UNRES-Dock-protein-protein and peptide-protein docking by coarse-grained replica-exchange MD simulations.

MOTIVATION: The majority of the proteins in living organisms occur as homo- or hetero-multimeric structures. Although there are many tools to predict the structures of single-chain proteins or protein complexes with small ligands, peptide-protein and protein-protein docking is more challenging. In this work, we utilized multiplexed replica-exchange molecular dynamics (MREMD) simulations with the physics-based heavily coarse-grained UNRES model, which provides more than a 1000-fold simulation speed-up compared with all-atom approaches to predict structures of protein complexes. RESULTS: We present a new protein-protein and peptide-protein docking functionality of the UNRES package, which includes a variable degree of conformational flexibility. UNRES-Dock protocol was tested on a set of 55 complexes with size from 43 to 587 amino-acid residues, showing that structures of the complexes can be predicted with good quality, if the sampling of the conformational space is sufficient, especially for flexible peptide-protein systems. The developed automatized protocol has been implemented in the standalone UNRES package and in the UNRES server. AVAILABILITY AND IMPLEMENTATION: UNRES server: http://unres-server.chem.ug.edu.pl; UNRES package and data used in testing of UNRES-Dock: http://unres.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Role of Glycosaminoglycans in Procathepsin B Maturation: Molecular Mechanism Elucidated by a Computational Study.

Procathepsins are an inactive, immature form of cathepsins, predominantly cysteine proteases present in the extracellular matrix (ECM) and in lysosomes that play a key role in various biological processes such as bone resorption or intracellular proteolysis. The enzymatic activity of cathepsins can be mediated by glycosaminoglycans (GAGs), long unbranched periodic negatively charged polysaccharides found in ECM that take part in many biological processes such as anticoagulation, angiogenesis, and tissue regeneration. In addition to the known effects on mature cathepsins, GAGs can mediate the maturation process of procathepsins, in particular, procathepsin B. However, the detailed mechanism of this mediation at the molecular level is still unknown. In this study, for the first time, we aimed to unravel the role of GAGs in this process using computational approaches. We rigorously analyzed procathepsin B-GAG complexes in terms of their dynamics, energetics, and potential allosteric regulation. We revealed that GAGs can stabilize the conformation of the procathepsin B structure with the active site accessible for the substrate and concluded that GAGs most probably bind to procathepsin B once the zymogen adopts the enzymatically active conformation. Our data provided a novel mechanistic view of the maturation process of procathepsin B, while the approaches elaborated here might be useful to study other procathepsins. Furthermore, our data can serve as a rational guide for experimental work on procathepsin-GAG systems that are not characterized in vivo and in vitro yet.

Improved Consensus-Fragment Selection in Template-Assisted Prediction of Protein Structures with the UNRES Force Field in CASP13.

The method for protein-structure prediction, which combines the physics-based coarse-grained UNRES force field with knowledge-based modeling, has been developed further and tested in the 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13). The method implements restraints from the consensus fragments common to server models. In this work, the server models to derive fragments have been chosen on the basis of quality assessment; a fully automatic fragment-selection procedure has been introduced, and Dynamic Fragment Assembly pseudopotentials have been fully implemented. The Global Distance Test Score (GDT_TS), averaged over our "Model 1" predictions, increased by over 10 units with respect to CASP12 for the free-modeling category to reach 40.82. Our "Model 1" predictions ranked 20 and 14 for all and free-modeling targets, respectively (upper 20.2% and 14.3% of all models submitted to CASP13 in these categories, respectively), compared to 27 (upper 21.1%) and 24 (upper 18.9%) in CASP12, respectively. For oligomeric targets, the Interface Patch Similarity (IPS) and Interface Contact Similarity (ICS) averaged over our best oligomer models increased from 0.28 to 0.36 and from 12.4 to 17.8, respectively, from CASP12 to CASP13, and top-ranking models of 2 targets (H0968 and T0997o) were obtained (none in CASP12). The improvement of our method in CASP13 over CASP12 was ascribed to the combined effect of the overall enhancement of server-model quality, our success in selecting server models and fragments to derive restraints, and improvements of the restraint and potential-energy functions.

UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics.

A server implementation of the UNRES package (http://www.unres.pl) for coarse-grained simulations of protein structures with the physics-based UNRES model, coined a name UNRES server, is presented. In contrast to most of the protein coarse-grained models, owing to its physics-based origin, the UNRES force field can be used in simulations, including those aimed at protein-structure prediction, without ancillary information from structural databases; however, the implementation includes the possibility of using restraints. Local energy minimization, canonical molecular dynamics simulations, replica exchange and multiplexed replica exchange molecular dynamics simulations can be run with the current UNRES server; the latter are suitable for protein-structure prediction. The user-supplied input includes protein sequence and, optionally, restraints from secondary-structure prediction or small x-ray scattering data, and simulation type and parameters which are selected or typed in. Oligomeric proteins, as well as those containing D-amino-acid residues and disulfide links can be treated. The output is displayed graphically (minimized structures, trajectories, final models, analysis of trajectory/ensembles); however, all output files can be downloaded by the user. The UNRES server can be freely accessed at http://unres-server.chem.ug.edu.pl.

Fragments of gD Protein as Inhibitors of BTLA/HVEM Complex Formation-Design, Synthesis, and Cellular Studies.

One of the major current trends in cancer immunotherapy is the blockade of immune checkpoint proteins that negatively regulate the immune response. This has been achieved through antibodies blocking PD-1/PD-L1 and CTLA-4/CD80/CD86 interactions. Such antibodies have revolutionized oncological therapy and shown a new way to fight cancer. Additional (negative) immune checkpoints are also promising targets in cancer therapy and there is a demand for inhibitors for these molecules. Our studies are focused on BTLA/HVEM complex, which inhibits T-cell proliferation and cytokine production and therefore has great potential as a new target for cancer treatment. The goal of the presented studies was the design and synthesis of compounds able to block BTLA/HVEM interactions. For that purpose, the N-terminal fragment of glycoprotein D (gD), which interacts with HVEM, was used. Based on the crystal structure of the gD/HVEM complex and MM/GBSA analysis performed on it, several peptides were designed and synthesized as potential inhibitors of the BTLA/HVEM interaction. Affinity tests, ELISA tests, and cellular-based reporter assays were performed on these compounds to check their ability to bind to HVEM and to inhibit BTLA/HVEM complex formation. For leading peptides candidates, all-atom and subsequent docking simulations with a coarse-grained force field were performed to determine their binding modes. To further evaluate their potential as drug candidates, their stability in plasma and their cytotoxicity effects on PBMCs were assessed. Our data indicate that the peptide gD(1-36)(K10C-T29C) is the best candidate as a future drug. It interacts with HVEM protein, blocks the BTLA/HVEM interaction, and is nontoxic to cells. The present study provides a new perspective on the development of BTLA/HVEM inhibitors that disrupt protein interactions.

Disulfide-Linked Peptides for Blocking BTLA/HVEM Binding.

Immune checkpoints are crucial in the maintenance of antitumor immune responses. The activation or blockade of immune checkpoints is dependent on the interactions between receptors and ligands; such interactions can provide inhibitory or stimulatory signals, including the enhancement or suppression of T-cell proliferation, differentiation, and/or cytokine secretion. B-and T-lymphocyte attenuator (BTLA) is a lymphoid-specific cell surface receptor which is present on T-cells and interacts with herpes virus entry mediator (HVEM), which is present on tumor cells. The binding of HVEM to BTLA triggers an inhibitory signal which attenuates the immune response. This feature is interesting for studying the molecular interactions between HVEM and BTLA, as they may be targeted for novel immunotherapies. This work was based on the crystal structure of the BTLA/HVEM complex showing that BTLA binds the N-terminal cysteine-rich domain of HVEM. We investigated the amino acid sequence of HVEM and used molecular modeling methods to develop inhibitors of the BTLA/HVEM interaction. We synthesized novel compounds and determined their ability to interact with the BTLA protein and inhibit the formation of the BTLA/HVEM complex. Our results suggest that the HVEM (14-39) peptide is a potent inhibitor of the formation of the BTLA/HVEM protein complex.

Assessment of chemical-crosslink-assisted protein structure modeling in CASP13.

With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest-to-date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Prediction of protein structure with the coarse-grained UNRES force field assisted by small X-ray scattering data and knowledge-based information.

