Performance of ZDOCK and ZRANK in CAPRI rounds 13-19

ENPP1 inhibits the transcription activity of the hepatitis B virus pregenomic promoter by upregulating the acetylation of LMNB1

Current therapies for hepatitis B virus (HBV) infection can slow disease progression but cannot cure the infection, as it is difficult to eliminate or permanently silence HBV covalently closed circular DNA (cccDNA). The interaction between host factors and cccDNA is essential for their formation, stability, and transcriptional activity. Here, we focused on the regulatory role of the host factor ENPP1 and its interacting transcription factor LMNB1 in HBV replication and transcription to better understand the network of host factors that regulate HBV, which may facilitate the development of new antiviral drugs. Overexpression of ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) in Huh7 cells decreased HBV pregenomic RNA (pgRNA) and hepatitis B core antigen (HBcAg) expression levels, whereas knockdown of ENPP1 increased them. A series of HBV promoter and mutant plasmids were constructed, and a luciferase reporter assay showed that overexpression of ENPP1 caused inhibition of the HBV promoter and its mutants. A DNA pull-down assay showed that lamin B1 (LMNB1), but not ENPP1, interacts directly with the HBV enhancer II/ basic core promoter (EnhII/BCP). ZDOCK and PyMOL software were used to predict the interaction of ENPP1 with LMNB1. Overexpression of LMNB1 inhibited the activity of the HBV promoter and its mutant. The acetylation levels at the amino acids 111K, 261K, and 483K of LMNB1 were reduced compared to the control, and an LMNB1 acetylation mutant containing 111R, 261Q, 261R, 483Q, and 483R showed increased promoter activity. In summary, ENPP1 together with LMNB1 increased the acetylation level at 111K and 261K, and LMNB1 inhibited the activity of HBV promoter and downregulated the expression of pregenomic RNA and HBcAg. Our follow-up studies will investigate the expression, clinical significance, and relevance of ENPP1 and LMNB1 in HBV patient tissues, explore the effect of LMNB1 on post-transcriptional progression, and examine whether ENPP1 can reduce cccDNA levels in the nucleus.

Ebola virus disease: In vivo protection provided by the PAMP restricted TLR3 agonist rintatolimod and its mechanism of action

Ebola virus (EBOV) is a highly infectious and lethal pathogen responsible for sporadic self-limiting clusters of Ebola virus disease (EVD) in Central Africa capable of reaching epidemic status. 100% protection from lethal EBOV-Zaire in Balb/c mice was achieved by rintatolimod (Ampligen) at the well tolerated human clinical dose of 6 mg/kg. The data indicate that the mechanism of action is rintatolimod's dual ability to act as both a competitive decoy for the IID domain of VP35 blocking viral dsRNA sequestration and as a pathogen-associated molecular pattern (PAMP) restricted agonist for direct TLR3 activation but lacking RIG-1-like cytosolic helicase agonist properties. These data show promise for rintatolimod as a prophylactic therapy against human Ebola outbreaks.

Pseudopotentials for coarse-grained cross-link-assisted modeling of protein structures

Pseudopotentials for the chemical cross-links comprising the glutamic- and aspartic-acid side chains bridged with adipic- (ADH) or pimelic-acid hydrazide (PDH), and the lysine side chains bridged with glutaric (BS² G) or suberic acid (BS³ ) for coarse-grained cross-link-assisted simulations were determined by canonical molecular dynamics with the Amber14sb force field. The potentials depend on the distance between side-chain ends and on side-chain orientation, this preventing from making cross-link contacts across the globule in simulations. The potentials were implemented in the UNRES coarse-grained force field and their effect on the quality of models was assessed with 11 monomeric and 1 dimeric proteins, using synthetic or experimental cross-link data. Simulations with the new potentials resulted in improvement of the generated models compared to unrestrained simulations in more instances compared to those with the statistical potentials.

