Cliques in mitotic spindle network bring kinetochore‐associated complexes to form dependence pathway

The mitotic spindle is an essential molecular machine for chromosome segregation during mitosis. Achieving a better understanding of its organization at the topological level remains a daunting task. To determine the functional connections among 137 mitotic spindle proteins, a protein–protein interaction network among queries was constructed. Many hub proteins, which connect more than one query and serve as highly plausible candidates for expanding the mitotic spindle proteome, are ranked by conventional degree centrality and a new subnetwork specificity score. Evaluation of the ranking results by literature reviews and empirical verification of SEPT6, a novel top‐ranked hub, suggests that the subnetwork specificity score could enrich for putative spindle‐related proteins. Topological analysis of this expanded network shows the presence of 30 3‐cliques and six 4‐cliques (fully connected subgraphs) that, respectively, reside in eight kinetochore‐associated complexes, of which seven are evolution conserved. Notably, these complexes strikingly form dependence pathways for the assembly of the kinetochore complex. These analyses indicate the feasibility of using network topology, i.e. cliques, to uncover novel pathways to accelerate our understanding of potential biological processes.     

ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

PPI spider: A tool for the interpretation of proteomics data in the context of protein–protein interaction networks

Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein–protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web‐based tool, PPI spider ( http://mips.helmholtz‐muenchen.de/proj/ppispider ). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.     

Structural templates for modeling homodimers

Oligomeric proteins are more abundant in nature than monomeric proteins, and involved in all biological processes. In the absence of an experimental structure, their subunits can be modeled from their sequence like monomeric proteins, but reliable procedures to build the oligomeric assembly are scarce. Template‐based methods, which start from known protein structures, are commonly applied to model subunits. We present a method to model homodimers that relies on a structural alignment of the subunits, and test it on a set of 511 target structures recently released by the Protein Data Bank, taking as templates the earlier released structures of 3108 homodimeric proteins (H‐set), and 2691 monomeric proteins that form dimer‐like assemblies in crystals (M‐set). The structural alignment identifies a H‐set template for 97% of the targets, and in half of the cases, it yields a correct model of the dimer geometry and residue–residue contacts in the target. It also identifies a M‐set template for most of the targets, and some of the crystal dimers are very similar to the target homodimers. The procedure efficiently detects homology at low levels of sequence identities, and points to erroneous quaternary structures in the Protein Data Bank. The high coverage of the target set suggests that the content of the Protein Data Bank already approaches the structural diversity of protein assemblies in nature, and that template‐based methods should become the choice method for modeling oligomeric as well as monomeric proteins.     

Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

The aim of the present study was to explore the underlying mechanisms involved in gastric cancer (GC) formation using data‐independent acquisition (DIA) quantitative proteomics analysis. We identified the differences in protein expression and related functions involved in biological metabolic processes in GC. Totally, 745 differentially expressed proteins (DEPs) were found in GC tissues vs. gastric normal tissues. Despite enormous complexity in the details of the underlying regulatory network, we find that clusters of proteins from the DEPs were mainly involved in 38 pathways. All of the identified DEPs involved in oxidative phosphorylation were down‐regulated. Moreover, GC possesses significantly altered biological metabolic processes, such as NADH dehydrogenase complex assembly and tricarboxylic acid cycle, which is mostly consistent with that in KEGG analysis. Furthermore the higher expression of UQCRQ, NDUFB7 and UQCRC2 were positively correlated with a better prognosis, implicating these proteins may as novel candidate diagnostic and prognostic biomarkers.     

The Bacillus subtilis spore coat protein interaction network

Bacterial spores are surrounded by a morphologically complex, mechanically flexible protein coat, which protects the spore from toxic molecules. The interactions among the over 50 proteins that make up the coat remain poorly understood. We have used cell biological and protein biochemical approaches to identify novel coat proteins in Bacillus subtilis and describe the network of their interactions, in order to understand coat assembly and the molecular basis of its protective functions and mechanical properties. Our analysis characterizes the interactions between 32 coat proteins. This detailed view reveals a complex interaction network. A key feature of the network is the importance of a small subset of proteins that direct the assembly of most of the coat. From an analysis of the network topology, we propose a model in which low-affinity interactions are abundant in the coat and account, to a significant degree, for the coat's mechanical properties as well as structural variation between spores.     

Predicting the helix‐helix interactions from correlated residue mutations

Helix‐helix interactions are crucial in the structure assembly, stability and function of helix‐rich proteins including many membrane proteins. In spite of remarkable progresses over the past decades, the accuracy of predicting protein structures from their amino acid sequences is still far from satisfaction. In this work, we focused on a simpler problem, the prediction of helix‐helix interactions, the results of which could facilitate practical protein structure prediction by constraining the sampling space. Specifically, we started from the noisy 2D residue contact maps derived from correlated residue mutations, and utilized ridge detection to identify the characteristic residue contact patterns for helix‐helix interactions. The ridge information as well as a few additional features were then fed into a machine learning model HHConPred to predict interactions between helix pairs. In an independent test, our method achieved an F‐measure of ～60% for predicting helix‐helix interactions. Moreover, although the model was trained mainly using soluble proteins, it could be extended to membrane proteins with at least comparable performance relatively to previous approaches that were generated purely using membrane proteins. All data and source codes are available at http://166.111.152.91/Downloads.html or https://github.com/dpxiong/HHConPred .     

Disorder transitions and conformational diversity cooperatively modulate biological function in proteins

Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered‐disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure‐function relationships related to order‐disorder transitions.     

Assembly,Target‐Signaling and Intracellular Transport of Tyrosinase Gene Family Proteins in the Initial Stage of Melanosome Biogenesis

Assembly, target‐signaling and transport of tyrosinase gene family proteins at the initial stage of melanosome biogenesis are reviewed based on our own discoveries. Melanosome biogenesis involves four stages of maturation with distinct morphological and biochemical characteristics that reflect distinct processes of the biosynthesis of structural and enzymatic proteins, subsequent structural organization and melanin deposition occurring in these particular cellular compartments. The melanosomes share many common biological properties with the lysosomes. The stage I melanosomes appear to be linked to the late endosomes. Most of melanosomal proteins are glycoproteins that should be folded or assembled correctly in the ER through interaction with calnexin, a chaperone associated with melanogenesis. These melanosomal glycoproteins are then accumulated in the trans Golgi network (TGN) and transported to the melanosomal compartment. During the formation of transport vesicles, coat proteins assemble on the cytoplasmic face of TGN to select their cargos by interacting directly or indirectly with melanosomal glycoproteins to be transported. Adapter protein‐3 (AP‐3) is important for intracellular transport of tyrosinase gene family proteins from TGN to melanosomes. Tyrosinase gene family proteins possess a di‐leucine motif in their cytoplasmic tail, to which AP‐3 appears to bind. Thus, the initial cascade of melanosome biogenesis is regulated by several factors including: 1) glycosylation of tyrosinase gene family proteins and their correct folding and assembly within ER and Golgi, and 2) supply of specific signals necessary for intracellular transport of these glycoproteins by vesicles from Golgi to melanosomes.     

Discovery of multiple interacting partners of gankyrin,a proteasomal chaperone and an oncoprotein—Evidence for a common hot spot site at the interface and its functional relevance

Gankyrin, a non‐ATPase component of the proteasome and a chaperone of proteasome assembly, is also an oncoprotein. Gankyrin regulates a variety of oncogenic signaling pathways in cancer cells and accelerates degradation of tumor suppressor proteins p53 and Rb. Therefore gankyrin may be a unique hub integrating signaling networks with the degradation pathway. To identify new interactions that may be crucial in consolidating its role as an oncogenic hub, crystal structure of gankyrin‐proteasome ATPase complex was used to predict novel interacting partners. EEVD, a four amino acid linear sequence seems a hot spot site at this interface. By searching for EEVD in exposed regions of human proteins in PDB database, we predicted 34 novel interactions. Eight proteins were tested and seven of them were found to interact with gankyrin. Affinity of four interactions is high enough for endogenous detection. Others require gankyrin overexpression in HEK 293 cells or occur endogenously in breast cancer cell line‐ MDA‐MB‐435, reflecting lower affinity or presence of a deregulated network. Mutagenesis and peptide inhibition confirm that EEVD is the common hot spot site at these interfaces and therefore a potential polypharmacological drug target. In MDA‐MB‐231 cells in which the endogenous CLIC1 is silenced, trans‐expression of Wt protein (CLIC1_EEVD) and not the hot spot site mutant (CLIC1_AAVA) resulted in significant rescue of the migratory potential. Our approach can be extended to identify novel functionally relevant protein‐protein interactions, in expansion of oncogenic networks and in identifying potential therapeutic targets. Proteins 2014; 82:1283–1300. © 2013 Wiley Periodicals, Inc.     

Heat induced stress proteins and the concept of molecular chaperones

Heat stress proteins can be assigned to eleven protein families conserved among bacteria, plants and animals. Most of them aid other proteins to maintain or regain their native conformation by stabilizing partially unfolded states. Hence, they are called molecular chaperones. Experimental data indicate that many of them form heterooligomeric complexes, so-called chaperone machines, interacting with each other to generate a network for maturation, assembly and intracellular targeting of proteins. In this review we summarize the essential information on the structure and function of chaperone and chaperone complexes. In addition we present a compilation ofin vivo andin vivo test systems used in the preceding ten years of chaperone research.     

Global microRNA level regulation of EGFR‐driven cell‐cycle protein network in breast cancer

The EGFR‐driven cell‐cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large‐scale miRNA screening approach with a high‐throughput proteomic readout and network‐based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3′‐UTR of target genes. Furthermore, the novel network‐analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co‐regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR‐124, miR‐147 and miR‐193a‐3p) as novel tumor suppressors that co‐target EGFR‐driven cell‐cycle network proteins and inhibit cell‐cycle progression and proliferation in breast cancer.     

Molecular basis of the head‐to‐tail assembly of giant muscle proteins obscurin‐like 1 and titin

Large filament proteins in muscle sarcomeres comprise many immunoglobulin‐like domains that provide a molecular platform for self‐assembly and interactions with heterologous protein partners. We have unravelled the molecular basis for the head‐to‐tail interaction of the carboxyl terminus of titin and the amino‐terminus of obscurin‐like‐1 by X‐ray crystallography. The binary complex is formed by a parallel intermolecular β‐sheet that presents a novel immunoglobulin‐like domain‐mediated assembly mechanism in muscle filament proteins. Complementary binding data show that the assembly is entropy‐driven rather than dominated data by specific polar interactions. The assembly observed leads to a V‐shaped zipper‐like arrangement of the two filament proteins.     

Information Flow Analysis of Interactome Networks

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.     

Distinctive Network Topology of Phase-Separated Proteins in Human Interactome

Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase-separated proteins at proteome-scale is a challenging task. Multivalent interaction among macromolecules is the driving force of LLPS, which suggests that phase-separated proteins may harbor distinct biological characteristics in protein–protein interactions (PPIs). In this study, we constructed an integrated human PPI network (HPIN) and mapped phase-separated proteins into it. Analysis of the network parameters revealed differences of network topology between phase-separated proteins and others. The results further suggested the efficiency when applying topological similarities in distinguishing components of MLOs. Furthermore, we found that affinity purification mass spectrometry (AP-MS) detects PPIs more effectively than yeast-two hybrid system (Y2H) in phase separation-driven condensates. Our work provides the first global view of the distinct network topology of phase-separated proteins in human interactome, suggesting incorporation of PPI network for LLPS prediction in further studies.     

The amino-conserved domain of human cytomegalovirus UL80a proteins is required for key interactions during early stages of capsid formation and virus production

Assembly of many spherical virus capsids is guided by an internal scaffolding protein or group of proteins that are often cleaved and eliminated in connection with maturation and incorporation of the genome. In cytomegalovirus there are at least two proteins that contribute to this scaffolding function; one is the maturational protease precursor (pUL80a), and the other is the assembly protein precursor (pUL80.5) encoded by a shorter genetic element within UL80a. Yeast GAL4 two-hybrid assays established that both proteins contain a carboxyl-conserved domain that is required for their interaction with the major capsid protein (pUL86) and an amino-conserved domain (ACD) that is required for their self-interaction and for their interaction with each other. In the work reported here, we demonstrate that when the ACD is deleted (deltaACD) or disrupted by a point mutation (L47A), the bacterially expressed mutant protein sediments as a monomer during rate-velocity centrifugation, whereas the wild-type protein sediments mainly as oligomers. We also show that the L47A mutation reduces the production of infectious virus by at least 90%, results in the formation of irregular nuclear capsids, gives rise to tube-like structures in the nucleus that resemble the capsid core in cross-section and contain UL80 proteins, slows nuclear translocation of the major capsid protein, and may slow cleavage by the maturational protease. We provide physical corroboration that mutating the ACD disrupts self-interaction of the UL80 proteins and biological support for the proposal that the ACD has a critical role in capsid assembly and production of infectious virus.