A new approach to assisted protein-structure prediction has been proposed, which is based on running multiplexed replica exchange molecular dynamics simulations with the coarse-grained UNRES force field with restraints derived from knowledge-based models and distance distribution from small angle X-ray scattering (SAXS) measurements. The latter restraints are incorporated into the target function as a maximum-likelihood term that guides the shape of the simulated structures towards that defined by SAXS. The approach was first verified with the 1KOY protein, for which the distance distribution was calculated from the experimental structure, and subsequently used to predict the structures of 11 data-assisted targets in the CASP12 experiment. Major improvement of the GDT_TS was obtained for 2 targets, minor improvement for other 2 while, for 6 target GDT_TS deteriorated compared with that calculated for predictions without the SAXS data, partly because of assuming a wrong multimeric state (for Ts866) or because the crystal conformation was more compact than the solution conformation (for Ts942). Particularly good results were obtained for Ts909, in which use of SAXS data resulted in the selection of a correctly packed trimer and, subsequently, increased the GDT_TS of monomer prediction. It was found that running simulations with correct oligomeric state is essential for the success in SAXS-data-assisted prediction.

Ergodicity and model quality in template-restrained canonical and temperature/Hamiltonian replica exchange coarse-grained molecular dynamics simulations of proteins.

Molecular simulations restrained to single or multiple templates are commonly used in protein-structure modeling. However, the restraints introduce additional barriers, thus impairing the ergodicity of simulations, which can affect the quality of the resulting models. In this work, the effect of restraint types and simulation schemes on ergodicity and model quality was investigated by performing template-restrained canonical molecular dynamics (MD), multiplexed replica-exchange molecular dynamics, and Hamiltonian replica exchange molecular dynamics (HREMD) simulations with the coarse-grained UNRES force field on nine selected proteins, with pseudo-harmonic log-Gaussian (unbounded) or Lorentzian (bounded) restraint functions. The best ergodicity was exhibited by HREMD. It has been found that non-ergodicity does not affect model quality if good templates are used to generate restraints. However, when poor-quality restraints not covering the entire protein are used, the improved ergodicity of HREMD can lead to significantly improved protein models. © 2017 Wiley Periodicals, Inc.

Performance of protein-structure predictions with the physics-based UNRES force field in CASP11.

Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed 3-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the CαRMSD was 3.8 Å for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template-based modeling methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://www.unres.pl/ CONTACT: has5@cornell.edu.

A Maximum-Likelihood Approach to Force-Field Calibration.

A new approach to the calibration of the force fields is proposed, in which the force-field parameters are obtained by maximum-likelihood fitting of the calculated conformational ensembles to the experimental ensembles of training system(s). The maximum-likelihood function is composed of logarithms of the Boltzmann probabilities of the experimental conformations, calculated with the current energy function. Because the theoretical distribution is given in the form of the simulated conformations only, the contributions from all of the simulated conformations, with Gaussian weights in the distances from a given experimental conformation, are added to give the contribution to the target function from this conformation. In contrast to earlier methods for force-field calibration, the approach does not suffer from the arbitrariness of dividing the decoy set into native-like and non-native structures; however, if such a division is made instead of using Gaussian weights, application of the maximum-likelihood method results in the well-known energy-gap maximization. The computational procedure consists of cycles of decoy generation and maximum-likelihood-function optimization, which are iterated until convergence is reached. The method was tested with Gaussian distributions and then applied to the physics-based coarse-grained UNRES force field for proteins. The NMR structures of the tryptophan cage, a small α-helical protein, determined at three temperatures (T = 280, 305, and 313 K) by Halabis et al. ( J. Phys. Chem. B 2012 , 116 , 6898 - 6907 ), were used. Multiplexed replica-exchange molecular dynamics was used to generate the decoys. The iterative procedure exhibited steady convergence. Three variants of optimization were tried: optimization of the energy-term weights alone and use of the experimental ensemble of the folded protein only at T = 280 K (run 1); optimization of the energy-term weights and use of experimental ensembles at all three temperatures (run 2); and optimization of the energy-term weights and the coefficients of the torsional and multibody energy terms and use of experimental ensembles at all three temperatures (run 3). The force fields were subsequently tested with a set of 14 α-helical and two α + ß proteins. Optimization run 1 resulted in better agreement with the experimental ensemble at T = 280 K compared with optimization run 2 and in comparable performance on the test set but poorer agreement of the calculated folding temperature with the experimental folding temperature. Optimization run 3 resulted in the best fit of the calculated ensembles to the experimental ones for the tryptophan cage but in much poorer performance on the training set, suggesting that use of a small α-helical protein for extensive force-field calibration resulted in overfitting of the data for this protein at the expense of transferability. The optimized force field resulting from run 2 was found to fold 13 of the 14 tested α-helical proteins and one small α + ß protein with the correct topologies; the average structures of 10 of them were predicted with accuracies of about 5 Å C(α) root-mean-square deviation or better. Test simulations with an additional set of 12 α-helical proteins demonstrated that this force field performed better on α-helical proteins than the previous parametrizations of UNRES. The proposed approach is applicable to any problem of maximum-likelihood parameter estimation when the contributions to the maximum-likelihood function cannot be evaluated at the experimental points and the dimension of the configurational space is too high to construct histograms of the experimental distributions.

