Human Protein Complexes Research Articles

Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new potentially true human protein complexes were suggested as candidates for further validation using experimental techniques.

Abstract 4173: Discovery at the interface: novel anticancer agents targeting human estrogen receptor/S100 interactions

Abstract Estrogen-triggered nuclear transcription factor, estrogen receptor (ER) is expressed in about 70% of breast cancer. ER mediates various cellular signaling events including the genomic regulation of target genes that affect reproduction, development, and general metabolism. The antiestrogen tamoxifen is currently administered to patients in order to induce regression of the tumor growth. However, upon continued administration, breast cancer patients develop resistance to tamoxifen. In addition, calcium binding proteins such as, calmodulin (CaM) which are significantly overexpressed in cancer cells, can activate transcription of target genes by directly binding to ER in lieu of estrogen. Calmodulin antagonists (w7 and melatonin) have been shown to significantly inhibit human estrogen receptor alpha (hERalpha) transcriptional activity. In search of a novel anti-cancer agent, we generated a library of interaction profiles of human ER-EF hand protein complexes and identified S100B and S100P (high sequence and structure homology with CaM) as potential targets. Furthermore, S100P is shown to mediate tamoxifen-acquired resistance and cell migration capacity in MCF-7 breast cancer cells. Our objective is to develop peptidomimetics and small molecules to block ER-S100 interactions using an integrated proteomics-assisted drug discovery approach. Briefly, using the fold information and contact regions obtained from the FT-ICR mass spectrometry, 3D structures of ER-S100 protein complexes were reconstructed. These structures of ER best represent the conformational state sensed by the specific interacting partner. ER-S100 complex is distinct from CaM. The lack of the connective peptidic region between the EF hand pairs in S100 attributed to less interaction coverage than CaM. The identified ER conformation-sensing regions of the interacting EF hand proteins were used to develop a library of top peptidomimetics and potential small molecule sensors targeting the interfaces of ER-S100 interactions. We have evaluated their ability to block specific ER-S100 interactions in a high throughput fluorescent polarization based screen, to inhibit cancer cell proliferation and ER-mediated transcription of target genes. We believe that the ensemble of ER-EF hand protein complexes generated by our integrated proteomics-assisted protein interaction profiling will shed light on the lingering issue of hormone independent activation of ER at the molecular level and novel therapeutics targeting ER-S100 interactions. Citation Format: David H. Lee, Bethany K. Asare, Matthew L. Hudson, Rajendram V. Rajnarayanan. Discovery at the interface: novel anticancer agents targeting human estrogen receptor/S100 interactions. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4173. doi:10.1158/1538-7445.AM2014-4173

Structural Genomics of Chromatin Regulators for Biological Discovery & Epigenetic Therapy

We are taking a protein family approach to understand how human protein domains, enzymes and complexes recognize specific histone tail sequences and their posttranslational modifications. These are key mechanisms of nuclear signaling that regulate epigenetic cellular states and gene expression programs. Systematic structural and biochemical analyses are revealing key features of selectivity and regulation among these factors, enabling structure-based development of potent, selective, cell-active small molecule inhibitors of individual epigenetic regulatory proteins.

An integrated map of HIV-human protein complexes that facilitate viral infection.

Recent proteomic and genetic studies have aimed to identify a complete network of interactions between HIV and human proteins and genes. This HIV-human interaction network provides invaluable information as to how HIV exploits the host machinery and can be used as a starting point for further functional analyses. We integrated this network with complementary datasets of protein function and interaction to nominate human protein complexes with likely roles in viral infection. Based on our approach we identified a global map of 40 HIV-human protein complexes with putative roles in HIV infection, some of which are involved in DNA replication and repair, transcription, translation, and cytoskeletal regulation. Targeted RNAi screens were used to validate several proteins and complexes for functional impact on viral infection. Thus, our HIV-human protein complex map provides a significant resource of potential HIV-host interactions for further study.

