Structural and molecular determinants for the interaction of ExbB from Serratia marcescens and HasB, a TonB paralog.

ExbB and ExbD are cytoplasmic membrane proteins that associate with TonB to convey the energy of the proton-motive force to outer membrane receptors in Gram-negative bacteria for iron uptake. The opportunistic pathogen Serratia marcescens (Sm) possesses both TonB and a heme-specific TonB paralog, HasB. ExbBSm has a long periplasmic extension absent in other bacteria such as E. coli (Ec). Long ExbB's are found in several genera of Alphaproteobacteria, most often in correlation with a hasB gene. We investigated specificity determinants of ExbBSm and HasB. We determined the cryo-EM structures of ExbBSm and of the ExbB-ExbDSm complex from S. marcescens. ExbBSm alone is a stable pentamer, and its complex includes two ExbD monomers. We showed that ExbBSm extension interacts with HasB and is involved in heme acquisition and we identified key residues in the membrane domain of ExbBSm and ExbBEc, essential for function and likely involved in the interaction with TonB/HasB. Our results shed light on the class of inner membrane energy machinery formed by ExbB, ExbD and HasB.

Modeling Perturbations in Protein Filaments at the Micro and Meso Scale Using NAMD and PTools/Heligeom.

Protein filaments are dynamic entities that respond to external stimuli by slightly or substantially modifying the internal binding geometries between successive protomers. This results in overall changes in the filament architecture, which are difficult to model due to the helical character of the system. Here, we describe how distortions in RecA nucleofilaments and their consequences on the filament-DNA and bound DNA-DNA interactions at different stages of the homologous recombination process can be modeled using the PTools/Heligeom software and subsequent molecular dynamics simulation with NAMD. Modeling methods dealing with helical macromolecular objects typically rely on symmetric assemblies and take advantage of known symmetry descriptors. Other methods dealing with single objects, such as MMTK or VMD, do not integrate the specificities of regular assemblies. By basing the model building on binding geometries at the protomer-protomer level, PTools/Heligeom frees the building process from a priori knowledge of the system topology and enables irregular architectures and symmetry disruption to be accounted for. Graphical abstract: Model of ATP hydrolysis-induced distortions in the recombinant nucleoprotein, obtained by combining RecA-DNA and two RecA-RecA binding geometries.

Phylogenetic and functional diversity of aldehyde-alcohol dehydrogenases in microalgae.

KEY MESSAGE: The study shows the biochemical and enzymatic divergence between the two aldehyde-alcohol dehydrogenases of the alga Polytomella sp., shedding light on novel aspects of the enzyme evolution amid unicellular eukaryotes. Aldehyde-alcohol dehydrogenases (ADHEs) are large metalloenzymes that typically perform the two-step reduction of acetyl-CoA into ethanol. These enzymes consist of an N-terminal acetylating aldehyde dehydrogenase domain (ALDH) and a C-terminal alcohol dehydrogenase (ADH) domain. ADHEs are present in various bacterial phyla as well as in some unicellular eukaryotes. Here we focus on ADHEs in microalgae, a diverse and polyphyletic group of plastid-bearing unicellular eukaryotes. Genome survey shows the uneven distribution of the ADHE gene among free-living algae, and the presence of two distinct genes in various species. We show that the non-photosynthetic Chlorophyte alga Polytomella sp. SAG 198.80 harbors two genes for ADHE-like enzymes with divergent C-terminal ADH domains. Immunoblots indicate that both ADHEs accumulate in Polytomella cells growing aerobically on acetate or ethanol. ADHE1 of ~ 105-kDa is found in particulate fractions, whereas ADHE2 of ~ 95-kDa is mostly soluble. The study of the recombinant enzymes revealed that ADHE1 has both the ALDH and ADH activities, while ADHE2 has only the ALDH activity. Phylogeny shows that the divergence occurred close to the root of the Polytomella genus within a clade formed by the majority of the Chlorophyte ADHE sequences, next to the cyanobacterial clade. The potential diversification of function in Polytomella spp. unveiled here likely took place after the loss of photosynthesis. Overall, our study provides a glimpse at the complex evolutionary history of the ADHE in microalgae which includes (i) acquisition via different gene donors, (ii) gene duplication and (iii) independent evolution of one of the two enzymatic domains.

MinOmics, an Integrative and Immersive Tool for Multi-Omics Analysis.

Proteomic and transcriptomic technologies resulted in massive biological datasets, their interpretation requiring sophisticated computational strategies. Efficient and intuitive real-time analysis remains challenging. We use proteomic data on 1417 proteins of the green microalga Chlamydomonas reinhardtii to investigate physicochemical parameters governing selectivity of three cysteine-based redox post translational modifications (PTM): glutathionylation (SSG), nitrosylation (SNO) and disulphide bonds (SS) reduced by thioredoxins. We aim to understand underlying molecular mechanisms and structural determinants through integration of redox proteome data from gene- to structural level. Our interactive visual analytics approach on an 8.3 m2 display wall of 25 MPixel resolution features stereoscopic three dimensions (3D) representation performed by UnityMol WebGL. Virtual reality headsets complement the range of usage configurations for fully immersive tasks. Our experiments confirm that fast access to a rich cross-linked database is necessary for immersive analysis of structural data. We emphasize the possibility to display complex data structures and relationships in 3D, intrinsic to molecular structure visualization, but less common for omics-network analysis. Our setup is powered by MinOmics, an integrated analysis pipeline and visualization framework dedicated to multi-omics analysis. MinOmics integrates data from various sources into a materialized physical repository. We evaluate its performance, a design criterion for the framework.

Great interactions: How binding incorrect partners can teach us about protein recognition and function.

Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Sites of Anesthetic Inhibitory Action on a Cationic Ligand-Gated Ion Channel.

Pentameric ligand-gated ion channels have been identified as the principal target of general anesthetics (GA), whose molecular mechanism of action remains poorly understood. Bacterial homologs, such as the Gloeobacter violaceus receptor (GLIC), have been shown to be valid functional models of GA action. The GA bromoform inhibits GLIC at submillimolar concentration. We characterize bromoform binding by crystallography and molecular dynamics (MD) simulations. GLIC's open form structure identified three intra-subunit binding sites. We crystallized the locally closed form with an additional bromoform molecule in the channel pore. We systematically compare binding with the multiple potential sites of allosteric channel regulation in the open, locally closed, and resting forms. MD simulations reveal differential exchange pathways between sites from one form to the other. GAs predominantly access the receptor from the lipid bilayer in all cases. Differential binding affinity among the channel forms is observed; the pore site markedly stabilizes the inactive versus active state.

Epock: rapid analysis of protein pocket dynamics.

SUMMARY: The volume of an internal protein pocket is fundamental to ligand accessibility. Few programs that compute such volumes manage dynamic data from molecular dynamics (MD) simulations. Limited performance often prohibits analysis of large datasets. We present Epock, an efficient command-line tool that calculates pocket volumes from MD trajectories. A plugin for the VMD program provides a graphical user interface to facilitate input creation, run Epock and analyse the results. AVAILABILITY AND IMPLEMENTATION: Epock C++ source code, Python analysis scripts, VMD Tcl plugin, documentation and installation instructions are freely available at http://epock.bitbucket.org. CONTACT: benoist.laurent@gmail.com or baaden@smplinux.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-resolution approach for interactively locating functionally linked ion binding sites by steering small molecules into electrostatic potential maps using a haptic device.

Metal ions drive important parts of biology, yet it remains experimentally challenging to locate their binding sites. Here we present an innovative computational approach. We use interactive steering of charged ions or small molecules in an electrostatic potential map in order to identify potential binding sites. The user interacts with a haptic device and experiences tactile feedback related to the strength of binding at a given site. The potential field is the first level of resolution used in this model. Any type of potential field can be used, implicitly taking into account various conditions such as ionic strength, dielectric constants or the presence of a membrane. At a second level, we represent the accessibility of all binding sites by modelling the shape of the target macromolecule via non-bonded van der Waals interactions between its static atomic or coarse-grained structure and the probe molecule(s). The third independent level concerns the representation of the molecular probe itself. Ion selectivity can be assessed by using multiple interacting ions as probes. This method was successfully applied to the DNase I enzyme, where we recently identified two new cation binding sites by computationally expensive extended molecular dynamics simulations.