DaReUS-Loop: a web server to model multiple loops in homology models.

Loop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.

The pepATTRACT web server for blind, large-scale peptide-protein docking.

Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. pepATTRACT is a novel docking protocol that is fully blind, i.e. it does not require any information about the binding site. In various stages of its development, pepATTRACT has participated in CAPRI, making successful predictions for five out of seven protein-peptide targets. Its performance is similar or better than state-of-the-art local docking protocols that do require binding site information. Here we present a novel web server that carries out the rigid-body stage of pepATTRACT. On the peptiDB benchmark, the web server generates a correct model in the top 50 in 34% of the cases. Compared to the full pepATTRACT protocol, this leads to some loss of performance, but the computation time is reduced from ∼18 h to ∼10 min. Combined with the fact that it is fully blind, this makes the web server well-suited for large-scale in silico protein-peptide docking experiments. The rigid-body pepATTRACT server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/pepATTRACT.

Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins.

Protein-RNA complexes are important for many biological processes. However, structural modeling of such complexes is hampered by the high flexibility of RNA. Particularly challenging is the docking of single-stranded RNA (ssRNA). We have developed a fragment-based approach to model the structure of ssRNA bound to a protein, based on only the protein structure, the RNA sequence and conserved contacts. The conformational diversity of each RNA fragment is sampled by an exhaustive library of trinucleotides extracted from all known experimental protein-RNA complexes. The method was applied to ssRNA with up to 12 nucleotides which bind to dimers of the RNA recognition motifs (RRMs), a highly abundant eukaryotic RNA-binding domain. The fragment based docking allows a precise de novo atomic modeling of protein-bound ssRNA chains. On a benchmark of seven experimental ssRNA-RRM complexes, near-native models (with a mean heavy-atom deviation of <3 Å from experiment) were generated for six out of seven bound RNA chains, and even more precise models (deviation < 2 Å) were obtained for five out of seven cases, a significant improvement compared to the state of the art. The method is not restricted to RRMs but was also successfully applied to Pumilio RNA binding proteins.

Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI.

The ATTRACT coarse-grained docking approach in combination with various types of atomistic, flexible refinement methods has been applied to predict protein-protein and peptide-protein complexes in CAPRI rounds 28-36. For a large fraction of CAPRI targets (12 out of 18), at least one model of acceptable or better quality was generated, corresponding to a success rate of 67%. In particular, for several peptide-protein complexes excellent predictions were achieved. In several cases, a combination of template-based modeling and extensive molecular dynamics-based refinement yielded medium and even high quality solutions. In one particularly challenging case, the structure of an ubiquitylation enzyme bound to the nucleosome was correctly predicted as a set of acceptable quality solutions. Based on the experience with the CAPRI targets, new interface refinement approaches and methods for ab-initio peptide-protein docking have been developed. Failures and possible improvements of the docking method with respect to scoring and protein flexibility will also be discussed. Proteins 2017; 85:391-398. © 2016 Wiley Periodicals, Inc.

Fast and accurate grid representations for atom-based docking with partner flexibility.

Macromolecular docking methods can broadly be divided into geometric and atom-based methods. Geometric methods use fast algorithms that operate on simplified, grid-like molecular representations, while atom-based methods are more realistic and flexible, but far less efficient. Here, a hybrid approach of grid-based and atom-based docking is presented, combining precalculated grid potentials with neighbor lists for fast and accurate calculation of atom-based intermolecular energies and forces. The grid representation is compatible with simultaneous multibody docking and can tolerate considerable protein flexibility. When implemented in our docking method ATTRACT, grid-based docking was found to be ∼35x faster. With the OPLSX forcefield instead of the ATTRACT coarse-grained forcefield, the average speed improvement was >100x. Grid-based representations may allow atom-based docking methods to explore large conformational spaces with many degrees of freedom, such as multiple macromolecules including flexibility. This increases the domain of biological problems to which docking methods can be applied. © 2017 Wiley Periodicals, Inc.

Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach.

Protein-RNA docking is hampered by the high flexibility of RNA, and particularly single-stranded RNA (ssRNA). Yet, ssRNA regions typically carry the specificity of protein recognition. The lack of methodology for modeling such regions limits the accuracy of current protein-RNA docking methods. We developed a fragment-based approach to model protein-bound ssRNA, based on the structure of the protein and the sequence of the RNA, without any prior knowledge of the RNA binding site or the RNA structure. The conformational diversity of each fragment is sampled by an exhaustive RNA fragment library that was created from all the existing experimental structures of protein-ssRNA complexes. A systematic and detailed analysis of fragment-based ssRNA docking was performed which constitutes a proof-of-principle for the fragment-based approach. The method was tested on two 8-homo-nucleotide ssRNA-protein complexes and was able to identify the binding site on the protein within 10 Å. Moreover, a structure of each bound ssRNA could be generated in close agreement with the crystal structure with a mean deviation of ~1.5 Å except for a terminal nucleotide. This is the first time a bound ssRNA could be modeled from sequence with high precision.

Investigating the reliability and validity of the Dutch versions of the illness management and recovery scales among clients with mental disorders.

BACKGROUND: The Illness Management and Recovery scales (IMRS) can measure the progress of clients' illness self-management and recovery. Previous studies have examined the psychometric properties of the IMRS. AIMS: This study examined the reliability and validity of the Dutch version of the IMRS. METHOD: Clients (n = 111) and clinicians (n = 40) completed the client and clinician versions of the IMRS, respectively. The scales were administered again 2 weeks later to assess stability over time. Validity was assessed with the Utrecht Coping List (UCL), Dutch Empowerment Scale (DES), and Brief Symptom Inventory (BSI). RESULTS: The client and clinician versions of the IMRS had moderate internal reliability, with α = 0.69 and 0.71, respectively. The scales showed strong test-retest reliability, r = 0.79, for the client version and r = 0.86 for the clinician version. Correlations between client and clinician versions ranged from r = 0.37 to 0.69 for the total and subscales. We also found relationships in expected directions between the client IMRS and UCL, DES and BSI, which supports validity of the Dutch version of the IMRS. CONCLUSIONS: The Dutch version of the IMRS demonstrated good reliability and validity. The IMRS could be useful for Dutch-speaking programs interested in evaluating client progress on illness self-management and recovery.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling.

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.

A web interface for easy flexible protein-protein docking with ATTRACT.

Protein-protein docking programs can give valuable insights into the structure of protein complexes in the absence of an experimental complex structure. Web interfaces can facilitate the use of docking programs by structural biologists. Here, we present an easy web interface for protein-protein docking with the ATTRACT program. While aimed at nonexpert users, the web interface still covers a considerable range of docking applications. The web interface supports systematic rigid-body protein docking with the ATTRACT coarse-grained force field, as well as various kinds of protein flexibility. The execution of a docking protocol takes up to a few hours on a standard desktop computer.

iATTRACT: simultaneous global and local interface optimization for protein-protein docking refinement.

Protein-protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure-based force field for intramolecular contributions. The approach was systematically evaluated on a large protein-protein docking benchmark, starting from an enriched decoy set of rigidly docked protein-protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%.

Dynamic control of selectivity in the ubiquitination pathway revealed by an ASP to GLU substitution in an intra-molecular salt-bridge network.

Ubiquitination relies on a subtle balance between selectivity and promiscuity achieved through specific interactions between ubiquitin-conjugating enzymes (E2s) and ubiquitin ligases (E3s). Here, we report how a single aspartic to glutamic acid substitution acts as a dynamic switch to tip the selectivity balance of human E2s for interaction toward E3 RING-finger domains. By combining molecular dynamic simulations, experimental yeast-two-hybrid screen of E2-E3 (RING) interactions and mutagenesis, we reveal how the dynamics of an internal salt-bridge network at the rim of the E2-E3 interaction surface controls the balance between an "open", binding competent, and a "closed", binding incompetent state. The molecular dynamic simulations shed light on the fine mechanism of this molecular switch and allowed us to identify its components, namely an aspartate/glutamate pair, a lysine acting as the central switch and a remote aspartate. Perturbations of single residues in this network, both inside and outside the interaction surface, are sufficient to switch the global E2 interaction selectivity as demonstrated experimentally. Taken together, our results indicate a new mechanism to control E2-E3 interaction selectivity at an atomic level, highlighting how minimal changes in amino acid side-chain affecting the dynamics of intramolecular salt-bridges can be crucial for protein-protein interactions. These findings indicate that the widely accepted sequence-structure-function paradigm should be extended to sequence-structure-dynamics-function relationship and open new possibilities for control and fine-tuning of protein interaction selectivity.

MTMDAT-HADDOCK: high-throughput, protein complex structure modeling based on limited proteolysis and mass spectrometry.

