models is analyzed in detail using multi-microsecond simulations. Finally, an extended (10 μs) simulation is used to characterize slow and infrequent conformational changes in DDD, leading to the identification of previously uncharacterized conformational states of this duplex which can explain biologically relevant conformational transitions. With a total of more than 43 μs of unrestrained molecular dynamics simulation, this study is the most extensive investigation of the dynamics of the most prototypical DNA duplex.
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http://dx.doi.org/10.1093/nar/gkw264 | DOI Listing |
J Chem Theory Comput
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Aix Marseille University, CNRS, ICR, Marseille 13397, France.
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The adaptation to the daily 24-h light-dark cycle is ubiquitous across animal species and is crucial for maintaining fitness. This free-running cycle occurs innately within multiple bodily systems, such as endogenous circadian rhythms in clock-gene expression and synaptic plasticity. These phenomena are well studied; however, it is unknown if and how the 24-h clock affects electrophysiologic network function in vivo.
View Article and Find Full Text PDFJ Chem Phys
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School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel.
We present an inference scheme of long timescale, non-exponential kinetics from molecular dynamics simulations accelerated by stochastic resetting. Standard simulations provide valuable insight into chemical processes but are limited to timescales shorter than ∼1μs. Slower processes require the use of enhanced sampling methods to expedite them and inference schemes to obtain the unbiased kinetics.
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November 2024
National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8565, Japan.
Recent advances in neural network-based computing have enabled human-like information processing in areas such as image classification and voice recognition. However, many neural networks run on conventional computers that operate at GHz clock frequency and consume considerable power compared to biological neural networks, such as human brains, which work with a much slower spiking rate. Although many electronic devices aiming to emulate the energy efficiency of biological neural networks have been explored, achieving long timescales while maintaining scalability remains an important challenge.
View Article and Find Full Text PDFbioRxiv
November 2024
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, CA 91016.
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