Performance of ZDOCK in CAPRI rounds 20-26

Targeted drug delivery to the thrombus by fusing streptokinase with a fibrin-binding peptide (CREKA): an in silico study

Aim: Streptokinase has poor selectivity and provokes the immune response. In this study, we used in silico studies to design a fusion protein to achieve targeted delivery to the thrombus. Materials & methods: Streptokinase was analyzed computationally for mapping. The fusion protein modeling and quality assessment were carried out on several servers. The enzymatic activity and the stability of the fusion protein and its complex with plasminogen were assessed through molecular docking analysis and molecular dynamics simulation respectively. Results: Physicochemical properties analysis, protein quality assessments, protein-protein docking and molecular dynamics simulations predicted that the designed fusion protein is functionally active. Conclusion: Our results showed that this fusion protein might be a prospective candidate as a novel thrombolytic agent with better selectivity.

Phosphorylation promotes the endonuclease-like activity of human centrin 2

Centrin is a member of the EF-hand superfamily of calcium-binding proteins, which is involved in the nucleotide excision repair (NER). Reversible phosphorylation of centrin is an important regulatory mechanism in vivo and is closely related to many physiological processes. To explore the possible role of centrin in NER, the endonuclease-like activity of human centrin 2 (HsCen2) regulated by phosphorylation in the absence or presence of Tb³⁺ was investigated by spectroscopy techniques, gel electrophoresis, and molecular docking simulation in 10 mM Hepes, pH 7.4. The results showed that phosphorylation weakened the binding of Tb³⁺ to HsCen2 and enhanced the binding of DNA to HsCen2. Phosphorylation improves the endonuclease-like activity of HsCen2. In addition, Tb³⁺ is favorable for DNA binding and endonuclease-like activity of HsCen2 before and after phosphorylation. These results provide clear insights into the effects of phosphorylation on the properties of HsCen2 and offer important clues for further exploration of how phosphorylation affects protein-driven functions.

Phosphorylation weakened the binding of Tb³⁺ to HsCen2, enhanced the binding of DNA to HsCen2; and improves the endonuclease-like activity of HsCen2; Additionally, the endonuclease-like activity of HsCen2 or HsCen2p is regulated up by Tb³⁺-binding.

Anti-epileptic Kunitz-like peptides discovered in the branching coral Acropora digitifera through transcriptomic analysis

Approximately 50 million people are suffering from epilepsy worldwide. Corals have been used for treating epilepsy in traditional Chinese medicine, but the mechanism of this treatment is unknown. In this study, we analyzed the transcriptome of the branching coral Acropora digitifera and obtained its Kyoto Encyclopedia of Genes and Genomes (KEGG), EuKaryotic Orthologous Groups (KOG) and Gene Ontology (GO) annotation. Combined with multiple sequence alignment and phylogenetic analysis, we discovered three polypeptides, we named them AdKuz1, AdKuz2 and AdKuz3, from A. digitifera that showed a close relationship to Kunitz-type peptides. Molecular docking and molecular dynamics simulation indicated that AdKuz1 to 3 could interact with GABA_A receptor but AdKuz2-GABA_A remained more stable than others. The biological experiments showed that AdKuz1 and AdKuz2 exhibited an anti-inflammatory effect by decreasing the aberrant level of nitric oxide (NO), IL-6, TNF-α and IL-1β induced by LPS in BV-2 cells. In addition, the pentylenetetrazol (PTZ)-induced epileptic effect on zebrafish was remarkably suppressed by AdKuz1 and AdKuz2. AdKuz2 particularly showed superior anti-epileptic effects compared to the other two peptides. Furthermore, AdKuz2 significantly decreased the expression of c-fos and npas4a, which were up-regulated by PTZ treatment. In addition, AdKuz2 reduced the synthesis of glutamate and enhanced the biosynthesis of gamma-aminobutyric acid (GABA). In conclusion, the results indicated that AdKuz2 may affect the synthesis of glutamate and GABA and enhance the activity of the GABA_A receptor to inhibit the symptoms of epilepsy. We believe, AdKuz2 could be a promising anti-epileptic agent and its mechanism of action should be further investigated.

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.

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.

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 .

Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.

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.

Exploration of the BF2*15 major histocompatibility complex class I binding motif and identification of cytotoxic T lymphocyte epitopes from the H5N1 influenza virus nucleoprotein in chickens

The binding motif of BF2*15 major histocompatibility complex (MHC) class I was explored by analyzing the interaction between an infectious bronchitis virus octapeptide and BF2*15, and the cytotoxic T lymphocyte (CTL) epitope from the nucleoprotein (NP) of H5N1 virus was identified using experimental methods. Computational methods, including homology modeling, molecular dynamics simulation, and molecular docking analysis, were used. The recombinant plasmid pCAGGS-NP was constructed, and NP expression was confirmed by indirect immunofluorescence and Western blot in transfected 293T cells. Antibodies against NP in pCAGGS-NP-inoculated specific-pathogen-free chickens were detected by enzyme-linked immunosorbent assay (ELISA). Interferon γ (IFN-γ) mRNA was quantified, and IFN-γ production was evaluated using quantitative reverse transcription PCR and capture ELISA, respectively. CD8(+) T-lymphocyte proliferation was detected using flow cytometric analysis. The BF2*15 MHC class I binding motif "x-Arg/Lys-x-x-x-Arg/Lys" was explored. Quantification of chicken IFN-γ mRNA, evaluation of IFN-γ production, and measurement of CD8(+) T-lymphocyte proliferation confirmed that the peptide NP67-74 of H5N1 was the BF2*15 MHC-class-I-restricted CTL epitope.

Computational analysis of the CB1 carboxyl-terminus in the receptor-G protein complex

Despite the important role of the carboxyl-terminus (Ct) of the activated brain cannabinoid receptor one (CB1) in the regulation of G protein signaling, a structural understanding of interactions with G proteins is lacking. This is largely due to the highly flexible nature of the CB1 Ct that dynamically adapts its conformation to the presence of G proteins. In the present study, we explored how the CB1 Ct can interact with the G protein by building on our prior modeling of the CB1-Gi complex (Shim, Ahn, and Kendall, The Journal of Biological Chemistry 2013;288:32449-32465) to incorporate a complete CB1 Ct (Glu416(Ct)-Leu472(Ct)). Based on the structural constraints from NMR studies, we employed ROSETTA to predict tertiary folds, ZDOCK to predict docking orientation, and molecular dynamics (MD) simulations to obtain two distinct plausible models of CB1 Ct in the CB1-Gi complex. The resulting models were consistent with the NMR-determined helical structure (H9) in the middle region of the CB1 Ct. The CB1 Ct directly interacted with both Gα and Gβ and stabilized the receptor at the Gi interface. The results of site-directed mutagenesis studies of Glu416(Ct), Asp423(Ct), Asp428(Ct), and Arg444(Ct) of CB1 Ct suggested that the CB1 Ct can influence receptor-G protein coupling by stabilizing the receptor at the Gi interface. This research provided, for the first time, models of the CB1 Ct in contact with the G protein.

Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

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

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.

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.