BACKGROUND: MTMDAT is a program designed to facilitate analysis of mass spectrometry data of proteins and biomolecular complexes that are probed structurally by limited proteolysis. This approach can provide information about stable fragments of multidomain proteins, yield tertiary and quaternary structure data, and help determine the origin of stability changes at the amino acid residue level. Here, we introduce a pipeline between MTMDAT and HADDOCK, that facilitates protein-protein complex structure probing in a high-throughput and highly automated fashion. RESULTS: A new feature of MTMDAT allows for the direct identification of residues that are involved in complex formation by comparing the mass spectra of bound and unbound proteins after proteolysis. If 3D structures of the unbound components are available, this data can be used to define restraints for data-driven docking to calculate a model of the complex. We describe here a new implementation of MTMDAT, which includes a pipeline to the data-driven docking program HADDOCK, thus streamlining the entire procedure. This addition, together with usability improvements in MTMDAT, enables high-throughput modeling of protein complexes from mass spectrometry data. The algorithm has been validated by using the protein-protein interaction between the ubiquitin-binding domain of proteasome component Rpn13 and ubiquitin. The resulting structural model, based on restraints extracted by MTMDAT from limited proteolysis and modeled by HADDOCK, was compared to the published NMR structure, which relied on twelve unambiguous intermolecular NOE interactions. The MTMDAT-HADDOCK structure was of similar quality to structures generated using only chemical shift perturbation data derived by NMR titration experiments. CONCLUSIONS: The new MTMDAT-HADDOCK pipeline enables direct high-throughput modeling of protein complexes from mass spectrometry data. MTMDAT-HADDOCK can be downloaded from http://www.ifm.liu.se/chemistry/molbiotech/maria_sunnerhagens_group/mtmdat/together with the manual and example files. The program is free for academic/non-commercial purposes.

Building macromolecular assemblies by information-driven docking: introducing the HADDOCK multibody docking server.

Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results.

SeamDock: An Interactive and Collaborative Online Docking Resource to Assist Small Compound Molecular Docking.

In silico assessment of protein receptor interactions with small ligands is now part of the standard pipeline for drug discovery, and numerous tools and protocols have been developed for this purpose. With the SeamDock web server, we propose a new approach to facilitate access to small molecule docking for nonspecialists, including students. The SeamDock online service integrates different docking tools in a common framework that allows ligand global and/or local docking and a hierarchical approach combining the two for easy interaction site identification. This service does not require advanced computer knowledge, and it works without the installation of any programs with the exception of a common web browser. The use of the Seamless framework linking the RPBS calculation server to the user's browser allows the user to navigate smoothly and interactively on the SeamDock web page. A major effort has been put into the 3D visualization of ligand, receptor, and docking poses and their interactions with the receptor. The advanced visualization features combined with the seamless library allow a user to share with an unlimited number of collaborators, a docking session, and its full visualization states. As a result, SeamDock can be seen as a free, simple, didactic, evolving online docking resource best suited for education and training.

Strengths and weaknesses of data-driven docking in critical assessment of prediction of interactions.

The recent CAPRI rounds have introduced new docking challenges in the form of protein-RNA complexes, multiple alternative interfaces, and an unprecedented number of targets for which homology modeling was required. We present here the performance of HADDOCK and its web server in the CAPRI experiment and discuss the strengths and weaknesses of data-driven docking. HADDOCK was successful for 6 out of 9 complexes (6 out of 11 targets) and accurately predicted the individual interfaces for two more complexes. The HADDOCK server, which is the first allowing the simultaneous docking of generic multi-body complexes, was successful in 4 out of 7 complexes for which it participated. In the scoring experiment, we predicted the highest number of targets of any group. The main weakness of data-driven docking revealed from these last CAPRI results is its vulnerability for incorrect experimental data related to the interface or the stoichiometry of the complex. At the same time, the use of experimental and/or predicted information is also the strength of our approach as evidenced for those targets for which accurate experimental information was available (e.g., the 10 three-stars predictions for T40!). Even when the models show a wrong orientation, the individual interfaces are generally well predicted with an average coverage of 60% ± 26% over all targets. This makes data-driven docking particularly valuable in a biological context to guide experimental studies like, for example, targeted mutagenesis.

A comprehensive framework of E2-RING E3 interactions of the human ubiquitin-proteasome system.