Interaction of BDE-47 with nuclear receptors (NRs) based on the cytotoxicity: In vitro investigation and molecular interaction

Polybrominated diphenyl ethers (PBDEs) are endocrine-disrupting chemicals that possess neuroendocrine and reproductive toxicity to humans and disturb thyroid hormone homeostasis, neurobehavior, and development. The most predominant congener of PBDEs in humans and other organisms is 2,2',4,4'-tetrabromodiphenyl ether (BDE-47); however, the molecular mechanisms underlying its cytotoxicity remain largely unknown. Here, we evaluated the toxic effect and underlying mechanism of nuclear receptors (NRs) induced by BDE-47 in SK-N-SH human neuroblastoma cells. The CCK-8 cell viability assay showed that the proliferation of human SK-N-SH cells exposed to BDE-47 was significantly inhibited in time- and dose-dependent manners, and flow cytometry showed that cell cycle was arrested at the S phase after BDE-47 exposure. Moreover, compared with the control group, the expression of retinoic acid receptor alpha (RXRα), pregnane X receptor (PXR), thyroid hormone receptors (TRs), and peroxisome proliferator-activated receptors (PPARs) at the mRNA and protein levels was significantly increased, as determined by quantitative PCR and western blot analysis, demonstrating that BDE-47 activated the NRs in vitro. Moreover, BDE-47 could bind to all four NRs in the affinity order of PPARγ > PXR > TRβ > RXRα under molecular dynamics. Because RXR is the promiscuous dimerization partner for a large number of NRs, ZDock was used to calculate its interaction with other three NRs. Taking the number of hydrogen bonds and ZDock scores into account, the rank of docking ability between RXRα and the NRs was PXR > TRβ > PPARγ. Further analysis of the interaction between BDE-47 and dimerized-NRs, the affinity order was RXRα > TRβ > PXR > PPARγ via Glide. The results of this study demonstrated that BDE-47 interfered the cross-talk among NRs, especially the promiscuous RXRα, which might be critical for the harmonized re-adjustment of cytotoxicity and biological regulation. Our findings provide a better understanding of the mechanisms underlying toxic effects and intermolecular interaction induced by BDE-47.

Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein–Protein Docking Poses

Scoring is a challenging step in protein–protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.

A Novel Index of Contact Frequency from Noise Protein-Protein Interaction Data Help for Accurate Interface Residue Pair Prediction

Protein-protein interactions are important for most biological processes and have been studied for decades. However, the detailed formation mechanism of protein-protein interaction interface is still ambiguous, which makes it difficult to accurately predict the protein-protein interaction interface residue pairs. Here, we extract the interface residue-residue contacts from the decoys in the ZDOCK protein-protein complex decoy set with RMSD mostly larger than 3 Å. To accurately compute the interface residue-residue contacts, we define a new constant called interface residue pairs frequency, which counts the atom contact numbers between two interface residues. We normalize interface residue pairs frequency to pick out the top residue-residue pairs from all the possible pairs preferential to be on correct protein-protein interaction interface. When tested on 37 protein dimers from the decoy set where most decoys are incorrect, our method successfully predicts 30 protein dimers with a success rate of up to 81.1%. Higher accuracy than some other state-of-the-art methods confirmed the performance of our method.

Performance of ZDOCK and IRAD in CAPRI rounds 39-45

We report docking performance on the six targets of Critical Assessment of PRedicted Interactions (CAPRI) rounds 39-45 that involved heteromeric protein-protein interactions and had the solved structures released since the rounds were held. Our general strategy involved protein-protein docking using ZDOCK, reranking using IRAD, and structural refinement using Rosetta. In addition, we made extensive use of experimental data to guide our docking runs. All the experimental information at the amino-acid level proved correct. However, for two targets, we also used protein-complex structures as templates for modeling interfaces. These resulted in incorrect predictions, presumably due to the low sequence identity between the targets and templates. Albeit a small number of targets, the performance described here compared somewhat less favorably with our previous CAPRI reports, which may be due to the CAPRI targets being increasingly challenging.

Inhibitor discovery for the E. coli meningitis virulence factor IbeA from homology modeling and virtual screening

