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Structure determination of complex molecular machines requires a combination of an increasing number of experimental methods with highly specialized software geared toward each data source to properly handle the gathered data. Recently, we introduced the two software packages PowerFit and DisVis. These combine high-resolution structures of atomic subunits with density maps from cryo-electron microscopy or distance restraints, typically acquired by chemical cross-linking coupled with mass spectrometry, respectively. Here, we report on recent advances in both GPGPU-accelerated software packages: PowerFit is a tool for rigid body fitting of atomic structures in cryo-electron density maps and has been updated to also output reliability indicators for the success of fitting, through the use of the Fisher z-transformation and associated confidence intervals; DisVis aims at quantifying the information content of distance restraints and identifying false-positive restraints. We extended its analysis capabilities to include an analysis of putative interface residues and to output an average shape representing the putative location of the ligand. To facilitate their use by a broad community, they have been implemented as web portals harvesting both local CPU resources and GPGPU-accelerated EGI grid resources. They offer user-friendly interfaces, while minimizing computational requirements, and provide a first interactive view of the results. The portals can be accessed freely after registration via http://milou.science.uu.nl/services/DISVIS and http://milou.science.uu.nl/services/POWERFIT., (Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Kallikrein-related peptidases (KLKs) are crucial for epidermal barrier function and are involved in the proteolytic regulation of the desquamation process. Elevated KLK levels were reported in atopic dermatitis. In skin, the proteolytic activity of KLKs is regulated by specific inhibitors of the serine protease inhibitor of Kazal-type (SPINK) family. SPINK6 was shown to be expressed in human stratum corneum and is able to inhibit several KLKs such as KLK4, -5, -12, -13 and -14. In order to understand the structural traits of the specific inhibition we solved the structure of SPINK6 in solution by NMR-spectroscopy and studied its interaction with KLKs. Thereby, beside the conserved binding mode, we identified an alternate binding mode which has so far not been observed for SPINK inhibitors., (Copyright © 2016 Elsevier Inc. All rights reserved.)

NMR is a well-established method to characterize the structure and dynamics of biomolecules in solution. High-quality structures can now be produced thanks to both experimental advances and computational developments that incorporate new NMR parameters and improved protocols and force fields in the structure calculation and refinement process. In this chapter, we give a short overview of the various types of NMR data that can provide structural information, and then focus on the structure calculation methodology itself. We discuss and illustrate with tutorial examples "classical" structure calculation, refinement, and structure validation approaches.

Protein-protein docking aims at predicting the three-dimensional structure of a protein complex starting from the free forms of the individual partners. As assessed in the CAPRI community-wide experiment, the most successful docking algorithms combine pure laws of physics with information derived from various experimental or bioinformatics sources. Of these so-called "information-driven" approaches, HADDOCK stands out as one of the most successful representatives. In this chapter, we briefly summarize which experimental information can be used to drive the docking prediction in HADDOCK, and then focus on the docking protocol itself. We discuss and illustrate with a tutorial example a "classical" protein-protein docking prediction, as well as more recent developments for modelling multi-body systems and large conformational changes.

Protein-protein interactions lie at the heart of most cellular processes. Determining their high-resolution structures by experimental methods is a nontrivial task, which is why complementary computational approaches have been developed over the years. To gain structural and dynamical insight on an atomic scale in these interactions, computational modeling must often be complemented by low-resolution experimental information. For this purpose, we developed the user-friendly HADDOCK webserver, the interface to our biomolecular docking program, which can make use of a variety of low-resolution data to drive the docking process. In this chapter, we explain the use of the HADDOCK webserver based on the real-life Lys48-linked di-ubiquitin case, which led to the 2BGF PDB model. We demonstrate the use of chemical shift perturbation data in combination with residual dipolar couplings and further highlight a few other cases where our software was successfully used. The HADDOCK webserver is available to the science community for free at haddock.science.uu.nl/services/HADDOCK.

High-resolution structural information is needed in order to unveil the underlying mechanistic of biomolecular function. Due to the technical limitations or the nature of the underlying complexes, acquiring atomic resolution information is difficult for many challenging systems, while, often, low-resolution biochemical or biophysical data can still be obtained. To make best use of all the available information and shed light on these challenging systems, integrative computational tools are required that can judiciously combine and accurately translate sparse experimental data into structural information. In this review we discuss the current state of integrative approaches, the challenges they are confronting and the advances made regarding those challenges. Recent developments are underpinned by noteworthy application examples taken from the literature. Within this context, we also position our data-driven docking approach, HADDOCK that can integrate a variety of information sources to drive the modeling of biomolecular complexes. Only a synergistic combination of experiment and modeling will allow us to tackle the challenges of adding the structural dimension to interactomes, shed "atomic" light onto molecular processes and understand the underlying mechanistic of biomolecular function. The current state of integrative approaches indicates that they are poised to take those challenges., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (K(d)), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein-protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure-affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein-protein recognition.

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Binding-induced backbone and large-scale conformational changes represent one of the major challenges in the modeling of biomolecular complexes by docking. To address this challenge, we have developed a flexible multidomain docking protocol that follows a "divide-and-conquer" approach to model both large-scale domain motions and small- to medium-scale interfacial rearrangements: the flexible binding partner is treated as an assembly of subparts/domains that are docked simultaneously making use of HADDOCK's multidomain docking ability. For this, the flexible molecules are cut at hinge regions predicted using an elastic network model. The performance of this approach is demonstrated on a benchmark covering an unprecedented range of conformational changes of 1.5 to 19.5 Å. We show from a statistical survey of known complexes that the cumulative sum of eigenvalues obtained from the elastic network has some predictive power to indicate the extent of the conformational change to be expected., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

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.

There is a growing interest in structural studies of DNA by both experimental and computational approaches. Often, 3D-structural models of DNA are required, for instance, to serve as templates for homology modeling, as starting structures for macro-molecular docking or as scaffold for NMR structure calculations. The conformational adaptability of DNA when binding to a protein is often an important factor and at the same time a limitation in such studies. As a response to the demand for 3D-structural models reflecting the intrinsic plasticity of DNA we present the 3D-DART server (3DNA-Driven DNA Analysis and Rebuilding Tool). The server provides an easy interface to a powerful collection of tools for the generation of DNA-structural models in custom conformations. The computational engine beyond the server makes use of the 3DNA software suite together with a collection of home-written python scripts. The server is freely available at http://haddock.chem.uu.nl/dna without any login requirement.

Hydramacin-1 is a novel antimicrobial protein recently discovered during investigations of the epithelial defense of the ancient metazoan Hydra. The amino acid sequence of hydramacin-1 shows no sequence homology to any known antimicrobial proteins. Determination of the solution structure revealed that hydramacin-1 possesses a disulfide bridge-stabilized alphabeta motif. This motif is the common scaffold of the knottin protein fold. The structurally closest relatives are the scorpion oxin-like superfamily. Within this superfamily hydramacin-1 establishes a new family of proteins that all share antimicrobial activity. Hydramacin-1 is potently active against Gram-positive and Gram-negative bacteria including multi-resistant human pathogenic strains. It leads to aggregation of bacteria as an initial step of its bactericidal mechanism. Aggregated cells are connected via electron-dense contacts and adopt a thorn apple-like morphology. Analysis of the hydramacin-1 structure revealed an unusual distribution of amino acid side chains on the surface. A belt of positively charged residues is sandwiched by two hydrophobic areas. Based on this characteristic surface feature and on biophysical analysis of protein-membrane interactions, we propose a model that describes the aggregation effect exhibited by hydramacin-1.

Nuclear magnetic resonance (NMR) has become a well-established method to characterize the structures of biomolecules in solution. High-quality structures are now produced, thanks to both experimental and computational developments, allowing the use of new NMR parameters and improved protocols and force fields in structure calculation and refinement. In this chapter, we give a short overview of the various types of NMR data that can provide structural information, and then focus on the structure calculation methodology itself. We discuss and illustrate with tutorial examples both "classical" structure calculation and refinement approaches as well as more recently developed protocols for modeling biomolecular complexes.

Proper assembly and function of cytochrome c oxidase, which catalyzes the reduction of O2 and generates the proton gradient driving ATP synthesis, depend on correct copper delivery and incorporation. Structural details about the protein-protein complexes involved in this process are still missing. We describe here models of four complexes along this pathway obtained by combining bioinformatics interface predictions with information-driven docking and discuss their relevance with respect to known and pathogenic mutations.

Motivation: Interfacial water, which plays an important role in mediating biomolecular interactions, has been neglected in the modelling of biomolecular complexes., Methods: We present a solvated docking approach that explicitly accounts for the presence of water in protein-protein complexes. Our solvated docking protocol is based on the concept of the first encounter complex in which a water layer is present in-between the molecules. It mimics the pathway from this initial complex towards the final assembly in which most waters have been expelled from the interface. Docking is performed from solvated biomolecules and waters are removed in a biased Monte Carlo procedure based on water-mediated contact propensities obtained from an analysis of high-resolution crystal structures., Results: We demonstrate the feasibility of this approach for protein-protein complexes representing both 'wet' and 'dry' interfaces. Solvated docking leads to improvements both in quality and scoring. Water molecules are recovered that closely match the ones in the crystal structures., Availabilty: Solvated docking will be made available in the future release of HADDOCK version 2.0 (http://www.nmr.chem.uu.nl/haddock).

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.

In this study, a new beta-helical model is proposed that explains the species barrier and strain variation in transmissible spongiform encephalopathies. The left-handed beta-helix serves as a structural model that can explain the seeded growth characteristics of beta-sheet structure in PrP(Sc) fibrils. Molecular dynamics simulations demonstrate that the left-handed beta-helix is structurally more stable than the right-handed beta-helix, with a higher beta-sheet content during the simulation and a better distributed network of inter-strand backbone-backbone hydrogen bonds between parallel beta-strands of different rungs. Multiple sequence alignments and homology modelling of prion sequences with different rungs of left-handed beta-helices illustrate that the PrP region with the highest beta-helical propensity (residues 105-143) can fold in just two rungs of a left-handed beta-helix. Even if no other flanking sequence participates in the beta-helix, the two rungs of a beta-helix can give the growing fibril enough elevation to accommodate the rest of the PrP protein in a tight packing at the periphery of a trimeric beta-helix. The folding of beta-helices is driven by backbone-backbone hydrogen bonding and stacking of side-chains in adjacent rungs. The sequence and structure of the last rung at the fibril end with unprotected beta-sheet edges selects the sequence of a complementary rung and dictates the folding of the new rung with optimal backbone hydrogen bonding and side-chain stacking. An important side-chain stack that facilitates the beta-helical folding is between methionine residues 109 and 129, which explains their importance in the species barrier of prions. Because the PrP sequence is not evolutionarily optimised to fold in a beta-helix, and because the beta-helical fold shows very little sequence preference, alternative alignments are possible that result in a different rung able to select for an alternative complementary rung. A different top rung results in a new strain with different growth characteristics. Hence, in the present model, sequence variation and alternative alignments clarify the basis of the species barrier and strain specificity in PrP-based diseases.

Intrinsic flexibility of DNA has hampered the development of efficient protein-DNA docking methods. In this study we extend HADDOCK (High Ambiguity Driven DOCKing) [C. Dominguez, R. Boelens and A. M. J. J. Bonvin (2003) J. Am. Chem. Soc. 125, 1731-1737] to explicitly deal with DNA flexibility. HADDOCK uses non-structural experimental data to drive the docking during a rigid-body energy minimization, and semi-flexible and water refinement stages. The latter allow for flexibility of all DNA nucleotides and the residues of the protein at the predicted interface. We evaluated our approach on the monomeric repressor-DNA complexes formed by bacteriophage 434 Cro, the Escherichia coli Lac headpiece and bacteriophage P22 Arc. Starting from unbound proteins and canonical B-DNA we correctly predict the correct spatial disposition of the complexes and the specific conformation of the DNA in the published complexes. This information is subsequently used to generate a library of pre-bent and twisted DNA structures that served as input for a second docking round. The resulting top ranking solutions exhibit high similarity to the published complexes in terms of root mean square deviations, intermolecular contacts and DNA conformation. Our two-stage docking method is thus able to successfully predict protein-DNA complexes from unbound constituents using non-structural experimental data to drive the docking.

Predicting the structure of protein-protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Flexibility can be addressed at several levels: implicitly, by smoothing the protein surfaces or allowing some degree of interpenetration (soft docking) or by performing multiple docking runs from various conformations (cross or ensemble docking); or explicitly, by allowing sidechain and/or backbone flexibility. Although significant improvements have been achieved in the modeling of sidechains, methods for the explicit inclusion of backbone flexibility in docking are still being developed. A few novel approaches have emerged involving collective degrees of motion, multicopy representations and multibody docking, which should allow larger conformational changes to be modeled.

With the amount of genetic information available, a lot of attention has focused on systems biology, in particular biomolecular interactions. Considering the huge number of such interactions, and their often weak and transient nature, conventional experimental methods such as X-ray crystallography and NMR spectroscopy are not sufficient to gain structural insight into these. A wealth of biochemical and/or biophysical data can, however, readily be obtained for biomolecular complexes. Combining these data with docking (the process of modeling the 3D structure of a complex from its known constituents) should provide valuable structural information and complement the classical structural methods. In this review we discuss and illustrate the various sources of data that can be used to map interactions and their combination with docking methods to generate structural models of the complexes. Finally a perspective on the future of this kind of approach is given.

Several studies have shown that biomolecular NMR structures are often of lower quality when compared to crystal structures, and consequently they are often excluded from structural analyses. We present a publicly available database of re-refined NMR structures, exhibiting significantly improved quality. This database (available at http://www.cmbi.kun.nl/dress/) presents a uniformly refined and validated set of structural models that improves the value of these NMR structures as input for experimental and theoretical studies in many fields of research., (Copyright 2004 Wiley-Liss, Inc.)

The high sensitivity of modern NMR instrumentation, in combination with full deuteration, enabled the measurement of long-range NOEs between amide protons in a fully deuterated protein corresponding to distances up to 8A. These are beyond the limit normally observed in protonated samples. Such long-distance NOEs could be observed using long mixing times, which became possible due to reduced spin diffusion and T1 relaxation of the amide protons in the fully deuterated sample. This information was used in combination with secondary structure restraints derived from secondary chemical shifts for structure calculations. With these backbone amide proton NOEs only, a unique fold could be obtained with positional root mean square deviations from the average of 1.30 and 2.25 A for backbone and heavy atoms, respectively. Despite the low density of restraints, no mirror image problems were observed. Addition of sidechain NOE information increased the precision of the ensemble and in particular of the core packing. The structures obtained in this way were close to the published crystal structure. NOE completeness analysis revealed that the cumulative completeness is still more than 80% for an 8.0 A cut-off distance.

We present a CPU efficient protocol for refinement of protein structures in a thin layer of explicit solvent and energy parameters with completely revised dihedral angle terms. Our approach is suitable for protein structures determined by theoretical (e.g., homology modeling or threading) or experimental methods (e.g., NMR). In contrast to other recently proposed refinement protocols, we put a strong emphasis on consistency with widely accepted covalent parameters and computational efficiency. We illustrate the method for NMR structure calculations of three proteins: interleukin-4, ubiquitin, and crambin. We show a comparison of their structure ensembles before and after refinement in water with and without a force field energy term for the dihedral angles; crambin was also refined in DMSO. Our results demonstrate the significant improvement of structure quality by a short refinement in a thin layer of solvent. Further, they show that a dihedral angle energy term in the force field is beneficial for structure calculation and refinement. We discuss the optimal weight for the energy constant for the backbone angle omega and include an extensive discussion of meaning and relevance of the calculated validation criteria, in particular root mean square Z scores for covalent parameters such as bond lengths., (Copyright 2003 Wiley-Liss, Inc.)

The feasibility of determining the relative populations of multi-conformer structures from NOE-derived distances alone is assessed. Without cross-validation of the NOE restraints, any population ratio can be refined to a similar quality of the fit. Complete cross-validation provides a less biased measure of fit and allows the estimation of the correct population ratio when used in conjunction with very tight distance restraints. With the qualitative distance restraints most commonly used in NMR structure determination, cross-validation is unsuccessful in providing the correct answer. Other experimental sources are therefore needed to determine relative populations of multi-conformer structures.