Before beginning the production phase of molecular dynamics simulations, i.e., the phase that produces the data to be analyzed, it is often necessary to first perform a series of one or more preparatory minimizations and/or molecular dynamics simulations in order to ensure that subsequent production simulations are stable. This is particularly important for simulations with explicit solvent molecules. Despite the preparatory minimizations and simulations being ubiquitous and essential for stable production simulations, there are currently no general recommended procedures to perform them and very few criteria to decide whether the system is capable of producing a stable simulation trajectory. Here, we propose a simple and well-defined ten step simulation preparation protocol for explicitly solvated biomolecules, which can be applied to a wide variety of system types, as well as a simple test based on the system density for determining whether the simulation is stabilized.
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http://dx.doi.org/10.1063/5.0013849 | DOI Listing |
Chem Sci
January 2025
Department of Chemical and Biological Physics, Weizmann Institute of Science Rehovot 761001 Israel
Proteins often harness extensive motions of domains and subunits to promote their function. Deciphering how these movements impact activity is key for understanding life's molecular machinery. The enzyme adenylate kinase is an intriguing example for this relationship; it ensures efficient catalysis by large-scale domain motions that lead to the enclosure of the bound substrates ATP and AMP.
View Article and Find Full Text PDFNat Mach Intell
January 2025
Engineering Laboratory, University of Cambridge, Cambridge, UK.
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time.
View Article and Find Full Text PDFJ Phys Chem C Nanomater Interfaces
January 2025
Institut für Chemie und Biochemie, Freie Universität Berlin, Arnimallee 22, 14195 Berlin, Germany.
Since water is both a product and a common reactant impurity in the (partial) methanol oxidation to methyl formate (MeFo) on gold, its effect on the isothermal selectivity to methyl formate was investigated under well-defined single-collision conditions employing pulsed molecular beam experiments and in situ IRAS measurements. Both a flat Au(111) and a stepped Au(332) surface were used as model catalysts to elucidate how water affects the reactivity of low-coordinated step sites as compared to (111) terrace sites employing a range of reaction conditions. The interactions of water with methanol/methoxy as well as with oxygen species are addressed.
View Article and Find Full Text PDFFront Immunol
January 2025
College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Alzheimer's disease (AD) is the most common neurodegenerative disorder, accounting for approximately 70% of dementia cases worldwide. Patients gradually exhibit cognitive decline, such as memory loss, aphasia, and changes in personality and behavior. Research has shown that mitochondrial dysfunction plays a critical role in the onset and progression of AD.
View Article and Find Full Text PDFRNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data.
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