MDAnalysis (http://mdanalysis.org) is a library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. MD simulations of biological molecules have become an important tool to elucidate the relationship between molecular structure and physiological function. Simulations are performed with highly optimized software packages on HPC resources but most codes generate output trajectories in their own formats so that the development of new trajectory analysis algorithms is confined to specific user communities and widespread adoption and further development is delayed. MDAnalysis addresses this problem by abstracting access to the raw simulation data and presenting a uniform object-oriented Python interface to the user. It thus enables users to rapidly write code that is portable and immediately usable in virtually all biomolecular simulation communities. The user interface and modular design work equally well in complex scripted work flows, as foundations for other packages, and for interactive and rapid prototyping work in IPython / Jupyter notebooks, especially together with molecular visualization provided by nglview and time series analysis with pandas. MDAnalysis is written in Python and Cython and uses NumPy arrays for easy interoperability with the wider scientific Python ecosystem. It is widely used and forms the foundation for more specialized biomolecular simulation tools.

The MARTINI coarse-grained model is used to gain insight into the association of WALP23 helices in three different lipid membranes: DMPC, DPPC and DOPC. Potentials of mean force describing the association of two WALP23 helices embedded in different lipid bilayers indicate no barrier of association and a stabilization of more than 20 kJ mol(-1) of the associated state relative to the fully dissociated state. Association is strongest in DMPC, followed by DPPC and DOPC. Helix-helix association appears to be enthalpically favorable in all lipid bilayers, while the entropic contribution appears favorable only in the presence of significant positive hydrophobic mismatch, in DMPC lipids. The interpretation of this requires care given the coarse-grained nature of the simulations, but the sign of the thermodynamic quantities agrees with experimental measurements on dimerization of (AALALAA)3 peptides and the observed association free energies are within the experimental range. Both protein-protein and lipid-lipid interactions appear to strongly favor protein dimerization, while the interactions between a dimer and lipid are unfavorable relative to the interactions between two separated monomers and lipids. Dimers with antiparallel orientation appear to be thermodynamically favored over parallel dimers, particularly in conditions of greater hydrophobic mismatch, but elucidating the detailed origin of this likely requires simulations of helices for which there is structural data on the dimer. We analyze 3D density, membrane order, and membrane thickness maps using new freely available analysis programs. Although these properties differ somewhat for each lipid, perturbations extend to about 1 nm for lipid density, ~2 nm for ordering and ~2.5 nm for thickness. A striking feature is the appearance and extent of systematic density fluctuations around the helices.

Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e. the classical secondary structures. . More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g. B-factor) and computational methodology (e.g. Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g. to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.