Covalent attachment of ubiquitin to substrates is crucial to protein degradation, transcription regulation and cell signalling. Highly specific interactions between ubiquitin-conjugating enzymes (E2) and ubiquitin protein E3 ligases fulfil essential roles in this process. We performed a global yeast-two hybrid screen to study the specificity of interactions between catalytic domains of the 35 human E2s with 250 RING-type E3s. Our analysis showed over 300 high-quality interactions, uncovering a large fraction of new E2-E3 pairs. Both within the E2 and the E3 cohorts, several members were identified that are more versatile in their interaction behaviour than others. We also found that the physical interactions of our screen compare well with reported functional E2-E3 pairs in in vitro ubiquitination experiments. For validation we confirmed the interaction of several versatile E2s with E3s in in vitro protein interaction assays and we used mutagenesis to alter the E3 interactions of the E2 specific for K63 linkages, UBE2N(Ubc13), towards the K48-specific UBE2D2(UbcH5B). Our data provide a detailed, genome-wide overview of binary E2-E3 interactions of the human ubiquitination system.

How proteins get in touch: interface prediction in the study of biomolecular complexes.

Protein-protein interface prediction is a booming field, with a substantial growth in the number of new methods being published the last two years. The increasing number of available three-dimensional structures of protein-protein complexes has enabled large-scale statistical analyses of protein interfaces, considering evolutionary, physicochemical and structural properties. Successful combinations of these properties have led to more accurate interface predictors in recent years. In addition to parametric combination, machine learning algorithms have become popular. In the meantime, assessing the absolute and relative performance of interface predictors remains very difficult: This is due to differences in both the output of the various interface predictors, and in the evaluation criteria used by their respective authors. This review provides an overview of the state of the art in the field, and discusses the performance of existing interface predictors. The focus is mainly on protein-protein interface prediction, although most issues are also valid for other kinds of interface prediction.

Higher-level cognitive functions in Dutch elite and sub-elite table tennis players.

This study aimed to investigate the higher-level cognitive functions (i.e. metacognition and executive functions) of Dutch competitive table tennis players to better understand its relevance for performance in this fast and complex sport. Thirty elite (age 16 ± 4) and thirty age and sex-matched sub-elite peers (age 16 ± 5) were assessed on metacognition and executive functions (working memory, inhibitory control, cognitive flexibility) using D-KEFS tests. Compared to norm scores, both the Dutch competitive elite and sub-elite table tennis players scored above average on all tests (p < 0.05). MANOVA showed a main effect for performance level (elites outscored sub-elites; p < 0.05). T-tests revealed that elite players make less mistakes on tests for inhibitory control (CWI-3: 0.9 ± 0.9; CWI-4: 1.1 ± 1.2) than sub-elite players (CWI-3: 1.8 ± 1.1; CWI-4: 2.6 ± 1.5) (p < 0.05). When controlling for training hours in a MANCOVA, no significant main effect of performance level remained (p > 0.05). In conclusion, Dutch elite and sub-elite table tennis players are characterized by above-average scores on higher-level cognitive functions compared to norm scores. A relation with performance level has been shown, which may be explained by the greater exposure to table tennis for elite compared to sub-elite players. However, longitudinal research is needed to indicate the direction of this association.

HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets.

Here we present version 2.0 of HADDOCK, which incorporates considerable improvements and new features. HADDOCK is now able to model not only protein-protein complexes but also other kinds of biomolecular complexes and multi-component (N > 2) systems. In the absence of any experimental and/or predicted information to drive the docking, HADDOCK now offers two additional ab initio docking modes based on either random patch definition or center-of-mass restraints. The docking protocol has been considerably improved, supporting among other solvated docking, automatic definition of semi-flexible regions, and inclusion of a desolvation energy term in the scoring scheme. The performance of HADDOCK2.0 is evaluated on the targets of rounds 4-11, run in a semi-automated mode using the original information we used in our CAPRI submissions. This enables a direct assessment of the progress made since the previous versions. Although HADDOCK performed very well in CAPRI (65% and 71% success rates, overall and for unbound targets only, respectively), a substantial improvement was achieved with HADDOCK2.0.

Intramolecular surface contacts contain information about protein-protein interface regions.

MOTIVATION: Some amino acids clearly show preferences over others in protein-protein interfaces. These preferences, or so-called interface propensities can be used for a priori interface prediction. We investigated whether the prediction accuracy could be improved by considering not single but pairs of residues in an interface. Here we present the first systematic analysis of intramolecular surface contacts in interface prediction. RESULTS: We show that preferences do exist for contacts within and around an interface region within one molecule: specific pairs of amino acids are more often occurring than others. Using intramolecular contact propensities in a blind test, higher average scores were assigned to interface residues than to non-interface residues. This effect persisted as small but significant when the contact propensities were corrected to eliminate the influence of single amino acid interface propensity. This indicates that intramolecular contact propensities may replace interface propensities in protein-protein interface prediction. AVAILABILITY: The source code is available on request from the authors.