Rigid-body Docking Research Articles

Small-Angle X-ray Scattering Data in Combination with RosettaDock Improves the Docking Energy Landscape.

We have performed a benchmark to evaluate the relative success of using small-angle X-ray scattering (SAXS) data as constraints (hereafter termed SAXSconstrain) in the RosettaDock protocol (hereafter termed RosettaDockSAXS). For this purpose, we have chosen 38 protein complex structures, calculated the theoretical SAXS data for the protein complexes using the program CRYSOL, and then used the SAXS data as constraints. We further considered a few examples where crystal structures and experimental SAXS data are available. SAXSconstrain were added to the protocol in the initial, low-resolution docking step, allowing fast rejection of complexes that violate the shape restraints imposed by the SAXS data. Our results indicate that the implementation of SAXSconstrain in general reduces the sampling space of possible protein-protein complexes significantly and can indeed increase the probability of finding near-native protein complexes. The methodology used is based on rigid-body docking and works for cases where no or minor conformational changes occur upon binding of the docking partner. In a wider perspective, the strength of RosettaDockSAXS lies in the combination of low-resolution structural information on protein complexes in solution from SAXS experiments with protein-protein interaction energies obtained from RosettaDock, which will allow the prediction of unknown three-dimensional atomic structures of protein-protein complexes.

LightDock: a new multi-scale approach to protein-protein docking.

Computational prediction of protein-protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. We describe here a new multi-scale protein-protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases. The source code of the software and installation instructions are available for download at https://life.bsc.es/pid/lightdock/. juanf@bsc.es. Supplementary data are available at Bioinformatics online.

NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins containing classical nuclear localization signals.

The presence of a nuclear localization signal (NLS) in proteins can be inferred by the presence of a stretch of basic amino acids (KRKK). These NLSs are termed classical NLS (cNLS). However, only a fraction of proteins containing the cNLS pattern are transported into the nucleus by binding to importin α. Hence, there must exist, additional structural determinants that guide the appropriate interaction between putative NLSs containing cargo and importin α. Using 52 protein structures containing cNLS obtained from RCSB PDB, we assembled a training set and a validation set such that both sets were comprised of a combination of proteins with proven nuclear localization and ones that were non-nuclear. We modeled the interface between cargoes containing cNLS and importin α. We conducted rigid body docking and produced induced-fit modes by allowing both side chain and the backbone to be flexible. The output of these studies and additional determinants such as energy of interaction, atomic contacts, hydrophilic interaction, cationic interaction, and penetration of the cargo protein were used to derive a 26 parameter quantitative structure activity relationship based regression equation. This was further optimized by a step-wise backward elimination approach to derive a 15 parameter score. This NLScore was not only able to correctly classify confirmed nuclear and non-nuclear localized proteins but it was able to perform better than currently implemented algorithms like NucPred, Euk-mPLoc 2.0, cNls Mapper, and NLStradamus. Leave-one-out cross validation (LOOCV) showed that NLScore correctly predicted 78.6% and 81.6% of non-nuclear and nuclear proteins respectively. Graphical abstract NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins.

Local destabilization, rigid body, and fuzzy docking facilitate the phosphorylation of the transcription factor Ets-1 by the mitogen-activated protein kinase ERK2

Mitogen-activated protein (MAP) kinase substrates are believed to require consensus docking motifs (D-site, F-site) to engage and facilitate efficient site-specific phosphorylation at specific serine/threonine-proline sequences by their cognate kinases. In contrast to other MAP kinase substrates, the transcription factor Ets-1 has no canonical docking motifs, yet it is efficiently phosphorylated by the MAP kinase ERK2 at a consensus threonine site (T38). Using NMR methodology, we demonstrate that this phosphorylation is enabled by a unique bipartite mode of ERK2 engagement by Ets-1 and involves two suboptimal noncanonical docking interactions instead of a single canonical docking motif. The N terminus of Ets-1 interacts with a part of the ERK2 D-recruitment site that normally accommodates the hydrophobic sidechains of a canonical D-site, retaining a significant degree of disorder in its ERK2-bound state. In contrast, the C-terminal region of Ets-1, including its Pointed (PNT) domain, engages in a largely rigid body interaction with a section of the ERK2 F-recruitment site through a binding mode that deviates significantly from that of a canonical F-site. This latter interaction is notable for the destabilization of a flexible helix that bridges the phospho-acceptor site to the rigid PNT domain. These two spatially distinct, individually weak docking interactions facilitate the highly specific recognition of ERK2 by Ets-1, and enable the optimal localization of its dynamic phospho-acceptor T38 at the kinase active site to enable efficient phosphorylation.