BTLA-derived peptides as inhibitors of BTLA/HVEM complex formation - design, synthesis and biological evaluation.

Immune checkpoints can be divided into co-stimulatory and co-inhibitory molecules that regulate the activation and effector functions of T cells. The co-inhibitory pathways mediated by ICPs are used by cancer cells to escape from immune surveillance, and therefore the blockade of these receptor/ligand interactions is one of the strategies used in the treatment of cancer. The two main pathways currently under investigation are CTLA-4/CD80/CD86 and PD-1/PD-L1, and the monoclonal Abs targeting them have shown potent immunomodulatory effects and activity in clinical environments. Another interesting target in cancer treatment is the BTLA/HVEM complex. Binding of BTLA protein on T cells to HVEM on cancer cells leads to inhibition of T cell proliferation and cytokine production. In the presented work, we focused on blocking the HVEM protein using BTLA-derived peptides. Based on the crystal structure of the BTLA/HVEM complex and MM/GBSA analysis performed here, we designed and synthesized peptides, specifically fragments of BTLA protein. We subsequently checked the inhibitory capacities of these compounds using ELISA and a cellular reporter platform. Two of these peptides, namely BTLA(35-43) and BTLA(33-64)C58Abu displayed the most promising properties, and we therefore performed further studies to evaluate their affinity to HVEM protein, their stability in plasma and their effect on viability of human PBMCs. In addition, the 3D structure for the peptide BTLA(33-64)C58Abu was determined using NMR. Obtained data confirmed that the BTLA-derived peptides could be the basis for future drugs and their immunomodulatory potential merits further examination.

Modeling the Structure, Dynamics, and Transformations of Proteins with the UNRES Force Field.

The physics-based united-residue (UNRES) model of proteins ( www.unres.pl ) has been designed to carry out large-scale simulations of protein folding. The force field has been derived and parameterized based on the principles of statistical-mechanics, which makes it independent of structural databases and applicable to treat nonstandard situations such as, proteins that contain D-amino-acid residues. Powered by Langevin dynamics and its replica-exchange extensions, UNRES has found a variety of applications, including ab initio and database-assisted protein-structure prediction, simulating protein-folding pathways, exploring protein free-energy landscapes, and solving biological problems. This chapter provides a summary of UNRES and a guide for potential users regarding the application of the UNRES package in a variety of research tasks.

Truncation of Huia versabilis Bowman-Birk inhibitor increases its selectivity, matriptase-1 inhibitory activity and proteolytic stability.

A gradual truncation of the primary structure of frog skin-derived Huia versabilis Bowman-Birk peptidic inhibitor (HV-BBI) resulted in 18-times stronger inhibitor of matriptase-1 (peptide 6, Ki = 8 nm) in comparison to the full-length HV-BBI (Ki = 155 nm). Analogous increase in the inhibitory activity in correlation with the peptide length reduction was not observed in case of other serine proteases, bovine trypsin (Ki = 151 nm for peptide 6 and Ki = 120 nm for HV-BBI) and plasmin (Ki = 120 nm for peptide 6 and 82 nm for HV-BBI). Weaker binding affinity to these enzymes emphasized an inhibitory specificity of peptide 6. Molecular dynamic analysis revealed that the observed variations in the binding affinity of peptide 6 and HV-BBI with matriptase-1 are associated with the entropic differences of the unbound peptides. Moreover, several aspects explaining differences in the inhibition of matriptase-1 by peptide 6 (bearing the C-terminal amide group) and its two analogues, peptide 6∗ (having the C-terminal carboxyl group, Ki = 473 nm) and cyclic peptide 6∗∗ (Ki = 533 nm), both exhibiting more than 50-fold reduced inhibitory potency, were discovered. It was also shown that peptide 6 presented significantly higher resistance to proteolytic degradation in human serum than HV-BBI. Additional investigations revealed that, in contrast to some amphibian-derived inhibitors, HV-BBI and its truncated analogues do not possess bactericidal activity, thus they cannot be considered as bifunctional agents.

Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers.

In this chapter the scale-consistent approach to the derivation of coarse-grained force fields developed in our laboratory is presented, in which the effective energy function originates from the potential of mean force of the system under consideration and embeds atomistically detailed interactions in the resulting energy terms through use of Kubo's cluster-cumulant expansion, appropriate selection of the major degrees of freedom to be averaged out in the derivation of analytical approximations to the energy terms, and appropriate expression of the interaction energies at the all-atom level in these degrees of freedom. Our approach enables the developers to find correct functional forms of the effective coarse-grained energy terms, without having to import them from all-atom force fields or deriving them on a heuristic basis. In particular, the energy terms derived in such a way exhibit correct dependence on coarse-grained geometry, in particular on site orientation. Moreover, analytical formulas for the multibody (correlation) terms, which appear to be crucial for coarse-grained modeling of many of the regular structures such as, e.g., protein α-helices and ß-sheets, can be derived in a systematic way. Implementation of the developed theory to the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules, which consists of the UNRES (for proteins), NARES-2P (for nucleic acids), and SUGRES-1P (for polysaccharides) components, and is being developed in our laboratory is described. Successful applications of UNICORN to the prediction of protein structure, simulating the folding and stability of proteins and nucleic acids, and solving biological problems are discussed.

A structural model of the immune checkpoint CD160-HVEM complex derived from HDX-mass spectrometry and molecular modeling.

CD160 is a T cell coinhibitory molecule that interacts with the herpes virus entry mediator (HVEM) on antigen-presenting cells to provide an inhibitory signal to T cells. To date, the structure of CD160 and its complex with HVEM are unknown. Here, we have identified the fragments of CD160 interacting with HVEM using ELISA tests, hydrogen/deuterium studies, affinity chromatography and mass spectrometry (MS). By combining hydrogen/deuterium exchange and mass spectrometry (HDX-MS) we obtained key information about the tertiary structure of CD160, predicting the 3D structure of the CD160-HVEM complex. Our results provide insights into the molecular architecture of this complex, serving as a useful basis for designing inhibitors for future immunotherapies.

Evaluation of the scale-consistent UNRES force field in template-free prediction of protein structures in the CASP13 experiment.

The recent NEWCT-9P version of the coarse-grained UNRES force field for proteins, with scale-consistent formulas for the local and correlation terms, has been tested in the CASP13 experiment of the blind-prediction of protein structure, in the ab initio, contact-assisted, and data-assisted modes. Significant improvement of the performance has been observed with respect to the CASP11 and CASP12 experiments (by over 10 GDT_TS units for the ab initio mode predictions and by over 15 GDT_TS units for the contact-assisted prediction, respectively), which is a result of introducing scale-consistent terms and improved handling of contact-distance restraints. As in previous CASP exercises, UNRES ranked higher in the free modeling category than in the general category that included template based modeling targets. Use of distance restraints from the predicted contacts, albeit many of them were wrong, resulted in the increase of GDT_TS by over 8 units on average and introducing sparse restraints from small-angle X-ray/neutron scattering and chemical cross-link-mass-spectrometry experiments, and ambiguous restraints from nuclear magnetic resonance experiments has also improved the predictions by 8.6, 9.7, and 10.7 GDT_TS units on average, respectively.

Use of the UNRES force field in template-assisted prediction of protein structures and the refinement of server models: Test with CASP12 targets.

Knowledge-based methods are, at present, the most effective ones for the prediction of protein structures; however, their results heavily depend on the similarity of a target sequence to those of proteins with known structures. On the other hand, the physics-based methods, although still less accurate and more expensive to execute, are independent of databases and give reasonable results where the knowledge-based methods fail because of weak sequence similarity. Therefore, a plausible approach seems to be the use of knowledge-based methods to determine the sections of the structures that correspond to sufficient sequence similarity and physics-based methods to determine the remaining structure. By participating in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) as the KIAS-Gdansk group, we tested our recently developed hybrid approach, in which protein-structure prediction is carried out by using the physics-based UNRES coarse-grained energy function, with restraints derived from the server models. Best predictions among all groups were obtained for 2 targets and 80% of our models were in the upper 50% of the models submitted to CASP. Our method was also able to exclude, with about 70% confidence, the information from the servers that performed poorly on a given target. Moreover, the method resulted in the best models of 2 refinement targets and performed remarkably well on oligomeric targets.