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Cancer cells make extensive use of the folate cycle to sustain increased anabolic metabolism. Multiple chemotherapeutic drugs interfere with the folate cycle, including methotrexate and 5-fluorouracil that are commonly applied for the treatment of leukemia and colorectal cancer (CRC), respectively. Despite high success rates, therapy-induced resistance causes relapse at later disease stages. Depletion of folylpolyglutamate synthetase (FPGS), which normally promotes intracellular accumulation and activity of natural folates and methotrexate, is linked to methotrexate and 5-fluorouracil resistance and its association with relapse illustrates the need for improved intervention strategies. Here, we describe a novel antifolate (C1) that, like methotrexate, potently inhibits dihydrofolate reductase and downstream one-carbon metabolism. Contrary to methotrexate, C1 displays optimal efficacy in FPGS-deficient contexts, due to decreased competition with intracellular folates for interaction with dihydrofolate reductase. We show that FPGS-deficient patient-derived CRC organoids display enhanced sensitivity to C1, whereas FPGS-high CRC organoids are more sensitive to methotrexate. Our results argue that polyglutamylation-independent antifolates can be applied to exert selective pressure on FPGS-deficient cells during chemotherapy, using a vulnerability created by polyglutamylation deficiency., (© 2023 van der Krift et al.)

Summary: Distinguishing biologically relevant interfaces from crystallographic ones in biological complexes is fundamental in order to associate cellular functions to the correct macromolecular assemblies. Recently, we described a detailed study reporting the differences in the type of intermolecular residue-residue contacts between biological and crystallographic interfaces. Our findings allowed us to develop a fast predictor of biological interfaces reaching an accuracy of 0.92 and competitive to the current state of the art. Here we present its web-server implementation, PRODIGY-CRYSTAL, aimed at the classification of biological and crystallographic interfaces. PRODIGY-CRYSTAL has the advantage of being fast, accurate and simple. This, together with its user-friendly interface and user support forum, ensures its broad accessibility., Availability and Implementation: PRODIGY-CRYSTAL is freely available without registration requirements at https://haddock.science.uu.nl/services/PRODIGY-CRYSTAL., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Our information-driven docking approach HADDOCK is a consistent top predictor and scorer since the start of its participation in the CAPRI community-wide experiment. This sustained performance is due, in part, to its ability to integrate experimental data and/or bioinformatics information into the modelling process, and also to the overall robustness of the scoring function used to assess and rank the predictions. In the CASP-CAPRI Round 1 scoring experiment we successfully selected acceptable/medium quality models for 18/14 of the 25 targets - a top-ranking performance among all scorers. Considering that for only 20 targets acceptable models were generated by the community, our effective success rate reaches as high as 90% (18/20). This was achieved using the standard HADDOCK scoring function, which, thirteen years after its original publication, still consists of a simple linear combination of intermolecular van der Waals and Coulomb electrostatics energies and an empirically derived desolvation energy term. Despite its simplicity, this scoring function makes sense from a physico-chemical perspective, encoding key aspects of biomolecular recognition. In addition to its success in the scoring experiment, the HADDOCK server takes the first place in the server prediction category, with 16 successful predictions. Much like our scoring protocol, because of the limited time per target, the predictions relied mainly on either an ab initio center-of-mass and symmetry restrained protocol, or on a template-based approach whenever applicable. These results underline the success of our simple but sensible prediction and scoring scheme. Proteins 2017; 85:417-423. © 2016 Wiley Periodicals, Inc., (© 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.)

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.)

Vγ9Vδ2 T cell activation plays an important role in antitumor and antimicrobial immune responses. However, there are conditions in which Vγ9Vδ2 T cell activation can be considered inappropriate for the host. Patients treated with aminobisphosphonates for hypercalcemia or metastatic bone disease often present with a debilitating acute phase response as a result of Vγ9Vδ2 T cell activation. To date, no agents are available that can clinically inhibit Vγ9Vδ2 T cell activation. In this study, we describe the identification of a single domain Ab fragment directed to the TCR of Vγ9Vδ2 T cells with neutralizing properties. This variable domain of an H chain-only Ab (VHH or nanobody) significantly inhibited both phosphoantigen-dependent and -independent activation of Vγ9Vδ2 T cells and, importantly, strongly reduced the production of inflammatory cytokines upon stimulation with aminobisphosphonate-treated cells. Additionally, in silico modeling suggests that the neutralizing VHH binds the same residues on the Vγ9Vδ2 TCR as the Vγ9Vδ2 T cell Ag-presenting transmembrane protein butyrophilin 3A1, providing information on critical residues involved in this interaction. The neutralizing Vγ9Vδ2 TCR VHH identified in this study might provide a novel approach to inhibit the unintentional Vγ9Vδ2 T cell activation as a consequence of aminobisphosphonate administration., (Copyright © 2016 by The American Association of Immunologists, Inc.)

Modeling protein-peptide interactions remains a significant challenge for docking programs due to the inherent highly flexible nature of peptides, which often adopt different conformations whether in their free or bound forms. We present here a protocol consisting of a hybrid approach, combining the most frequently found peptide conformations in complexes with representative conformations taken from molecular dynamics simulations of the free peptide. This approach intends to broaden the range of conformations sampled during docking. The resulting ensemble of conformations is used as a starting point for information-driven flexible docking with HADDOCK. We demonstrate the performance of this protocol on six cases of increasing difficulty, taken from a protein-peptide benchmark set. In each case, we use knowledge of the binding site on the receptor to drive the docking process. In the majority of cases where MD conformations are added to the starting ensemble for docking, we observe an improvement in the quality of the resulting models.

Protein docking procedures carry out the task of predicting the structure of a protein-protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved 'target' complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody-antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark, and is updated on a weekly basis in synchrony with new PDB releases. Proteins 2016; 85:10-16. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given protein-protein complex. Here we present PROtein binDIng enerGY prediction (PRODIGY), a web server to predict the binding affinity of protein-protein complexes from their 3D structure. The PRODIGY server implements our simple but highly effective predictive model based on intermolecular contacts and properties derived from non-interface surface., Availability and Implementation: PRODIGY is freely available at: http://milou.science.uu.nl/services/PRODIGY CONTACT: a.m.j.j.bonvin@uu.nl, a.vangone@uu.nl., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Subversion of the host immune system by viruses is often mediated by molecular decoys that sequester host proteins pivotal to mounting effective immune responses. The widespread mammalian pathogen parapox Orf virus deploys GIF, a member of the poxvirus immune evasion superfamily, to antagonize GM-CSF (granulocyte macrophage colony-stimulating factor) and IL-2 (interleukin-2), two pleiotropic cytokines of the mammalian immune system. However, structural and mechanistic insights into the unprecedented functional duality of GIF have remained elusive. Here we reveal that GIF employs a dimeric binding platform that sequesters two copies of its target cytokines with high affinity and slow dissociation kinetics to yield distinct complexes featuring mutually exclusive interaction footprints. We illustrate how GIF serves as a competitive decoy receptor by leveraging binding hotspots underlying the cognate receptor interactions of GM-CSF and IL-2, without sharing any structural similarity with the cytokine receptors. Our findings contribute to the tracing of novel molecular mimicry mechanisms employed by pathogenic viruses.

Iron is a limiting nutrient in bacterial infection putting it at the centre of an evolutionary arms race between host and pathogen. Gram-negative bacteria utilize TonB-dependent outer membrane receptors to obtain iron during infection. These receptors acquire iron either in concert with soluble iron-scavenging siderophores or through direct interaction and extraction from host proteins. Characterization of these receptors provides invaluable insight into pathogenesis. However, only a subset of virulence-related TonB-dependent receptors have been currently described. Here we report the discovery of FusA, a new class of TonB-dependent receptor, which is utilized by phytopathogenic Pectobacterium spp. to obtain iron from plant ferredoxin. Through the crystal structure of FusA we show that binding of ferredoxin occurs through specialized extracellular loops that form extensive interactions with ferredoxin. The function of FusA and the presence of homologues in clinically important pathogens suggests that small iron-containing proteins represent an iron source for bacterial pathogens.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Molecular-docking programs coupled with suitable scoring functions are now established and very useful tools enabling computational chemists to rapidly screen large chemical databases and thereby to identify promising candidate compounds for further experimental processing. In a broader scenario, predicting binding affinity is one of the most critical and challenging components of computer-aided structure-based drug design. The development of a molecular docking scoring function which in principle could combine both features, namely ranking putative poses and predicting complex affinity, would be of paramount importance. Here, we systematically investigated the performance of the MM-PBSA approach, using two different Poisson-Boltzmann solvers (APBS and DelPhi), in the currently rising field of protein-peptide interactions (PPIs), identifying the correct binding conformations of 19 different protein-peptide complexes and predicting their binding free energies. First, we scored the decoy structures from HADDOCK calculation via the MM-PBSA approach in order to assess the capability of retrieving near-native poses in the best-scoring clusters and of evaluating the corresponding free energies of binding. MM-PBSA behaves well in finding the poses corresponding to the lowest binding free energy, however the built-in HADDOCK score shows a better performance. In order to improve the MM-PBSA-based scoring function, we dampened the MM-PBSA solvation and coulombic terms by 0.2, as proposed in the HADDOCK score and LIE approaches. The new dampened MM-PBSA (dMM-PBSA) outperforms the original MM-PBSA and ranks the decoys structures as the HADDOCK score does. Second, we found a good correlation between the dMM-PBSA and HADDOCK scores for the near-native clusters of each system and the experimental binding energies, respectively. Therefore, we propose a new scoring function, dMM-PBSA, to be used together with the built-in HADDOCK score in the context of protein-peptide docking simulations.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Phospho-MurNAc-pentapeptide translocase (MraY) catalyzes the synthesis of Lipid I, a bacterial peptidoglycan precursor. As such, MraY is essential for bacterial survival and therefore is an ideal target for developing novel antibiotics. However, the understanding of its catalytic mechanism, despite the recently determined crystal structure, remains limited. In the present study, the kinetic properties of Bacillus subtilis MraY (BsMraY) were investigated by fluorescence enhancement using dansylated UDP-MurNAc-pentapeptide and heptaprenyl phosphate (C35-P, short-chain homolog of undecaprenyl phosphate, the endogenous substrate of MraY) as second substrate. Varying the concentrations of both of these substrates and fitting the kinetics data to two-substrate models showed that the concomitant binding of both UDP-MurNAc-pentapeptide-DNS and C35-P to the enzyme is required before the release of the two products, Lipid I and UMP. We built a model of BsMraY and performed docking studies with the substrate C35-P to further deepen our understanding of how MraY accommodates this lipid substrate. Based on these modeling studies, a novel catalytic role was put forward for a fully conserved histidine residue in MraY (His-289 in BsMraY), which has been experimentally confirmed to be essential for MraY activity. Using the current model of BsMraY, we propose that a small conformational change is necessary to relocate the His-289 residue, such that the translocase reaction can proceed via a nucleophilic attack of the phosphate moiety of C35-P on bound UDP-MurNAc-pentapeptide., (© 2016 by The American Society for Biochemistry and Molecular Biology, Inc.)

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.

Molecular modelling and simulations are nowadays an integral part of research in areas ranging from physics to chemistry to structural biology, as well as pharmaceutical drug design. This popularity is due to the development of high-performance hardware and of accurate and efficient molecular mechanics algorithms by the scientific community. These improvements are also benefitting scientific education. Molecular simulations, their underlying theory, and their applications are particularly difficult to grasp for undergraduate students. Having hands-on experience with the methods contributes to a better understanding and solidification of the concepts taught during the lectures. To this end, we have created a computer practical class, which has been running for the past five years, composed of several sessions where students characterize the conformational landscape of small peptides using molecular dynamics simulations in order to gain insights on their binding to protein receptors. In this report, we detail the ingredients and recipe necessary to establish and carry out this practical, as well as some of the questions posed to the students and their expected results. Further, we cite some examples of the students' written reports, provide statistics, and share their feedbacks on the structure and execution of the sessions. These sessions were implemented alongside a theoretical molecular modelling course but have also been used successfully as a standalone tutorial during specialized workshops. The availability of the material on our web page also facilitates this integration and dissemination and lends strength to the thesis of open-source science and education., (© 2016 The International Union of Biochemistry and Molecular Biology.)

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.)

Peptides that bind to ion channels have attracted much interest as potential lead molecules for the development of new drugs and insecticides. However, the structure determination of large peptide-channel complexes using experimental methods is challenging. Thus structural models are often derived from combining experimental information with restraint-driven docking approaches. Using the complex formed by the venom peptide PcTx1 and the acid sensing ion channel (ASIC) 1a as a case study, we have examined the effect of different combinations of restraints and input structures on the statistical likelihood of (a) correctly predicting the structure of the binding interface and (b) the ability to predict which residues are involved in specific pairwise peptide-channel interactions. For this, we have analyzed over 200,000 water-refined docked structures obtained with various amounts and types of restraints of the peptide-channel complex predicted using the docking program HADDOCK. We found that increasing the number of restraints or even the use of pairwise interaction data resulted in only a modest improvement in the likelihood of finding a structure within a given accuracy. This suggests that shape complementarity and the force field make a large contribution to the accuracy of the predicted structure. The results also showed that there are large variations in the accuracy of the predicted structure depending on the precise combination of residues used as restraints. Finally, we reflect on the limitations of relying on geometric criteria such as root-mean square deviations to assess the accuracy of docking procedures. We propose that in addition to currently used measures, the likelihood of finding a structure within a given level of accuracy should be also used to evaluate docking methods.

The Antimicrobial peptides (e.g. defensins, hevein-like molecules and food-protecting peptides like nisin) are able to interact specifically with contact structures on pathogen surfaces. Besides protein receptors, important recognition points for such contacts are provided by pathogen glycan chains or surface lipids. Therefore, structural data concerning surface exposed glycans and lipids are of the highest clinical interest since these recognition functions play a key role when optimising anti-infection therapies. Approaches in nanomedicine and nanopharmacology in which various biophysical techniques such as NMR (Nuclear Magnetic Resonance), AFM (Atomic Force Microscopy), SPR (Surface Plasmon Resonance) and X-ray crystallography can be combined with biochemical and cell-biological methods will lead to improved antimicrobial peptides by this rational drug design approach. Such a strategy is extremely well suited to support clinical studies focussing on an effective fight against multiresistant pathogens. The data sets which are described here can be considered as universal for the design of various antimicrobial drugs against certain pathogens (bacteria, viruses and fungi) which cause severe diseases in humans and animals. Furthermore, these insights are also helpful for progressing developments in the field of food conservation and food preservation. A detailed analysis of the structure-function relationships between antimicrobial peptides and contact molecules on pathogen surfaces at the sub-molecular level will lead to a higher degree of specificity of antimicrobial peptides.

The human Argonaute 2 (hAgo2) protein is a key player of RNA interference (RNAi). Upon complex formation with small non-coding RNAs, the protein initially interacts with the 5'-end of a given guide RNA through multiple interactions within the MID domain. This interaction has been reported to show a strong bias for U and A over C and G at the 5'-position. Performing molecular dynamics simulations of binary hAgo2/OH-guide-RNA complexes, we show that hAgo2 is a highly flexible protein capable of binding to guide strands with all four possible 5'-bases. Especially, in the case of C and G this is associated with rather large individual conformational rearrangements affecting the MID, PAZ and even the N-terminal domains to different degrees. Moreover, a 5'-G induces domain motions in the protein, which trigger a previously unreported interaction between the 5'-base and the L2 linker domain. Combining our in silico analyses with biochemical studies of recombinant hAgo2, we find that, contrary to previous observations, hAgo2 is capable of functionally accommodating guide strands regardless of the 5'-base.

Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)

Unlabelled: We present DisVis, a Python package and command line tool to calculate the reduced accessible interaction space of distance-restrained binary protein complexes, allowing for direct visualization and quantification of the information content of the distance restraints. The approach is general and can also be used as a knowledge-based distance energy term in FFT-based docking directly during the sampling stage., Availability and Implementation: The source code with documentation is freely available from https://github.com/haddocking/disvis., Contact: a.m.j.j.bonvin@uu.nl, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press.)

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes., (Copyright © 2015. Published by Elsevier Ltd.)

We present here the performance of the WeNMR CS-Rosetta3 web server in CASD-NMR, the critical assessment of automated structure determination by NMR. The CS-Rosetta server uses only chemical shifts for structure prediction, in combination, when available, with a post-scoring procedure based on unassigned NOE lists (Huang et al. in J Am Chem Soc 127:1665-1674, 2005b, doi: 10.1021/ja047109h). We compare the original submissions using a previous version of the server based on Rosetta version 2.6 with recalculated targets using the new R3FP fragment picker for fragment selection and implementing a new annotation of prediction reliability (van der Schot et al. in J Biomol NMR 57:27-35, 2013, doi: 10.1007/s10858-013-9762-6), both implemented in the CS-Rosetta3 WeNMR server. In this second round of CASD-NMR, the WeNMR CS-Rosetta server has demonstrated a much better performance than in the first round since only converged targets were submitted. Further, recalculation of all CASD-NMR targets using the new version of the server demonstrates that our new annotation of prediction quality is giving reliable results. Predictions annotated as weak are often found to provide useful models, but only for a fraction of the sequence, and should therefore only be used with caution.

Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?, (Copyright © 2015 Elsevier Ltd. All rights reserved.)

BamA is the main component of the β-barrel assembly machinery (BAM) that folds and inserts outer membrane proteins in Gram-negative bacteria. Crystal structures have suggested that this process involves conformational changes in the transmembrane β-barrel of BamA that allow for lateral opening, as well as large overall rearrangements of its periplasmic POTRA domains. Here, we identify local dynamics of the BamA POTRA 5 domain by solution and solid-state nuclear magnetic resonance. The protein region undergoing conformational exchange is highly conserved and contains residues critical for interaction with BamD and correct β-barrel assembly in vivo. We show that mutations known to affect the latter processes influence the conformational equilibrium, suggesting that the plasticity of POTRA 5 is related to its interaction with BamD and possibly to substrate binding. Taken together, a view emerges in which local protein plasticity may be critically involved in the different stages of outer membrane protein folding and insertion., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Rapid neurotransmitter release depends on the Ca2+ sensor Synaptotagmin-1 (Syt1) and the SNARE complex formed by synaptobrevin, syntaxin-1 and SNAP-25. How Syt1 triggers release has been unclear, partly because elucidating high-resolution structures of Syt1-SNARE complexes has been challenging. An NMR approach based on lanthanide-induced pseudocontact shifts now reveals a dynamic binding mode in which basic residues in the concave side of the Syt1 C2B-domain β-sandwich interact with a polyacidic region of the SNARE complex formed by syntaxin-1 and SNAP-25. The physiological relevance of this dynamic structural model is supported by mutations in basic residues of Syt1 that markedly impair SNARE-complex binding in vitro and Syt1 function in neurons. Mutations with milder effects on binding have correspondingly milder effects on Syt1 function. Our results support a model whereby dynamic interaction facilitates cooperation between Syt1 and the SNAREs in inducing membrane fusion.

Studying biomolecules at atomic resolution in their native environment is the ultimate aim of structural biology. We investigated the bacterial type IV secretion system core complex (T4SScc) by cellular dynamic nuclear polarization-based solid-state nuclear magnetic resonance spectroscopy to validate a structural model previously generated by combining in vitro and in silico data. Our results indicate that T4SScc is well folded in the cellular setting, revealing protein regions that had been elusive when studied in vitro.

Nanovesicles self-assembled from amphiphilic peptides are promising candidates for applications in drug delivery. However, complete high-resolution data on the local and supramolecular organization of such materials has been elusive thus far, which is a substantial obstacle to their rational design. In the absence of precise information, nanovesicles built of amphiphilic "lipid-like" peptides are generally assumed to resemble liposomes that are organized from bilayers of peptides with a tail-to-tail ordering. Using the nanocarrier formed by the amphiphilic self-assembling peptide 2 (SA2 peptide) as an example, we derive the local and global organization of a multimega-Dalton peptide-based nanocarrier at high molecular detail and at close-to physiological conditions. By integrating a multitude of experimental techniques (solid-state NMR, AFM, SLS, DLS, FT-IR, CD) with large- and multiscale MD simulations, we show that SA2 nanocarriers are built of interdigitated antiparallel β-sheets, which bear little resemblance to phospholipid liposomes. Our atomic level study allows analyzing the vesicle surface structure and dynamics as well as the intermolecular forces between peptides, providing a number of potential leads to improve and tune the biophysical properties of the nanocarrier. The herein presented approach may be of general utility to investigate peptide-based nanomaterials at high-resolution and at physiological conditions.

With the increasingly rapid growth of data in life sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. Such approaches necessitate the use of large-scale computational resources and e-infrastructures, such as the European Grid Infrastructure (EGI). EGI, one of key the enablers of the digital European Research Area, is a federation of resource providers set up to deliver sustainable, integrated and secure computing services to European researchers and their international partners. Here we aim to provide the state of the art of Grid/Cloud computing in EU research as viewed from within the field of life sciences, focusing on key infrastructures and projects within the life sciences community. Rather than focusing purely on the technical aspects underlying the currently provided solutions, we outline the design aspects and key characteristics that can be identified across major research approaches. Overall, we aim to provide significant insights into the road ahead by establishing ever-strengthening connections between EGI as a whole and the life sciences community.

Protein-protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure-based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein-protein binding, even for simple lock-and-key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein-protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein-protein surfaces. We compare properties of the interface, rim, and non-interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non-interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non-interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein-protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein-protein complexes from unintended protein-protein interactions., (© 2014 Wiley Periodicals, Inc.)

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.

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are difficult to study experimentally at the structural level because of the inherent flexibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplified by the recent introduction of protein-peptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high flexibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to define the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The flexibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fit molecular recognition theories.

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.

Bromodomains are epigenetic reader domains, which have come under increasing scrutiny both from academic and pharmaceutical research groups. Effective targeting of the BAZ2B bromodomain by small molecule inhibitors has been recently reported, but no structural information is yet available on the interaction with its natural binding partner, acetylated histone H3K14ac. We have assigned the BAZ2B bromodomain and studied its interaction with H3K14ac acetylated peptides by NMR spectroscopy using both chemical shift perturbation (CSP) data and clean chemical exchange (CLEANEX-PM) NMR experiments. The latter was used to characterize water molecules known to play an important role in mediating interactions. Besides the anticipated Kac binding site, we consistently found the bromodomain BC loop as hotspots for the interaction. This information was used to create a data-driven model for the complex using HADDOCK. Our findings provide both structure and dynamics characterization that will be useful in the quest for potent and selective inhibitors to probe the function of the BAZ2B bromodomain.

The integration of biophysical data from multiple sources is critical for developing accurate structural models of large multiprotein systems and their regulators. Mass spectrometry (MS) can be used to measure the insertion location for a wide range of topographically sensitive chemical probes, and such insertion data provide a rich, but disparate set of modeling restraints. We have developed a software platform that integrates the analysis of label-based MS and tandem MS (MS(2)) data with protein modeling activities (Mass Spec Studio). Analysis packages can mine any labeling data from any mass spectrometer in a proteomics-grade manner, and link labeling methods with data-directed protein interaction modeling using HADDOCK. Support is provided for hydrogen/deuterium exchange (HX) and covalent labeling chemistries, including novel acquisition strategies such as targeted HX-MS(2) and data-independent HX-MS(2). The latter permits the modeling of highly complex systems, which we demonstrate by the analysis of microtubule interactions., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

Protein-protein complexes orchestrate most cellular processes such as transcription, signal transduction and apoptosis. The factors governing their affinity remain elusive however, especially when it comes to describing dissociation rates (koff). Here we demonstrate that, next to direct contributions from the interface, the non-interacting surface (NIS) also plays an important role in binding affinity, especially polar and charged residues. Their percentage on the NIS is conserved over orthologous complexes indicating an evolutionary selection pressure. Their effect on binding affinity can be explained by long-range electrostatic contributions and surface-solvent interactions that are known to determine the local frustration of the protein complex surface. Including these in a simple model significantly improves the affinity prediction of protein complexes from structural models. The impact of mutations outside the interacting surface on binding affinity is supported by experimental alanine scanning mutagenesis data. These results enable the development of more sophisticated and integrated biophysical models of binding affinity and open new directions in experimental control and modulation of biomolecular interactions., (Copyright © 2014. Published by Elsevier Ltd.)

Protein interactions define the homeostatic state of the cell. Our ability to understand these interactions and their role in both health and disease is tied to our knowledge of the 3D atomic structure of the interacting partners and their complexes. Despite advances in experimental method of structure determination, the majority of known protein interactions are still missing an atomic structure. High-resolution methods such as X-ray crystallography and NMR spectroscopy struggle with the high-throughput demand, while low-resolution techniques such as cryo-electron microscopy or small-angle X-ray scattering provide data that are too coarse. Computational structure prediction of protein complexes, or docking, was first developed to complement experimental research and has since blossomed into an independent and lively field of research. Its most successful products are hybrid approaches that combine powerful algorithms with experimental data from various sources to generate high-resolution models of protein complexes. This minireview introduces the concept of docking and docking with the help of experimental data, compares and contrasts the available integrative docking methods, and provides a guide for the experimental researcher for what types of data and which particular software can be used to model a protein complex., (© 2014 FEBS.)

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., (Copyright © 2013 Wiley Periodicals, Inc.)

Availability of high-resolution atomic structures is one of the prerequisites for a mechanistic understanding of biomolecular function. This atomic information can, however, be difficult to acquire for interesting systems such as high molecular weight and multi-subunit complexes. For these, low-resolution and/or sparse data from a variety of sources including NMR are often available to define the interaction between the subunits. 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 the sparse experimental data into structural information. In this Perspective we discuss NMR techniques and data sources available for the modeling of large and multi-subunit complexes. Recent developments are illustrated by particularly challenging application examples taken from the literature. Within this context, we also position our data-driven docking approach, HADDOCK, which can integrate a variety of information sources to drive the modeling of biomolecular complexes. It is the synergy between experimentation and computational modeling that will provides us with detailed views on the machinery of life and lead to a mechanistic understanding of biomolecular function., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

The HADDOCK score, a scoring function for both protein-protein and protein-nucleic acid modeling, has been successful in selecting near-native docking poses in a variety of cases, including those of the CAPRI blind prediction experiment. However, it has yet to be optimized for small molecules, and in particular inhibitors of protein-protein interactions, that constitute an "unmined gold reserve" for drug design ventures. We describe here HADDOCK(2P2I), a biophysical model capable of predicting the binding affinity of protein-protein complex inhibitors close to experimental error (~2-fold larger). The algorithm was trained and 4-fold cross-validated against experimental data for 27 inhibitors targeting 7 protein-protein complexes of various functions and tested on an independent set of 24 different inhibitors for which K(d)/IC50 data are available. In addition, two popular ligand topology generation and parametrization methods (ACPYPE and PRODRG) were assessed. The resulting HADDOCK(2P2I) model, derived from the original HADDOCK score, provides insights into inhibition determinants: while the role of electrostatics and desolvation energies is case-dependent, the interface area plays a more critical role compared to protein-protein interactions.

Circadian timing in cyanobacteria is determined by the Kai system consisting of KaiA, KaiB, and KaiC. Interactions between Kai proteins change the phosphorylation status of KaiC, defining the phase of circadian timing. The KaiC-KaiB interaction is crucial for the circadian rhythm to enter the dephosphorylation phase but it is not well understood. Using mass spectrometry to characterize Kai complexes, we found that KaiB forms monomers, dimers, and tetramers. The monomer is the unit that interacts with KaiC, with six KaiB monomers binding to one KaiC hexamer. Hydrogen-deuterium exchange MS reveals structural changes in KaiC upon binding of KaiB in both the CI and CII domains, showing allosteric coupling upon KaiB binding. Based on this information we propose a model of the KaiB-KaiC complex and hypothesize that the allosteric changes observed upon complex formation relate to coupling KaiC ATPase activity with KaiB binding and to sequestration of KaiA dimers into KaiCBA complexes., Competing Interests: The authors declare no conflict of interest.

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.

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., (Copyright © 2013 Wiley Periodicals, Inc.)