Design, synthesis of novel furan appended benzothiazepine derivatives and in vitro biological evaluation as potent VRV-PL-8a and H+/K+ ATPase inhibitors

A series of new of furan derivatised [1,4] benzothiazepine analogues were synthesized starting from 1-(furan-2-yl)ethanone. 1-(Furan-2-yl)ethanone was converted into chalcones by its reaction with various aromatic aldehydes, then were reacted with 2-aminobenzenethiol in acidic conditions to obtain the title compounds in good yields. The synthesized new compounds were characterized by 1H NMR, 13C NMR, Mass spectral studies and elemental analyses. All the new compounds were evaluated for their in vitro VRV-PL-8a and H+/K+ ATPase inhibitor properties. Preliminary studies revealed that, some molecules amongst the designed series showed promising VRV-PL-8a and H+/K+ ATPase inhibitor properties. Further, rigid body docking studies were performed to understand possible docking sites of the molecules on the target proteins and the mode of binding. This finding presents a promising series of lead molecules that can serve as prototypes for the treatment of inflammatory related disorder that can mitigate the ulcer inducing side effect shown by other NSAIDs.

ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT.

SummaryWe present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptide’s final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions.Availability and ImplementationThe method is available as part of the protein-protein docking server ClusPro at https://peptidock.cluspro.org/nousername.php.Supplementary information Supplementary data are available at Bioinformatics online.

A systematic analysis of scoring functions in rigid-body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining.

Protein-protein interactions play fundamental roles in biological processes including signaling, metabolism, and trafficking. While the structure of a protein complex reveals crucial details about the interaction, it is often difficult to acquire this information experimentally. As the number of interactions discovered increases faster than they can be characterized, protein-protein docking calculations may be able to reduce this disparity by providing models of the interacting proteins. Rigid-body docking is a widely used docking approach, and is often capable of generating a pool of models within which a near-native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. Recently, more than 100 scoring functions from the CCharPPI server were evaluated for this task using decoy structures generated with SwarmDock. Here, we extend this analysis to identify the predictive success rates of the scoring functions on decoys from three rigid-body docking programs, ZDOCK, FTDock, and SDOCK, allowing us to assess the transferability of the functions. We also apply set-theoretic measure to test whether the scoring functions are capable of identifying near-native poses within different subsets of the benchmark. This information can provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts. Proteins 2017; 85:1287-1297. © 2017 Wiley Periodicals, Inc.

Interaction Surfaces of Proteins Involved in Bacterial Chemotaxis with Rigid-Body Docking Decoys

To understand protein interaction mechanism, protein interaction interfaces are investigated using properties of protein interaction surfaces derived from sets of protein complex structures generated by rigid-body docking, referred as docking decoys. Because each docking decoys includes information of sets of possible interacting amino acid pairs we used profiles of protein interaction surfaces for improving the precision of protein-protein interaction (PPI) prediction in process of cluster analysis [Uchikoga & Hirokawa 2010 BMC Bioinfo.11:236]. By using profiles of protein interaction surfaces, we have developed methods for exploring docking search spaces in rigid-body docking process, Re-docking scheme [Uchikoga et al. 2013 PLoS ONE 8:e69365]. Protein interaction surfaces of whole decoys would contain clue for understanding protein interaction mechanisms. We then proposed a profile generated by assembling whole docking decoy interaction surfaces, referred as broad interaction surfaces (BIPs). And differences of BIPs between various protein pairs in a docking benchmark dataset were analyzed, indicating that BIPs are different between true and false protein interaction partners [Uchikoga et al. 2016 Biophys. Phisicobiol. 13:105-]. In this work, we apply the BIP method to PPI analysis involved in chemotaxis systems of several bacteria species. We then performed all-to-all docking analysis using a rigid-body docking software MEGADOCK for docking process. Cluster analysis could examine for differences between true interaction protein pairs and false pairs.

