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In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources., Comment: 26 pages, 5 figures

Molecular dynamics (MD) simulations play a crucial role in resolving the underlying conformational dynamics of molecular systems. However, their capability to correctly reproduce and predict dynamics in agreement with experiments is limited by the accuracy of the force-field model. This capability can be improved by refining the structural ensembles or the force-field parameters. Furthermore, discrepancies with experimental data can be due to imprecise forward models, namely, functions mapping simulated structures to experimental observables. Here, we introduce MDRefine, a Python package aimed at implementing the refinement of the ensemble, the force-field and/or the forward model by comparing MD-generated trajectories with experimental data. The software consists of several tools that can be employed separately from each other or combined together in different ways, providing a seamless interpolation between these three different types of refinement. We use some benchmark cases to show that the combined approach is superior to separately applied refinements. Source code, documentation and examples are freely available at https://pypi.org/project/MDRefine and https://github.com/bussilab/MDRefine., Comment: 39 pages, 20 figures

RNA molecules exhibit various biological functions intrinsically dependent on their diverse ecosystem of highly flexible structures. This flexibility arises from complex hydrogen-bonding networks defined by canonical and non-canonical base pairs that require protonation events to stabilize or perturb these interactions. Constant pH molecular dynamics (CpHMD) methods provide a reliable framework to explore the conformational and protonation space of dynamic structures and for robust calculations of pH-dependent properties, such as the pK$_\mathrm{a}$ of titrable sites. Despite growing biological evidence concerning pH regulation of certain motifs and in biotechnological applications, pH-sensitive in silico methods have rarely been applied to nucleic acids. In this work, we extended the stochastic titration CpHMD method to include RNA parameters from the standard $\chi$OL3 AMBER force field and highlighted its capability to depict titration events of nucleotides in single-stranded RNAs. We validated the method using trimers and pentamers with a single central titrable site while integrating a well-tempered metadynamics approach into the st-CpHMD methodology (CpH-MetaD) using PLUMED. This approach enhanced the convergence of the conformational landscape and enabled more efficient sampling of protonation-conformation coupling. Our pK$_\mathrm{a}$ estimates agree with experimental data, validating the method's ability to reproduce electrostatic changes around a titrable nucleobase in single-stranded RNA. These findings provided molecular insight into intramolecular phenomena, such as nucleobase stacking and phosphate interactions, that dictate the experimentally observed pK$_\mathrm{a}$ shifts between different strands. Overall, this work validates both the st-CpHMD and the metadynamics integration as reliable tools for studying biologically relevant RNA systems.

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

N6-methyladenosine (m6A) is a prevalent RNA post-transcriptional modification that plays crucial roles in RNA stability, structural dynamics, and interactions with proteins. The YT521-B (YTH) family of proteins, which are notable m6A readers, function through their highly conserved YTH domain. Recent structural investigations and molecular dynamics (MD) simulations have shed light on the recognition mechanism of m6A by the YTHDC1 protein. Despite advancements, using MD to predict the stabilization induced by m6A on the free energy of binding between RNA and YTH proteins remains challenging, due to inaccuracy of the employed force field and limited sampling. For instance, simulations often fail to sufficiently capture the hydration dynamics of the binding pocket. This study addresses these challenges through an innovative methodology that integrates metadynamics, alchemical simulations, and force-field refinement. Importantly, our research identifies hydration of the binding pocket as giving only a minor contribution to the binding free energy and emphasizes the critical importance of precisely tuning force-field parameters to experimental data. By employing a fitting strategy built on alchemical calculations, we refine the m6A partial charges parameters, thereby enabling the simultaneous reproduction of N6 methylation on both the protein binding free energy and the thermodynamic stability of nine RNA duplexes. Our findings underscore the sensitivity of binding free energies to partial charges, highlighting the necessity for thorough parameterization and validation against experimental observations across a range of structural contexts.

Ribonucleic acids (RNA) are unique in that they can store genetic information, replicate and perform catalysis. Importantly, RNA molecules are highly dynamic, and thus determining the ensemble of conformations that they populate is crucial not only to elucidate their biological functions, but also for their potential use as therapeutic targets. Computational and experimental techniques provide complementary views on RNA dynamics, and their integration is fundamental to improve the accuracy of computations and the resolution of experiments. Recent exciting developments in this field, were discussed at the CECAM workshop ``RNA dynamics from experimental and computational approaches'', in Paris, June 26-28, 2023. This report outlines key `take-home' messages that emerged during this workshop from the presentations and discussions.