Escherichia coli (E. coli) K1 is the most common Gram-negative bacteria cause of neonatal meningitis. The penetration of E. coli through the blood-brain barrier is a key step of the meningitis pathogenesis. A host receptor protein, Caspr1, interacts with the E. coli virulence factor IbeA and thus facilitates bacterial penetration through the blood-brain barrier. Based on this result, we have now predicted the binding pattern between Caspr1 and IbeA by an integrated computational protocol. Based on the predicted model, we have identified a putative molecular binding pocket in IbeA, that directly bind with Caspr1. This evidence indicates that the IbeA (229-343aa) region might play a key role in mediating the bacteria invasion. Virtual screening with the molecular model was conducted to search for potential inhibitors from 213,279 commercially available chemical compounds. From the top 50 identified compounds, 9 demonstrated a direct binding ability to the residues within the Caspr1 binding site on IbeA. By using human brain microvascular endothelial cells (hBMEC) with E. coli strain RS218, four molecules were characterized that significantly attenuated the bacteria invasions at concentrations devoid of cell toxicity. Our study provides useful structural information for understanding the pathogenesis of neonatal meningitis, and have identified drug-like compounds that could be used to develop effective anti-meningitis agents.

Computational Study of Natural Compounds for the Clearance of Amyloid-Βeta: A Potential Therapeutic Management Strategy for Alzheimer's Disease

Alzheimer’s disease (AD) is a widespread dynamic neurodegenerative malady. Its etiology is still not clear. One of the foremost pathological features is the extracellular deposits of Amyloid-beta (Aβ) peptides in senile plaques. The interaction of Aβ and the receptor for advanced glycation end products at the blood-brain barrier is also observed in AD, which not only causes the neurovascular anxiety and articulation of proinflammatory cytokines, but also directs reduction of cerebral bloodstream by upgrading the emission of endothelin-1 to induce vasoconstriction. In this process, RAGE is deemed responsible for the influx of Aβ into the brain through BBB. In the current study, we predicted the interaction potential of the natural compounds vincamine, ajmalicine and emetine with the Aβ peptide concerned in the treatment of AD against the standard control, curcumin, to validate the Aβ peptide–compounds results. Protein-protein interaction studies have also been carried out to see their potential to inhibit the binding process of Aβ and RAGE. Moreover, the current study verifies that ligands are more capable inhibitors of a selected target compared to positive control with reference to ΔG values. The inhibition of Aβ and its interaction with RAGE may be valuable in proposing the next round of lead compounds for effective Alzheimer’s disease treatment.

Decrypting protein surfaces by combining evolution, geometry, and molecular docking

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physicochemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET² algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.

Protein-protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking

Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.

Mechanisms of TPGS and its derivatives inhibiting P-glycoprotein efflux pump and application for reversing multidrug resistance in hepatocellular carcinoma

We have developed a TPGS–GA conjugate and TPGS–LA conjugate which possess more effective P-gp inhibition compared to TPGS because of the enhancement of hydrophilicity and negative charge.

Finding correct protein-protein docking models using ProQDock

Motivation: Protein–protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models.

Results: Here, we present ProQDock, a scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein–protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further.

Availability and implementation:http://bioinfo.ifm.liu.se/ProQDock

Contact:bjornw@ifm.liu.se

Supplementary information:Supplementary data are available at Bioinformatics online.

AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking

BACKGROUND

Computational analysis of protein-protein interaction provided the crucial information to increase the binding affinity without a change in basic conformation. Several docking programs were used to predict the near-native poses of the protein-protein complex in 10 top-rankings. The universal criteria for discriminating the near-native pose are not available since there are several classes of recognition protein. Currently, the explicit criteria for identifying the near-native pose of ankyrin-protein complexes (APKs) have not been reported yet.

RESULTS

In this study, we established an ensemble computational model for discriminating the near-native docking pose of APKs named "AnkPlex". A dataset of APKs was generated from seven X-ray APKs, which consisted of 3 internal domains, using the reliable docking tool ZDOCK. The dataset was composed of 669 and 44,334 near-native and non-near-native poses, respectively, and it was used to generate eleven informative features. Subsequently, a re-scoring rank was generated by AnkPlex using a combination of a decision tree algorithm and logistic regression. AnkPlex achieved superior efficiency with ≥1 near-native complexes in the 10 top-rankings for nine X-ray complexes compared to ZDOCK, which only obtained six X-ray complexes. In addition, feature analysis demonstrated that the van der Waals feature was the dominant near-native pose out of the potential ankyrin-protein docking poses.

CONCLUSION

The AnkPlex model achieved a success at predicting near-native docking poses and led to the discovery of informative characteristics that could further improve our understanding of the ankyrin-protein complex. Our computational study could be useful for predicting the near-native poses of binding proteins and desired targets, especially for ankyrin-protein complexes. The AnkPlex web server is freely accessible at http://ankplex.ams.cmu.ac.th .

