Systematic computational prediction of protein interaction networks

Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use.     

Crystal structure of the motor domain of the human kinetochore protein CENP-E

The human kinetochore is a highly complex macromolecular structure that connects chromosomes to spindle microtubules (MTs) in order to facilitate accurate chromosome segregation. Centromere-associated protein E (CENP-E), a member of the kinesin superfamily, is an essential component of the kinetochore, since it is required to stabilize the attachment of chromosomes to spindle MTs, to develop tension across aligned chromosomes, to stabilize spindle poles and to satisfy the mitotic checkpoint. Here we report the 2.5A resolution crystal structure of the motor domain and linker region of human CENP-E with MgADP bound in the active site. This structure displays subtle but important differences compared to the structures of human Eg5 and conventional kinesin. Our structure reveals that the CENP-E linker region is in a "docked" position identical to that in the human plus-end directed conventional kinesin. CENP-E has many advantages as a potential anti-mitotic drug target and this crystal structure of human CENP-E will provide a starting point for high throughput virtual screening of potential inhibitors.     

Global protein function prediction from protein-protein interaction networks

Determining protein function is one of the most challenging problems of the post-genomic era. The availability of entire genome sequences and of high-throughput capabilities to determine gene coexpression patterns has shifted the research focus from the study of single proteins or small complexes to that of the entire proteome. In this context, the search for reliable methods for assigning protein function is of primary importance. There are various approaches available for deducing the function of proteins of unknown function using information derived from sequence similarity or clustering patterns of co-regulated genes, phylogenetic profiles, protein-protein interactions (refs. 5-8 and Samanta, M.P. and Liang, S., unpublished data), and protein complexes. Here we propose the assignment of proteins to functional classes on the basis of their network of physical interactions as determined by minimizing the number of protein interactions among different functional categories. Function assignment is proteome-wide and is determined by the global connectivity pattern of the protein network. The approach results in multiple functional assignments, a consequence of the existence of multiple equivalent solutions. We apply the method to analyze the yeast Saccharomyces cerevisiae protein-protein interaction network. The robustness of the approach is tested in a system containing a high percentage of unclassified proteins and also in cases of deletion and insertion of specific protein interactions.     

Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. AVAILABILITY: http://cytoprophet.cse.nd.edu.     

Inferring protein function by domain context similarities in protein-protein interaction networks

Background  

Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions.     

Architecture of basic building blocks in protein and domain structural interaction networks

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.     

Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps

Background

Recent computational techniques have facilitated analyzing genome-wide protein-protein interaction data for several model organisms. Various graph-clustering algorithms have been applied to protein interaction networks on the genomic scale for predicting the entire set of potential protein complexes. In particular, the density-based clustering algorithms which are able to generate overlapping clusters, i.e. the clusters sharing a set of nodes, are well-suited to protein complex detection because each protein could be a member of multiple complexes. However, their accuracy is still limited because of complex overlap patterns of their output clusters.

Results

We present a systematic approach of refining the overlapping clusters identified from protein interaction networks. We have designed novel metrics to assess cluster overlaps: overlap coverage and overlapping consistency. We then propose an overlap refinement algorithm. It takes as input the clusters produced by existing density-based graph-clustering methods and generates a set of refined clusters by parameterizing the metrics. To evaluate protein complex prediction accuracy, we used the f-measure by comparing each refined cluster to known protein complexes. The experimental results with the yeast protein-protein interaction data sets from BioGRID and DIP demonstrate that accuracy on protein complex prediction has increased significantly after refining cluster overlaps.

Conclusions

The effectiveness of the proposed cluster overlap refinement approach for protein complex detection has been validated in this study. Analyzing overlaps of the clusters from protein interaction networks is a crucial task for understanding of functional roles of proteins and topological characteristics of the functional systems.

     

Analyses of domains and domain fusions in human proto-oncogenes

Background  

Understanding the constituent domains of oncogenes, their origins and their fusions may shed new light about the initiation and the development of cancers.     

Domain-mediated protein interaction prediction: From genome to network

Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease.     

Ontology integration to identify protein complex in protein interaction networks

Background

Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.

Methods

We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.

Results

The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.

     

RNAi knockdown of human kinetochore protein CENP-H

The inner kinetochore protein complex binds to centromeres during the whole cell cycle. It serves as the basis for the binding of further kinetochore proteins during mitosis. CENP-H is one of the inner kinetochore proteins which is conserved amongst many eukaryotes. By specific RNAi knockdown, we reduced the CENP-H protein level in human HEp-2 cells down to less than 5% of its normal value. In these CENP-H knocked-down cells, we observed severe mitotic phenotypes like misaligned chromosomes and multipolar spindles, however, no mitotic arrest. Strong reduction of CENP-H resulted in a slightly reduced CENP-C level at the kinetochores and normal localisation of hBubR1, indicating a functional mitotic checkpoint at the hBubR1 protein level. In CENP-H knocked-down human cells, the misaligned chromosomes contained only reduced levels of CENP-E. Our data clearly indicate that CENP-H has an important impact on the architecture and function of the human kinetochore complex.     

hZwint-1 bridges the inner and outer kinetochore: identification of the kinetochore localization domain and the hZw10-interaction domain

Accurate chromosome segregation in mitosis is required to maintain genetic stability. hZwint-1 [human Zw10 (Zeste white 10)-interacting protein 1] is a kinetochore protein known to interact with the kinetochore checkpoint protein hZw10. hZw10, along with its partners Rod (Roughdeal) and hZwilch, form a complex which recruits dynein-dynactin and Mad1-Mad2 complexes to the kinetochore and are essential components of the mitotic checkpoint. hZwint-1 localizes to the kinetochore in prophase, before hZw10 localization, and remains at the kinetochore until anaphase, after hZw10 has dissociated. This difference in localization timing may reflect a role for hZwint-1 as a structural kinetochore protein. In addition to hZw10, we have found that hZwint-1 interacts with components of the conserved Ndc80 and Mis12 complexes in yeast two-hybrid and GST (glutathione transferase) pull-down assays. Furthermore, hZwint-1 was found to have stable FRAP (fluorescence recovery after photobleaching) dynamics similar to hHec1, hSpc24 and hMis12. As such, we proposed that hZwint-1 is a structural protein, part of the inner kinetochore scaffold and recruits hZw10 to the kinetochore. To test this, we performed mutagenesis-based domain mapping to determine which regions of hZwint-1 are necessary for kinetochore localization and which are required for interaction with hZw10. hZwint-1 localizes to the kinetochore through the N-terminal region and interacts with hZw10 through the C-terminal coiled-coil domain. The two domains are at opposite ends of the protein as expected for a protein that bridges the inner and outer kinetochore.     

Scale-free behavior in protein domain networks

Several technical, social, and biological networks were recently found to demonstrate scale-free and small-world behavior instead of random graph characteristics. In this work, the topology of protein domain networks generated with data from the ProDom, Pfam, and Prosite domain databases was studied. It was found that these networks exhibited small-world and scale-free topologies with a high degree of local clustering accompanied by a few long-distance connections. Moreover, these observations apply not only to the complete databases, but also to the domain distributions in proteomes of different organisms. The extent of connectivity among domains reflects the evolutionary complexity of the organisms considered.     

CENP-B: a major human centromere protein located beneath the kinetochore

The family of three structurally related autoantigens CENP-A (17 kD), CENP-B (80 kD), and CENP-C (140 kD) are the best characterized components of the human centromere, and they have been widely assumed to be components of the kinetochore. Kinetochore components are currently of great interest since this structure, which has long been known to be the site of microtubule attachment to the chromosome, is now believed to be a site of force production for anaphase chromosome movement. In the present study we have mapped the distribution of CENP-B in mitotic chromosomes by immunoelectron microscopy using two monospecific polyclonal antibodies together with a newly developed series of ultra-small 1-nm colloidal gold probes. We were surprised to find that greater than 95% of CENP-B is distributed throughout the centromeric heterochromatin beneath the kinetochore. This strongly supports other emerging evidence that CENP-B is specifically associated with alpha-satellite heterochromatin. Although in certain instances CENP-B can be seen to be concentrated immediately adjacent to the lower surface of the kinetochore, the outer plate remains virtually unlabeled. Similar analysis with a human autoimmune serum that recognizes all three CENP antigens reveals an additional unsuspected feature of kinetochore structure. In addition to recognizing antigens in the centromeric heterochromatin, the autoantiserum recognizes a concentration of antigens lateral to the kinetochore. This difference in staining pattern may reflect the presence of a "collar" of chromatin rich in CENP-C and/or CENP-A encircling the kinetochore plates.     