Concordance of gene expression in human protein complexes reveals tissue specificity and pathology

Disease-causing variants in human genes usually lead to phenotypes specific to only a few tissues. Here, we present a method for predicting tissue specificity based on quantitative deregulation of protein complexes. The underlying assumption is that the degree of coordinated expression among proteins in a complex within a given tissue may pinpoint tissues that will be affected by a mutation in the complex and coordinated expression may reveal the complex to be active in the tissue. We identified known disease genes and their protein complex partners in a high-quality human interactome. Each susceptibility gene's tissue involvement was ranked based on coordinated expression with its interaction partners in a non-disease global map of human tissue-specific expression. The approach demonstrated high overall area under the curve (0.78) and was very successfully benchmarked against a random model and an approach not using protein complexes. This was illustrated by correct tissue predictions for three case studies on leptin, insulin-like-growth-factor 2 and the inhibitor of NF-κB kinase subunit gamma that show high concordant expression in biologically relevant tissues. Our method identifies novel gene-phenotype associations in human diseases and predicts the tissues where associated phenotypic effects may arise.

Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets

Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets.

Dissecting the Characteristics and Dynamics of Human Protein Complexes at Transcriptome Cascade Using RNA-Seq Data

Human protein complexes play crucial roles in various biological processes as the functional module. However, the expression features of human protein complexes at the transcriptome cascade are poorly understood. Here, we used the RNA-Seq data from 16 disparate tissues and four types of human cancers to explore the characteristics and dynamics of human protein complexes. We observed that many individual components of human protein complexes can be generated by multiple distinct transcripts. Similar with yeast, the human protein complex constituents are inclined to co-express in diverse tissues. The dominant isoform of the genes involved in protein complexes tend to encode the complex constituents in each tissue. Our results indicate that the protein complex dynamics not only correlate with the presence or absence of complexes, but may also be related to the major isoform switching for complex subunits. Between any two cancers of breast, colon, lung and prostate, we found that only a few of the differentially expressed transcripts associated with complexes were identical, but 5–10 times more protein complexes involved in differentially expressed transcripts were common. Collectively, our study reveals novel properties and dynamics of human protein complexes at the transcriptome cascade in diverse normal tissues and different cancers.

Improving protein complex classification accuracy using amino acid composition profile

Protein complex prediction approaches are based on the assumptions that complexes have dense protein–protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties. Principal component analysis is carried out to determine the major features. Adopting amino acid composition profile information with the SVM classifier serves as an effective post-processing step for complexes classification. Improvement is based on primary sequence information only, which is easy to obtain.

Benchmarking human protein complexes to investigate drug-related systems and evaluate predicted protein complexes.

Protein complexes are key entities to perform cellular functions. Human diseases are also revealed to associate with some specific human protein complexes. In fact, human protein complexes are widely used for protein function annotation, inference of human protein interactome, disease gene prediction, and so on. Therefore, it is highly desired to build an up-to-date catalogue of human complexes to support the research in these applications. Protein complexes from different databases are as expected to be highly redundant. In this paper, we designed a set of concise operations to compile these redundant human complexes and built a comprehensive catalogue called CHPC2012 (Catalogue of Human Protein Complexes). CHPC2012 achieves a higher coverage for proteins and protein complexes than those individual databases. It is also verified to be a set of complexes with high quality as its co-complex protein associations have a high overlap with protein-protein interactions (PPI) in various existing PPI databases. We demonstrated two distinct applications of CHPC2012, that is, investigating the relationship between protein complexes and drug-related systems and evaluating the quality of predicted protein complexes. In particular, CHPC2012 provides more insights into drug development. For instance, proteins involved in multiple complexes (the overlapping proteins) are potential drug targets; the drug-complex network is utilized to investigate multi-target drugs and drug-drug interactions; and the disease-specific complex-drug networks will provide new clues for drug repositioning. With this up-to-date reference set of human protein complexes, we believe that the CHPC2012 catalogue is able to enhance the studies for protein interactions, protein functions, human diseases, drugs, and related fields of research. CHPC2012 complexes can be downloaded from http://www1.i2r.a-star.edu.sg/xlli/CHPC2012/CHPC2012.htm.

Walking on a Tissue-Specific Disease-Protein-Complex Heterogeneous Network for the Discovery of Disease-Related Protein Complexes

Besides the pinpointing of individual disease-related genes, associating protein complexes to human inherited diseases is also of great importance, because a biological function usually arises from the cooperative behaviour of multiple proteins in a protein complex. Moreover, knowledge about disease-related protein complexes could also enhance the inference of disease genes and pathogenic genetic variants. Here, we have designed a computational systems biology approach to systematically analyse potential relationships between diseases and protein complexes. First, we construct a heterogeneous network which is composed of a disease-disease similarity layer, a tissue-specific protein-protein interaction layer, and a protein complex membership layer. Then, we propose a random walk model on this disease-protein-complex network for identifying protein complexes that are related to a query disease. With a series of leave-one-out cross-validation experiments, we show that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We further predict a landscape of associations between human diseases and protein complexes. This landscape can be used to facilitate the inference of disease genes, thereby benefiting studies on pathology of diseases.

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.

PCDq: human protein complex database with quality index which summarizes different levels of evidences of protein complexes predicted from H-Invitational protein-protein interactions integrative dataset

BackgroundProteins interact with other proteins or biomolecules in complexes to perform cellular functions. Existing protein-protein interaction (PPI) databases and protein complex databases for human proteins are not organized to provide protein complex information or facilitate the discovery of novel subunits. Data integration of PPIs focused specifically on protein complexes, subunits, and their functions. Predicted candidate complexes or subunits are also important for experimental biologists.DescriptionBased on integrated PPI data and literature, we have developed a human protein complex database with a complex quality index (PCDq), which includes both known and predicted complexes and subunits. We integrated six PPI data (BIND, DIP, MINT, HPRD, IntAct, and GNP_Y2H), and predicted human protein complexes by finding densely connected regions in the PPI networks. They were curated with the literature so that missing proteins were complemented and some complexes were merged, resulting in 1,264 complexes comprising 9,268 proteins with 32,198 PPIs. The evidence level of each subunit was assigned as a categorical variable. This indicated whether it was a known subunit, and a specific function was inferable from sequence or network analysis. To summarize the categories of all the subunits in a complex, we devised a complex quality index (CQI) and assigned it to each complex. We examined the proportion of consistency of Gene Ontology (GO) terms among protein subunits of a complex. Next, we compared the expression profiles of the corresponding genes and found that many proteins in larger complexes tend to be expressed cooperatively at the transcript level. The proportion of duplicated genes in a complex was evaluated. Finally, we identified 78 hypothetical proteins that were annotated as subunits of 82 complexes, which included known complexes. Of these hypothetical proteins, after our prediction had been made, four were reported to be actual subunits of the assigned protein complexes.ConclusionsWe constructed a new protein complex database PCDq including both predicted and curated human protein complexes. CQI is a useful source of experimentally confirmed information about protein complexes and subunits. The predicted protein complexes can provide functional clues about hypothetical proteins. PCDq is freely available at http://h-invitational.jp/hinv/pcdq/.

A Census of Human Soluble Protein Complexes

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

Improved methodology for the affinity isolation of human protein complexes expressed at near endogenous levels

An efficient and reliable procedure for the capture of affinity-tagged proteins and associated complexes from human cell lines is reported. Through multiple optimizations, high yield and low background affinity-purifications are achieved from modest quantities of human cells expressing endogenous-level tagged proteins. Isolations of triple-FLAG and GFP-tagged fusion proteins involved in RNA metabolism are presented.

Alternative splicing as regulator of protein-protein interactions

Motivations. In recent years, the role of alternative splicing in protein function regulation has been widely investigated. Examples have been reported in which alternative splicing modulates the interaction between two proteins by removing interacting regions. However, despite the large amount of data available about alternative splicing isoforms and protein-protein interactions, only a few systematic studies have been carried to assess how much widespread and general is this form of regulation. Some of these works lack specificity, while others are based on a relatively small number of cases; in addition, they show conflicting results. In this work, we have created a non redundant residue-level dataset of protein-protein interfaces derived from the three dimensional structure of human protein complexes in order to determine whether the removal of protein-protein interfaces via alternative splicing in the human proteome could have evolved as a form of interaction regulation. Methods. To create a non-redundant dataset of human protein-protein interfaces, we first obtained from the Protein Data Bank (PDB) all the Biological Units containing at least two human protein chains that could be assigned to Uniprot entries according to SIFTS. From this dataset we extracted all the protein-protein interfaces (defined as sufficiently large sets residues involved in Van der Waals contacts) and filtered out those that were not present in the Asymmetric Unit. Using this procedure, we obtained a set of about 12100 protein-protein interfaces, from which, we derived a non redundant dataset of nearly 2300 interfaces, using a multi-level clustering procedure based on sequence identity between chains, evaluated using the BLASTClust software (ftp://ftp.ncbi.nih.gov/blast/documents/blastclust.html), and on a simple measure of the similarity between two interfaces. For each interface, we mapped the interacting residues to each Ensembl splicing isoform (if present) of the gene encoding it using residue genomic coordinates retrieved using Biomart, excluding those isoforms which undergo nonsense mediated decay. To evaluate whether splicing has a significant effect on the availability of the interface in the mature protein product, we compared the rate of splicing-mediated interface deletion with that of random controls using a chi-square test. Controls were created picking groups of residues that shared the same dispersion in the sequence with the corresponding interface. An interface, or a control, was considered to be removed from a splicing isoform if the corresponding protein product did not contain at least a certain fraction of its residues: we repeated the procedure using different values of this cutoff. This general procedure was differently adapted for hetero- and homo-dimeric interfaces, taking into account their different nature that might also imply a different evolution of their regulation. Results. Initial results showed that, if we consider all the isoforms, there isn't any statistically significant difference between the frequency of removal of real interfaces and random controls, both for heterodimeric and homodimeric interactions. However, discarding all the isoforms that determine a large change in the sequence of the interacting protein (which are not likely to regulate specific functions of that protein), we detected a significant tendency to avoid removal of small fractions of the homodimeric interfaces, suggesting that interaction interfaces are protected from being partially spliced and are more often completely included or removed in the mature transcript. This effect was also seen for compact heterodimeric interfaces. This effect is mainly ascribed to interfaces that are small and whose residues are relatively less scattered on the sequence, suggesting that alternative splicing is less likely to have evolved as regulative agent for large interfaces having more dispersed residues.

The protein-protein interaction network within human AF4 and AF4-MLL protein complexes

We have recently established the composition and function of the human AF4 and AF4-MLL fusion protein complex (Benedikt et al., 2011). The AF4 complex is responsible for transcriptional elongation, while the AF4-MLL complex is causing ectopic transcription, ectopic histone signatures and was shown to induce proB ALL in a mouse model. The composition of both complexes has been eludidated by using affinity-purification from human cells, nano LC-MS/MS analyses, Western blot and co-IP experiments. Functional analysis was performed by using different in vitro and in vivo assays. Here, we will present our preliminary data on the dissection of the protein-protein interaction network of complex components. This work has been done by subcloning about 35 protein-coding fragments in bait and prey vectors, and to use these vectors in combination with the yeast-2-hybrid system to unravel the AF4-N protein interactome.

Identification of human protein complexes from local sub-graphs of protein–protein interaction network based on random forest with topological structure features

In the post-genomic era, one of the most important and challenging tasks is to identify protein complexes and further elucidate its molecular mechanisms in specific biological processes. Previous computational approaches usually identify protein complexes from protein interaction network based on dense sub-graphs and incomplete priori information. Additionally, the computational approaches have little concern about the biological properties of proteins and there is no a common evaluation metric to evaluate the performance. So, it is necessary to construct novel method for identifying protein complexes and elucidating the function of protein complexes. In this study, a novel approach is proposed to identify protein complexes using random forest and topological structure. Each protein complex is represented by a graph of interactions, where descriptor of the protein primary structure is used to characterize biological properties of protein and vertex is weighted by the descriptor. The topological structure features are developed and used to characterize protein complexes. Random forest algorithm is utilized to build prediction model and identify protein complexes from local sub-graphs instead of dense sub-graphs. As a demonstration, the proposed approach is applied to protein interaction data in human, and the satisfied results are obtained with accuracy of 80.24%, sensitivity of 81.94%, specificity of 80.07%, and Matthew's correlation coefficient of 0.4087 in 10-fold cross-validation test. Some new protein complexes are identified, and analysis based on Gene Ontology shows that the complexes are likely to be true complexes and play important roles in the pathogenesis of some diseases. PCI-RFTS, a corresponding executable program for protein complexes identification, can be acquired freely on request from the authors.

Global landscape of HIV–human protein complexes

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host's cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV-human protein-protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.

Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation

BackgroundPhenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases.Methodology/Principal FindingsWe proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes.Conclusions/SignificanceGuilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations.