GSoC Report: Curvature for MDA Universes

The main goal of my GSoC project was to develop a tool to calculate membrane curvature from MD simulations using MDAnalysis. We aimed to have the following features in the membrane curvature analysis tool:


  • Surfaces derived from an AtomGroup of reference.
  • Calculation of mean and Gaussian curvature.
  • Multiframe and averaged-over-frames analysis.
  • Plug-and-play with visualization libraries to obtain 2D curvature profiles.
  • Data visualization made easy.

Why Membrane Curvature?

In the wide range of tools that are available to analyze Molecular Dynamics (MD) simulations, user-friendly, actively-maintained, and well-documented tools to calculate membrane curvature are still difficult to find. I was motivated to share a tool to calculate membrane curvature that I initially developed as part of my PhD at the Biocomputing Group. Membrane curvature is a phenomenon that can be investigated via MD simulations, and I had an interest to share this tool with the wider MD community.


Keeping in mind the goals and motivations behind my GSoC project, I would like to hightlight three areas of contributions that were key to the MembraneCurvature MDAnalysis tool: Core functions, AnalysisBase, and Documentation.

Core functions (#9, #34, #40, #44)

The initial version of the code contained functions to map elements from the AtomGroup of reference into a grid of dimensions defined by the simulation box. The initial version also included the functions to calculate mean and Gaussian curvature. After refactoring, the core functions of MembraneCurvature were cleaned and tuned up. In summary, our core functions can be split into two groups:

  • Surface : derive_surface(), get_z_surface(), normalize_grid().
  • Curvature : mean_curvature(), gaussian_curvature().

These functions are our bricks to build the MembraneCurvature AnalysisBase.

Analysis Base (#43, #48)

The analysis in MembraneCurvature used the MDAnalysis AnalysisBase building block, from where we obtained the basic structure to run multilframe analysis.

In the MembraneCurvature subclass of AnalysisBase, we define the initial arrays for surface, mean, and Gaussian curvature in the _prepare() method. In _single_frame(), AnalysisBase runs the membrane curvature analysis in every frame, and populates the arrays previously defined in _prepare(). In _conclude, we compute the average over frames for the surface and curvature arrays (#43).

The derived surface and calculated arrays of mean and Gaussian curvature values are stored in the results attribute. This makes AnalysisBase the most fundamental part of the MembraneCurvature analysis, allowing us to perform multiframe and average-over-frame curvature analysis.

We also added coordinate wrapping to our Analysis base, which enables users to run MembraneCurvature with all atoms in the primary unit cell (#48). Having an option to wrap coordinates is particularly useful when we want to calculate curvature with elements in the AtomGroup that may fall outside the boundaries of the grid.

With #48, we also achieved a significant milestone: reaching 100% code coverage in MembraneCurvature! 100% coverage means that every line of code included was executed by pytest, the test suite used by MDAnalysis, to check that the code works as it should.

Documentation (#57, #62, #64, #69)

One of the strongest motivations to contribute an MDAnalysis curvature tool was to provide a well-documented package to analyze membrane curvature from MD simulations.

The membrane curvature tool includes solid documentation that can be found in the following pages:

We included two different tutorials: One where we use Membrane Curvature to derive surfaces and calculate curvature of a membrane-only system. A second tutorial to calculate curvature in a membrane-protein system is currently under development (#69).

How can I use it?

Membrane-curvature uses MDAnalysis under the hood. We can install Membrane-curvature via pip:

pip install membrane-curvature

Here you can find more installation instructions.

Running MembraneCurvature

MembraneCurvature was designed to be user friendly. No counterintuitive commands, and no long lines of code. With MembraneCurvature you can calculate curvature in just a few lines! The snippet below illustrates how easy it gets to extract mean and Gaussian curvature from MD simulations using MembraneCurvature:

import MDAnalysis as mda
from membrane_curvature.base import MembraneCurvature
from membrane_curvature.tests.datafiles import (MEMB_GRO, 

u = mda.Universe(MEMB_GRO, MEMB_XTC)

mc_upper = MembraneCurvature(u, 
                             select="resid 103-1023 and name PO4",

mc_lower = MembraneCurvature(u, 
                             select='resid 1024-2046 and name PO4', 

In the example above, we use two files included in the MembraneCurvature tests: MEMB_GRO and XTC_GRO, which comprises a membrane of lipid composition POPC:POPE:CHOL, in a 5:4:1 ratio:


We use the selection "resid 103-1023 and name PO4" as an AtomGroup of reference to derive the surface associated to the upper leaflet. Similarly, we derive the surface from the lower leaflet with the AtomGroup defined by the selection "resid 1024-2046 and name PO4" . In this example, PO4 is the name of the phospholipid head groups.

After running MembraneCurvature, the calculated values of the derived surface (H), and Gaussian curvature (K) are stored in the _results() attribute. We can easily extract the average results with:

# surface
surf_upper = mc_upper.results.average_z_surface
surf_lower = mc_lower.results.average_z_surface

# mean curvature
mean_upper = mc_upper.results.average_mean
mean_lower = mc_lower.results.average_mean

# Gaussian curvature
gauss_upper = mc_upper.results.average_gaussian
gauss_lower = mc_lower.results.average_gaussian


To visualize the results from, we can use contourf or imshow from Matplotlib. For example, here is the plot of the averaged results for the upper leaflet using contours:


In biological membranes of higher complexity, lipid composition between leaflets is commonly assymetric. With the results obtained from MembraneCurvature, direct comparison between leaflets can be easily performed. The following snippet generates a side-by-side plot of the mean curvature results for each leaflet with its respective color bar:

import matplotlib.pyplot as plt
from scipy import ndimage
import numpy as np

results = [mean_lower, mean_upper]
leaflets = ["Lower", "Upper"]

fig, [ax1, ax2] = plt.subplots(ncols=2, figsize=(4,3.5))
max_ = max([np.max(abs(h)) for h in results])

for ax, rs, lf in zip((ax1, ax2), results, leaflets):
    rs = ndimage.zoom(rs, 4, mode='wrap', order=1)
    levs = np.linspace(-max_, max_, 40)
    im = ax.contourf(rs, cmap='bwr', 
                     origin='lower', levels=levs,
                     vmin=-max_, vmax=max_)

    ax.set_title('{} Leaflet'.format(lf), fontsize=6)

cbar = plt.colorbar(im, ticks=[-max_, 0, max_], 
                    ax=[ax1, ax2], shrink=0.4, 
                    aspect=10, pad=0.05), width=0.5)
cbar.set_label('H (nm$^{-1}$)', fontsize=6, labelpad=2) 


Mean curvature plots provide information about the “inverted shape” of the surface, which in this example is derived from phospholipids headgroups in each leaflet. Positive mean curvature indicates valleys (red coloured), negative mean curvature is associated with peaks (blue coloured). Regions where H=0 indicates a flat region (white coloured). In the example considered here, the contour plot of mean curvature shows:

  • Coupling between leaflets. Regions of positive curvature (red coloured) in the lower leaflet match those in the upper leaflet. The same is observed for regions of negative curvature (blue coloured).

  • A central region of negative curvature. For both upper and lower leaflets, there is a central region of negative mean curvature (blue coloured) along the y axis, while regions of positive curvature (red coloured) are localized in the bulk of the membrane, in particular bottom left and upper right regions.

In progress

Currently, we are working on implementing interpolation as an option for the user #52.

In some situations, when selecting a very high number of bins in the grid, or when having regions of the grid with low sampling we may find regions of undefined values. For example, think of a membrane-protein system, where the bins occupied by the protein won’t be populated by lipids, and therefore, will have a region of undefined values in the grid. Such undefined values spread in the array during the calculation of curvature, which may result in meaningless output.

By adding an optional interpolation, we will be able to patch up undefined values in bins inside the embedded element (i.e. protein). With this improvement, calculation of membrane curvature won’t be hamstrung by the presence of undefined values in the grid.

What’s next?

There is always room for improvement, and MembraneCurvature is not an exception. One of the main limitations of the current version of MembraneCurvature is the inability to calculate curvature in systems like vesicles, capsids, or micelles. This would definitely be a nice improvement for a future release of MembraneCurvature!


We acknowledge that scientific research would benefit from a tool to calculate membrane curvature in these types of systems, so we are considering possible approaches to include more topologies in MembraneCurvature!


MembraneCurvature is a well-documented and easy-to-use tool to derive 2D maps of mean and Gaussian curvature from Molecular Dynamics (MD) simulations. Since it uses the MDAnalysis AnalysisBase building block, MembraneCurvature enable users to perform multiframe and average-over-frames curvature analyses.

From MD simulations to 2D curvature profiles in only a few lines of code!



Participating in GSoC with MDAnalysis has been a unique experience. I had the opportunity to learn best practices in software development mentored by a group of incredibly talented people:

I also would like to thank @richardjgowers (Richard) and @tylerjereddy (Tyler) from the MDA community, who participated in our discussions and provided valuable insights.
Thanks for all your valuable lessons.

MembraneCurvature has launched! 🚀


Chan Zuckerberg Initiative EOSS4 Award

MDAnalysis is excited to announce that we have been awarded an Essential Open Source Software for Science (EOSS) grant by the Chan Zuckerberg Initiative for our proposal “MDAnalysis: Faster, extensible molecular analysis for reproducible science”.

Over the next two years, this award will fund work on two major aims that will improve MDAnalysis for our users and make it easier for scientists to contribute code, with a view towards increasing code reuse, interoperability, and reproducibility:

(1) Improving the performance of essential core components by rewriting Python code in Cython

As MDAnalysis has now become a mature library with a stable API, we want to focus on improving the performance of core library components by rewriting essential code in Cython. This will enable us to handle the increasingly large and complex datasets generated to solve the biomedical challenges of tomorrow.

(2) Launching the “MDAKits” ecosystem

We will launch the MDAnalysis Toolkits (“MDAKit”) ecosystem. Inspired by SciPy’s SciKits, an MDAKit will contain code based on MDAnalysis that solves specific scientific problems. An MDAKit can be written by anyone and hosted anywhere. In order to streamline the process we will provide a cookiecutter-based template repository. MDAKits will have a well-defined API, have documentation, will be registered with MDAnalysis, and will be continuously tested for compatibility with the current version of MDAnalysis. As part of the registration process, code will be reviewed by MDAnalysis developers and we will encourage the scientific publication of the package, e.g., in the Journal of Open Source Software. We will also refactor a number of analysis modules as separate MDAKits. Overall, the introduction of MDAKits will enable the rapid development and dissemination of new components and tools that can be released independently of the main package. We will soon describe the details of MDAKits in a white paper and seek a discussion with the wider community. Ultimately, MDAKits should help address two long-standing challenges in the biomolecular simulation community: effort duplication and reproducibility.

Along the same lines, we will focus on software interoperability with other packages by expanding our converters framework.

By validating, maintaining and publicizing new code through a common process, we encourage scientists to reuse and extend existing packages rather than re-creating their own ones. We hope that this combined approach will improve consistency, transparency, and accessibility in methods used across scientific studies.

This project will be led by Irfan Alibay, Lily Wang, Fiona Naughton, Richard Gowers, and Oliver Beckstein.

About MDAnalysis

The MDAnalysis package is one of the most widely used Python-based libraries for the analysis and manipulation of molecular simulations. The objective of MDAnalysis is to provide simple, flexible, and efficient means of handling molecular structure data from simulations and experiments and support research in biophysics, biochemistry, materials science and beyond.

MDAnalysis is a fiscally-sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open source scientific computing community.

MDAnalysis 2.0 is here

We are happy to release version 2.0.0 of MDAnalysis!

This is a new major version, and includes major API breaking changes in addition to a large number of updates and enhancements. This release concludes our current roadmap, an updated roadmap detailing our future steps will shortly be published.

This version supports Python 3.6 through to 3.9 on Windows (32- and 64-bit), MacOS, and Linux. It also offers preliminary support for ppc64le and ARM64, but does not currently support Apple M1 processors.

Support for legacy version of Python (e.g. 2.7 and 3.5) is only available in MDAnalysis 1.x releases.

Upgrading to MDAnalysis version 2.0.0

To install with conda from the conda-forge channel run

conda update -c conda-forge mdanalysis

To install from PyPi with pip run

pip install --upgrade MDAnalysis

For more help with installation see the installation instructions in the User Guide.

Notable new additions

There were many awesome contributions to MDAnalysis for version 2.0.0. Here we list a few notable example of these. For more information please see the CHANGELOG.

RDKit converter

Google Summer of Code student @cbouy implemented a new RDKit converter which allows for seamless conversions from RDKit to MDAnalysis structures. See @cbouy’s GSoC RDKit report for more information.

It currently accurately converts 90% of chemical space (based on ChEMBL27), but future changes will improve this.

OpenMM converter

@ahy3nz implemented an OpenMM converter which can read OpenMM objects directly into MDAnalysis.

Universe serialization

Google Summer of Code student @yuxuanzhuang implemented a means to serialize MDAnalysis.Universe objects, paving the way for parallel analysis. See @yuxuanzhuang’s GSoC Universe serialization report for more information.

Mean Square Displacement

@hmacdope implemented a new Mean Square Displacement analysis class.

Results class

@PicoCentauri implemented a Results class to store results from analysis classes. This change streamlines the API for analysis classe and paves the way for a soon-to-be released command line interface for MDAnalysis.

H5MD support

NSF REU student @edisj implemented both a reader and writer for the H5MD format.

Bond-Angle-Torsion coordinates

@daveminh implemented a means to translate from Cartesian to Bond-Angle-Torsion coordinates.

Important fixes

For a full list of bugfixes see the CHANGELOG. The following are selected fixes that may have lead to wrong results depending on your use case.


  • Fixes an issue where select_atom, AtomGroup.unique, ResidueGroup.unique, and SegmentGroup.unique did not sort the output atoms (Issues #3364 #2977)
  • Fixes the sometimes wrong sorting of atoms into fragments when unwrapping (Issue #3352)
  • now works correctly for compounds + unwrapping (Issue #2984)

File formats

  • Fixes support for DL_POLY HISTORY files that contain cell information even if there are no periodic boundary conditions (Issue #3314)
  • PDBWriter will use chainID instead of segID (Issue #3144)
  • PDBParser and PDBWriter now assign and use the element attribute (Issues #2422 #2423)


  • Fixes issue when attempting to use/pass mean positions to PCA analysis (Issue #2728)
  • Fixes issue with WaterBridgeAnalysis double counting waters (Issue #3119)
  • Documents and fixes the density keyword for rdf.InterRDF_s (Issue #2811)
  • Fixed Janin analysis residue filtering, including CYSH (Issue #2898)

Changes to functionality

  • Deprecated hbonds.hbond_analysis has been removed in favour of hydrogenbonds.hbond_analysis (Issues #2739, #2746)
  • TPRParser now loads TPR files with tpr_resid_from_one=True by default, which starts TPR resid indexing from 1 (instead of 0 as in previous MDAnalysis versions) (Issue #2364, PR #3152)
  • analysis.hole has now been removed in favour of analysis.hole2.hole (Issue #2739)
  • Writer.write(Timestep) and Writer.write_next_timestep have been removed. Please use write() instead (Issue #2739)
  • Removes deprecated density_from_Universe, density_from_PDB, Bfactor2RMSF, and notwithin_coordinates_factory from MDAnalysis.analysis.density (Issue #2739)
  • Removes deprecated waterdynamics.HydrogenBondLifetimes (PR #2842)
  • hbonds.WaterBridgeAnalysis has been moved to hydrogenbonds.WaterBridgeAnalysis (Issue #2739 PR #2913)


  • The bfactors attribute is now aliased to tempfactors and will be removed in 3.0.0 (Issue #1901)
  • WaterBridgeAnalysis.generate_table() now returns table information, with the table attribute being deprecated
  • Various analysis result attributes which are now stored in Results will be deprecated in 3.0.0 (Issue #3261)
  • In 3.0.0 the ParmEd classes will only be accessible from the MDAnalysis.converters module
  • In 2.1.0 the TRZReader will default to a dt of 1.0 ps when failing to read it from the input TRZ trajectory

Author statistics

Altogether, this represents the work of 35 authors, 17 of which were new contributors:

MDAnalysis thanks NumFOCUS’s continued support as the organisation’s fiscal sponsor.

  • The MDAnalysis Team