DOCKSCORE: a webserver for ranking protein-protein docked poses

Role of the redox state of the Pirin-bound cofactor on interaction with the master regulators of inflammation and other pathways

Persistent cellular stress induced perpetuation and uncontrolled amplification of inflammatory response results in a shift from tissue repair toward collateral damage, significant alterations of tissue functions, and derangements of homeostasis which in turn can lead to a large number of acute and chronic pathological conditions, such as chronic heart failure, atherosclerosis, myocardial infarction, neurodegenerative diseases, diabetes, rheumatoid arthritis, and cancer. Keeping the vital role of balanced inflammation in maintaining tissue integrity in mind, the way to combating inflammatory diseases may be through identification and characterization of mediators of inflammation that can be targeted without hampering normal body function. Pirin (PIR) is a non-heme iron containing protein having two different conformations depending on the oxidation state of the iron. Through exploration of the Pirin interactome and using molecular docking approaches, we identified that the Fe2+-bound Pirin directly interacts with BCL3, NFKBIA, NFIX and SMAD9 with more resemblance to the native binding pose and higher affinity than the Fe3+-bound form. In addition, Pirin appears to have a function in the regulation of inflammation, the transition between the canonical and non-canonical NF-κB pathways, and the remodeling of the actin cytoskeleton. Moreover, Pirin signaling appears to have a critical role in tumor invasion and metastasis, as well as metabolic and neuro-pathological complications. There are regulatory variants in PIR that can influence expression of not only PIR but also other genes, including VEGFD and ACE2. Disparity exists between South Asian and European populations in the frequencies of variant alleles at some of these regulatory loci that may lead to differential occurrence of Pirin-mediated pathogenic conditions.

Bioinformatics Analysis of Mutations Sheds Light on the Evolution of Dengue NS1 Protein With Implications in the Identification of Potential Functional and Druggable Sites

Non-structural protein (NS1) is a 350 amino acid long conserved protein in the dengue virus. Conservation of NS1 is expected due to its importance in dengue pathogenesis. The protein is known to exist in dimeric and hexameric states. The dimeric state is involved in its interaction with host proteins and viral replication, and the hexameric state is involved in viral invasion. In this work, we performed extensive structure and sequence analysis of NS1 protein, and uncovered the role of NS1 quaternary states in its evolution. A three-dimensional modeling of unresolved loop regions in NS1 structure is performed. "Conserved" and "Variable" regions within NS1 protein were identified from sequences obtained from patient samples and the role of compensatory mutations in selecting destabilizing mutations were identified. Molecular dynamics (MD) simulations were performed to extensively study the effect of a few mutations on NS1 structure stability and compensatory mutations. Virtual saturation mutagenesis, predicting the effect of every individual amino acid substitution on NS1 stability sequentially, revealed virtual-conserved and variable sites. The increase in number of observed and virtual-conserved regions across NS1 quaternary states suggest the role of higher order structure formation in its evolutionary conservation. Our sequence and structure analysis could enable in identifying possible protein-protein interfaces and druggable sites. Virtual screening of nearly 10,000 small molecules, including FDA-approved drugs, permitted us to recognize six drug-like molecules targeting the dimeric sites. These molecules could be promising due to their stable interactions with NS1 throughout the simulation.

TIMP-1 is a novel ligand of Amyloid Precursor Protein and triggers a proinflammatory phenotype in human monocytes

The emerging cytokine tissue inhibitor of metalloproteinases-1 (TIMP-1) correlates with the progression of inflammatory diseases, including cancer. However, the effects of TIMP-1 on immune cell activation and underlying molecular mechanisms are largely unknown. Unbiased ligand-receptor-capture-screening revealed TIMP-1-interaction with Amyloid Precursor Protein (APP) family members, namely APP and Amyloid Precursor-like Protein-2 (APLP2), which was confirmed by pull-down assays and confocal microscopy. We found that TIMP-1 triggered glucose uptake and proinflammatory cytokine expression in human monocytes. In cancer patients, TIMP-1 expression positively correlated with proinflammatory cytokine expression and processes associated with monocyte activation. In pancreatic cancer, TIMP-1 plasma levels correlated with the monocyte activation marker sCD163, and the combined use of both clinically accessible plasma proteins served as a powerful prognostic indicator. Mechanistically, TIMP-1 triggered monocyte activation by its C-terminal domain and via APP as demonstrated by in vitro interference, in silico docking, and the employment of recombinant TIMP-1 variants. Identification of TIMP-1 as a trigger of monocyte activation opens new therapeutic perspectives for inflammatory diseases.

