dataesr/scientific-paragraphs-categorization huggingface dataset:
NMRlipids Databank-API: programmatic access to the MD simulation data The NMRlipids Databank-API provides programmatic access to all simulation data in the NMRlipids Databank through application programming interface (API). This enables wide range of novel data-driven applications-from construction of machine learning models that predict membrane properties, to automatic analysis of virtually any property across all simulations in the Databank. The flowchart in Fig. 1D  illustrates the practical implementation of such an analysis. After cloning the databank repository to a local computer, raw data of each simulation can be accessed and analyzed with the help of functions delivered by the NMRlipids Databank-API. Analyses over simulations with different naming convetions can be automatically   S5.
D Flowchart for performing an analysis of properties through all MD simulations in the NMRlipids Databank using the API. E Currently available lipid mixtures in the NMRlipids Databank. Colorbar shows the number of available simulations with the darkest green indicating three or more. performed with the help of mapping files that associate the specific naming conventions in each simulation with the universal molecule and atom names used by the Databank. Finally, the analysis results can be stored using the same structure as in the Databank layer. Documentation of the NMRlipids Databank-API and template for new user-defined analyses with further instructions are available at the NMRlipids databank documentation  19. While analysis codes and results for basic membrane properties are included in the Databank layer, unlimited further analyses can be implemented by anyone in separate repositories in the Application layer.
When Application layer repositories are organized by mimicking the Databank layer structure, they can be accessed programmatically and further analyzed using the tools in the NMRlipids Databank-API by implementing the flowchart demonstrated in Fig. S1. Novel analyses that demonstrate the power of NMRlipids Databank in selecting the best simulation models, analysing rare phenomena, and extending MD simulations to new fields are implemented in an Application-layer repository located at github. com/NMRlipids/DataBankManuscript. The related codes are listed in Table  S2.
Selecting simulation parameters using NMRlipids Databank: Best models for most abundant neutral membrane lipids Molecular dynamics (MD) simulations have been particularly useful in understanding membrane systems, although their accuracy has often been compromised by artefacts such as the quality of model parameters  20, 21. Presently, the accuracy of models is becoming increasingly important as researches are progressing from simulations of individual molecules to simulating whole organelles or even cells using interdisciplinary approaches  [21] [22] [23]. Such systems exhibit intricate emergent behaviour making inaccuracies more difficult to detect, and accumulation of even modest errors may have a dramatic impact on the conclusions drawn. To minimize the detrimental consequences of artificial MD simulation results for their applications, the quality of lipid bilayer MD simulations has to be carefully assessed  20.
Qualities against X-ray scattering experiments (FF q ) were estimated as the difference in the experimental and simulated locations of the first form factor minimum. These measures are good proxies for membrane properties because they correlate with the membrane lateral packing and thickness (Figs. 2G, S2,  and S3 ). Ergodicity of conformational sampling of lipids was estimated by calculating τ rel , the convergence time of the slowest principal component divided by the simulation length. Fig. 2  demonstrates how the automatic simulation-quality evaluation and the NMRlipids Databank-API enable rapid selection of the best models for membrane simulations. Fig. 2A  illustrates that predictions for the lateral packing of membranes composed of two most biologically-abundant neutral membrane lipids, POPC and POPE  7  , diverge between different force fields.
To find the most realistic parameters to simulate membranes with these lipids, we first ranked all simulations based on order parameter quality (Fig. S4 ), then picked force fields that occurr in Fig. 2A  (that is: force fields for both POPC and POPE in the Databank), and then ranked them according to the quality of the sn-1 chain of POPC (Fig. 2B ) and of POPE (Fig. 2C ). Simulations with τ rel clearly above one (larger than 1. 3) were discarded in this analysis. Because the average sn-1 chain order parameter is a good proxy for the membrane packing (Fig. 2G ), rankings in Figs. 2B  and C  can be used to select the simulations giving the most realistic results in Fig. 2A. Based on this, Lipid17 and Slipids simulations are most realistic for a POPC membrane, while CHARMM36 and GROMOS-CKP simulations predict overly packed bilayers (overestimated order in Fig.
S5 ). For POPE, on the other hand, GROMOS-CKP and Slipids are most realistic, while CHARMM36 and Lipid17 predict too packed membranes. In conclusion, the quality evaluation based on the NMRlipids Databank suggests that the Slipids parameters are the best currently available choice for simulations with PC and PE lipids, at least for applications where membrane packing is relevant. Also direct comparisons with the experimental data for the most relevant simulations are shown in Figs. 2E-F and S5 a. Fig. 2D  shows the overall highest-ranked simulation, POPC bilayer with OPLS3e parameters, for the reference.
Using NMRlipids Databank as a training set for machine learning applications: Predicting multi-component membrane properties To demonstrate the usage of the NMRlipids databank to construct machine learning (ML) models that predict membrane properties, we trained a model that predicts area per lipids and thicknesses of membranes with diverse compositions. First we used randomly selected 80% of the databank simulations to choose the hyperparameters for, and optimize, a set of machine learning models with the goal of predicting the area per lipid from the membrane composition. Examples of data obtained using NMRlipids Databank. A Area per lipid of POPC and POPE lipid bilayers predicted by different force fields at 310 K in simulations that are available in the NMRlipids Databank. The data points from the best-performing simulations, based on rankings in panels B and C, are surrounded by black circles.
B Best POPC simulations ranked based on the sn-1 acyl chain order parameter quality (P sn1 ). Also sn-2 acyl chain (P sn2 ), headgroup (P hg ) and total (P total ) order parameter qualities, form factor quality (FF q ), and relative equilibration time for conformations (τ rel ) are shown. Note that the best possible order parameter quality is one, while the best possible form factor quality is zero. C Best POPE simulations ranked based on the sn-1 acyl chain order parameter quality. D-F Direct comparison against experimental (NMR order parameters and X-ray scattering ) data exemplified for a simulation with the best overall order parameter quality (D), the best quality for POPE lipid (E), and the headgroup quality for POPE (F). G Scatter plots and Pearson correlation coefficients, r, for the membrane area per lipid, thickness, first minimum of X-ray scattering form factor and average order parameter of the sn-1 acyl chain extracted from the NMRlipids Databank.
All correlation coefficients have p-value below 0. 001. For more correlations see Fig. S2. After the parameter optimization, we tested predictions from different ML models for the area per lipid both against the remaining 20% of the databank and against area per lipids reported from simulations of membranes containing mixtures of POPC, POPE, POPS, PI, sphingomyelin lipids and cholesterol  [25] [26] [27]  that are not included in the databank. Essential differences between models were not observed when predicting area per lipids of 20% of simulations selected as the test set, but linear regression and Ridge models gave the best correlations with the literature data (predictions from the linear regression model are shown in Fig. 3  and from other models in Fig. S6  in the supplementary information).
The linear regression model was selected for further studies due to its simplicity. To demonstrate the usefulness of the constructed models for understanding multi-component membrane properties, we predicted later packing (ares per lipids) and membrane thicknesses of common biological membranes based on their lipid compositions reported in the literature, see table  1. The model predicts substantial 50% difference in area per lipid between most densely (influenza virus) and loosely (mitochondria) packed membranes. Difference of 0. 8 nm in thickness is predicted between thinnest (bacterial) and thickest (plasma) membranes. Such differences are expected to effect on many biologically relevant functions of membranes, such as permeation, cholesterol flip-flops (see next sections), and interactions with proteins  28  , demonstrating that NMRlipids databank can be used to give valuable insights on biologically relevant properties of complex biological membranes.
The atomistic studies report an increase in cholesterol flip-flop rates with increasing acyl chain unsaturation level and decreasing cholesterol concentration  37, 38  , but the amount of data in these individual studies was not sufficient to systematically assess correlations between cholesterol flip-flop rates and membrane properties. Here, we demonstrate that the NMRlipids Databank-API makes analyses of such rare phenomena accessible for all by enabling access to a large amount of MD simulation data as illustrated in Fig. 1. This is particularly useful for scientists in various fields of science and industry who lack access to the computational resources or the expertise to produce the large amounts of MD simulation data required for such analyses. Using the general workflow depicted in Fig.
1D , we first calculated the flip-flop rates from all the simulations available in the NMRlipids Databank. Flip-flops were observed for cholesterol, DCHOL (18,19-di-nor-cholesterol), DOG (1,2-dioleoyl-snglycerol), and SDG (1-stearoyl-2-docosahexaenoyl-sn-glycerol). The observed cholesterol flip-flop rates, ranging between 0. 001-1. 6 µs -1 with the mean of 0. 16 µs -1 and median of 0. 07 µs -1 , are in line with the previously reported values from atomistic MD simulations  [37] [38] [39]. The flip-flop rate of DCHOL, 0. 2 µs -1 , was close to the average value of cholesterol, while the average rates for diacylglycerols DOG (0. 4 µs -1 ) and SDG (0. 5 µs -1 ) were higher than for cholesterol. Flip-flops were not observed for other lipids, giving the upper limits for PC-lipid flip-flop rate as 9×10 -6 µs -1 and for ceramide (N-palmitoyl-Derythro-sphingosine) as 0.
002 µs -1. Thus, the available data in the NMRlipids Databank suggest that the lipid flip-flop rate decreases in the order: diacylglycerols > cholesterol > other lipids including ceramides. However, the amount of data for diacylgycerols (8 simulations with the Lipid17 force field) and ceramide (3 simulations with CHARMM36) is less than that for cholesterol (83 simulations); thus we cannot fully exclude the effect of force field or composition on this comparison. Nevertheless, we used the general workflow depicted in Fig. 1E  to analyse how the flip-flop rates calculated from the NMRlipids Databank depend on membrane properties. Figures  4B-D  show cholesterol flip-flop rates and their histograms as a function of membrane thickness, lateral density, and acyl chain order. The results reveal a non-linear correlation between cholesterol flip-flop rate and membrane packing (depicted as area per lipid): Flip-flop rates increase by an order of magnitude when membrane packing density decreases, and a major jump is observed at low membrane packing.
Such order-of-magnitude changes in cholesterol flip-flop rate with the membrane composition may have major implications in understanding lipid trafficking and membrane biochemistry  37, 39. Because the results from the NMRlipids Databank are averaged over a large range of membrane compositions and force fields, they show that the strong dependence of cholesterol flip-flop rate on membrane properties is not limited to the particular lipid compositions or force fields used in the previous studies  [37] [38] [39]. For the mean value in each bin, average weighted with the simulation lengths was used, and error bars show the standard error of the mean. Only bins with more than one microsecond of data in total were used for water permeation.
Extending the scope of MD simulations to new fields using NMRlipids Databank: Water diffusion anisotropy in membrane systems The anisotropic diffusion of water and hydrophilic molecules in directions parallel and perpendicular to membranes is an important parameter in models describing the translocation of drugs through biological material, particularly in the skin  12, [40] [41] [42]. Water anisotropic diffusion plays a role also in the signal formation in diffusion-tensor MRI imaging  11. MD simulations are rarely used to analyze the anisotropic diffusion of water, since only a few membrane permeation events of water are typically observed in a single MD simulation trajectory  43, 44  , thereby making the collection of a sufficient amount of data challenging. Here, we show that the API access to the data in NMRlipids Databank enables systematic analysis on how the anisotropic diffusion of water depends on membrane properties in multilamellar membrane systems, thereby extending the application of MD simulations to new fields.
To this end, we first calculated the water permeability through membranes from all simulations in the NMRlipids Databank using the general workflow depicted in Fig. 1D. The resulting non-zero values range between 0. 3-322 µm/s with the mean of 14 µm/s and median of 8 µm/s. These values agree with the previously reported simulation results  43, 44  , but are on average larger than experimental values reported for PC lipids in the liquid crystalline phase, 0. 19-0. 33 µm/s  46. Using the workflow depicted in Fig. 1E , we then plotted the observed permeabilities and their histogrammed values in Figs. 5B-E  as a function of temperature, membrane thickness, area per lipid, and acyl chain order. As expected, the permeability increases with the temperature, giving an average energy barrier of 14 ± 3 k B T for the water permeation from the Arrhenius plot in Fig.
5B. On the other hand, the water permeability on average decreases when membranes become more packed, that is, with decreasing area per lipid and increasing thickness and acyl chain order (Figs. 5C-E ). Permeation of water through bilayers depends on membrane properties also according to previous studies, but there is no established consensus on whether the area per lipid  47  or bilayer thickness  48  is the main parameter determining the permeability. Our analysis over the NMRlipids Databank, containing significantly more data than what was available in previous studies, suggest non-linear dependencies on both of these parameters. Clear dependencies of permeability on hydration level or the fraction of charged lipids, cholesterol, or POPE in the membrane were not observed (Fig. S7 ). To examine how water diffusion anisotropy depends on membrane properties in a multi-lamellar lipid bilayer system, we analyzed the water diffusion parallel to the membrane surface from all simulations in the NMRlipids Databank using the general workflows depicted in Figs.
Discussion Sharing of biomolecular MD simulation and other data is becoming increasingly important in the age of big data and artificial intelligence  1, 4. Besides the data itself, also programmatic access is a necessary requirement for data-driven and machine learning applications. This is particularly challenging when field-specific smart guidelines and community-consensus bestpractices have not yet been defined, which is the case in biomolecular simulations  4. The NMRlipids Databank demonstrates how these issues can be solved by the overlay databank design, where the raw data are distributed to already publicly available decentralized locations, while the core of the databank is composed only of the metadata stored in a version-controlled git repository with an open-access license. On the other hand, the open-collaboration approach developed in the NMRlipids Project  6  creates incentives for sharing the data by offering authorship in published articles to the contributors.
Advantages of such an approach are demonstrated here for membrane simulations, yet the concept can be applied to any other biomolecules, as well as in any other field where similar barriers hinder the AI revolution, such as the assignment of NMR spectra  5. The NMRlipids Databank-API delivers programmatic access to MD simulation data that can be used as training set for diverse data-driven and machine learning applications that predict membrane properties. Such applications could be analogous to AlphaFold 3 and other tools  53, 54  that predict protein structures from their sequence using artificial intelligence. This is demonstrated here by building ML models to predict multi-component membrane properties. Furthermore, the analysis of cholesterol flip-flop events (Fig. 4 ) and water permeation through membranes (Fig.
Simultaneously, machine-learning-based models for predicting the behaviour of biomolecules and automatic approaches to parametrize models are emerging  3, 20. The resources delivered by the NMRlipids Databank will support developments in both of these directions. Automatic quality evaluation and ranking of simulations against experimental Analyses of rare phenomena (for example, Lipid flip-flops and water permeation in Figs. 4  and 5 ), data-driven analyzes and machine learning models predicting correlations between properties and compositions of biomolecular assemblies (Figs. 2  and 5 ) Students and teachers Graphical and programmatic access to biomolecular structure and dynamics Illustration of disorder and dynamics in biomolecules, example data for bioinformatics and data-science courses Reviewers and publishers Tool to facilitate FAIRness of data, transparency and reproducibility All publicly available data can be included in an overlay databank irrespectively on its location Table  2.
Examples of impact of NMRlipids and other overlay databanks in different disciplines. data enable the selection of best simulations for specific applications without laborious manual force field evaluation. This also streamlines automatic parametrization procedures for atomistic and coarse grained simulations by, for example, pinpointing typical failures of force fields and highlighting points of improvement. Such practises for fostering the accuracy of simulations are becoming increasingly important as small errors accumulate when complexity and size of simulated systems are increasing. Examples of impact of NMRlipids and other overlay databanks in different disciplines are listed in Table  2 , yet the scope of applications is expected to further widen with increasing amount of publicly shared data.
Structure of the Databank The overlay structure designed for the NMRlipids Databank is composed of three layers (Fig. 1A ). The Data layer contains raw data that can be distributed to publicly available servers such as Zenodo (zenodo. org). The core content of the Databank locates in the Databank layer, which is a git repository at github. com/NMRlipids/Databank and is also permanently stored in a Zenodo repository (DOI: 10. 5281/zenodo. 7875567). The essential information of each simulation is stored in a human-and-machinereadable README. yaml file located in a subfolder of the /Data/Simulations folder in the Databank layer repository; each subfolder has a unique name constructed based on a hash code of the trajectory and topology files of each simulation. The README. yaml files in these folders contain access to all information that is needed for further analysis of simulations, such as links to the raw data and associations with the universal molecule and atom names.
The content of these files is described in detail in Table  S3  in the supplementary information and in the NMRlipids databank documentation nmrlipids. github. io/. Results from analyses of basic membrane properties (area per lipid, thickness, C-H bond order parameters, X-ray scattering form factors, and relaxation of principal components) are stored in the same folders as the README. yaml files. Experimental data used for ranking is stored in the /Data/experiments folder, the ranking results in /Data/Ranking, and the relevant scripts in the /Scripts/ folder in the Databank layer repository. The scripts in the NMRlipids Databank are mainly written in Python and many of them use the MDAnalysis module  55, 56. The Databank structure is illustrated in more detail in Fig. S9  in the supplementary information.
Universal naming convention for molecules and atoms When analysing simulation trajectories, atoms and molecules often need to be called by the names used in the trajectory. However, these names typically vary between force fields, as a universal naming convention has not been defined for lipids. To enable automatic analyses over all the simulations in the NMRlipids Databank, we have defined universal naming conventions for the molecules and atoms therein. The universal abbreviations used in the NMRlipids Databank for each molecule are listed in the NMRlipids Databank documentation  18. The atom names used in simulation trajectories are connected to the universal atom names using the mapping files defined in the NMRlipids Project  18. These files are located at /Scripts/BuildDatabank/mapping_files in the NMRlipids Databank repository.
Adding data to NMRlipids Databank The NMRlipids Databank is open for additions of simulation data by anyone. In practise, the required information is first manually entered into an info. yaml file that is then added into the /Scripts/BuildDatabank/info_files folder trough a git pull request. Rest of the information to be stored in the README. yaml files will be then automatically extracted using the /Scripts/BuildDatabank/AddData. py script. The required manually entered and automatically extracted information are described in detail in Table  S3  in the supplementary information and in the NMRlipids databank documentation  57. The documentation includes also detailed instructions to add data. To avoid ineligible entries and minimize human errors, the pull requests are monitored before the acceptance and generation of the README.
Experimental data Experimental data used in the quality evaluation, currently composed of C-H bond order parameters and X-ray scattering form factors, are stored in /Data/experiments in the NMRlipids Databank repository. Similarly to simulations, each experimental data set has a README. yaml file containing all the relevant information about the experiment. The keys and their descriptions for the experimental data, as well as detailed instructions to add the date, are given in Table  S4  and in the NMRlipids databank documentation  58. The NMR data currently in the NMRlipids Databank are taken from Refs. 15, 16, 59-63 and the X-ray scattering data from Refs. 63-69. In addition, previously unpublished NMR data for POPE, POPG, and DOPC was acquired as described in the supplementary information (Figs.