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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 Highlights The HADDOCK Web server has been upgraded to use HADDOCK version 2.2. It supports mixed-type molecules, for example, protein–nucleic acid, such as nucleosomes. NMR-based pseudocontact shifts and radius of gyration restraints are included. The multi-body and solvated docking protocol are extended. The HADDOCK Web server counts over 6000 users and has served more than 100,000 jobs

In molecular simulations of macromolecules, it is desirable to limit the amount of solvent in the system to avoid spending computational resources on uninteresting solvent−solvent interactions. As a consequence, periodic boundary conditions are commonly used, with a simulation box chosen as small as possible, for a given minimal distance between images. Here, we describe how such a simulation cell can be set up for ensembles, taking into account a priori available or estimable information regarding conformational flexibility. Doing so ensures that any conformation present in the input ensemble will satisfy the distance criterion during the simulation. This helps avoid periodicity artifacts due to conformational changes. The method introduces three new approaches in computational geometry: (1) The first is the derivation of an optimal packing of ensembles, for which the mathematical framework is described. (2) A new method for approximating the α-hull and the contact body for single bodies and ensembles is presented, which is orders of magnitude faster than existing routines, allowing the calculation of packings of large ensembles and/or large bodies. 3. A routine is described for searching a combination of three vectors on a discretized contact body forming a reduced base for a lattice with minimal cell volume. The new algorithms reduce the time required to calculate packings of single bodies from minutes or hours to seconds. The use and efficacy of the method is demonstrated for ensembles obtained from NMR, MD simulations, and elastic network modeling. An implementation of the method has been made available online at http://haddock.chem.uu.nl/services/SQUEEZE/ and has been made available as an option for running simulations through the weNMR GRID MD server at http://haddock.science.uu.nl/enmr/services/ GROMACS/main.php.

We have shown previously that given high-resolution structures of the unbound molecules, structure determination of protein com- plexes is possible by including biochemical and/or biophysical data as highly ambiguous distance re- straints in a docking approach. We applied this method, implemented in the HADDOCK (High Ambi- guity Driven DOCKing) package (Dominguez et al., J Am Chem Soc 2003;125:1731-1737), to the targets in the fourth and fifth rounds of CAPRI. Here we describe our results and analyze them in detail. Special attention is given to the role of flexibility in our docking method and the way in which this improves the docking results. We describe exten- sions to our approach that were developed as a direct result of our participation in CAPRI. In addi- tion to experimental information, we also included interface residue predictions from PPISP (Protein- Protein Interaction Site Predictor; Zhou and Shan, Proteins 2001;44:336 -343), a neural network method. Using HADDOCK we were able to generate accept- able structures for 6 of the 8 targets, and to submit at least 1 acceptable structure for 5 of them. Of these 5 submissions, 3 were of medium quality (Targets 10, 11, and 15) and 2 of high quality (Targets 13 and 14). In all cases, predictions were obtained containing at least 40% of the correct epitope at the interface for both ligand and receptor simultaneously. Proteins 2005;60:232-238. © 2005 Wiley-Liss, Inc.

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.

Advances in biophysics and biochemistry have pushed back the limits of the structural characterization of biomolecular assemblies. Mixing even a limited amount of experimental and/or bioinformatics data with modeling methods such as macromolecular docking represents a valuable strategy to predict the three-dimensional structures of complexes. In this chapter, we discuss the HADDOCK data-driven approach to the modeling of complexes. The program supports a wide range of NMR and other experimental data as well as bioinformatics predictions. It is also available as a user-friendly web server, facilitating the modeling of biomolecular complexes for a wide community.

The protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experimental data sets with unassigned nuclear Overhauser effect spectroscopy (NOESY) peak lists for various proteins of unknown structure, computed structures for each of them using different, fully automatic programs, and compared the results to each other and to the manually solved reference structures that were not available at the time the data were provided. This constitutes a stringent “blind” assessment similar to the CASP and CAPRI initiatives. This study demonstrates the feasibility of routine, fully automated protein structure determination by NMR.

In molecular simulations of macromolecules, it is desirable to limit the amount of solvent in the system to avoid spending computational resources on uninteresting solvent−solvent interactions. As a consequence, periodic boundary conditions are commonly used, with a simulation box chosen as small as possible, for a given minimal distance between images. Here, we describe how such a simulation cell can be set up for ensembles, taking into account a priori available or estimable information regarding conformational flexibility. Doing so ensures that any conformation present in the input ensemble will satisfy the distance criterion during the simulation. This helps avoid periodicity artifacts due to conformational changes. The method introduces three new approaches in computational geometry: (1) The first is the derivation of an optimal packing of ensembles, for which the mathematical framework is described. (2) A new method for approximating the α-hull and the contact body for single bodies and ensembles is presented, which is orders of magnitude faster than existing routines, allowing the calculation of packings of large ensembles and/or large bodies. 3. A routine is described for searching a combination of three vectors on a discretized contact body forming a reduced base for a lattice with minimal cell volume. The new algorithms reduce the time required to calculate packings of single bodies from minutes or hours to seconds. The use and efficacy of the method is demonstrated for ensembles obtained from NMR, MD simulations, and elastic network modeling. An implementation of the method has been made available online at http://haddock.chem.uu.nl/services/SQUEEZE/ and has been made available as an option for running simulations through the weNMR GRID MD server at http://haddock.science.uu.nl/enmr/services/ GROMACS/main.php.

The WeNMR (http://www.wenmr.eu) project is an EU-funded international effort to streamline and automate structure determination from Nuclear Magnetic Resonance (NMR) data. Conventionally calculation of structure requires the use of various softwares, considerable user expertise and ample computational resources. To facilitate the use of NMR spectroscopy in life sciences the eNMR/WeNMR consortium has set out to provide protocolized services through easy-to-use web interfaces, while still retaining sufficient flexibility to handle more specific requests. Thus far, a number of programs often used in Structural Biology have been made available through portals, including HADDOCK, XPLOR-NIH, CYANA and CS-ROSETTA, MARS, MDDNMR. The implementation of these services, in particular the distribution of calculations to the Grid, involves a novel mechanism for submission and handling of jobs that is independent of the type of job being run. With over 280 registered users (April 2011), eNMR/WeNMR is currently one of the largest Virtual Organization (VO) in life sciences. With its large and worldwide user community, WeNMR has become the first Virtual Research Community officially recognized by the European Grid Infrastructure (EGI)

BACKGROUND: Macromolecular complexes are the molecular machines of the cell. Knowledge at the atomic level is essential to understand and influence their function. However, their number is huge and a significant fraction is extremely difficult to study using classical structural methods such as NMR and X-ray crystallography. Therefore, the importance of large-scale computational approaches in structural biology is evident. This study combines two of these computational approaches, interface prediction and docking, to obtain atomic-level structures of protein-protein complexes, starting from their unbound components. METHODOLOGY/PRINCIPAL FINDINGS: Here we combine six interface prediction web servers into a consensus method called CPORT (Consensus Prediction Of interface Residues in Transient complexes). We show that CPORT gives more stable and reliable predictions than each of the individual predictors on its own. A protocol was developed to integrate CPORT predictions into our data-driven docking program HADDOCK. For cases where experimental information is limited, this prediction-driven docking protocol presents an alternative to ab initio docking, the docking of complexes without the use of any information. Prediction-driven docking was performed on a large and diverse set of protein-protein complexes in a blind manner. Our results indicate that the performance of the HADDOCK-CPORT combination is competitive with ZDOCK-ZRANK, a state-of-the-art ab initio docking/scoring combination. Finally, the original interface predictions could be further improved by interface post-prediction (contact analysis of the docking solutions). CONCLUSIONS/SIGNIFICANCE: The current study shows that blind, prediction-driven docking using CPORT and HADDOCK is competitive with ab initio docking methods. This is encouraging since prediction-driven docking represents the absolute bottom line for data-driven docking: any additional biological knowledge will greatly improve the results obtained by prediction-driven docking alone. Finally, the fact that original interface predictions could be further improved by interface post-prediction suggests that prediction-driven docking has not yet been pushed to the limit. A web server for CPORT is freely available at http://haddock.chem.uu.nl/services/CPORT.

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.

The e-NMR project is an European e-infrastructure that aims at providing the bio-NMR community with a software platform integrating and streamlining computational approaches necessary for NMR data analysis. The infrastructure is grid enabled with fteen gLite based partners sharing computational resources. A main focus of the consortium is to provide protocoled services through easy-to-use web interfaces, while retaining su cient exibility to handle speci c requests by expert users. Various programs relevant for structural biology scientists are grid ported and already available through the e-NMR web portal, including HADDOCK, XPLORNIH, CYANA and CS-ROSETTA among others. A general overview of the project current status toward EGEE/EGI integration, as well as brief guidelines on how to become an e-NMR site/user will be considered. With more than 170 registered users, enmr.eu is currently the second largest virtual organization in the life sciences. The state of the project can be found on the web page http://www.enmr.eu.

Computational docking is the prediction or modeling of the three-dimensional structure of a biomolecular complex, starting from the structures of the individual molecules in their free, unbound form. HADDOC K is a popular docking program that takes a datadriven approach to docking, with support for a wide range of experimental data. Here we present the HADDOC K web server protocol, facilitating the modeling of biomolecular complexes for a wide community. The main web interface is user-friendly, requiring only the structures of the individual components and a list of interacting residues as input. Additional web interfaces allow the more advanced user to exploit the full range of experimental data supported by HADDOC K and to customize the docking process. The HADDOC K server has access to the resources of a dedicated cluster and of the e-NMR GRID infrastructure. Therefore, a typical docking run takes only a few minutes to prepare and a few hours to complete.