Protein-Protein Complex Structure Prediction using the Solution Theory in the Energy Representation

Proteins conduct their functions through interactions with other molecules. Thus, accurate prediction of protein complex is a key to understand functional mechanisms. We have developed a method to evaluate binding free energy differences of complex models generated by docking prediction using the all-atom molecular dynamics simulation and solution theory in the energy representation. By evaluating binding free energy difference with the method, we previously show that “near-native” models similar to crystal structure are successfully selected as the lowest energy structures in two protein-protein complexes, bovine trypsin with CMTI-1 squash inhibitor and RNase SA with barstar [Takemura, J. Chem. Phys. 2012]. The method requires relatively short MD simulations (2 ns) and can calculate binding free energy difference among several hundreds of complex models. As a protein-protein complex structure prediction method, we combined the evaluation method with CyClus [Omori, Proteins, 2013] that performs fast clustering/reranking using a cylindrical approximation of interface and improves the results of the rigid-body docking.The initial complex models were prepared using the protein-protein rigid body docking program, ZDOCK. The generated complex models were then clustered and reranked using CyClus. Free energy analysis for top 100 or 300 complex models returned by CyClus were conducted using conformational energies, solute entropies, and solvation free energies calculated with the solution theory in the energy representation. Our analysis improved the results of CyClus and the complex models similar to the native structure have the lowest binding free energies, suggesting that this procedure is effective in protein-protein complex structure predictions. We further improved the procedure by placing interface waters into protein-protein interface before free energy evaluations.

New additions to the ClusPro server motivated by CAPRI.

The heavily used protein-protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for small angle X-ray scattering data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally, we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server. Proteins 2017; 85:435-444. © 2016 Wiley Periodicals, Inc.

Accurate Structure Prediction of CDR H3 Loops Enabled by a Novel Structure-Based C-Terminal Constraint.

Ab structure prediction has made great strides, but accurately modeling CDR H3 loops remains elusive. Unlike the other five CDR loops, CDR H3 does not adopt canonical conformations and must be modeled de novo. During Antibody Modeling Assessment II, we found that biasing simulations toward kinked conformations enables generating low-root mean square deviation models (Weitzner et al. 2014. Proteins 82: 1611-1623), and since then, we have presented new geometric parameters defining the kink conformation (Weitzner et al. 2015. Structure 23: 302-311). In this study, we use these parameters to develop a new biasing constraint. When applied to a benchmark set of high-quality CDR H3 loops, the average minimum root mean square deviation sampled is 0.93 Å, compared with 1.34 Å without the constraint. We then test the performance of the constrained de novo method for homology modeling and rigid-body docking and present the results for 1) the Antibody Modeling Assessment II targets, 2) the 2009 RosettaAntibody benchmark set, and 3) the high-quality set.

Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins.

The recent outbreak of Zika virus (ZIKV) infection in Brazil has developed to a global health concern due to its likely association with birth defects (primary microcephaly) and neurological complications. Consequently, there is an urgent need to develop a vaccine to prevent or a medicine to treat the infection. In this study, immunoinformatics approach was employed to predict antigenic epitopes of Zika viral proteins to aid in development of a peptide vaccine against ZIKV. Both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted for ZIKV Envelope (E), NS3 and NS5 proteins. We further investigated the binding interactions of altogether 15 antigenic CTL epitopes with three class I major histocompatibility complex (MHC I) proteins after docking the peptides to the binding groove of the MHC I proteins. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlight the limits of rigid-body docking methods. Some of the antigenic epitopes predicted and analyzed in this work might present a preliminary set of peptides for future vaccine development against ZIKV.

Computer prediction of paratope on antithrombotic antibody 10B12 and epitope on platelet glycoprotein VI via molecular dynamics simulation.

BackgroundInteraction between immunoglobulin-like receptor glycoprotein VI (GPVI) and collagen plays a central role in platelet activation and sequent firm adhesion. Of various antithrombotic agents targeting GPVI, antibody 10B12 is of great potential to block the GPVI-collagen interaction, but less is known about 10B12 paratope and GPVI epitope.MethodsAlong the pathway in the computer strategy presented in our previous work, the 10B12/GPVI complex model was constructed through homology modeling and rigid-body docking, and the molecular dynamics (MD) simulation was used to detect the paratope residues on 10B12 and their partners on GPVI. Quantified by free and steered MD simulations, the stabilities of hydrogen bonds and salt bridges were used to rank the contributions of interface residues to binding of 10B12 and GPVI.ResultsWe predicted 12 key and seven dispensable residues in interaction of 10B12 to GPVI with present computational procedure. Besides of the 12 key residues, two are epitope residues (LYS41 and LYS59) which had been identified by previous mutation experiments, and others, including four epitope residues (ARG38, SER44, ARG46 and TYR47 on GPVI) and six paratope residues (GLU1, ASP98, GLU102, ASP107, ASP108 and ASP111 on 10B12), were newly found and also might be important for the 10B12–GPVI binding. The seven predicted dispensable residues on GPVI were had been illustrated in previous mutation experiments.ConclusionsThe present computer strategy combining homology modeling, rigid body docking and MD simulation was illustrated to be effective in mapping paratope on antithrombotic antibody 10B12 to epitope on GPVI, and have large potential in drug discovery and antibody research.

