High functional coherence in k-partite protein cliques of protein interaction networks

We introduce a new topological concept called k-partite protein cliques to study protein interaction (PPI) networks. In particular, we examine functional coherence of proteins in k-partite protein cliques. A k-partite protein clique is a k-partite maximal clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI’s k-partite maximal cliques, we propose to transform PPI networks into induced K-partite graphs with proteins as vertices where edges only exist among the graph’s partites. Then, we present a k-partite maximal clique mining (MaCMik) algorithm to enumerate k-partite maximal cliques from K-partite graphs. Our MaCMik algorithm is applied to a yeast PPI network. We observe that there does exist interesting and unusually high functional coherence in k-partite protein cliques—most proteins in k-partite protein cliques, especially those in the same partites, share the same functions. Therefore, the idea of k-partite protein cliques suggests a novel approach to characterizing PPI networks, and may help function prediction for unknown proteins.

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.

Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

Identification of protein interaction regions of VINC/NEAT1/Men epsilon RNA

The virus inducible non-coding RNA (VINC) was detected initially in the brain of mice infected with Japanese encephalitis virus (JEV) and rabies virus. VINC is also known as NEAT1 or Men epsilon RNA. It is localized in the nuclear paraspeckles of several murine as well as human cell lines and is essential for paraspeckle formation. We demonstrate that VINC interacts with the paraspeckle protein, P54nrb through three different protein interaction regions (PIRs) one of which (PIR-1) is localized near the 50 end while the other two (PIR-2, PIR-3) are localized near the 30 region of VINC. Our studies suggest that VINC may interact with P54nrb through a novel mechanism which is different from that reported for protein coding RNAs. (C) 2010 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserved.

Glioblastoma-Specific Protein Interaction Network Identifies PP1A and CSK21 as Connecting Molecules between Cell Cycle-Associated Genes

Glioblastoma (GBM; grade IV astrocytoma) is a very aggressive form of brain cancer with a poor survival and few qualified predictive markers. This study integrates experimentally validated genes that showed specific upregulation in GBM along with their protein-protein interaction information. A system level analysis was used to construct GBM-specific network. Computation of topological parameters of networks showed scale-free pattern and hierarchical organization. From the large network involving 1,447 proteins, we synthesized subnetworks and annotated them with highly enriched biological processes. A careful dissection of the functional modules, important nodes, and their connections identified two novel intermediary molecules CSK21 and protein phosphatase 1 alpha (PP1A) connecting the two subnetworks CDC2-PTEN-TOP2A-CAV1-P53 and CDC2-CAV1-RB-P53-PTEN, respectively. Real-time quantitative reverse transcription-PCR analysis revealed CSK21 to be moderately upregulated and PP1A to be overexpressed by 20-fold in GBM tumor samples. