Surviving the deluge of biosimulation data

MDverse, shedding light on the dark matter of molecular dynamics simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.

Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050

In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.

MD-DATA: the legacy of the ABC Consortium

The ABC Consortium has been generating nucleic-acids MD trajectories for more than 20 years. This brief comment highlights the importance of this data for the field, which triggered a number of critical studies, including force-field parameterization and development of new coarse-grained and mesoscopic models. With the world entering into a new data-driven era led by artificial intelligence, where data is becoming more essential than ever, the ABC initiative is leading the way for nucleic acid flexibility.

MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

From complex data to clear insights: visualizing molecular dynamics trajectories

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology

Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Sequence-Dependent Properties of the RNA Duplex

Sequence-dependent properties of the DNA duplex have been accurately described using extensive molecular dynamics simulations. The RNA duplex meanwhile─which is typically represented as a sequence-averaged rigid rod─does not benefit from having equivalent molecular dynamics simulations. In this paper, we present a massive simulation effort using a set of ABC-optimized duplexes from which we derived tetramer-resolution properties of the RNA duplex and a simple mesoscopic model that can represent elastic properties of long RNA duplexes. Despite the extreme chemical similarity between DNA and RNA, the local and global elastic properties of the duplexes are very different. DNA duplexes show a complex and nonelastic pattern of flexibility, for instance, while RNA duplexes behave as an elastic system whose deformations can be represented by simple harmonic potentials. In RNA duplexes (RNA2), not only are intra- and interbase pair parameters (equilibrium and mechanical) different from those in the equivalent DNA duplex sequences (DNA2) but the correlations between movements also differ. Simple statements on the relative flexibility or stability of both polymers are meaningless and should be substituted by a more detailed description depending on the sequence and the type of deformation considered.

A perspective on the sharing of docking data

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case

Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under:Data Science > Computer Algorithms and ProgrammingData Science > Databases and Expert SystemsMolecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods.

BioExcel Building Blocks Workflows (BioBB-Wfs), an integrated web-based platform for biomolecular simulations

We present BioExcel Building Blocks Workflows, a web-based graphical user interface (GUI) offering access to a collection of transversal pre-configured biomolecular simulation workflows assembled with the BioExcel Building Blocks library. Available workflows include Molecular Dynamics setup, protein-ligand docking, trajectory analyses and small molecule parameterization. Workflows can be launched in the platform or downloaded to be run in the users’ own premises. Remote launching of long executions to user's available High-Performance computers is possible, only requiring configuration of the appropriate access credentials. The web-based graphical user interface offers a high level of interactivity, with integration with the NGL viewer to visualize and check 3D structures, MDsrv to visualize trajectories, and Plotly to explore 2D plots. The server requires no login but is recommended to store the users’ projects and manage sensitive information such as remote credentials. Private projects can be made public and shared with colleagues with a simple URL. The tool will help biomolecular simulation users with the most common and repetitive processes by means of a very intuitive and interactive graphical user interface. The server is accessible at https://mmb.irbbarcelona.org/biobb-wfs.

The Impact of the HydroxyMethylCytosine epigenetic signature on DNA structure and function

We present a comprehensive, experimental and theoretical study of the impact of 5-hydroxymethylation of DNA cytosine. Using molecular dynamics, biophysical experiments and NMR spectroscopy, we found that Ten-Eleven translocation (TET) dioxygenases generate an epigenetic variant with structural and physical properties similar to those of 5-methylcytosine. Experiments and simulations demonstrate that 5-methylcytosine (mC) and 5-hydroxymethylcytosine (hmC) generally lead to stiffer DNA than normal cytosine, with poorer circularization efficiencies and lower ability to form nucleosomes. In particular, we can rule out the hypothesis that hydroxymethylation reverts to unmodified cytosine physical properties, as hmC is even more rigid than mC. Thus, we do not expect dramatic changes in the chromatin structure induced by differences in physical properties between d(mCpG) and d(hmCpG). Conversely, our simulations suggest that methylated-DNA binding domains (MBDs), associated with repression activities, are sensitive to the substitution d(mCpG) ➔ d(hmCpG), while MBD3 which has a dual activation/repression activity is not sensitive to the d(mCpG) d(hmCpG) change. Overall, while gene activity changes due to cytosine methylation are the result of the combination of stiffness-related chromatin reorganization and MBD binding, those associated to 5-hydroxylation of methylcytosine could be explained by a change in the balance of repression/activation pathways related to differential MBD binding.

In Eukaryotic cells, DNA epigenetic modifications play an important role in gene expression and regulation, and protein recognition. In this work we investigate the physical implications of cytosine 5-hydroxymethylation on DNA, its structural and flexibility differences with methylated and unmodified cytosine using molecular dynamics, biophysical experiments and NMR spectroscopy.

In particular the effect of hydroxyl group on free energy of nucleosome and Methyl binding Protein (MBD) binding, comparing in silico and experimental data to shed light on the effect of the reduced flexibility and the direct protein-DNA recognition.

Ferreira T, Ollila OHS, Miettinen MS. Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics

Molecular dynamics (MD) simulations are widely used to monitor time-resolved motions of biomacromolecules, although it often remains unknown how closely the conformational dynamics correspond to those occurring in real life. Here, we used a large set of open-access MD trajectories of phosphatidylcholine (PC) lipid bilayers to benchmark the conformational dynamics in several contemporary MD models (force fields) against nuclear magnetic resonance (NMR) data available in the literature: effective correlation times and spin-lattice relaxation rates. We found none of the tested MD models to fully reproduce the conformational dynamics. That said, the dynamics in CHARMM36 and Slipids are more realistic than in the Amber Lipid14, OPLS-based MacRog, and GROMOS-based Berger force fields, whose sampling of the glycerol backbone conformations is too slow. The performance of CHARMM36 persists when cholesterol is added to the bilayer, and when the hydration level is reduced. However, for conformational dynamics of the PC headgroup, both with and without cholesterol, Slipids provides the most realistic description because CHARMM36 overestimates the relative weight of ∼1 ns processes in the headgroup dynamics. We stress that not a single new simulation was run for the present work. This demonstrates the worth of open-access MD trajectory databanks for the indispensable step of any serious MD study: benchmarking the available force fields. We believe this proof of principle will inspire other novel applications of MD trajectory databanks and thus aid in developing biomolecular MD simulations into a true computational microscope-not only for lipid membranes but for all biomacromolecular systems.

Bioactive Conformational Ensemble Server and Database. A Public Framework to Speed Up In Silico Drug Discovery

Modern high-throughput structure-based drug discovery algorithms consider ligand flexibility, but typically with low accuracy, which results in a loss of performance in the derived models. Here we present the bioactive conformational ensemble (BCE) server and its associated database. The server creates conformational ensembles of drug-like ligands and stores them in the BCE database, where a variety of analyses are offered to the user. The workflow implemented in the BCE server combines enhanced sampling molecular dynamics with self-consistent reaction field quantum mechanics (SCRF/QM) calculations. The server automatizes all of the steps to transform one-dimensional (1D) or 2D representation of drugs into 3D molecules, which are then titrated, parametrized, hydrated, and optimized before being subjected to Hamiltonian replica-exchange (HREX) molecular dynamics simulations. Ensembles are collected and subjected to a clustering procedure to derive representative conformers, which are then analyzed at the SCRF/QM level of theory. All structural data are organized in a noSQL database accessible through a graphical interface and in a programmatic manner through a REST API. The server allows the user to define a private workspace and offers a deposition protocol as well as input files for "in house" calculations in those cases where confidentiality is a must. The database and the associated server are available at https://mmb.irbbarcelona.org/BCE.