Proteins are the wheels and mill stones of the complex machinery that underlies human life. In carrying out their functions, proteins work in close association with other proteins, forming protein complexes. A huge network of protein-protein interactions enables the cell to respond quickly to changes in the environment and to communicate with other cells. When the balance of this network is disrupted, diseases such as cancer may result, and for this reason, pharmaceutical drugs are very often targeted at proteins. To fully understand how proteins work together, knowledge at the atomic level is required. X-ray crystallography and NMR are the classical methods for this, solving the three-dimensional structure of many individual proteins as well as protein complexes. However, the number of complexes in the cell is at least one order of magnitude larger than the number of proteins. Moreover, associations between proteins and other macromolecules are often transient and reversible, especially the biologically interesting signal transduction complexes. For many complexes, the 3D structures of the individual proteins in their free, unbound form are known, but the structure of the protein complex itself remains elusive. This thesis deals with two fields of study that aim to shed light on protein complexes by computational means: data-driven docking and interface prediction. Docking, in general, is predicting the structure of a complex starting from the free, unbound structures. Data-driven docking, in particular, uses experimental information during the docking process. Chemical shift perturbation (CSP), hydrogen-deuterium (H/D) exchange and site-directed mutagenesis can identify the interface region; residual dipolar couplings and relaxation anisotropy can provide information about the relative orientation of the proteins. All of this information can be used in HADDOCK, the data-driven docking method developed in our group, and an improved version of HADDOCK is presented in chapter 6 of this thesis. HADDOCK is not limited to complexes between two proteins, but can deal with up to six molecules of proteins, nucleic acids, sugars or small ligands. HADDOCK finished second in the recent cycle of the CAPRI international docking competition. Finally, a web server interface for HADDOCK is presented in chapter 7, facilitating data-driven docking for a larger community. It is shown that even in the absence of experimental data, data-driven docking can be successful when the interface region between the proteins is predicted by computational means. Chapter 4 describes WHISCY, a general-purpose interface prediction program and web server. In chapter 5, pairwise propensities and their use in interface prediction are evaluated. Finally, in chapter 8, CPORT is introduced: a consensus method that combines six interface predictors and that is specifically designed for data-driven docking. In this chapter, prediction-driven docking is successfully applied to a large and diverse benchmark of protein complexes, including signal transduction complexes. While correct solutions could not be obtained for all complexes, the success rate is comparable with state-of-the-art ab initio docking methods, and it is argued that further improvement is still possible.

Human P-glycoprotein is an ATP-binding cassette transporter that plays an important role in the defence against potentially harmful molecules from the environment. It is involved in conferring resistance against cancer therapeutics and plays an important role for the pharmacokinetics of drugs. The lack of a high resolution structure of P-glycoprotein has hindered its functional understanding and represents an obstacle for structure based drug development. The homologous bacterial exporter Sav1866 has been shown to share a common architecture and overlapping substrate specificity with P-glycoprotein. The structure of Sav1866 suggests that helices in the transmembrane domains diverge at the extracytoplasmic face, whereas cross-link information and a combination of small angle X-ray scattering and cryo-electron crystallography data indicate that helices 6 and 12 of P-glycoprotein are closer in P-glycoprotein than in the crystal structure of Sav1866. Using homology modelling, we present evidence that the protein possesses intrinsic structural flexibility to allow cross-links to occur between helices 6 and 12 of P-glycoprotein, thereby reconciling crystallographic models with available experimental data from cross-linking.

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.

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.

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.

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.

Protein-protein interactions play a key role in biological processes. Identifying the interacting residues is a first step toward understanding these interactions at a structural level. In this study, the interface prediction program WHISCY is presented. It combines surface conservation and structural information to predict protein-protein interfaces. The accuracy of the predictions is more than three times higher than a random prediction. These predictions have been combined with another interface prediction program, ProMate [Neuvirth et al. J Mol Biol 2004;338:181-199], resulting in an even more accurate predictor. The usefulness of the predictions was tested using the data-driven docking program HADDOCK [Dominguez et al. J Am Chem Soc 2003;125:1731-1737] in an unbound docking experiment, with the goal of generating as many near-native structures as possible. Unrefined rigid body docking solutions within 10 A ligand RMSD from the true structure were generated for 22 out of 25 docked complexes. For 18 complexes, more than 100 of the 8000 generated models were correct. Our results demonstrates the potential of using interface predictions to drive protein-protein docking.