Predicting the structure of protein-protein complexes using the SwarmDock Web Server

Targeting breast and pancreatic cancer metastasis using a dual-cadherin antibody

The successful application of antibody-based therapeutics in either primary or metastatic cancer depends upon the selection of rare cell surface epitopes that distinguish cancer cells from surrounding normal epithelial cells. By contrast, as circulating tumor cells (CTCs) transit through the bloodstream, they are surrounded by hematopoietic cells with dramatically distinct cell surface proteins, greatly expanding the number of targetable epitopes. Here, we show that an antibody (23C6) against cadherin proteins effectively suppresses blood-borne metastasis in mouse isogenic and xenograft models of triple negative breast and pancreatic cancers. The 23C6 antibody is remarkable in that it recognizes both the epithelial E-cadherin (CDH1) and mesenchymal OB-cadherin (CDH11), thus overcoming considerable heterogeneity across tumor cells. Despite its efficacy against single cells in circulation, the antibody does not suppress primary tumor formation, nor does it elicit detectable toxicity in normal epithelial organs, where cadherins may be engaged within intercellular junctions and hence inaccessible for antibody binding. Antibody-mediated suppression of metastasis is comparable in matched immunocompetent and immunodeficient mouse models. Together, these studies raise the possibility of antibody targeting CTCs within the vasculature, thereby suppressing blood-borne metastasis.

The p.P1127S pathogenic variant lowers von Willebrand factor levels through higher affinity for the macrophagic scavenger receptor LRP1: Clinical phenotype and pathogenic mechanisms

BACKGROUND

The index case is a 21-year-old Italian woman with a mild hemorrhagic syndrome and von Willebrand factor antigen (VWF:Ag) = 34.3 U/dl, VWF recombinant glycoprotein Ib (VWF:GpIbR) = 32.8 U/dl, and factor VIII (FVIII) = 55.3 IU/dl.

AIMS

The aim of this study is to characterize from a genetic and biochemical standpoint this low VWF phenotype.

METHODS

Coagulation and biochemical methods were used to study the structural and functional pattern of VWF multimers in the index case's plasma. Recombinant wild-type and p.P1127S VWF variants were produced using human embryonic kidney (HEK)-293 cells. In addition, genetic screening was carried out to detect single nucleotide variants of some scavenger VWF/FVIII receptor genes such as CLEC4M, STAB2, and ASGR2.

RESULTS

Genetic investigation revealed that the index case inherited from her mother the heterozygous missense mutation c.3379C > T (VWF exon 25), causing the p.P1127S substitution in the VWF D'D3 domain. The index case was also homozygous for the scavenger receptor ASGR2 c.-95 CC-genotype. Desmopressin normalized the VWF level of the patient, although its clearance was faster (t1/2  = 6.7 h) than in normal subjects (t1/2  = 12 ± 0.7 h). FVIII-VWF interaction, A Disintegrin And Metalloprotease with ThromboSpondin type 1 motif-13 levels, ristocetin-induced-platelet-aggregation, and VWF multimeric pattern were normal. The p.P1127S variant was normally synthesized and secreted by HEK-293 cells, and molecular modeling predicts a conformational change showing higher affinity for the macrophagic scavenger receptor lipoprotein receptor-related protein 1 (LRP1), as also experimentally verified.

CONCLUSIONS

The p.P1127S variant may cause a low VWF phenotype, stemming from an increased VWF affinity for the scavenger receptor LRP1 and, consequently, an accelerated clearance of VWF.

PEA-15 engages in allosteric interactions using a common scaffold in a phosphorylation-dependent manner

Phosphoprotein enriched in astrocytes, 15 kDa (PEA-15) is a death-effector domain (DED) containing protein involved in regulating mitogen-activated protein kinase and apoptosis pathways. In this molecular dynamics study, we examined how phosphorylation of the PEA-15 C-terminal tail residues, Ser-104 and Ser-116, allosterically mediates conformational changes of the DED and alters the binding specificity from extracellular-regulated kinase (ERK) to Fas-associated death domain (FADD) protein. We delineated that the binding interfaces between the unphosphorylated PEA-15 and ERK2 and between the doubly phosphorylated PEA-15 and FADD are similarly composed of a scaffold that includes both the DED and the C-terminal tail residues of PEA-15. While the unphosphorylated serine residues do not directly interact with ERK2, the phosphorylated Ser-116 engages in strong electrostatic interactions with arginine residues on FADD DED. Upon PEA-15 binding, FADD repositions its death domain (DD) relative to the DED, an essential conformational change to allow the death-inducing signaling complex (DISC) assembly.

Alpha-Synuclein defects autophagy by impairing SNAP29-mediated autophagosome-lysosome fusion

Dopaminergic (DA) cell death in Parkinson's disease (PD) is associated with the gradual appearance of neuronal protein aggregates termed Lewy bodies (LBs) that are comprised of vesicular membrane structures and dysmorphic organelles in conjunction with the protein alpha-Synuclein (α-Syn). Although the exact mechanism of neuronal aggregate formation and death remains elusive, recent research suggests α-Syn-mediated alterations in the lysosomal degradation of aggregated proteins and organelles - a process termed autophagy. Here, we used a combination of molecular biology and immunochemistry to investigate the effect of α-Syn on autophagy turnover in cultured human DA neurons and in human post-mortem brain tissue. We found α-Syn overexpression to reduce autophagy turnover by compromising the fusion of autophagosomes with lysosomes, thus leading to a decrease in the formation of autolysosomes. In accord with a compensatory increase in the plasma membrane fusion of autophagosomes, α-Syn enhanced the number of extracellular vesicles (EV) and the abundance of autophagy-associated proteins in these EVs. Mechanistically, α-Syn decreased the abundance of the v-SNARE protein SNAP29, a member of the SNARE complex mediating autophagolysosome fusion. In line, SNAP29 knockdown mimicked the effect of α-Syn on autophagy whereas SNAP29 co-expression reversed the α-Syn-induced changes on autophagy turnover and EV release and ameliorated DA neuronal cell death. In accord with our results from cultured neurons, we found a stage-dependent reduction of SNAP29 in SNc DA neurons from human post-mortem brain tissue of Lewy body pathology (LBP) cases. In summary, our results thus demonstrate a previously unknown effect of α-Syn on intracellular autophagy-associated SNARE proteins and, as a consequence, a reduced autolysosome fusion. As such, our findings will therefore support the investigation of autophagy-associated pathological changes in PD.

von Willebrand factor propeptide missense variants affect anterograde transport to Golgi resulting in ER retention

von Willebrand disease (VWD), the most prevalent congenital bleeding disorder, arises from a deficiency in von Willebrand factor (VWF), which has crucial roles in hemostasis. The present study investigated functional consequences and underlying pathomolecular mechanisms of several VWF propeptide (VWFpp) missense variants detected in our cohort of VWD patients for the first time. Transient expression experiments in HEK293T cells demonstrated that four out of the six investigated missense variants (p.Gly55Glu, p.Val86Glu, p.Trp191Arg, and p.Cys608Trp) severely impaired secretion. Their cotransfections with the wild-type partly corrected VWF secretion, displaying loss of large/intermediate multimers. Immunostaining of the transfected HEK293 cells illustrated the endoplasmic reticulum (ER) retention of the VWF variants. Docking of the COP I and COP II cargo recruitment proteins, ADP-ribosylation factor 1 and Sec24, onto the N-terminal VWF model (D1D2D'D3) revealed that these variants occur at VWFpp putative interfaces, which can hinder VWF loading at the ER exit quality control. Furthermore, quantitative and automated morphometric exploration of the three-dimensional immunofluorescence images showed changes in the number/size of the VWF storage organelles, Weibel-Palade body (WPB)-like vesicles. The result of this study highlighted the significance of the VWFpp variants on anterograde ER-Golgi trafficking of VWF as well as the biogenesis of WPB-like vesicles.

Designing of a Novel Fusion Protein Vaccine Candidate Against Human Visceral Leishmaniasis (VL) Using Immunoinformatics and Structural Approaches

Leishmaniasis is caused by an obligate intracellular protozoan parasite. The clinical forms of leishmaniasis differ from cutaneous leishmaniasis, mucocutaneous leishmaniasis and visceral leishmaniasis (VL) which depend on the parasite species and the host’s immune responses. There are significant challenges to the available anti-leishmanial drug therapy, particularly in severe forms of disease, and the rise of drug resistance has made it more difficult. Currently, no licensed vaccines have been introduced to the market for the control and elimination of VL. A potential target for use in candidate vaccines against leishmaniasis has been shown to be leishmania Kinetoplastid membrane protein-11 (KMP-11) antigen. In this study, we chose KMP-11 antigen as target antigen in our vaccine construct. In addition, B-type flagellin (fliC) was used as an adjuvant for enhancing vaccine immunogenicity. The GSGSGSGSGSG linker was applied to link the KMP-11 antigen and fliC (KMP-11-fliC) to construct our fusion protein. Bioinformatics approaches such as; 3D homology modeling, CTL, B-cell, MHC class I and II epitopes prediction, allergenicity, antigenicity evaluations, molecular docking, fast simulations of flexibility of docked complex and in silico cloning were employed to analysis and evaluation of various properties of the designed fusion construct. Computational results showed that our engineered structure has the potential for proper stimulation of cellular and humoral immune responses against VL. Consequently, it could be proposed as a candidate vaccine against VL according to these data and after verifying the efficacy of the candidate vaccine through in vivo and in vitro immunological tests.

Amalgamation of 3D structure and sequence information for protein-protein interaction prediction

Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein-protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein-protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

Enhanced sampling of protein conformational states for dynamic cross-docking within the protein-protein docking server SwarmDock

The formation of specific protein-protein interactions is often a key to a protein's function. During complex formation, each protein component will undergo a change in the conformational state, for some these changes are relatively small and reside primarily at the sidechain level; however, others may display notable backbone adjustments. One of the classic problems in the protein-docking field is to be able to a priori predict the extent of such conformational changes. In this work, we investigated three protocols to find the most suitable input structure conformations for cross-docking, including a robust sampling approach in normal mode space. Counterintuitively, knowledge of the theoretically best combination of normal modes for unbound-bound transitions does not always lead to the best results. We used a novel spatial partitioning library, Aether Engine (see Supplementary Materials), to efficiently search the conformational states of 56 receptor/ligand pairs, including a recent CAPRI target, in a systematic manner and selected diverse conformations as input to our automated docking server, SwarmDock, a server that allows moderate conformational adjustments during the docking process. In essence, here we present a dynamic cross-docking protocol, which when benchmarked against the simpler approach of just docking the unbound components shows a 10% uplift in the quality of the top docking pose.

A Guide for Protein-Protein Docking Using SwarmDock

Many of the biological functions of the cell are driven by protein-protein interactions. However, determining which proteins interact and exactly how they do so to enable their functions, remain major research questions. Functional interactions are dependent on a number of complicated factors; therefore, modeling the three-dimensional structure of protein-protein complexes is still considered a complex endeavor. Nevertheless, the rewards for modeling protein interactions to atomic level detail are substantial, and there are numerous examples of how models can provide useful information for drug design, protein engineering, systems biology, and understanding of the immune system. Here, we provide practical guidelines for docking proteins using the web-server, SwarmDock, a flexible protein-protein docking method. Moreover, we provide an overview of the factors that need to be considered when deciding whether docking is likely to be successful.

Evaluation of specificity determinants in Mycobacterium tuberculosis σ/anti-σ factor interactions

Extra Cytoplasmic Function (ECF) σ factor/regulatory protein (anti-σ factor) pairs govern environment mediated changes in gene expression in bacteria. The release of the ECF σ factor from an inactive σ/anti-σ factor complex is triggered by specific environmental stimuli. The free σ factor then associates with the RNA polymerase and drives the expression of genes in its target regulon. Multiple ECF σ/anti-σ pairs ensure calibrated changes in the expression profile by correlating diverse environmental stimuli with changes in the intracellular levels of different ECF σ factors. Specificity in σ/anti-σ factor interaction is thus essential for accurate signal transduction. Here we describe experiments to evaluate interactions between different M. tuberculosis σ and anti-σ proteins in vitro. The interaction parameters suggest that cross-talk between non-cognate σ/anti-σ pairs is likely. The sequence and conformational determinants that govern interaction specificity in a σ/anti-σ complex are not immediately evident due to substantial structural conservation. Sequence-structure analysis of all σ/anti-σ pairs suggest that conserved residues are not the primary determinants of σ/anti-σ interactions-a finding that suggests a potential route to set tolerance limits in interaction specificity. Non-specific σ/anti-σ interactions are likely to be biologically significant as it can contribute to heterogeneity in cellular responses in a bacterial population under less stringent requirements. This finding is relevant for synthetic biology approaches to engineer bacteria using σ/anti-σ transcription initiation modules for diverse applications in biotechnology.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

Flexible Protein-Protein Docking with SwarmDock

The atomic structures of protein complexes can provide useful information for drug design, protein engineering, systems biology, and understanding pathology. Obtaining this information experimentally can be challenging. However, if the structures of the subunits are known, then it is often possible to model the complex computationally. This chapter provide practical guidelines for docking proteins using the SwarmDock flexible protein-protein docking method, providing an overview of the factors that need to be considered when deciding whether docking is likely to be successful, the preparation of structural input, generation of docked poses, analysis and ranking of docked poses, and the validation of models using external data.

Protein interaction evolution from promiscuity to specificity with reduced flexibility in an increasingly complex network

A key question regarding protein evolution is how proteins adapt to the dynamic environment in which they function and how in turn their evolution shapes the protein interaction network. We used extant and resurrected ancestral plant MADS-domain transcription factors to understand how SEPALLATA3, a protein with hub and glue properties, evolved and takes part in network organization. Although the density of dimeric interactions was saturated in the network, many new interactions became mediated by SEPALLATA3 after a whole genome triplication event. By swapping SEPALLATA3 and its ancestors between dimeric networks of different ages, we found that the protein lost the capacity of promiscuous interaction and acquired specificity in evolution. This was accompanied with constraints on conformations through proline residue accumulation, which made the protein less flexible. SHORT VEGETATIVE PHASE on the other hand (non-hub) was able to gain protein-protein interactions due to a C-terminal domain insertion, allowing for a larger interaction interface. These findings illustrate that protein interaction evolution occurs at the level of conformational dynamics, when the binding mechanism concerns an induced fit or conformational selection. Proteins can evolve towards increased specificity with reduced flexibility when the complexity of the protein interaction network requires specificity.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

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.

GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ

Background

Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target.

Results

Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface.

Conclusions

The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter’s druggable intrafilament interface as a potential drug target.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-016-2149-5) contains supplementary material, which is available to authorized users.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues

Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.