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.

Insight towards the effect of the multi basic cleavage site of SARS-CoV-2 spike protein on cellular proteases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection presents an immense global health problem. Spike (S) protein of coronavirus is the primary determinant of its entry into the host as it consists of both receptor binding and fusion domain. Besides tissue tropism, and host range, coronavirus pathogenesis are primarily controlled by the interaction of S protein with the cell receptor. Moreover, the proteolytic activation of S protein by host cell proteases plays a decisive role. The host-cell proteases have shown to be involved in the proteolysis of S protein and cleaving it into two functional subunits, S1 and S2, during the maturation process. In the present study, the interaction of the S protein of SARS-CoV-2 with different host proteases like furin, cathepsin B, and plasmin has been analyzed using molecular docking and molecular dynamics (MD) simulation. Incorporation of the furin cleavage site (R-R-A-R) in the S protein of SARS-CoV-2 has been studied by mutating the individual amino acid. MD simulation results suggest the polytropic nature of the S protein. Our analysis indicated that a single amino acid substitution in the polybasic cleavage site of S protein perturb the binding of cellular proteases. This mutation study might help to generate an attenuated SARS-CoV-2. Besides, targeting host proteases by inhibitors may result in a practical approach to stop the cellular spread of SARS-CoV-2 and develop its antiviral.

ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features

MOTIVATION

The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius $r$ of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grade their placement within the protein as a whole. Moreover, we have shown the potential of ZoomQA to identify problematic regions of the SARS-CoV-2 protein complex.

RESULTS

We benchmark ZoomQA on CASP14, and it outperforms other state-of-the-art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features and shows that our method is able to match the performance of other state-of-the-art methods without the use of homology searching against databases or PSSM matrices.

AVAILABILITY

http://zoomQA.renzhitech.com.

Homology modelling of Trypanosoma brucei major surface proteases and molecular docking of variant surface glycoproteins and inhibitor ligands for drug design

Trypanosomes, which cause animal African trypanosomiasis, escape host immune responses by renewing their variable surface glycoprotein (VSG) coat. Chemotherapy is currently the only form of external intervention available. However, the efficacy of current trypanocides is poor due to overuse leading to an increase in drug resistance. Major surface proteases (MSPs) of trypanosomes, which are zinc-dependent metalloproteases, are possible drug targets. A Trypanosoma brucei MSP-B (TbMSP-B) mediates parasite antigenic variation via cleavage of 60% of VSG molecules. Whilst TbMSP-A has no apparent role in VSG cleavage; it is not known if TbMSP-C is involved in VSG cleavage. In this study, three-dimensional structures of TbMSP-A, TbMSP-B and TbMSP-C were modelled. By comparing the docking poses of the C-terminal domains of VSG substrates into the models, TbMSP-C showed an affinity for similar VSG substrate sites as TbMSP-B, but these sites differed from those recognised by TbMSP-A. This observation suggests that TbMSP-C may be involved in VSG cleavage during antigenic variation. Furthermore, by docking small inhibitor ligands into the TbMSP-B and TbMSP-C homology models, followed by molecular dynamics simulations, ligands with potential anti-trypanosomal activity were identified. Docking studies also revealed the depth of the S₁' pockets of TbMSP-B and TbMSP-C, which is influential in ligand and substrate binding, thereby identifying the protease subsite pocket that should be targeted in drug design.

From virus to diabetes therapy: Characterization of a specific insulin-degrading enzyme inhibitor for diabetes treatment

Inhibition of insulin-degrading enzyme (IDE) is a possible target for treating diabetes. However, it has not yet evolved into a medical intervention, mainly because most developed inhibitors target the zinc in IDE's catalytic site, potentially causing toxicity to other essential metalloproteases. Since IDE is a cellular receptor for the varicella-zoster virus (VZV), we constructed a VZV-based inhibitor. We computationally characterized its interaction site with IDE showing that the peptide specifically binds inside IDE's central cavity, however, not in close proximity to the zinc ion. We confirmed the peptide's effective inhibition on IDE activity in vitro and showed its efficacy in ameliorating insulin-related defects in types 1 and 2 diabetes mouse models. In addition, we suggest that inhibition of IDE may ameliorate the pro-inflammatory profile of CD4⁺ T-cells toward insulin. Together, we propose a potential role of a designed VZV-derived peptide to serve as a selectively-targeted and as an efficient diabetes therapy.

Classification and prediction of protein-protein interaction interface using machine learning algorithm

Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein-protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein-protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein-protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/ .

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.

Guidelines To Predict Binding Poses of Antibody-Integrin Complexes

Integrins are cell adhesion receptors that transmit bidirectional signals across the plasma membrane. They are noncovalently linked heterodimeric molecules consisting of two subunits and act as biomarkers in several pathologies. Thus, according to the increase of therapeutic antibody production, some efforts have been applied to produce anti-integrin antibodies. Here, we purposed to evaluate methods of generation and identification of the binding pose of integrin-antibody complexes, through protein-protein docking and molecular dynamics simulations, and propose a strategy to assure the confidence of the final model and avoid false-positive poses. The results show that ClusPro and GRAMM-X were the best programs to generate the native pose of integrin-antibody complexes. Furthermore, we were able to recover and to ensure that the selected pose is the native one by using a simple rule. All complexes from ClusPro in which the first model had the lowest energy, at least 5% more negative than the second one, were correctly predicted. Therefore, our methodology seems to be efficient to avoid misranking of wrong poses for integrin-antibody complexes. In cases where the rule is inconclusive, we proposed the use of heated molecular dynamics to identify the native pose characterized by RMSDi <0.5 nm. We believe that the set of methods presented here helps in the rational design of anti-integrin antibodies, giving some insights on the development of new biopharmaceuticals.

COZOID: contact zone identifier for visual analysis of protein-protein interactions

BACKGROUND

Studying the patterns of protein-protein interactions (PPIs) is fundamental for understanding the structure and function of protein complexes. The exploration of the vast space of possible mutual configurations of interacting proteins and their contact zones is very time consuming and requires the proteomic expert knowledge.

RESULTS

In this paper, we propose a novel tool containing a set of visual abstraction techniques for the guided exploration of PPI configuration space. It helps proteomic experts to select the most relevant configurations and explore their contact zones at different levels of detail. The system integrates a set of methods that follow and support the workflow of proteomics experts. The first visual abstraction method, the Matrix view, is based on customized interactive heat maps and provides the users with an overview of all possible residue-residue contacts in all PPI configurations and their interactive filtering. In this step, the user can traverse all input PPI configurations and obtain an overview of their interacting amino acids. Then, the models containing a particular pair of interacting amino acids can be selectively picked and traversed. Detailed information on the individual amino acids in the contact zones and their properties is presented in the Contact-Zone list-view. The list-view provides a comparative tool to rank the best models based on the similarity of their contacts to the template-structure contacts. All these techniques are interactively linked with other proposed methods, the Exploded view and the Open-Book view, which represent individual configurations in three-dimensional space. These representations solve the high overlap problem associated with many configurations. Using these views, the structural alignment of the best models can also be visually confirmed.

CONCLUSIONS

We developed a system for the exploration of large sets of protein-protein complexes in a fast and intuitive way. The usefulness of our system has been tested and verified on several docking structures covering the three major types of PPIs, including coiled-coil, pocket-string, and surface-surface interactions. Our case studies prove that our tool helps to analyse and filter protein-protein complexes in a fraction of the time compared to using previously available techniques.

Integrative modelling of TIR domain-containing adaptor molecule inducing interferon-β (TRIF) provides insights into its autoinhibited state

BACKGROUND

TRIF is a key protein in antiviral innate immunity, operating downstream of TLRs. TRIF activation leads to the production of interferon-β and pro-inflammatory cytokines. There is evidence from experiments to suggest that the N-terminal domain of TRIF binds to its TIR domain to avoid constitutive activation. However, no structure of a complex between the N-terminal domain and the TIR domain exists till date. The disordered nature of the region connecting the N-terminal domain and the TIR domain compounds the issue of elucidating the mechanism of autoinhibition of TRIF. In this study, we have employed an integrative approach consisting of mutual information analysis, docking, molecular dynamics simulations and residue network analysis, in combination with existing experimental data to provide a glimpse of TRIF in its autoinhibited state.

