An NMR-based scoring function improves the accuracy of binding pose predictions by docking by two orders of magnitude

Low-affinity ligands can be efficiently optimized into high-affinity drug leads by structure based drug design when atomic-resolution structural information on the protein/ligand complexes is available. In this work we show that the use of a few, easily obtainable, experimental restraints improves the accuracy of the docking experiments by two orders of magnitude. The experimental data are measured in nuclear magnetic resonance spectra and consist of protein-mediated NOEs between two competitively binding ligands. The methodology can be widely applied as the data are readily obtained for low-affinity ligands in the presence of non-labelled receptor at low concentration. The experimental inter-ligand NOEs are efficiently used to filter and rank complex model structures that have been pre-selected by docking protocols. This approach dramatically reduces the degeneracy and inaccuracy of the chosen model in docking experiments, is robust with respect to inaccuracy of the structural model used to represent the free receptor and is suitable for high-throughput docking campaigns.     

Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies

Protein folding and protein binding are similar processes. In both, structural units combinatorially associate with each other. In the case of folding, we mostly handle relatively small units, building blocks or domains, that are covalently linked. In the case of multi-molecular binding, the subunits are relatively large and are associated only by non-covalent bonds. Experimentally, the difficulty in the determination of the structures of such large assemblies increases with the complex size and the number of components it contains. Computationally, the prediction of the structures of multi-molecular complexes has largely not been addressed, probably owing to the magnitude of the combinatorial complexity of the problem. Current docking algorithms mostly target prediction of pairwise interactions. Here our goal is to predict the structures of multi-unit associations, whether these are chain-connected as in protein folding, or separate disjoint molecules in the assemblies. We assume that the structures of the single units are known, either through experimental determination or modeling. Our aim is to combinatorially assemble these units to predict their structure. To address this problem we have developed CombDock. CombDock is a combinatorial docking algorithm for the structural units assembly problem. Below, we briefly describe the algorithm and present examples of its various applications to folding and to multi-molecular assemblies. To test the robustness of the algorithm, we use inaccurate models of the structural units, derived either from crystal structures of unbound molecules or from modeling of the target sequences. The algorithm has been able to predict near-native arrangements of the input structural units in almost all of the cases, suggesting that a combinatorial approach can overcome the imperfect shape complementarity caused by the inaccuracy of the models. In addition, we further show that through a combinatorial docking strategy it is possible to enhance the predictions of pairwise interactions involved in a multi-molecular assembly.     

Cryo-EM structure of the DNA-dependent protein kinase catalytic subunit at subnanometer resolution reveals alpha helices and insight into DNA binding

The DNA-dependent protein kinase catalytic subunit (DNA-PKcs) regulates the nonhomologous end joining pathway for repair of double-stranded DNA (dsDNA) breaks. Here, we present a 7A resolution structure of DNA-PKcs determined by cryo-electron microscopy single-particle reconstruction. This structure is composed of density rods throughout the molecule that are indicative of alpha helices and reveals structural features not observed in lower resolution EM structures. Docking of homology models into the DNA-PKcs structure demonstrates that up to eight helical HEAT repeat motifs fit well within the density. Surprisingly, models for the kinase domain can be docked into either the crown or base of the molecule at this resolution, although real space refinement suggests that the base location is the best fit. We propose a model for the interaction of DNA with DNA-PKcs in which one turn of dsDNA enters the central channel and interacts with a resolved alpha-helical protrusion.     

The Ligand-Free State of the TPP Riboswitch: A Partially Folded RNA Structure

Riboswitches are elements of mRNA that regulate gene expression by undergoing structural changes upon binding of small ligands. Although the structures of several riboswitches have been solved with their ligands bound, the ligand-free states of only a few riboswitches have been characterized. The ligand-free state is as important for the functionality of the riboswitch as the ligand-bound form, but the ligand-free state is often a partially folded structure of the RNA, with conformational heterogeneity that makes it particularly challenging to study. Here, we present models of the ligand-free state of a thiamine pyrophosphate riboswitch that are derived from a combination of complementary experimental and computational modeling approaches. We obtain a global picture of the molecule using small-angle X-ray scattering data and use an RNA structure modeling software, MC-Sym, to fit local structural details to these data on an atomic scale. We have used two different approaches to obtaining these models. Our first approach develops a model of the RNA from the structures of its constituent junction fragments in isolation. The second approach treats the RNA as a single entity, without bias from the structure of its individual constituents. We find that both approaches give similar models for the ligand-free form, but the ligand-bound models differ for the two approaches, and only the models from the second approach agree with the ligand-bound structure known previously from X-ray crystallography. Our models provide a picture of the conformational changes that may occur in the riboswitch upon binding of its ligand. Our results also demonstrate the power of combining experimental small-angle X-ray scattering data with theoretical structure prediction tools in the determination of RNA structures beyond riboswitches.     

Computational approaches to understand alpha-conotoxin interactions at neuronal nicotinic receptors.

Recent and increasing use of computational tools in the field of nicotinic receptors has led to the publication of several models of ligand-receptor interactions. These models are all based on the crystal structure at 2.7 A resolution of a protein related to the extracellular N-terminus of nicotinic acetylcholine receptors (nAChRs), the acetylcholine binding protein. In the absence of any X-ray or NMR information on nAChRs, this new structure has provided a reliable alternative to study the nAChR structure. We are now able to build homology models of the binding domain of any nAChR subtype and fit in different ligands using docking programs. This strategy has already been performed successfully for the docking of several nAChR agonists and antagonists. This minireview focuses on the interaction of alpha-conotoxins with neuronal nicotinic receptors in light of our new understanding of the receptor structure. Computational tools are expected to reveal the molecular recognition mechanisms that govern the interaction between alpha-conotoxins and neuronal nAChRs at the molecular level. An accurate determination of their binding modes on the neuronal nAChR may allow the rational design of alpha-conotoxin-based ligands with novel nAChR selectivity.     

Reevaluation of ANS binding to human and bovine serum albumins: key role of equilibrium microdialysis in ligand - receptor binding characterization

In this work we return to the problem of the determination of ligand-receptor binding stoichiometry and binding constants. In many cases the ligand is a fluorescent dye which has low fluorescence quantum yield in free state but forms highly fluorescent complex with target receptor. That is why many researchers use dye fluorescence for determination of its binding parameters with receptor, but they leave out of account that fluorescence intensity is proportional to the part of the light absorbed by the solution rather than to the concentration of bound dye. We showed how ligand-receptor binding parameters can be determined by spectrophotometry of the solutions prepared by equilibrium microdialysis. We determined the binding parameters of ANS - human serum albumin (HSA) and ANS - bovine serum albumin (BSA) interaction, absorption spectra, concentration and molar extinction coefficient, as well as fluorescence quantum yield of the bound dye. It was found that HSA and BSA have two binding modes with significantly different affinity to ANS. Correct determination of the binding parameters of ligand-receptor interaction is important for fundamental investigations and practical aspects of molecule medicine and pharmaceutics. The data obtained for albumins are important in connection with their role as drugs transporters.     

Macromolecular docking restrained by a small angle X-ray scattering profile

While many structures of single protein components are becoming available, structural characterization of their complexes remains challenging. Methods for modeling assembly structures from individual components frequently suffer from large errors, due to protein flexibility and inaccurate scoring functions. However, when additional information is available, it may be possible to reduce the errors and compute near-native complex structures. One such type of information is a small angle X-ray scattering (SAXS) profile that can be collected in a high-throughput fashion from a small amount of sample in solution. Here, we present an efficient method for protein–protein docking with a SAXS profile (FoXSDock): generation of complex models by rigid global docking with PatchDock, filtering of the models based on the SAXS profile, clustering of the models, and refining the interface by flexible docking with FireDock. FoXSDock is benchmarked on 124 protein complexes with simulated SAXS profiles, as well as on 6 complexes with experimentally determined SAXS profiles. When induced fit is less than 1.5 Å interface C_α RMSD and the fraction residues of missing from the component structures is less than 3%, FoXSDock can find a model close to the native structure within the top 10 predictions in 77% of the cases; in comparison, docking alone succeeds in only 34% of the cases. Thus, the integrative approach significantly improves on molecular docking alone. The improvement arises from an increased resolution of rigid docking sampling and more accurate scoring.     

