NMRlipids Databank makes data-driven analysis of biomembrane properties accessible for all

Abstract

Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. Molecular dynamics (MD) simulations have been useful in understanding membrane systems, but they require significant computational resources and often suffer from inaccuracies in model parameters. Applications of data-driven and machine learning methods, currently revolutionising many fields, remain of limited use for membrane systems due to the lack of suitable training sets. Here we present the NMRlipids Databank—a community-driven, open-for-all database featuring programmatic access to quality- evaluated atom-resolution MD simulations of lipid bilayers. The NMRlipids Databank will benefit scientists in different disciplines by providing automatic ranking of simulations based on their quality against experiments, programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface. To demonstrate how it unlocks possibilities beyond current MD simulation studies, we analyzed the NMRlipids Databank to reveal how anisotropic diffusion of water and cholesterol flip-flop rates depend on membrane properties.

Quantitative Comparison against Experiments Reveals Imperfections in Force Fields’ Descriptions of POPC–Cholesterol Interactions

“…CHOL-induced ordering straightens the acyl chains, which leads to membrane thickening. While acyl chain order and membrane thickness are well correlated, lipid bilayer dimensions can be accessed more directly by measuring the X-ray form factor, which is related to the electron density along membrane normal via Fourier transform. ,,, Electron density profile, area per phospholipid, and bilayer thickness can all be extracted from the form factor using the scattering density profile (SDP) model or its combination with MD simulations. ,,− To complement the evaluation of CHOL-induced ordering against NMR order parameters, we also measured the X-ray form factors from POPC–CHOL mixtures at systematically increasing CHOL concentrations. Scattering intensities from experiments are shown in Figures S1 and S2, form factors from experiments and simulations in Figure S3, and density profiles in Figure S4.…”

“…Also the form factor profiles depend on simulation system size (see Figure S2 of ref 48) which complicates their direct comparison with experimental data. Nevertheless, the minima in X-ray form factors are independent of the simulation box size and correlate with membrane dimensions . To avoid the effects from simulation system sizes, we thus interpolated the locations of the first two minima in the form factors over the entire studied range of CHOL concentrations (bottom right panel of Figure ) for a more direct comparison between simulations and experiments.…”

“…Our results demonstrate that quality evaluation of lipid mixture simulations against experimental NMR and X-ray scattering data gives consistent results for how accurately force field parameters capture intermolecular interactions. The interpolation approach introduced here extends the NMRlipids Databank quality metrics beyond individual systems: this enables the automatic ranking of not only lipid mixtures but also membranes mixed with other molecules such as ions. Such tools will strongly support the emerging endeavors for automatic improvement of force field parameters …”

“…While simulating complex membranes with CHOL has become a relatively straightforward task, estimating the trustworthiness of MD simulations remains a challenge, especially as the complexity and number of membrane components increase. Our earlier work has demonstrated that the conformational ensembles of lipids from an MD simulation can be evaluated against the C–H bond order parameters from NMR experiments. − This approach has been useful for finding the best force fields to describe the headgroups of phosphatidylcholine (PC), , phosphatidylserine (PS), phoshatidylethanolamine (PE), and phosphatidylglycerol (PG) lipids; to evaluate and improve membrane interactions with ions − ,, and small molecules; and to find simulation parameters predicting most realistic packing properties of membranes. , Furthermore, quantitative quality measures based on the C–H bond order parameters have been recently defined and used to rank simulations in the NMRlipids Databank . However, such automatic quality evaluation is limited to simulations for which experimental data at the corresponding composition and temperature are available.…”

Dynamic Nature ofStaphylococcus aureusType I Signal Peptidases

“…66 Maintaining the ratio, two different lipid mixtures differing solely in their lipid tail lengths (one being myriostol (14:0) acyl chains for PG and CL; the other having PO (16:0, 18:1) for PG and P (18:1) for CL; in both cases lysyl-PG was modeled as lysyl-DPPG (DPPGK, 16:0)). The resulting characterization of the S. aureus model bilayer is performed using in-house scripts as well as the NMRlipids Databank 67 .…”