RESULTS

Our extensive docking approach reveals that the N-terminal domain binds to the BB loop-B helix region of the TIR domain, consistent with experimental observations. Long length molecular dynamics simulations of 1 microsecond performed on the docked model highlights residues participating in hydrogen bonding and hydrophobic interactions at the interface. A pair of residues present in the vicinity of the interface is also predicted by mutual information analysis, to co-evolve. Residues mediating long-range interactions within the TIR domain of TRIF were identified using residue network analysis.

CONCLUSIONS

Based on the results of the modelling and residue network analysis, we propose that the N-terminal domain binds to the BB loop region of the TIR domain, thereby preventing its homodimersation. The binding of TRIF to TLR3 or TRAM could induce a slight conformational change, causing the interactions between the N-terminal domain and TIR domain to disrupt, thereby exposing the BB loop and rendering it amenable for higher-order oligomerisation.

REVIEWERS

This article was reviewed by Michael Gromiha, Srikrishna Subramaniam and Peter Bond (nominated by Chandra Verma).

Molecular and structural characterization of novel cystatins from the taiga tick Ixodes persulcatus

Cystatins are cysteine peptidase inhibitors that in ticks mediate processes such as blood feeding and digestion. The ixodid tick Ixodes persulcatus is endemic to the Eurasia, where it is the principal vector of Lyme borreliosis. To date, no I. persulcatus cystatin has been characterized. In the present work, we describe three novel cystatins from I. persulcatus, named JpIpcys2a, JpIpcys2b and JpIpcys2c. In addition, the potential of tick cystatins as cross-protective antigens was evaluated by vaccination of hamsters using BrBmcys2c, a cystatin from Rhipicephalus microplus, against I. persulcatus infestation. Sequence analysis showed that motifs that are characteristic of cystatins type 2 are fully conserved in JpIpcys2b, while mutations are present in both JpIpcys2a and JpIpcys2c. Protein-protein docking simulations further revealed that JpIpcys2a, JpIpcys2b and JpIpcys2c showed conserved binding sites to human cathepsins L, all of them covering the active site cleft. Cystatin transcripts were detected in different I. persulcatus tissues and instars, showing their ubiquitous expression during I. persulcatus development. Serological analysis showed that although hamsters immunized with BrBmcys2c developed a humoral immune response, this response was not adequate to protect against a heterologous challenge with I. persulcatus adult ticks. The lack of cross-protection provided by BrBmcys2c immunization is perhaps linked to the fact that cystatins cluster into multigene protein families that are expressed differentially and exhibit functional redundancy. How to target such small proteins that are secreted in low quantities remains a challenge in the development of suitable anti-tick vaccine antigens.

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.

G-actin guides p53 nuclear transport: potential contribution of monomeric actin in altered localization of mutant p53

p53 preserves genomic integrity by restricting anomaly at the gene level. Till date, limited information is available for cytosol to nuclear shuttling of p53; except microtubule-based trafficking route, which utilizes minus-end directed motor dynein. The present study suggests that monomeric actin (G-actin) guides p53 traffic towards the nucleus. Histidine-tag pull-down assay using purified p53(1–393)-His and G-actin confirms direct physical association between p53 and monomeric G-actin. Co-immunoprecipitation data supports the same. Confocal imaging explores intense perinuclear colocalization between p53 and G-actin. To address atomistic details of the complex, constraint-based docked model of p53:G-actin complex was generated based on crystal structures. MD simulation reveals that p53 DNA-binding domain arrests very well the G-actin protein. Docking benchmark studies have been carried out for a known crystal structure, 1YCS (complex between p53DBD and BP2), which validates the docking protocol we adopted. Co-immunoprecipitation study using “hot-spot” p53 mutants suggested reduced G-actin association with cancer-associated p53 conformational mutants (R175H and R249S). Considering these findings, we hypothesized that point mutation in p53 structure, which diminishes p53:G-actin complexation results in mutant p53 altered subcellular localization. Our model suggests p53Arg249 form polar-contact with Arg357 of G-actin, which upon mutation, destabilizes p53:G-actin interaction and results in cytoplasmic retention of p53R249S.