Protein interaction networks from yeast to human

Protein interaction networks summarize large amounts of protein-protein interaction data, both from individual, small-scale experiments and from automated high-throughput screens. The past year has seen a flood of new experimental data, especially on metazoans, as well as an increasing number of analyses designed to reveal aspects of network topology, modularity and evolution. As only minimal progress has been made in mapping the human proteome using high-throughput screens, the transfer of interaction information within and across species has become increasingly important. With more and more heterogeneous raw data becoming available, proper data integration and quality control have become essential for reliable protein network reconstruction, and will be especially important for reconstructing the human protein interaction network.     

CIDeR: multifactorial interaction networks in human diseases

ABSTRACT: The pathobiology of common diseases is influenced by heterogeneous factors interacting in complex networks. CIDeR http://mips.helmholtz-muenchen.de/cider/ is a publicly available, manually curated, integrative database of metabolic and neurological disorders. The resource provides structured information on 18,813 experimentally validated interactions between molecules, bioprocesses and environmental factors extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make CIDeR a versatile knowledge base for biologists, analysis of large-scale data and systems biology approaches.     

From protein interaction networks to novel therapeutic strategies

Cellular mechanisms that sustain health or contribute to disease emerge mostly from the complex interplay among various molecular entities. To understand the underlying relationships between genotype, environment and phenotype, one has to consider the intricate and nonsequential interaction patterns formed between the different sets of cellular players. Biological networks capture a variety of molecular interactions and thus provide an excellent opportunity to consider physiological characteristics of individual molecules within their cellular context. In particular, the concept of network biology and its applications contributed largely to recent advances in biomedical research. In this review, we show (i) how biological networks, i.e., protein-protein interaction networks, facilitate the understanding of pathogenic mechanisms that trigger the onset and progression of diseases and (ii) how this knowledge can be translated into effective diagnostic and therapeutic strategies. In particular, we focus on the impact of network pharmacological concepts that go beyond the classical view on individual drugs and targets aiming for combinational therapies with improved clinical efficacy and reduced safety risks.     

Protein interaction domain mapping of human kinetochore protein Blinkin reveals a consensus motif for binding of spindle assembly checkpoint proteins Bub1 and BubR1

The kinetochore is a supramolecular structure essential for microtubule attachment and the mitotic checkpoint. Human blinkin/human Spc105 (hSpc105)/hKNL1 was identified originally as a mixed-lineage leukemia (MLL) fusion partner and later as a kinetochore component. Blinkin directly binds to several structural and regulatory proteins, but the precise binding sites have not been defined. Here, we report distinct and essential binding domains for Bub1 and BubR1 (here designated Bubs) at the N terminus of blinkin and for Zwint-1 and hMis14/hNsl1 at the C terminus. The minimal binding sites for Bub1 and BubR1 are separate but contain a consensus KI motif, KI(D/N)XXXF(L/I)XXLK. RNA interference (RNAi)-mediated replacement with mutant blinkin reveals that the Bubs-binding domain is functionally important for chromosome alignment and segregation. We also provide evidence that hMis14 mediates hNdc80 binding to blinkin at the kinetochore. The C-terminal fragment of blinkin locates at kinetochores in a dominant-negative fashion by displacing endogenous blinkin from kinetochores. This negative dominance is relieved by mutations of the hMis14 binding PPSS motif on the C terminus of blinkin or by fusion of the N sequence that binds to Bub1 and BubR1. Taken together, these results indicate that blinkin functions to connect Bub1 and BubR1 with the hMis12, Ndc80, and Zwint-1 complexes, and disruption of this connection may lead to tumorigenesis.