A structural model of the erythrocyte spectrin heterodimer initiation site determined using homology modeling and chemical cross-linking

Spectrin assembles into an anti-parallel heterodimeric flexible rod-like molecule through a multistep process initiated by a high affinity interaction between discrete complementary homologous motifs or "repeats" near the actin binding domain. Attempts to determine crystallographic structures of this critical dimer initiation complex have so far been unsuccessful. Therefore, in this study we determined the subunit-subunit docking interface and a plausible medium resolution structure of the heterodimer initiation site using homology modeling coupled with structural refinement based on experimentally determined distance constraints. Intramolecular and intermolecular cross-links formed by the "zero length" cross-linking reagent, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide were identified after trypsin digestion of cross-linked heterodimer complex using liquid chromatography-tandem mass spectrometry analysis. High confidence assignment of cross-linked peptides was facilitated by determination of cross-linked peptide masses with an uncertainty of a few parts per million using a high sensitivity linear ion trap mass spectrometer equipped with a Fourier-transform ion cyclotron resonance detector. Six interchain cross-links distinguished between alternative docking models, and these distance constraints, as well as three intrachain cross-links, were used to further refine an initial homology-based structure. The final model is consistent with all available physical data, including protease protection experiments, isothermal titration calorimetry analyses, and location of a common polymorphism that destabilizes dimerization. This model supports the hypothesis that initial docking of the correct alpha and beta repeats from among many very similar repeats in both subunits is driven primarily by long range electrostatic interactions.     

Docking by least-squares fitting of molecular surface patterns.

Molecular surfaces are fitted to each other by a new solution to the problem of docking a ligand into the active site of a protein molecule. The procedure constructs patterns of points on the surfaces and superimposes them upon each other using a least-squares best-fit algorithm. This brings the surfaces into contact and provides a direct measure of their local complementarity. The search over the ligand surface produces a large number of dockings, of which a small fraction having the best complementarity and the least steric hindrance are evaluated for electrostatic interaction energy. When applied to molecules taken from crystallographically observed complexes, this procedure consistently assigns the lowest electrostatic energies to correct dockings. On independently determined structures, the ability of the method to discern correct dockings depends on how much conformational difference there is between the free and complexed forms of the molecules. The procedure is found to be fast enough on contemporary workstation computers to permit many conformations to be considered, and tolerant enough to make rather coarse bond dihedral sampling a practicable way to overcome the problem of structural flexibility.     

Shape complementarity of protein-protein complexes at multiple resolutions

Biological complexes typically exhibit intermolecular interfaces of high shape complementarity. Many computational docking approaches use this surface complementarity as a guide in the search for predicting the structures of protein-protein complexes. Proteins often undergo conformational changes to create a highly complementary interface when associating. These conformational changes are a major cause of failure for automated docking procedures when predicting binding modes between proteins using their unbound conformations. Low resolution surfaces in which high frequency geometric details are omitted have been used to address this problem. These smoothed, or blurred, surfaces are expected to minimize the differences between free and bound structures, especially those that are due to side chain conformations or small backbone deviations. Despite the fact that this approach has been used in many docking protocols, there has yet to be a systematic study of the effects of such surface smoothing on the shape complementarity of the resulting interfaces. Here we investigate this question by computing shape complementarity of a set of 66 protein-protein complexes represented by multiresolution blurred surfaces. Complexed and unbound structures are available for these protein-protein complexes. They are a subset of complexes from a nonredundant docking benchmark selected for rigidity (i.e. the proteins undergo limited conformational changes between their bound and unbound states). In this work, we construct the surfaces by isocontouring a density map obtained by accumulating the densities of Gaussian functions placed at all atom centers of the molecule. The smoothness or resolution is specified by a Gaussian fall-off coefficient, termed "blobbyness." Shape complementarity is quantified using a histogram of the shortest distances between two proteins' surface mesh vertices for both the crystallographic complexes and the complexes built using the protein structures in their unbound conformation. The histograms calculated for the bound complex structures demonstrate that medium resolution smoothing (blobbyness = -0.9) can reproduce about 88% of the shape complementarity of atomic resolution surfaces. Complexes formed from the free component structures show a partial loss of shape complementarity (more overlaps and gaps) with the atomic resolution surfaces. For surfaces smoothed to low resolution (blobbyness = -0.3), we find more consistency of shape complementarity between the complexed and free cases. To further reduce bad contacts without significantly impacting the good contacts we introduce another blurred surface, in which the Gaussian densities of flexible atoms are reduced. From these results we discuss the use of shape complementarity in protein-protein docking.     

Pushing Structural Information into the Yeast Interactome by High-Throughput Protein Docking Experiments

The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.     

The use of in vitro peptide binding profiles and in silico ligand-receptor interaction profiles to describe ligand-induced conformations of the retinoid X receptor alpha ligand-binding domain

It is hypothesized that different ligand-induced conformational changes can explain the different interactions of nuclear receptors with regulatory proteins, resulting in specific biological activities. Understanding the mechanism of how ligands regulate cofactor interaction facilitates drug design. To investigate these ligand-induced conformational changes at the surface of proteins, we performed a time-resolved fluorescence resonance energy transfer assay with 52 different cofactor peptides measuring the ligand-induced cofactor recruitment to the retinoid X receptor-alpha (RXRalpha) in the presence of 11 compounds. Simultaneously we analyzed the binding modes of these compounds by molecular docking. An automated method converted the complex three-dimensional data of ligand-protein interactions into two-dimensional fingerprints, the so-called ligand-receptor interaction profiles. For a subset of compounds the conformational changes at the surface, as measured by peptide recruitment, correlate well with the calculated binding modes, suggesting that clustering of ligand-receptor interaction profiles is a very useful tool to discriminate compounds that may induce different conformations and possibly different effects in a cellular environment. In addition, we successfully combined ligand-receptor interaction profiles and peptide recruitment data to reveal structural elements that are possibly involved in the ligand-induced conformations. Interestingly, we could predict a possible binding mode of LG100754, a homodimer antagonist that showed no effect on peptide recruitment. Finally, the extensive analysis of the peptide recruitment profiles provided novel insight in the potential cellular effect of the compound; for the first time, we showed that in addition to the induction of coactivator peptide binding, all well-known RXRalpha agonists also induce binding of corepressor peptides to RXRalpha.     

Novel inhibitors of anthrax edema factor

Several pathogenic bacteria produce adenylyl cyclase toxins, such as the edema factor (EF) of Bacillus anthracis. These disturb cellular metabolism by catalyzing production of excessive amounts of the regulatory molecule cAMP. Here, a structure-based method, where a 3D-pharmacophore that fit the active site of EF was constructed from fragments, was used to identify non-nucleotide inhibitors of EF. A library of small molecule fragments was docked to the EF-active site in existing crystal structures, and those with the highest HINT scores were assembled into a 3D-pharmacophore. About 10,000 compounds, from over 2.7 million compounds in the ZINC database, had a similar molecular framework. These were ranked according to their docking scores, using methodology that was shown to achieve maximum accuracy (i.e., how well the docked position matched the experimentally determined site for ATP analogues in crystal structures of the complex). Finally, 19 diverse compounds with the best AutoDock binding/docking scores were assayed in a cell-based assay for their ability to reduce cAMP secretion induced by EF. Four of the test compounds, from different structural groups, inhibited in the low micromolar range. One of these has a core structure common to phosphatase inhibitors previously identified by high-throughput assays of a diversity library. Thus, the fragment-based pharmacophore identified a small number of diverse compounds for assay, and greatly enhanced the selection process of advanced lead compounds for combinatorial design.     

Profiling the interaction of 1‐phenylbenzimidazoles to cyclooxygenases

In the design of 1‐phenylbenzimidazoles as model cyclooxygenase (COX) inhibitors, docking to a series of crystallographic COX structures was performed to evaluate their potential for high‐affinity binding and to reproduce the interaction profile of well‐known COX inhibitors. The effect of ligand‐specific induced fit on the calculations was also studied. To quantitatively compare the pattern of interactions of model compounds to the profile of several cocrystallized COX inhibitors, a geometric parameter, denominated ligand‐receptor contact distance (LRCD), was developed. The interaction profile of several model complexes showed similarity to the profile of COX complexes with inhibitors such as iodosuprofen, iodoindomethacin, diclofenac, and flurbiprofen. Shaping of high‐affinity binding sites upon ligand‐specific induced fit mostly determined both the affinity and the binding mode of the ligands in the docking calculations. The results suggest potential of 1‐phenylbenzimidazole derivatives as COX inhibitors on the basis of their predicted affinity and interaction profile to COX enzymes. The analyses also provided insights into the role of induced fit in COX enzymes. While inhibitors produce different local structural changes at the COX ligand binding site, induced fit allows inhibitors in diverse chemical classes to share characteristic interaction patterns that ensure key contacts to be achieved. Different interaction patterns may also be associated with different inhibitory mechanisms.     