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

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.

DockQ: A Quality Measure for Protein-Protein Docking Models

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (<3.0Å). This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field. DockQ is available at http://github.com/bjornwallner/DockQ/.

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.

Great interactions: How binding incorrect partners can teach us about protein recognition and function

Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Visualizing ensembles in structural biology

Displaying a single representative conformation of a biopolymer rather than an ensemble of states mistakenly conveys a static nature rather than the actual dynamic personality of biopolymers. However, there are few apparent options due to the fixed nature of print media. Here we suggest a standardized methodology for visually indicating the distribution width, standard deviation and uncertainty of ensembles of states with little loss of the visual simplicity of displaying a single representative conformation. Of particular note is that the visualization method employed clearly distinguishes between isotropic and anisotropic motion of polymer subunits. We also apply this method to ligand binding, suggesting a way to indicate the expected error in many high throughput docking programs when visualizing the structural spread of the output. We provide several examples in the context of nucleic acids and proteins with particular insights gained via this method. Such examples include investigating a therapeutic polymer of FdUMP (5-fluoro-2-deoxyuridine-5-O-monophosphate) - a topoisomerase-1 (Top1), apoptosis-inducing poison - and nucleotide-binding proteins responsible for ATP hydrolysis from Bacillus subtilis. We also discuss how these methods can be extended to any macromolecular data set with an underlying distribution, including experimental data such as NMR structures.

Evaluation of the coarse-grained OPEP force field for protein-protein docking

Background

Knowing the binding site of protein–protein complexes helps understand their function and shows possible regulation sites. The ultimate goal of protein–protein docking is the prediction of the three-dimensional structure of a protein–protein complex. Docking itself only produces plausible candidate structures, which must be ranked using scoring functions to identify the structures that are most likely to occur in nature.

Methods

In this work, we rescore rigid body protein–protein predictions using the optimized potential for efficient structure prediction (OPEP), which is a coarse-grained force field. Using a force field based on continuous functions rather than a grid-based scoring function allows the introduction of protein flexibility during the docking procedure. First, we produce protein–protein predictions using ZDOCK, and after energy minimization via OPEP we rank them using an OPEP-based soft rescoring function. We also train the rescoring function for different complex classes and demonstrate its improved performance for an independent dataset.

Results

The trained rescoring function produces a better ranking than ZDOCK for more than 50 % of targets, rising to over 70 % when considering only enzyme/inhibitor complexes.

Conclusions

This study demonstrates for the first time that energy functions derived from the coarse-grained OPEP force field can be employed to rescore predictions for protein–protein complexes.

Humidity responsive self-healing based on intermolecular hydrogen bonding and metal–ligand coordination

Self-healing process occurring when a self-healing Co–CS/PAA PEM film is integrated (I), damaged (II), self-healing (III), and self-healed (IV).

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

Background

Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs.

Results

To address this problem, we developed eRank^PPI, an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank^PPI employs multiple features including interface probability estimates calculated by eFindSite^PPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank^PPI consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %.

Conclusions

eRank^PPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi.

Performance of ZDOCK in CAPRI rounds 20-26

We report the performance of our approaches for protein-protein docking and interface analysis in CAPRI rounds 20-26. At the core of our pipeline was the ZDOCK program for rigid-body protein-protein docking. We then reranked the ZDOCK predictions using the ZRANK or IRAD scoring functions, pruned and analyzed energy landscapes using clustering, and analyzed the docking results using our interface prediction approach RCF. When possible, we used biological information from the literature to apply constraints to the search space during or after the ZDOCK runs. For approximately half of the standard docking challenges we made at least one prediction that was acceptable or better. For the scoring challenges we made acceptable or better predictions for all but one target. This indicates that our scoring functions are generally able to select the correct binding mode.

Contacts-based prediction of binding affinity in protein-protein complexes

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

Contacts-based prediction of binding affinity in protein-protein complexes

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

In vitro affinity maturation and characterization of anti-P24 antibody for HIV diagnostic assay

P24 antigen is the main structural protein of HIV-1, its detection provide a means to aid the early diagnosis of HIV-1 infection. The aim of this study was to improve the selectivity and sensitivity of the HIV P24 diagnostic assay by developing a cohort of 9E8 affinity-matured antibodies through in vitro phage affinity maturation which was performed by complementarity determining region (CDR)-hot spot mutagenesis strategy. Antibody 9E8-491 had an affinity constant of 5.64 × 10(-11) M, which was 5.7-fold higher than that of the parent antibody (9E8). Furthermore, the affinity, sensitivity and specificity of 9E8-491 were higher than those of 9E8, which indicate that 9E8-491 is a good candidate detection antibody for HIV P24 assay. Structure analysis of matured variants revealed that most hydrogen bonds resided in HCDR3. Among the antibody-antigen predicted binding residues, Tyr(100A/100B) was the original conserved residue that was commonly present in HCDR3 of 9E8 and variants. Arg(100)/Asp(100C) was the major variant substitution that most likely influenced the binding differences among variants and 9E8 monoclonal antibody. Both efficient library panning and predicted structural data were in agreement that the binding residues were mostly located in HCDR3 and enabled identification of key residues that influence antibody affinity.

Fully Blind Peptide-Protein Docking with pepATTRACT

Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. Here, we present a new fully blind flexible peptide-protein docking protocol, pepATTRACT, which combines a rapid coarse-grained global peptide docking search of the entire protein surface with a two-stage atomistic flexible refinement. Global unbound-unbound docking yielded near-native models for 70% of the docking cases when testing the protocol on the largest benchmark of peptide-protein complexes available to date. This performance is similar to that of state-of-the-art local docking protocols that rely on information about the binding site. Upon restricting the search to the peptide binding region, the resulting pepATTRACT-local approach outperformed existing methods. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html.

Studying the binding interactions of allosteric agonists and antagonists of the CXCR4 receptor

Several examples of allosteric modulators of GPCRs have been reported recently in the literature, but understanding their molecular mechanism presents a new challenge for medicinal chemistry. For the specific case of the cellular receptor CXCR4, it is known that pepducins (lipidated fragments of intracellular GPCR loops) such as ATI-2341 modulate CXCR4 activity agonistically via an allosteric mechanism. Moreover, there are also examples of small organic molecules such as AMD11070 and GSK812397 which may also act as allosteric antagonists. However, incomplete knowledge of the ligand-binding sites has hampered a detailed molecular understanding of how these inhibitors work. Here, we attempt to answer this question by analysing the binding interactions between the CXCR4 receptor and the above-mentioned allosteric modulators. We propose two different allosteric binding sites, one located in the intracellular loops 1, 2 and 3 (ICL1, ICL2 and ICL3) which binds the pepducin agonist ATI-2341, and the other at a subsite of the main extracellular orthosteric binding pocket between extracellular loops 1 and 2 and the N-terminus, which binds the antagonists AMD11070 and GSK812397. Allosteric interactions between the CXCR4 and ATI-2341 were predicted by combining different modeling approaches. First, a rotational blind docking search was applied and the best poses were subsequently refined using flexible docking methods and molecular dynamic simulations. For the AMD11070 and GSK812397 antagonists, the entire CXCR4 protein surface was explored by blind docking in order to define the binding region. A second docking analysis by subsites was then performed to refine the allosteric interactions. Finally, we identified the binding residues that appear to be essential for CXCR4 allosteric modulators.

A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes

T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein-protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near-native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes.

Molecular Docking of Known Carcinogen 4- (Methyl-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK) with Cyclin Dependent Kinases towards Its Potential Role in Cell Cycle Perturbation

Cell cycle is maintained almost all the times and is controlled by various regulatory proteins and their complexes (Cdk+Cyclin) in different phases of interphase (G1, S and G2) and mitosis of cell cycle. A number of mechanisms have been proposed for the initiation and progression of carcinogenesis by abruption in cell cycle process. One of the important features of cancer/carcinogenesis is functional loss of these cell cycle regulatory proteins particularly in CDKs and cyclins. We hypothesize that there is a direct involvement of these cell cycle regulatory proteins not only at the genetic level but also proteins level, during the initiation of carcinogenesis. Therefore, it becomes significant to determine inconsistency in the functioning of regulatory proteins due to interaction with carcinogen 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Hence, we investigated the interaction efficiency of NNK, against cell cycle regulatory proteins. We found a different value of ΔG (free energy of binding) among the studied proteins ranging between -3.29 to -7.25 kcal/mol was observed. To validate the results, we considered Human Oxy-Hemoglobin at 1.25 Å Resolution, [PDB_ID:1HHO] as a +ve control, (binding energy -6.06 kcal/mol). Finally, the CDK8 (PDB_ID:3RGF) and CDK2 (PDB_ID:3DDP) regulatory proteins showing significantly strong molecular interaction with NNK -7.25 kcal/mol, -6.19 kcal/mol respectively were analyzed in details. In this study we predicted that CDK8 protein fails to form functional complex with its complementary partner cyclin C in presence of NNK. Consequently, inconsistency of functioning in regulatory proteins might lead to the abruption in cell cycle progression; contribute to the loss of cell cycle control and subsequently increasing the possibility of carcinogenesis.

Crystal structure of an antiviral ankyrin targeting the HIV-1 capsid and molecular modeling of the ankyrin-capsid complex

Ankyrins are cellular repeat proteins, which can be genetically modified to randomize amino-acid residues located at defined positions in each repeat unit, and thus create a potential binding surface adaptable to macromolecular ligands. From a phage-display library of artificial ankyrins, we have isolated Ank(GAG)1D4, a trimodular ankyrin which binds to the HIV-1 capsid protein N-terminal domain (NTD(CA)) and has an antiviral effect at the late steps of the virus life cycle. In this study, the determinants of the Ank(GAG)1D4-NTD(CA) interaction were analyzed using peptide scanning in competition ELISA, capsid mutagenesis, ankyrin crystallography and molecular modeling. We determined the Ank(GAG)1D4 structure at 2.2 Å resolution, and used the crystal structure in molecular docking with a homology model of HIV-1 capsid. Our results indicated that NTD(CA) alpha-helices H1 and H7 could mediate the formation of the capsid-Ank(GAG)1D4 binary complex, but the interaction involving H7 was predicted to be more stable than with H1. Arginine-18 (R18) in H1, and R132 and R143 in H7 were found to be the key players of the Ank(GAG)1D4-NTD(CA) interaction. This was confirmed by R-to-A mutagenesis of NTD(CA), and by sequence analysis of trimodular ankyrins negative for capsid binding. In Ank(GAG)1D4, major interactors common to H1 and H7 were found to be S45, Y56, R89, K122 and K123. Collectively, our ankyrin-capsid binding analysis implied a significant degree of flexibility within the NTD(CA) domain of the HIV-1 capsid protein, and provided some clues for the design of new antivirals targeting the capsid protein and viral assembly.

Network mapping among the functional domains of Chikungunya virus nonstructural proteins

Formation of virus specific replicase complex is among the most important steps that determines the fate of viral transcription and replication during Chikungunya virus (CHIKV) infection. In the present study, the authors have computationally generated a 3D structure of CHIKV late replicase complex on the basis of the interactions identified among the domains of CHIKV nonstructural proteins (nsPs) which make up the late replicase complex. The interactions among the domains of CHIKV nsPs were identified using systems such as pull down, protein interaction ELISA, and yeast two-hybrid. The structures of nsPs were generated using I-TASSER and the biological assembly of the replicase complex was determined using ZRANK and RDOCK. A total of 36 interactions among the domains and full length proteins were tested and 12 novel interactions have been identified. These interactions included the homodimerization of nsP1 and nsP4 through their respective C-ter domains; the associations of nsP2 helicase domain and C-ter domain of nsP4 with methyltransferase and membrane binding domains of nsP1; the interaction of nsP2 protease domain with C-ter domain of nsP4; and the interaction of nsP3 macro and alphavirus unique domains with the C-ter domain of nsP1. The novel interactions identified in the current study form a network of organized associations that suggest the spatial arrangement of nsPs in the late replicase complex of CHIKV.

Weak protein-ligand interactions studied by small-angle X-ray scattering

Small-angle X-ray scattering (SAXS) is a powerful technique for studying weak interactions between proteins and their ligands (other proteins, DNA/RNA or small molecules) in solution. SAXS provides knowledge about the equilibrium state, the stoichiometry of binding and association-dissociation processes. The measurements are conducted in a solution environment that allows easy monitoring of modifications in protein-ligand association state upon environmental changes. Model-free parameters such as the molecular mass of a system and the radius of gyration can be obtained directly from the SAXS data and give indications about the association state. SAXS is also widely employed to build models of biological assemblies at a resolution of approximately 10-20 Å. Low-resolution shapes can be generated ab initio, although more detailed and biologically interpretable information can be obtained by hybrid modelling. In the latter approach, composite structures of protein-ligand complexes are constructed using atomic models of individual molecules. These may be predicted homology models or experimental structures from X-ray crystallography or NMR. This review focuses on using SAXS data to model structures of protein-ligand complexes and to study their dynamics. The combination of SAXS with other methods such as size exclusion chromatography and dynamic light scattering is discussed.

ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers

SUMMARY

Protein-protein interactions are essential to cellular and immune function, and in many cases, because of the absence of an experimentally determined structure of the complex, these interactions must be modeled to obtain an understanding of their molecular basis. We present a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers. With a goal of providing an accessible and intuitive interface, we provide options for users to guide the scoring and the selection of output models, in addition to dynamic visualization of input structures and output docking models. This server enables the research community to easily and quickly produce structural models of protein-protein complexes and symmetric multimers for their own analysis.

AVAILABILITY

The ZDOCK server is freely available to all academic and non-profit users at: http://zdock.umassmed.edu. No registration is required.

DockRank: ranking docked conformations using partner-specific sequence homology-based protein interface prediction

Selecting near-native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner-specific sequence homology-based protein-protein interface predictor (PS-HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state-of-the-art docking scoring functions using Success Rate (the percentage of cases that have at least one near-native conformation among the top m conformations) and Hit Rate (the percentage of near-native conformations that are included among the top m conformations). In cases where it is possible to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state-of-the-art energy-based scoring functions (improving Success Rate by up to 4-fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39-fold). The latter result underscores the importance of using partner-specific interface residues in scoring docked conformations. We show that DockRank, when used to re-rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/.

Optimized atomic statistical potentials: assessment of protein interfaces and loops

MOTIVATION

Statistical potentials have been widely used for modeling whole proteins and their parts (e.g. sidechains and loops) as well as interactions between proteins, nucleic acids and small molecules. Here, we formulate the statistical potentials entirely within a statistical framework, avoiding questionable statistical mechanical assumptions and approximations, including a definition of the reference state.

RESULTS

We derive a general Bayesian framework for inferring statistically optimized atomic potentials (SOAP) in which the reference state is replaced with data-driven 'recovery' functions. Moreover, we restrain the relative orientation between two covalent bonds instead of a simple distance between two atoms, in an effort to capture orientation-dependent interactions such as hydrogen bonds. To demonstrate this general approach, we computed statistical potentials for protein-protein docking (SOAP-PP) and loop modeling (SOAP-Loop). For docking, a near-native model is within the top 10 scoring models in 40% of the PatchDock benchmark cases, compared with 23 and 27% for the state-of-the-art ZDOCK and FireDock scoring functions, respectively. Similarly, for modeling 12-residue loops in the PLOP benchmark, the average main-chain root mean square deviation of the best scored conformations by SOAP-Loop is 1.5 Å, close to the average root mean square deviation of the best sampled conformations (1.2 Å) and significantly better than that selected by Rosetta (2.1 Å), DFIRE (2.3 Å), DOPE (2.5 Å) and PLOP scoring functions (3.0 Å). Our Bayesian framework may also result in more accurate statistical potentials for additional modeling applications, thus affording better leverage of the experimentally determined protein structures.

AVAILABILITY AND IMPLEMENTATION

SOAP-PP and SOAP-Loop are available as part of MODELLER (http://salilab.org/modeller).

How to use not-always-reliable binding site information in protein-protein docking prediction

In many protein-protein docking algorithms, binding site information is used to help predicting the protein complex structures. Using correct and accurate binding site information can increase protein-protein docking success rate significantly. On the other hand, using wrong binding sites information should lead to a failed prediction, or, at least decrease the success rate. Recently, various successful theoretical methods have been proposed to predict the binding sites of proteins. However, the predicted binding site information is not always reliable, sometimes wrong binding site information could be given. Hence there is a high risk to use the predicted binding site information in current docking algorithms. In this paper, a softly restricting method (SRM) is developed to solve this problem. By utilizing predicted binding site information in a proper way, the SRM algorithm is sensitive to the correct binding site information but insensitive to wrong information, which decreases the risk of using predicted binding site information. This SRM is tested on benchmark 3.0 using purely predicted binding site information. The result shows that when the predicted information is correct, SRM increases the success rate significantly; however, even if the predicted information is completely wrong, SRM only decreases success rate slightly, which indicates that the SRM is suitable for utilizing predicted binding site information.

Binding interface prediction by combining protein-protein docking results

We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein-protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision-recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta-PPISP (AUC = 0.43), which is one of the best monomer-based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta-PPISP and another monomer-based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta-PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues.

Replica exchange improves sampling in low-resolution docking stage of RosettaDock

Many protein-protein docking protocols are based on a shotgun approach, in which thousands of independent random-start trajectories minimize the rigid-body degrees of freedom. Another strategy is enumerative sampling as used in ZDOCK. Here, we introduce an alternative strategy, ReplicaDock, using a small number of long trajectories of temperature replica exchange. We compare replica exchange sampling as low-resolution stage of RosettaDock with RosettaDock's original shotgun sampling as well as with ZDOCK. A benchmark of 30 complexes starting from structures of the unbound binding partners shows improved performance for ReplicaDock and ZDOCK when compared to shotgun sampling at equal or less computational expense. ReplicaDock and ZDOCK consistently reach lower energies and generate significantly more near-native conformations than shotgun sampling. Accordingly, they both improve typical metrics of prediction quality of complex structures after refinement. Additionally, the refined ReplicaDock ensembles reach significantly lower interface energies and many previously hidden features of the docking energy landscape become visible when ReplicaDock is applied.

Ranking multiple docking solutions based on the conservation of inter-residue contacts

Molecular docking is the method of choice for investigating the molecular basis of recognition in a large number of functional protein complexes. However, correctly scoring the obtained docking solutions (decoys) to rank native-like (NL) conformations in the top positions is still an open problem. Herein we present CONSRANK, a simple and effective tool to rank multiple docking solutions, which relies on the conservation of inter-residue contacts in the analyzed decoys ensemble. First it calculates a conservation rate for each inter-residue contact, then it ranks decoys according to their ability to match the more frequently observed contacts. We applied CONSRANK to 102 targets from three different benchmarks, RosettaDock, DOCKGROUND, and Critical Assessment of PRedicted Interactions (CAPRI). The method performs consistently well, both in terms of NL solutions ranked in the top positions and of values of the area under the receiver operating characteristic curve. Its ideal application is to solutions coming from different docking programs and procedures, as in the case of CAPRI targets. For all the analyzed CAPRI targets where a comparison is feasible, CONSRANK outperforms the CAPRI scorers. The fraction of NL solutions in the top ten positions in the RosettaDock, DOCKGROUND, and CAPRI benchmarks is enriched on average by a factor of 3.0, 1.9, and 9.9, respectively. Interestingly, CONSRANK is also able to specifically single out the high/medium quality (HMQ) solutions from the docking decoys ensemble: it ranks 46.2 and 70.8% of the total HMQ solutions available for the RosettaDock and CAPRI targets, respectively, within the top 20 positions.

Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency

Affinity improvement of proteins, including antibodies, by computational chemistry broadly relies on physics-based energy functions coupled with refinement. However, achieving significant enhancement of binding affinity (>10-fold) remains a challenging exercise, particularly for cross-reactive antibodies. We describe here an empirical approach that captures key physicochemical features common to antigen-antibody interfaces to predict protein-protein interaction and mutations that confer increased affinity. We apply this approach to the design of affinity-enhancing mutations in 4E11, a potent cross-reactive neutralizing antibody to dengue virus (DV), without a crystal structure. Combination of predicted mutations led to a 450-fold improvement in affinity to serotype 4 of DV while preserving, or modestly increasing, affinity to serotypes 1-3 of DV. We show that increased affinity resulted in strong in vitro neutralizing activity to all four serotypes, and that the redesigned antibody has potent antiviral activity in a mouse model of DV challenge. Our findings demonstrate an empirical computational chemistry approach for improving protein-protein docking and engineering antibody affinity, which will help accelerate the development of clinically relevant antibodies.