Identification of transient hub proteins and the possible structural basis for their multiple interactions

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Vitamin D analog calcitriol for breast cancer therapy; an integrated drug discovery approach

As breast cancer remains leading cause of cancer death globally, it is essential to develop an affordable breast cancer therapy in underdeveloped countries. Drug repurposing offers potential to address gaps in breast cancer treatment. Molecular networking studies were performed for drug repurposing approach by using heterogeneous data. The PPI networks were built to select the target genes from the EGFR overexpression signaling pathway and its associated family members. The selected genes EGFR, ErbB2, ErbB4 and ErbB3 were allowed to interact with 2637 drugs, leads to PDI network construction of 78, 61, 15 and 19 drugs, respectively. As drugs approved for treating non cancer-related diseases or disorders are clinically safe, effective, and affordable, these drugs were given considerable attention. Calcitriol had shown significant binding affinities with all four receptors than standard neratinib. The RMSD, RMSF, and H-bond analysis of protein-ligand complexes from molecular dynamics simulation (100 ns), confirmed the stable binding of calcitriol with ErbB2 and EGFR receptors. In addition, MMGBSA and MMP BSA also affirmed the docking results. These in-silico results were validated with in-vitro cytotoxicity studies in SK-BR-3 and Vero cells. The IC50 value of calcitriol (43.07 mg/ml) was found to be lower than neratinib (61.50 mg/ml) in SK-BR-3 cells. In Vero cells the IC50 value of calcitriol (431.05 mg/ml) was higher than neratinib (404.95 mg/ml). It demonstrates that calcitriol suggestively downregulated the SK-BR-3 cell viability in a dose-dependent manner. These implications revealed calcitriol has shown better cytotoxicity and decreased the proliferation rate of breast cancer cells than neratinib.Communicated by Ramaswamy H. Sarma.

Construction and analysis of protein-protein interaction networks based on nuclear proteomics data of the desiccation-tolerant Xerophyta schlechteri leaves subjected to dehydration stress

In order to understand the mechanism of desiccation tolerance in Xerophyta schlechteri, we carried out an in silico study to identify hub proteins and functional modules in the nuclear proteome of the leaves. Protein-protein interaction networks were constructed and analyzed from proteome data obtained from Abdalla and Rafudeen. We constructed networks in Cytoscape using the GeneMania software and analyzed them using a Network Analyzer. Functional enrichment analysis of key proteins in the respective networks was done using GeneMania network enrichment analysis, and GO (Gene Ontology) terms were summarized using REViGO. Also, community analysis of differentially expressed proteins was conducted using the Cytoscape Apps, GeneMania and ClusterMaker. Functional modules associated with the communities were identified using an online tool, ShinyGO. We identified HSP 70-2 as the super-hub protein among the up-regulated proteins. On the other hand, 40S ribosomal protein S2-3 (a protein added by GeneMANIA) was identified as a super-hub protein associated with the down-regulated proteins. For up-regulated proteins, the enriched biological process terms were those associated with chromatin organization and negative regulation of transcription. In the down-regulated protein-set, terms associated with protein synthesis were significantly enriched. Community analysis identified three functional modules that can be categorized as chromatin organization, anti-oxidant activity and metabolic processes.

Differentially Expressed Genes in Clear Cell Renal Cell Carcinoma as a Potential Marker for Prognostic and Immune Signatures

Clear cell renal cell carcinoma (ccRCC) is characterized by the inactivation of the von Hippel–Lindau (VHL) gene. Of note, no other gene is mutated as frequently as VHL in ccRCC, turning out that patients with inactivated VHL constitute the majority of ccRCC-related character. Thus, differentially expressed genes (DEGs) and their molecular networks caused by VHL mutation were considered as important factors for influencing the prognosis of ccRCC. Here, we first screened out six DEGs (GSTA1, GSTA2, NAT8, FABP7, SLC17A3, and SLC17A4) which downregulated in ccRCC patients with VHL non-mutation than with the mutation. Generally, most DEGs with high expression were associated with a favorable prognosis and low-risk score. Meanwhile, we spotted transcription factors and their kinases as hubs of DEGs. Finally, we clustered ccRCC patients into three subgroups according to the expression of hub proteins, and analyzed these subgroups with clinical profile, outcome, immune infiltration, and potential Immune checkpoint blockade (ICB) response. Herein, DEGs might be a promising biomarker panel for immunotherapy and prognosis in ccRCC. Moreover, the ccRCC subtype associated with high expression of hubs fit better for ICB therapy.

The Honey Bee Gene Bee Antiviral Protein-1 Is a Taxonomically Restricted Antiviral Immune Gene

Insects have evolved a wide range of strategies to combat invading pathogens, including viruses. Genes that encode proteins involved in immune responses often evolve under positive selection due to their co-evolution with pathogens. Insect antiviral defense includes the RNA interference (RNAi) mechanism, which is triggered by recognition of non-self, virally produced, double-stranded RNAs. Indeed, insect RNAi genes (e.g., dicer and argonaute-2) are under high selective pressure. Honey bees (Apis mellifera) are eusocial insects that respond to viral infections via both sequence specific RNAi and a non-sequence specific dsRNA triggered pathway, which is less well-characterized. A transcriptome-level study of virus-infected and/or dsRNA-treated honey bees revealed increased expression of a novel antiviral gene, GenBank: MF116383, and in vivo experiments confirmed its antiviral function. Due to in silico annotation and sequence similarity, MF116383 was originally annotated as a probable cyclin-dependent serine/threonine-protein kinase. In this study, we confirmed that MF116383 limits virus infection, and carried out further bioinformatic and phylogenetic analyses to better characterize this important gene-which we renamed bee antiviral protein-1 (bap1). Phylogenetic analysis revealed that bap1 is taxonomically restricted to Hymenoptera and Blatella germanica (the German cockroach) and that the majority of bap1 amino acids are evolving under neutral selection. This is in-line with the results from structural prediction tools that indicate Bap1 is a highly disordered protein, which likely has relaxed structural constraints. Assessment of honey bee gene expression using a weighted gene correlation network analysis revealed that bap1 expression was highly correlated with several immune genes-most notably argonaute-2. The coexpression of bap1 and argonaute-2 was confirmed in an independent dataset that accounted for the effect of virus abundance. Together, these data demonstrate that bap1 is a taxonomically restricted, rapidly evolving antiviral immune gene. Future work will determine the role of bap1 in limiting replication of other viruses and examine the signal cascade responsible for regulating the expression of bap1 and other honey bee antiviral defense genes, including coexpressed ago-2, and determine whether the virus limiting function of bap1 acts in parallel or in tandem with RNAi.

The transcription regulator BrsR serves as a network hub of natural competence protein-protein interactions in Streptococcus mutans

Genome evolution is an essential and stringently regulated aspect of biological fitness. For bacteria, natural competence is one of the principal mechanisms of genome evolution and is frequently subject to multiple layers of regulation derived from a plethora of environmental and physiological stimuli. Here, we present a regulatory mechanism that illustrates how such disparate stimuli can be integrated into the Streptococcus mutans natural competence phenotype. S. mutans possesses an intriguing, but poorly understood ability to coordinately control its independently regulated natural competence and bacteriocin genetic pathways as a means to acquire DNA released from closely related, bacteriocin-susceptible streptococci. Our results reveal how the bacteriocin-specific transcription activator BrsR directly mediates this coordination by serving as an anti-adaptor protein responsible for antagonizing the proteolysis of the inherently unstable, natural competence-specific alternative sigma factor ComX. This BrsR ability functions entirely independent of its transcription regulator function and directly modulates the timing and severity of the natural competence phenotype. Additionally, many of the DNA uptake proteins produced by the competence system were surprisingly found to possess adaptor abilities, which are employed to terminate the BrsR regulatory circuit via negative feedback. BrsR-competence protein heteromeric complexes directly inhibit nascent brsR transcription as well as stimulate the Clp-dependent proteolysis of extant BrsR proteins. This study illustrates how critical genetic regulatory abilities can evolve in a potentially limitless variety of proteins without disrupting their conserved ancestral functions. These unrecognized regulatory abilities are likely fundamental for transducing information through complex genetic networks.

Drug Repurposing for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer which presents a high rate of relapse, metastasis, and mortality. Nowadays, the absence of approved specific targeted therapies to eradicate TNBC remains one of the main challenges in clinical practice. Drug discovery is a long and costly process that can be dramatically improved by drug repurposing, which identifies new uses for existing drugs, both approved and investigational. Drug repositioning benefits from improvements in computational methods related to chemoinformatics, genomics, and systems biology. To the best of our knowledge, we propose a novel and inclusive classification of those approaches whereby drug repurposing can be achieved in silico: structure-based, transcriptional signatures-based, biological networks-based, and data-mining-based drug repositioning. This review specially emphasizes the most relevant research, both at preclinical and clinical settings, aimed at repurposing pre-existing drugs to treat TNBC on the basis of molecular mechanisms and signaling pathways such as androgen receptor, adrenergic receptor, STAT3, nitric oxide synthase, or AXL. Finally, because of the ability and relevance of cancer stem cells (CSCs) to drive tumor aggressiveness and poor clinical outcome, we also focus on those molecules repurposed to specifically target this cell population to tackle recurrence and metastases associated with the progression of TNBC.

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions

Molecular recognition features (MoRFs) are short protein-binding regions that undergo disorder-to-order transitions (induced folding) upon binding protein partners. These regions are abundant in nature and can be predicted from protein sequences based on their distinctive sequence signatures. This first-of-its-kind survey covers 14 MoRF predictors and six related methods for the prediction of short protein-binding linear motifs, disordered protein-binding regions and semi-disordered regions. We show that the development of MoRF predictors has accelerated in the recent years. These predictors depend on machine learning-derived models that were generated using training datasets where MoRFs are annotated using putative disorder. Our analysis reveals that they generate accurate predictions. We identified eight methods that offer area under the ROC curve (AUC) ≥ 0.7 on experimentally-validated test datasets. We show that modern MoRF predictors accurately find experimentally annotated MoRFs even though they were trained using the putative disorder annotations. They are relatively highly-cited, particularly the methods available as webservers that on average secure three times more citations than methods without this option. MoRF predictions contribute to the experimental discovery of protein-protein interactions, annotation of protein functions and computational analysis of a variety of proteomes, protein families, and pathways. We outline future development and application directions for these tools, stressing the importance to develop novel tools that would target interactions of disordered regions with other types of partners.

Reciprocal Perspective for Improved Protein-Protein Interaction Prediction

All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method.

Structure, function, and control of the human musculoskeletal network

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle's role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.

Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs

Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.

Interacting partners of FEN1 and its role in the development of anticancer therapeutics

Protein-protein interaction (PPI) plays a key role in cellular communication, Protein-protein interaction connected with each other with hubs and nods involved in signaling pathways. These interactions used to develop network based biomarkers for early diagnosis of cancer. FEN1(Flap endonuclease 1) is a central component in cellular metabolism, over expression and decrease of FEN1 levels may cause cancer, these regulation changes of Flap endonuclease 1reported in many cancer cells, to consider this data may needs to develop a network based biomarker. The current review focused on types of PPI, based on nature, detection methods and its role in cancer. Interacting partners of Flap endonuclease 1 role in DNA replication repair and development of anticancer therapeutics based on Protein-protein interaction data.

At the Interface of Three Nucleic Acids: The Role of RNA-Binding Proteins and Poly(ADP-ribose) in DNA Repair

RNA-binding proteins (RBPs) regulate RNA metabolism, from synthesis to decay. When bound to RNA, RBPs act as guardians of the genome integrity at different levels, from DNA damage prevention to the post-transcriptional regulation of gene expression. Recently, RBPs have been shown to participate in DNA repair. This fact is of special interest as DNA repair pathways do not generally involve RNA. DNA damage in higher organisms triggers the formation of the RNA-like polymer - poly(ADP-ribose) (PAR). Nucleic acid-like properties allow PAR to recruit DNA- and RNA-binding proteins to the site of DNA damage. It is suggested that poly(ADP-ribose) and RBPs not only modulate the activities of DNA repair factors, but that they also play an important role in the formation of transient repairosome complexes in the nucleus. Cytoplasmic biomolecules are subjected to similar sorting during the formation of RNA assemblages by functionally related mRNAs and promiscuous RBPs. The Y-box-binding protein 1 (YB-1) is the major component of cytoplasmic RNA granules. Although YB-1 is a classic RNA-binding protein, it is now regarded as a non-canonical factor of DNA repair.

Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at http://bsaltools.ym.edu.tw/predppis.

Deciphering the cause of evolutionary variance within intrinsically disordered regions in human proteins

Why the intrinsically disordered regions evolve within human proteome has became an interesting question for a decade. Till date, it remains an unsolved yet an intriguing issue to investigate why some of the disordered regions evolve rapidly while the rest are highly conserved across mammalian species. Identifying the key biological factors, responsible for the variation in the conservation rate of different disordered regions within the human proteome, may revisit the above issue. We emphasized that among the other biological features (multifunctionality, gene essentiality, protein connectivity, number of unique domains, gene expression level and expression breadth) considered in our study, the number of unique protein domains acts as a strong determinant that negatively influences the conservation of disordered regions. In this context, we justified that proteins having a fewer types of domains preferably need to conserve their disordered regions to enhance their structural flexibility which in turn will facilitate their molecular interactions. In contrast, the selection pressure acting on the stretches of disordered regions is not so strong in the case of multi-domains proteins. Therefore, we reasoned that the presence of conserved disordered stretches may compensate the functions of multiple domains within a single domain protein. Interestingly, we noticed that the influence of the unique domain number and expression level acts differently on the evolution of disordered regions from that of well-structured ones.

Molecular recognition features (MoRFs) in three domains of life

MoRFs are widespread intrinsically disordered protein-binding regions that have similar abundance and amino acid composition across the three domains of life.

Evolution of specificity in protein-protein interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.

Protein-protein interaction network prediction by using rigid-body docking tools: application to bacterial chemotaxis

Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures. The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation requires massive computational resources, recent advancements in the computational sciences have made such large-scale calculations feasible. different rigid-body docking tools with different scoring models. We found that the predicted interactions were different between the results from the two tools. When the positive predictions from both of the docking tools were combined, all the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation. Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications. This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior.

MEGADOCK: an all-to-all protein-protein interaction prediction system using tertiary structure data

The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock.

Protein-protein interaction networks studies and importance of 3D structure knowledge

Protein-protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes' stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype-phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.

Community-based network study of protein-carbohydrate interactions in plant lectins using glycan array data

Lectins play major roles in biological processes such as immune recognition and regulation, inflammatory responses, cytokine signaling, and cell adhesion. Recently, glycan microarrays have shown to play key roles in understanding glycobiology, allowing us to study the relationship between the specificities of glycan binding proteins and their natural ligands at the omics scale. However, one of the drawbacks in utilizing glycan microarray data is the lack of systematic analysis tools to extract information. In this work, we attempt to group various lectins and their interacting carbohydrates by using community-based analysis of a lectin-carbohydrate network. The network consists of 1119 nodes and 16769 edges and we have identified 3 lectins having large degrees of connectivity playing the roles of hubs. The community based network analysis provides an easy way to obtain a general picture of the lectin-glycan interaction and many statistically significant functional groups.

From sequence and forces to structure, function, and evolution of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs), which lack persistent structure, are a challenge to structural biology due to the inapplicability of standard methods for characterization of folded proteins as well as their deviation from the dominant structure/function paradigm. Their widespread presence and involvement in biological function, however, has spurred the growing acceptance of the importance of IDPs and the development of new tools for studying their structure, dynamics, and function. The interplay of folded and disordered domains or regions for function and the existence of a continuum of protein states with respect to conformational energetics, motional timescales, and compactness are shaping a unified understanding of structure-dynamics-disorder/function relationships. In the 20(th) anniversary of Structure, we provide a historical perspective on the investigation of IDPs and summarize the sequence features and physical forces that underlie their unique structural, functional, and evolutionary properties.

Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods

Background

Elucidation of protein-protein interaction (PPI) networks is important for understanding disease mechanisms and for drug discovery. Tertiary-structure-based in silico PPI prediction methods have been developed with two typical approaches: a method based on template matching with known protein structures and a method based on de novo protein docking. However, the template-based method has a narrow applicable range because of its use of template information, and the de novo docking based method does not have good prediction performance. In addition, both of these in silico prediction methods have insufficient precision, and require validation of the predicted PPIs by biological experiments, leading to considerable expenditure; therefore, PPI prediction methods with greater precision are needed.

Results

We have proposed a new structure-based PPI prediction method by combining template-based prediction and de novo docking prediction. When we applied the method to the human apoptosis signaling pathway, we obtained a precision value of 0.333, which is higher than that achieved using conventional methods (0.231 for PRISM, a template-based method, and 0.145 for MEGADOCK, a non-template-based method), while maintaining an F-measure value (0.285) comparable to that obtained using conventional methods (0.296 for PRISM, and 0.220 for MEGADOCK).

Conclusions

Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.

Promiscuity as a functional trait: intrinsically disordered regions as central players of interactomes

Because of their pervasiveness in eukaryotic genomes and their unique properties, understanding the role that ID (intrinsically disordered) regions in proteins play in the interactome is essential for gaining a better understanding of the network. Especially critical in determining this role is their ability to bind more than one partner using the same region. Studies have revealed that proteins containing ID regions tend to take a central role in protein interaction networks; specifically, they act as hubs, interacting with multiple different partners across time and space, allowing for the co-ordination of many cellular activities. There appear to be three different modules within ID regions responsible for their functionally promiscuous behaviour: MoRFs (molecular recognition features), SLiMs (small linear motifs) and LCRs (low complexity regions). These regions allow for functionality such as engaging in the formation of dynamic heteromeric structures which can serve to increase local activity of an enzyme or store a collection of functionally related molecules for later use. However, the use of promiscuity does not come without a cost: a number of diseases that have been associated with ID-containing proteins seem to be caused by undesirable interactions occurring upon altered expression of the ID-containing protein.

Computational analysis of C-reactive protein for assessment of molecular dynamics and interaction properties

Serum C-reactive protein (CRP) is used as a marker of inflammation in several diseases including autoimmune disease and cardiovascular disease. CRP, a member of the pentraxin family, is comprised of five identical subunits. CRP has diverse ligand-binding properties which depend upon different structural states of CRP. However, little is known about the molecular dynamics and interaction properties of CRP. In this study, we used SAPS, SCRATCH protein predictor, PDBsum, ConSurf, ProtScale, Drawhca, ASAView, SCide and SRide server and performed comprehensive analyses of molecular dynamics, protein-protein and residue-residue interactions of CRP. We used 1GNH.pdb file for the crystal structure of human CRP which generated two pentamers (ABCDE and FGHIJ). The number of residues involved in residue-residue interactions between A-B, B-C, C-D, D-E, F-G, G-H, H-I, I-J, A-E and F-J subunits were 12, 11, 10, 11, 12, 11, 10, 11, 10 and 10, respectively. Fifteen antiparallel β sheets were involved in β-sheet topology, and five β hairpins were involved in forming the secondary structure. Analysis of hydrophobic segment distribution revealed deviations in surface hydrophobicity at different cavities present in CRP. Approximately 33 % of all residues were involved in the stabilization centers. We show that the bioinformatics tools can provide a rapid method to predict molecular dynamics and interaction properties of CRP. Our prediction of molecular dynamics and interaction properties of CRP combined with the modeling data based on the known 3D structure of CRP is helpful in designing stable forms of CRP mutants for structure-function studies of CRP and may facilitate in silico drug design for therapeutic targeting of CRP.

Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles

The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein–protein interaction prediction and design methods.

Regulation of protein-protein binding by coupling between phosphorylation and intrinsic disorder: analysis of human protein complexes

Phosphorylation offers a dynamic way to regulate protein activity, subcellular localization, and stability. The majority of signaling pathways involve an extensive set of protein-protein interactions, and phosphorylation is widely used to regulate protein-protein binding by affecting the stability, kinetics and specificity of interactions. Previously it was found that phosphorylation sites tend to be located on protein-protein binding interfaces and may orthosterically modulate the strength of interactions. Here we studied the effect of phosphorylation on protein binding in relation to intrinsic disorder for different types of human protein complexes with known structure of the binding interface. Our results suggest that the processes of phosphorylation, binding and disorder-order transitions are coupled to each other, with about one quarter of all disordered interface Ser/Thr/Tyr sites being phosphorylated. Namely, residue site disorder and interfacial states significantly affect the phosphorylation of serine and to a lesser extent of threonine. Tyrosine phosphorylation might not be directly associated with binding through disorder, and is often observed in ordered interface regions which are not predicted to be disordered in the unbound state. We analyze possible mechanisms of how phosphorylation might regulate protein-protein binding via intrinsic disorder, and specifically focus on how phosphorylation could prevent disorder-order transitions upon binding.

Exploring the binding diversity of intrinsically disordered proteins involved in one-to-many binding

Molecular recognition features (MoRFs) are intrinsically disordered protein regions that bind to partners via disorder-to-order transitions. In one-to-many binding, a single MoRF binds to two or more different partners individually. MoRF-based one-to-many protein-protein interaction (PPI) examples were collected from the Protein Data Bank, yielding 23 MoRFs bound to 2-9 partners, with all pairs of same-MoRF partners having less than 25% sequence identity. Of these, 8 MoRFs were bound to 2-9 partners having completely different folds, whereas 15 MoRFs were bound to 2-5 partners having the same folds but with low sequence identities. For both types of partner variation, backbone and side chain torsion angle rotations were used to bring about the conformational changes needed to enable close fits between a single MoRF and distinct partners. Alternative splicing events (ASEs) and posttranslational modifications (PTMs) were also found to contribute to distinct partner binding. Because ASEs and PTMs both commonly occur in disordered regions, and because both ASEs and PTMs are often tissue-specific, these data suggest that MoRFs, ASEs, and PTMs may collaborate to alter PPI networks in different cell types. These data enlarge the set of carefully studied MoRFs that use inherent flexibility and that also use ASE-based and/or PTM-based surface modifications to enable the same disordered segment to selectively associate with two or more partners. The small number of residues involved in MoRFs and in their modifications by ASEs or PTMs may simplify the evolvability of signaling network diversity.

Dynamic hubs show competitive and static hubs non-competitive regulation of their interaction partners

Date hub proteins have 1 or 2 interaction interfaces but many interaction partners. This raises the question of whether all partner proteins compete for the interaction interface of the hub or if the cell carefully regulates aspects of this process? Here, we have used real-time rendering of protein interaction networks to analyse the interactions of all the 1 or 2 interface hubs of Saccharomyces cerevisiae during the cell cycle. By integrating previously determined structural and gene expression data, and visually hiding the nodes (proteins) and their edges (interactions) during their troughs of expression, we predict when interactions of hubs and their partners are likely to exist. This revealed that 20 out of all 36 one- or two- interface hubs in the yeast interactome fell within two main groups. The first was dynamic hubs with static partners, which can be considered as ‘competitive hubs’. Their interaction partners will compete for the interaction interface of the hub and the success of any interaction will be dictated by the kinetics of interaction (abundance and affinity) and subcellular localisation. The second was static hubs with dynamic partners, which we term ‘non-competitive hubs’. Regulatory mechanisms are finely tuned to lessen the presence and/or effects of competition between the interaction partners of the hub. It is possible that these regulatory processes may also be used by the cell for the regulation of other, non-cell cycle processes.

Conditional disorder in chaperone action

Protein disorder remains an intrinsically fuzzy concept. Its role in protein function is difficult to conceptualize and its experimental study is challenging. Although a wide variety of roles for protein disorder have been proposed, establishing that disorder is functionally important, particularly in vivo, is not a trivial task. Several molecular chaperones have now been identified as conditionally disordered proteins; fully folded and chaperone-inactive under non-stress conditions, they adopt a partially disordered conformation upon exposure to distinct stress conditions. This disorder appears to be vital for their ability to bind multiple aggregation-sensitive client proteins and to protect cells against the stressors. The study of these conditionally disordered chaperones should prove useful in understanding the functional role for protein disorder in molecular recognition.

A small molecule screen in yeast identifies inhibitors targeting protein-protein interactions within the vaccinia virus replication complex

Genetic and biochemical data have identified at least four viral proteins essential for vaccinia virus (VACV) DNA synthesis: the DNA polymerase E9, its processivity factor (the heterodimer A20/D4) and the primase/helicase D5. These proteins are part of the VACV replication complex in which A20 is a central subunit interacting with E9, D4 and D5. We hypothesised that molecules able to modulate protein-protein interactions within the replication complex may represent a new class of compounds with anti-orthopoxvirus activities. In this study, we adapted a forward duplex yeast two-hybrid assay to screen more than 27,000 molecules in order to identify inhibitors of A20/D4 and/or A20/D5 interactions. We identified two molecules that specifically inhibited both interactions in yeast. Interestingly, we observed that these compounds displayed a similar antiviral activity to cidofovir (CDV) against VACV in cell culture. We further showed that these molecules were able to inhibit the replication of another orthopoxvirus (i.e. cowpox virus), but not the herpes simplex virus type 1 (HSV-1), an unrelated DNA virus. We also demonstrated that the antiviral activity of both compounds correlated with an inhibition of VACV DNA synthesis. Hence, these molecules may represent a starting point for the development of new anti-orthopoxvirus drugs.

Detection of protein complexes using a protein ranking algorithm

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method.

The network interaction of the human cytosolic 90 kDa heat shock protein Hsp90: A target for cancer therapeutics

In the cell, proteins interact within a network in which a small number of proteins are highly connected nodes or hubs. A disturbance in the hub proteins usually has a higher impact on the cell physiology than a disturbance in poorly connected nodes. In eukaryotes, the cytosolic Hsp90 is considered to be a hub protein as it interacts with molecular chaperones and co-chaperones, and has key regulatory proteins as clients, such as transcriptional factors, protein kinases and hormone receptors. The large number of Hsp90 partners suggests that Hsp90 is involved in very important functions, such as signaling, proteostasis and epigenetics. Some of these functions are dysregulated in cancer, making Hsp90 a potential target for therapeutics. The number of Hsp90 interactors appears to be so large that a precise answer to the question of how many proteins interact with this chaperone has no definitive answer yet, not even if the question refers to specific Hsp90s as one of the human cytosolic forms. Here we review the major chaperones and co-chaperones that interact with cytosolic Hsp90s, highlighting the latest findings regarding client proteins and the role that posttranslational modifications have on the function and interactions of these molecular chaperones. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Identifying solubility-promoting buffers for intrinsically disordered proteins prior to purification

Intrinsically disordered proteins are anticipated to be more prone to aggregation than folded, stable proteins. Chemical additives included in the buffer can help maintain proteins in a soluble, monomeric state. However, the array of chemicals that impact protein solubility is staggering, precluding iterative testing of chemical conditions during purification. Herein, we describe a filter-based aggregation assay to rapidly identify chemical additives that maintain solubility for a protein of interest. A hierarchical approach to buffer selection is provided, in which the type of chemical which best improves solubility is first determined, followed by identifying the optimal chemical and its most effective concentration. Finally, combinations of chemical additives can be assessed if necessary. Although this assay can be applied to purified protein, partially purified protein, or aggregated protein, this protocol specifically details the use of this assay for crude cell lysate. This approach allows identification of solubility-promoting buffers prior to the initial protein purification.

Assessing the utility of gene co-expression stability in combination with correlation in the analysis of protein-protein interaction networks

Background

Gene co-expression, in the form of a correlation coefficient, has been valuable in the analysis, classification and prediction of protein-protein interactions. However, it is susceptible to bias from a few samples having a large effect on the correlation coefficient. Gene co-expression stability is a means of quantifying this bias, with high stability indicating robust, unbiased co-expression correlation coefficients. We assess the utility of gene co-expression stability as an additional measure to support the co-expression correlation in the analysis of protein-protein interaction networks.

Results

We studied the patterns of co-expression correlation and stability in interacting proteins with respect to their interaction promiscuity, levels of intrinsic disorder, and essentiality or disease-relatedness. Co-expression stability, along with co-expression correlation, acts as a better classifier of hub proteins in interaction networks, than co-expression correlation alone, enabling the identification of a class of hubs that are functionally distinct from the widely accepted transient (date) and obligate (party) hubs. Proteins with high levels of intrinsic disorder have low co-expression correlation and high stability with their interaction partners suggesting their involvement in transient interactions, except for a small group that have high co-expression correlation and are typically subunits of stable complexes. Similar behavior was seen for disease-related and essential genes. Interacting proteins that are both disordered have higher co-expression stability than ordered protein pairs. Using co-expression correlation and stability, we found that transient interactions are more likely to occur between an ordered and a disordered protein while obligate interactions primarily occur between proteins that are either both ordered, or disordered.

Conclusions

We observe that co-expression stability shows distinct patterns in structurally and functionally different groups of proteins and interactions. We conclude that it is a useful and important measure to be used in concert with gene co-expression correlation for further insights into the characteristics of proteins in the context of their interaction network.

Intrinsic protein disorder in human pathways

We analyze human-specific KEGG pathways trying to understand the functional role of intrinsic disorder in proteins. Pathways provide a comprehensive picture of biological processes and allow better understanding of a protein's function within the specific context of its surroundings. Our study pinpoints a few specific pathways significantly enriched in disorder-containing proteins and identifies the role of these proteins within the framework of pathway relationships. Three major categories of relations are shown to be significantly enriched in disordered proteins: gene expression, protein binding and to a lesser degree, protein phosphorylation. Finally we find that relations involving protein activation and to some extent inhibition are characterized by low disorder content.

Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions

The integration of molecular networks with other types of data, such as changing levels of gene expression or protein-structural features, can provide richer information about interactions than the simple node-and-edge representations commonly used in the network community. For example, the mapping of 3D-structural data onto networks enables classification of proteins into singlish- or multi-interface hubs (depending on whether they have >2 interfaces). Similarly, interactions can be classified as permanent or transient, depending on whether their interface is used by only one or by multiple partners. Here, we incorporate an additional dimension into molecular networks: dynamic conformational changes. We parse the entire PDB structural databank for alternate conformations of proteins and map these onto the protein interaction network, to compile a first version of the Dynamic Structural Interaction Network (DynaSIN). We make this network available as a readily downloadable resource file, and we then use it to address a variety of downstream questions. In particular, we show that multi-interface hubs display a greater degree of conformational change than do singlish-interface ones; thus, they show more plasticity which perhaps enables them to utilize more interfaces for interactions. We also find that transient associations involve smaller conformational changes than permanent ones. Although this may appear counterintuitive, it is understandable in the following framework: as proteins involved in transient interactions shuttle between interchangeable associations, they interact with domains that are similar to each other and so do not require drastic structural changes for their activity. We provide evidence for this hypothesis through showing that interfaces involved in transient interactions bind fewer classes of domains than those in a control set.

Structure and function of the N-terminal nucleolin binding domain of nuclear valosin-containing protein-like 2 (NVL2) harboring a nucleolar localization signal

The N-terminal regions of AAA-ATPases (ATPase associated with various cellular activities) often contain a domain that defines the distinct functions of the enzymes, such as substrate specificity and subcellular localization. As described herein, we have determined the solution structure of an N-terminal unique domain isolated from nuclear valosin-containing protein (VCP)-like protein 2 (NVL2(UD)). NVL2(UD) contains three α helices with an organization resembling that of a winged helix motif, whereas a pair of β-strands is missing. The structure is unique and distinct from those of other known type II AAA-ATPases, such as VCP. Consequently, we identified nucleolin from a HeLa cell extract as a binding partner of this domain. Nucleolin contains a long (∼300 amino acids) intrinsically unstructured region, followed by the four tandem RNA recognition motifs and the C-terminal glycine/arginine-rich domain. Binding analyses revealed that NVL2(UD) potentially binds to any of the combinations of two successive RNA binding domains in the presence of RNA. Furthermore, NVL2(UD) has a characteristic loop, in which the key basic residues RRKR are exposed to the solvent at the edge of the molecule. The mutation study showed that these residues are necessary and sufficient for nucleolin-RNA complex binding as well as nucleolar localization. Based on the observations presented above, we propose that NVL2 serves as an unfoldase for the nucleolin-RNA complex. As inferred from its RNA dependence and its ATPase activity, NVL2 might facilitate the dissociation and recycling of nucleolin, thereby promoting efficient ribosome biogenesis.

Transient protein-protein interactions: structural, functional, and network properties

Transient interactions, which involve protein interactions that are formed and broken easily, are important in many aspects of cellular function. Here we describe structural and functional properties of transient interactions between globular domains and between globular domains, short peptides, and disordered regions. The importance of posttranslational modifications in transient interactions is also considered. We review techniques used in the detection of the different types of transient protein-protein interactions. We also look at the role of transient interactions within protein-protein interaction networks and consider their contribution to different aspects of these networks.

Systematic bioinformatics and experimental validation of yeast complexes reduces the rate of attrition during structural investigations

For high-throughput structural studies of protein complexes of composition inferred from proteomics data, it is crucial that candidate complexes are selected accurately. Herein, we exemplify a procedure that combines a bioinformatics tool for complex selection with in vivo validation, to deliver structural results in a medium-throughout manner. We have selected a set of 20 yeast complexes, which were predicted to be feasible by either an automated bioinformatics algorithm, by manual inspection of primary data, or by literature searches. These complexes were validated with two straightforward and efficient biochemical assays, and heterologous expression technologies of complex components were then used to produce the complexes to assess their feasibility experimentally. Approximately one-half of the selected complexes were useful for structural studies, and we detail one particular success story. Our results underscore the importance of accurate target selection and validation in avoiding transient, unstable, or simply nonexistent complexes from the outset.

Functional dissection of an intrinsically disordered protein: understanding the roles of different domains of Knr4 protein in protein-protein interactions

Knr4, recently characterized as an intrinsically disordered Saccharomyces cerevisiae protein, participates in cell wall formation and cell cycle regulation. It is constituted of a functional central globular core flanked by a poorly structured N-terminal and large natively unfolded C-terminal domains. Up to now, about 30 different proteins have been reported to physically interact with Knr4. Here, we used an in vivo two-hybrid system approach and an in vitro surface plasmon resonance (BIAcore) technique to compare the interaction level of different Knr4 deletion variants with given protein partners. We demonstrate the indispensability of the N-terminal domain of Knr4 for the interactions. On the other hand, presence of the unstructured C-terminal domain has a negative effect on the interaction strength. In protein interactions networks, the most highly connected proteins or "hubs" are significantly enriched in unstructured regions, and among them the transient hub proteins contain the largest and most highly flexible regions. The results presented here of our analysis of Knr4 protein suggest that these large disordered regions are not always involved in promoting the protein-protein interactions of hub proteins, but in some cases, might rather inhibit them. We propose that this type of regions could prevent unspecific protein interactions, or ensure the correct timing of occurrence of transient interactions, which may be of crucial importance for different signaling and regulation processes.

Intrinsic disorder and protein multibinding in domain, terminal, and linker regions

Intrinsic disorder is believed to contribute to the ability of some proteins to interact with multiple partners which is important for protein functional promiscuity and regulation of the cross-talk between pathways. To better understand the mechanisms of molecular recognition through disordered regions, here, we systematically investigate the coupling between disorder and binding within domain families in a structure interaction network and in terminal and inter-domain linker regions. We showed that the canonical domain-domain interaction model should take into account contributions of N- and C-termini and inter-domain linkers, which may form all or part of the binding interfaces. For the majority of proteins, binding interfaces on domain and terminal regions were predicted to be less disordered than non-interface regions. Analysis of all domain families revealed several exceptions, such as kinases, DNA/RNA binding proteins, certain enzymes, and regulatory proteins, which are candidates for disorder-to-order transitions that can occur upon binding. Domain interfaces that bind single or multiple partners do not exhibit significant difference in disorder content if normalized by the number of interactions. In general, protein families with more diverse interactions exhibit less average disorder over all members of the family. Our results shed light on recent controversies regarding the relationship between disorder and binding of multiple partners at common interfaces. In particular, they support the hypothesis that protein domains with many interacting partners should have a pleiotropic effect on functional pathways and consequently might be more constrained in evolution.

Analysis of protein-protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes

Background

Protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. Here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. This combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles.

Results

To test for the applicability of this combined method, various CaM-ligand complexes were reconstructed from the NMR structures of unbound CaM. For the purpose of reconstruction, we used three known CaM-ligands, namely, the CaM-binding peptides of cyclic nucleotide gateway (CNG), CaM kinase kinase (CaMKK) and the plasma membrane Ca²⁺ ATPase pump (PMCA), and thirty-one structurally diverse CaM conformations. For each ligand, 62000 CaM-ligand complexes were generated in the docking step and the relationship between their energy profiles and structural similarities to the native complex were analyzed using interaction fingerprint and RMSD. Near-native clusters were obtained in the case of CNG and CaMKK.

Conclusions

The interaction fingerprint method discriminated near-native structures better than the RMSD method in cluster analysis. We showed that a combined method that includes the interaction fingerprint is very useful for protein-protein docking analysis of certain cases.

Analysis of hot region organization in hub proteins

Protein interaction maps constructed from binary interactions reveal that some proteins are highly connected to others (acting as hub proteins), whereas some others have a few interactions (at the edges of the map). This paper addresses hub proteins from a structural point: interfaces. It investigates how hot spots are organized in hub proteins (hot regions). We annotate interfaces as the ones between two date-hubs (DD), two party hubs (PP), and two non-hubs (NN). We investigate the physico-chemical properties of these three types of interfaces focusing on the accessible surface area distribution, hot region organization, and amino acid composition differences. Results reveal that there are significant differences between DD and PP interfaces. More of the hot spots are organized into the hot regions in DD interfaces compared to PP ones. A high fraction of the interfaces are covered by hot regions in DD interfaces. There are more distinct hot regions in DDs. Since the same (or overlapping) DD interfaces should be used repeatedly, different hot regions can be used to bind to different partners. Further, these hot region characteristics can be used to predict whether a given hub interface is involved in a DD or a PP interface type with 80% accuracy.

Hub promiscuity in protein-protein interaction networks

Hubs are proteins with a large number of interactions in a protein-protein interaction network. They are the principal agents in the interaction network and affect its function and stability. Their specific recognition of many different protein partners is of great interest from the structural viewpoint. Over the last few years, the structural properties of hubs have been extensively studied. We review the currently known features that are particular to hubs, possibly affecting their binding ability. Specifically, we look at the levels of intrinsic disorder, surface charge and domain distribution in hubs, as compared to non-hubs, along with differences in their functional domains.

Human cancer protein-protein interaction network: a structural perspective

Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.

Protein-protein interaction networks provide a global picture of cellular function and biological processes. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. The structural details of interfaces are immensely useful in efforts to answer some fundamental questions such as: (i) what features of cancer-related protein interfaces make them act as hubs; (ii) how hub protein interfaces can interact with tens of other proteins with varying affinities; and (iii) which interactions can occur simultaneously and which are mutually exclusive. Addressing these questions, we propose a method to characterize interactions in a human protein-protein interaction network using three-dimensional protein structures and interfaces. Protein interface analysis shows that the strength and specificity of the interactions of hub proteins and cancer proteins are different than the interactions of non-hub and non-cancer proteins, respectively. In addition, distinguishing overlapping from non-overlapping interfaces, we illustrate how a fourth dimension, that of the sequence of processes, is integrated into the network with case studies. We believe that such an approach should be useful in structural systems biology.

Exploring functional roles of multibinding protein interfaces

Cellular processes are highly interconnected and many proteins are shared in different pathways. Some of these shared proteins or protein families may interact with diverse partners using the same interface regions; such multibinding proteins are the subject of our study. The main goal of our study is to attempt to decipher the mechanisms of specific molecular recognition of multiple diverse partners by promiscuous protein regions. To address this, we attempt to analyze the physicochemical properties of multibinding interfaces and highlight the major mechanisms of functional switches realized through multibinding. We find that only 5% of protein families in the structure database have multibinding interfaces, and multibinding interfaces do not show any higher sequence conservation compared with the background interface sites. We highlight several important functional mechanisms utilized by multibinding families. (a) Overlap between different functional pathways can be prevented by the switches involving nearby residues of the same interfacial region. (b) Interfaces can be reused in pathways where the substrate should be passed from one protein to another sequentially. (c) The same protein family can develop different specificities toward different binding partners reusing the same interface; and finally, (d) inhibitors can attach to substrate binding sites as substrate mimicry and thereby prevent substrate binding.