Ezgi Karaca

The HADDOCK2.2 Web Server : User-Friendly Integrative Modeling of Biomolecular Complexes

The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2.

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.

Understanding the role of the josephin domain in the polyub binding and cleavage properties of ataxin-3

Ataxin-3, the disease protein in the neurodegenerative disorder Spinocerebellar Ataxia Type 3 or Machado Joseph disease, is a cysteine protease implicated in the ubiquitin proteasome pathway. It contains multiple ubiquitin binding sites through which it anchors polyubiquitin chains of different linkages that are then cleaved by the N-terminal catalytic (Josephin) domain. The properties of the ubiquitin interacting motifs (UIMs) in the C-terminus of ataxin-3 are well established. Very little is known, however, about how two recently identified ubiquitin-binding sites in the Josephin domain contribute to ubiquitin chain binding and cleavage. In the current study, we sought to define the specific contribution of the Josephin domain to the catalytic properties of ataxin-3 and assess how the topology and affinity of these binding sites modulate ataxin-3 activity. Using NMR we modeled the structure of diUb/Josephin complexes and showed that linkage preferences are imposed by the topology of the two binding sites. Enzymatic studies further helped us to determine a precise hierarchy between the sites. We establish that the structure of Josephin dictates specificity for K48-linked chains. Site 1, which is close to the active site, is indispensable for cleavage. Our studies open the way to understand better the cellular function of ataxin-3 and its link to pathology.

On the usefulness of ion-mobility mass spectrometry and SAXS data in scoring docking decoys

Scoring, the process of selecting the biologically relevant solution from a pool of generated conformations, is one of the major challenges in the field of biomolecular docking. A prominent way to cope with this challenge is to incorporate information-based terms into the scoring function. Within this context, low-resolution shape data obtained from either ion-mobility mass spectrometry (IM-MS) or SAXS experiments have been integrated into the conventional scoring function of the information-driven docking program HADDOCK. Here, the strengths and weaknesses of IM-MS-based and SAXS-based scoring, either in isolation or in combination with the HADDOCK score, are systematically assessed. The results of an analysis of a large docking decoy set composed of dimers generated by running HADDOCK in ab initio mode reveal that the content of the IM-MS data is of too low resolution for selecting correct models, while scoring with SAXS data leads to a significant improvement in performance. However, the effectiveness of SAXS scoring depends on the shape and the arrangement of the complex, with prolate and oblate systems showing the best performance. It is observed that the highest accuracy is achieved when SAXS scoring is combined with the energy-based HADDOCK score.

Clustering biomolecular complexes by residue contacts similarity

Inaccuracies in computational molecular modeling methods are often counterweighed by brute‐force generation of a plethora of putative solutions. These are then typically sieved via structural clustering based on similarity measures such as the root mean square deviation (RMSD) of atomic positions. Albeit widely used, these measures suffer from several theoretical and technical limitations (e.g., choice of regions for fitting) that impair their application in multicomponent systems (N > 2), large‐scale studies (e.g., interactomes), and other time‐critical scenarios. We present here a simple similarity measure for structural clustering based on atomic contacts—the fraction of common contacts—and compare it with the most used similarity measure of the protein docking community—interface backbone RMSD. We show that this method produces very compact clusters in remarkably short time when applied to a collection of binary and multicomponent protein–protein and protein–DNA complexes. Furthermore, it allows easy clustering of similar conformations of multicomponent symmetrical assemblies in which chain permutations can occur. Simple contact‐based metrics should be applicable to other structural biology clustering problems, in particular for time‐critical or large‐scale endeavors.Proteins 2012;

Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein–protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community‐wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions—20 groups submitted a total of 195 models—were assessed by measuring the recall fraction of water‐mediated protein contacts. Of the 176 high‐ or medium‐quality docking models—a very good docking performance per se—only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high‐quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein–water interactions and their role in stabilizing protein complexes. Proteins 2014; 82:620–632.

Defining the limits of homology modeling in information-driven protein docking

Information‐driven docking is currently one of the most successful approaches to obtain structural models of protein interactions as demonstrated in the latest round of CAPRI. While various experimental and computational techniques can be used to retrieve information about the binding mode, the availability of three‐dimensional structures of the interacting partners remains a limiting factor. Fortunately, the wealth of structural information gathered by large‐scale initiatives allows for homology‐based modeling of a significant fraction of the protein universe. Defining the limits of information‐driven docking based on such homology models is therefore highly relevant. Here we show, using previous CAPRI targets, that out of a variety of measures, the global sequence identity between template and target is a simple but reliable predictor of the achievable quality of the docking models. This indicates that a well‐defined overall fold is critical for the interaction. Furthermore, the quality of the data at our disposal to characterize the interaction plays a determinant role in the success of the docking. Given reliable interface information we can obtain acceptable predictions even at low global sequence identity. These results, which define the boundaries between trustworthy and unreliable predictions, should guide both experts and nonexperts in defining the limits of what is achievable by docking. This is highly relevant considering that the fraction of the interactome amenable for docking is only bound to grow as the number of experimentally solved structures increases. Proteins 2013; 81:2119–2128.

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. Proteins 2010.

Next challenges in protein-protein docking: from proteome to interactome and beyond

Advances in biophysics and biochemistry have pushed back the limits for the structural characterization of biomolecular assemblies. Large efforts have been devoted to increase both resolution and accuracy of the methods, probe into the smallest biomolecules as well as the largest macromolecular machineries, unveil transient complexes along with dynamic interaction processes, and, lately, dissect whole organism interactomes using high‐throughput strategies. However, the atomic description of such interactions, rarely reached by large‐scale projects in structural biology, remains indispensable to fully understand the subtleties of the recognition process, measure the impact of a mutation or predict the effect of a drug binding to a complex. Mixing even a limited amount of experimental and/or bioinformatic data with modeling methods, such as macromolecular docking, presents a valuable strategy to predict the three‐dimensional structures of complexes. Recent developments indicate that the docking community is seething to tackle the greatest challenge of adding the structural dimension to interactomes.

M3: an integrative framework for structure determination of molecular machines

We present a broadly applicable, user-friendly protocol that incorporates sparse and hybrid experimental data to calculate quasi-atomic-resolution structures of molecular machines. The protocol uses the HADDOCK framework, accounts for extensive structural rearrangements both at the domain and atomic levels and accepts input from all structural and biochemical experiments whose data can be translated into interatomic distances and/or molecular shapes.