On the use of structural templates for high‐resolution docking

Reliable high‐resolution prediction of protein complex structures starting from the free monomers is a considerable challenge toward large‐scale mapping of the structural details of protein‐protein interactions. The current major bottleneck is to model the conformational changes of the monomer backbone upon binding. We evaluate the use of homolog structures as source for conformational diversity, within the framework of RosettaDock—a leading high‐resolution docking protocol. We find that the use of homolog templates can improve significantly the modeling of a complex structure, including known difficult cases. Several conformational changes however are not sampled by any of the templates, indicating the need for additional sources of conformational variability. Interestingly, the successful homolog templates are not restricted to a confined range of sequence identity, highlighting the importance of the backbone conformation rather than the sequence. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

VORFFIP-Driven Dock: V-D2OCK,a Fast and Accurate Protein Docking Strategy

The experimental determination of the structure of protein complexes cannot keep pace with the generation of interactomic data, hence resulting in an ever-expanding gap. As the structural details of protein complexes are central to a full understanding of the function and dynamics of the cell machinery, alternative strategies are needed to circumvent the bottleneck in structure determination. Computational protein docking is a valid and valuable approach to model the structure of protein complexes. In this work, we describe a novel computational strategy to predict the structure of protein complexes based on data-driven docking: VORFFIP-driven dock (V-D²OCK). This new approach makes use of our newly described method to predict functional sites in protein structures, VORFFIP, to define the region to be sampled during docking and structural clustering to reduce the number of models to be examined by users. V-D²OCK has been benchmarked using a validated and diverse set of protein complexes and compared to a state-of-art docking method. The speed and accuracy compared to contemporary tools justifies the potential use of VD²OCK for high-throughput, genome-wide, protein docking. Finally, we have developed a web interface that allows users to browser and visualize V-D²OCK predictions from the convenience of their web-browsers.     

Refining the structure of the strongly bound actin-myosin complex by protein docking

The structure of the strongly bound complex of the globular myosin head and F-actin is a key for understanding some important details of the mechanism of the actin-myosin motor. Current knowledge about the structure is based on the docking of known atomic structures of actin and myosin heads into low-resolution EM electron density maps. To refine the structure, we suggested a new approach based on energy minimization using the ICM-Pro software. The minimization includes rigid-body movement of protein backbone and side chain optimization on the protein interface. Our best model structure is similar to that obtained from EM. It also provides the highest calculated interaction energy and agrees with a number of mutagenesis experiments. Using the structure, we suggest molecular explanations for actin activation of product release from myosin and actin-induced myosin dissociation.     

Modeling of protein binary complexes using structural mass spectrometry data

In this article, we describe a general approach to modeling the structure of binary protein complexes using structural mass spectrometry data combined with molecular docking. In the first step, hydroxyl radical mediated oxidative protein footprinting is used to identify residues that experience conformational reorganization due to binding or participate in the binding interface. In the second step, a three-dimensional atomic structure of the complex is derived by computational modeling. Homology modeling approaches are used to define the structures of the individual proteins if footprinting detects significant conformational reorganization as a function of complex formation. A three-dimensional model of the complex is constructed from these binary partners using the ClusPro program, which is composed of docking, energy filtering, and clustering steps. Footprinting data are used to incorporate constraints-positive and/or negative-in the docking step and are also used to decide the type of energy filter-electrostatics or desolvation-in the successive energy-filtering step. By using this approach, we examine the structure of a number of binary complexes of monomeric actin and compare the results to crystallographic data. Based on docking alone, a number of competing models with widely varying structures are observed, one of which is likely to agree with crystallographic data. When the docking steps are guided by footprinting data, accurate models emerge as top scoring. We demonstrate this method with the actin/gelsolin segment-1 complex. We also provide a structural model for the actin/cofilin complex using this approach which does not have a crystal or NMR structure.     

SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models

High resolution structures of antibody-antigen complexes are useful for analyzing the binding interface and to make rational choices for antibody engineering. When a crystallographic structure of a complex is unavailable, the structure must be predicted using computational tools. In this work, we illustrate a novel approach, named SnugDock, to predict high-resolution antibody-antigen complex structures by simultaneously structurally optimizing the antibody-antigen rigid-body positions, the relative orientation of the antibody light and heavy chains, and the conformations of the six complementarity determining region loops. This approach is especially useful when the crystal structure of the antibody is not available, requiring allowances for inaccuracies in an antibody homology model which would otherwise frustrate rigid-backbone docking predictions. Local docking using SnugDock with the lowest-energy RosettaAntibody homology model produced more accurate predictions than standard rigid-body docking. SnugDock can be combined with ensemble docking to mimic conformer selection and induced fit resulting in increased sampling of diverse antibody conformations. The combined algorithm produced four medium (Critical Assessment of PRediction of Interactions-CAPRI rating) and seven acceptable lowest-interface-energy predictions in a test set of fifteen complexes. Structural analysis shows that diverse paratope conformations are sampled, but docked paratope backbones are not necessarily closer to the crystal structure conformations than the starting homology models. The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions.