EncoMPASS: An encyclopedia of membrane proteins analyzed by structure and symmetry.

Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about protein structure and structural relationships. For membrane proteins, however, there is limited agreement among available online tools for highlighting and mapping such structural similarities. Moreover, no available resource provides a systematic overview of quaternary and internal symmetries, and their orientation relative to the membrane, despite the fact that these properties can provide key insights into membrane protein function and evolution. Here, we describe the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS), a database for relating integral membrane proteins of known structure from the points of view of sequence, structure, and symmetry. EncoMPASS is accessible through a web interface, and its contents can be easily downloaded. This allows the user not only to focus on specific proteins, but also to study general properties of the structure and evolution of membrane proteins.

A modular phage vector platform for targeted photodynamic therapy of Gram-negative bacterial pathogens.

Growing antibiotic resistance has encouraged the revival of phage-inspired antimicrobial approaches. On the other hand, photodynamic therapy (PDT) is considered a very promising research domain for the protection against infectious diseases. Yet, very few efforts have been made to combine the advantages of both approaches in a modular, retargetable platform. Here, we foster the M13 bacteriophage as a multifunctional scaffold, enabling the selective photodynamic killing of bacteria. We took advantage of the well-defined molecular biology of M13 to functionalize its capsid with hundreds of photo-activable Rose Bengal sensitizers and contemporarily target this light-triggerable nanobot to specific bacterial species by phage display of peptide targeting moieties fused to the minor coat protein pIII of the phage. Upon light irradiation of the specimen, the targeted killing of diverse Gram(-) pathogens occurred at subnanomolar concentrations of the phage vector. Our findings contribute to the development of antimicrobials based on targeted and triggerable phage-based nanobiotherapeutics.

iBIS2Analyzer: a web server for a phylogeny-driven coevolution analysis of protein families.

Residue coevolution within and between proteins is used as a marker of physical interaction and/or residue functional cooperation. Pairs or groups of coevolving residues are extracted from multiple sequence alignments based on a variety of computational approaches. However, coevolution signals emerging in subsets of sequences might be lost if the full alignment is considered. iBIS2Analyzer is a web server dedicated to a phylogeny-driven coevolution analysis of protein families with different evolutionary pressure. It is based on the iterative version, iBIS2, of the coevolution analysis method BIS, Blocks in Sequences. iBIS2 is designed to iteratively select and analyse subtrees in phylogenetic trees, possibly large and comprising thousands of sequences. With iBIS2Analyzer, openly accessible at http://ibis2analyzer.lcqb.upmc.fr/, the user visualizes, compares and inspects clusters of coevolving residues by mapping them onto sequences, alignments or structures of choice, greatly simplifying downstream analysis steps. A rich and interactive graphic interface facilitates the biological interpretation of the results.

Crystal structure of chloroplast fructose-1,6-bisphosphate aldolase from the green alga Chlamydomonas reinhardtii.

The Calvin-Benson cycle fixes carbon dioxide into organic triosephosphates through the collective action of eleven conserved enzymes. Regeneration of ribulose-1,5-bisphosphate, the substrate of Rubisco-mediated carboxylation, requires two lyase reactions catalyzed by fructose-1,6-bisphosphate aldolase (FBA). While cytoplasmic FBA has been extensively studied in non-photosynthetic organisms, functional and structural details are limited for chloroplast FBA encoded by oxygenic phototrophs. Here we determined the crystal structure of plastidial FBA from the unicellular green alga Chlamydomonas reinhardtii (Cr). We confirm that CrFBA folds as a TIM barrel, describe its catalytic pocket and homo-tetrameric state. Multiple sequence profiling classified the photosynthetic paralogs of FBA in a distinct group from non-photosynthetic paralogs. We mapped the sites of thiol- and phospho-based post-translational modifications known from photosynthetic organisms and predict their effects on enzyme catalysis.

AlignMe: an update of the web server for alignment of membrane protein sequences.

The AlignMe web server is dedicated to accurately aligning sequences of membrane proteins, a particularly challenging task due to the strong evolutionary divergence and the low compositional complexity of hydrophobic membrane-spanning proteins. AlignMe can create pairwise alignments of either two primary amino acid sequences or two hydropathy profiles. The web server for AlignMe has been continuously available for >10 years, supporting 1000s of users per year. Recent improvements include anchoring, multiple submissions, and structure visualization. Anchoring is the ability to constrain a position in an alignment, which allows expert information about related residues in proteins to be incorporated into an alignment without manual modification. The original web interface to the server limited the user to one alignment per submission, hindering larger scale studies. Now, batches of alignments can be initiated with a single submission. Finally, to provide structural context for the relationship between proteins, sequence similarity can now be mapped onto one or more structures (or structural models) of the proteins being aligned, by links to MutationExplorer, a web-based visualization tool. Together with a refreshed user interface, these features further enhance an important resource in the membrane protein community. The AlignMe web server is freely available at https://www.bioinfo.mpg.de/AlignMe/.

Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors.

The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly matched by a standard pairwise sequence alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the aligned regions either side of the newly matched positions. Previous studies have introduced restraints as a means to impose the matching of positions during sequence alignments, originally in the context of genome assembly. Here we introduce position restraints, or "anchors" as a feature in our alignment tool AlignMe, providing an aid to pairwise global sequence alignment of alpha-helical membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of anchors can be used to modify alignments, while still maintaining the reproducibility and rigor of the rest of the alignment. Anchored alignments can be generated using the online version of AlignMe available at www.bioinfo.mpg.de/AlignMe/.

FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution.

Predicting three-dimensional protein structure and assembling protein complexes using sequence information belongs to the most prominent tasks in computational biology. Recently substantial progress has been obtained in the case of single proteins using a combination of unsupervised coevolutionary sequence analysis with structurally supervised deep learning. While reaching impressive accuracies in predicting residue-residue contacts, deep learning has a number of disadvantages. The need for large structural training sets limits the applicability to multi-protein complexes; and their deep architecture makes the interpretability of the convolutional neural networks intrinsically hard. Here we introduce FilterDCA, a simpler supervised predictor for inter-domain and inter-protein contacts. It is based on the fact that contact maps of proteins show typical contact patterns, which results from secondary structure and are reflected by patterns in coevolutionary analysis. We explicitly integrate averaged contacts patterns with coevolutionary scores derived by Direct Coupling Analysis, improving performance over standard coevolutionary analysis, while remaining fully transparent and interpretable. The FilterDCA code is available at http://gitlab.lcqb.upmc.fr/muscat/FilterDCA.

MemSTATS: A Benchmark Set of Membrane Protein Symmetries and Pseudosymmetries.

In membrane proteins, symmetry and pseudosymmetry often have functional or evolutionary implications. However, available symmetry detection methods have not been tested systematically on this class of proteins because of the lack of an appropriate benchmark set. Here we present MemSTATS, a publicly available benchmark set of both quaternary- and internal-symmetries in membrane protein structures. The symmetries are described in terms of order, repeated elements, and orientation of the axis with respect to the membrane plane. Moreover, using MemSTATS, we compare the performance of four widely used symmetry detection algorithms and highlight specific challenges and areas for improvement in the future.

EncoMPASS: an online database for analyzing structure and symmetry in membrane proteins.

The EncoMPASS online database (http://encompass.ninds.nih.gov) collects, organizes, and presents information about membrane proteins of known structure, emphasizing their structural similarities as well as their quaternary and internal symmetries. Unlike, e.g. SCOP, the EncoMPASS database does not aim for a strict classification of membrane proteins, but instead is organized as a protein chain-centric network of sequence and structural homologues. The online server for the EncoMPASS database provides tools for comparing the structural features of its entries, making it a useful resource for homology modeling and active site identification studies. The database can also be used for inferring functionality, which for membrane proteins often involves symmetry-related mechanisms. To this end, the online database also provides a comprehensive description of both the quaternary and internal symmetries in known membrane protein structures, with a particular focus on their orientation relative to the membrane.

Toward a unified scoring function for native state discrimination and drug-binding pocket recognition.

Protein folding and receptor-ligand recognition are fundamental processes for any living organism. Although folding and ligand recognition are based on the same chemistry, the existing empirical scoring functions target just one problem: predicting the correct fold or the correct binding pose. We here introduce a statistical potential which considers moieties as fundamental units. The scoring function is able to deal with both folding and ligand pocket recognition problems with a performance comparable to the scoring functions specifically tailored for one of the two tasks. We foresee that the capability of the new scoring function to tackle both problems in a unified framework will be a key to deal with the induced fit phenomena, in which a target protein changes significantly its conformation upon binding. Moreover, the new scoring function might be useful in docking protocols towards intrinsically disordered proteins, whose flexibility cannot be handled with the available docking software.

Protein-protein structure prediction by scoring molecular dynamics trajectories of putative poses.

The prediction of protein-protein interactions and their structural configuration remains a largely unsolved problem. Most of the algorithms aimed at finding the native conformation of a protein complex starting from the structure of its monomers are based on searching the structure corresponding to the global minimum of a suitable scoring function. However, protein complexes are often highly flexible, with mobile side chains and transient contacts due to thermal fluctuations. Flexibility can be neglected if one aims at finding quickly the approximate structure of the native complex, but may play a role in structure refinement, and in discriminating solutions characterized by similar scores. We here benchmark the capability of some state-of-the-art scoring functions (BACH-SixthSense, PIE/PISA and Rosetta) in discriminating finite-temperature ensembles of structures corresponding to the native state and to non-native configurations. We produce the ensembles by running thousands of molecular dynamics simulations in explicit solvent starting from poses generated by rigid docking and optimized in vacuum. We find that while Rosetta outperformed the other two scoring functions in scoring the structures in vacuum, BACH-SixthSense and PIE/PISA perform better in distinguishing near-native ensembles of structures generated by molecular dynamics in explicit solvent. Proteins 2016; 84:1312-1320. © 2016 Wiley Periodicals, Inc.

Native fold and docking pose discrimination by the same residue-based scoring function.

Structure prediction and quality assessment are crucial steps in modeling native protein conformations. Statistical potentials are widely used in related algorithms, with different parametrizations typically developed for different contexts such as folding protein monomers or docking protein complexes. Here, we describe BACH-SixthSense, a single residue-based statistical potential that can be successfully employed in both contexts. BACH-SixthSense shares the same approach as BACH, a knowledge-based potential originally developed to score monomeric protein structures. A term that penalizes steric clashes as well as the distinction between polar and apolar sidechain-sidechain contacts are crucial novel features of BACH-SixthSense. The performance of BACH-SixthSense in discriminating correctly the native structure among a competing set of decoys is significantly higher than other state-of-the-art scoring functions, that were specifically trained for a single context, for both monomeric proteins (QMEAN, Rosetta, RF_CB_SRS_OD, benchmarked on CASP targets) and protein dimers (IRAD, Rosetta, PIE*PISA, HADDOCK, FireDock, benchmarked on 14 CAPRI targets). The performance of BACH-SixthSense in recognizing near-native docking poses within CAPRI decoy sets is good as well.