A novel evolutionary rigid body docking algorithm for medical image registration

One of today's motivating medical image processing problem is registration. Medical images acquired from different modalities give rise to a practical problem in image registration. For many years, mutual information has been utilized as a similarity criterion for registration. Therefore, it is believed to be the state-of-the-art in this field. Nonetheless, it is understood to be non-convex and possess many local maxima. To overcome this problem, we introduce a novel evolutionary rigid body docking (ERBD) algorithm for medical image registration. Here, the ligand is taken as the non-aligned (target) image (to be registered) and protein as the reference image. The alignment of the target image is changed as per the optimal configuration. This paper introduces a firsthand objective (fitness) function for finding optimal configurations. Genetic algorithm (GA) is used to optimize the fitness function. The proposed method is useful for recovery of rotation and translation parameters. Different image data formats like magnetic resonance image (MRI), computed tomography (CT) and positron emission tomography (PET) images from Retrospective Image Registration Evaluation (RIRE) project are used to demonstrate the effectiveness of our proposed method. Experimental results are presented to reveal the fact that our approach seems to be efficient.

Lessons from (co-)evolution in the docking of proteins and peptides for CAPRI Rounds 28-35.

Computational protein-protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real-life cases and help accelerate methodology development. For CAPRI Rounds 28-35, we used an automatic docking pipeline integrating the coarse-grained co-evolution-based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co-evolved interfaces. Together with Zdock/Frodock for rigid-body docking, SOAP-PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein-peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1st by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co-evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378-390. © 2016 Wiley Periodicals, Inc.

Performance of ZDOCK and IRAD in CAPRI rounds 28-34.

We report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking. Proteins 2017; 85:408-416. © 2016 Wiley Periodicals, Inc.

Protein Dynamics and Contact Topology Reveal Protein-DNA Binding Orientation.

Structure-encoded conformational dynamics are crucial for biomolecular functions. However, there is insufficient evidence to support the notion that dynamics play a role in guiding protein-nucleic acid interactions. Here, we show that protein-DNA docking orientation is a function of protein intrinsic dynamics, but the binding site itself does not display unique patterns in the examined spectrum of motions. This revelation is made possible by a novel technique that locates "dynamics interfaces" in proteins across which protein parts are anticorrelated in their slowest dynamics. A striking statistic is that such interfaces intersect the DNA in 97% of the 104 examined cases. These findings were then used to screen decoys generated by rigid-body docking of DNA molecules onto DNA-binding proteins. Using our method, the chance to discern near-native poses from non-native decoys increased by 2.5- and 1.6-fold, as compared to a random guess and methods based on surface complementarity, respectively. Hence, dynamically allowed protein-DNA docking orientations can work as new filters to cull and rerank docking poses and therefore enhance the predictability of DNA-binding sites that themselves do not have distinct dynamics features. Computer software implementing the method can be accessed via http://dyn.life.nthu.edu.tw/IDD/DNA.htm .

Predicting bioactive conformations and binding modes of macrocycles

Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein-protein interactions. From a computational viewpoint, these molecules are difficult to treat. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70% of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40% of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50% of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.

PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets.

Protein-protein docking methods are of great importance for understanding interactomes at the structural level. It has become increasingly appealing to use not only experimental structures but also homology models of unbound subunits as input for docking simulations. So far we are missing a large scale assessment of the success of rigid-body free docking methods on homology models. We explored how we could benefit from comparative modelling of unbound subunits to expand docking benchmark datasets. Starting from a collection of 3157 non-redundant, high X-ray resolution heterodimers, we developed the PPI4DOCK benchmark containing 1417 docking targets based on unbound homology models. Rigid-body docking by Zdock showed that for 1208 cases (85.2%), at least one correct decoy was generated, emphasizing the efficiency of rigid-body docking in generating correct assemblies. Overall, the PPI4DOCK benchmark contains a large set of realistic cases and provides new ground for assessing docking and scoring methodologies. Benchmark sets can be downloaded from http://biodev.cea.fr/interevol/ppi4dock/ CONTACT: guerois@cea.frSupplementary information: Supplementary data are available at Bioinformatics online.

Improved Accuracy in RNA-Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function.

RNA-protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA-protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA-protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA-protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein-protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA-protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA-protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA-protein complexes.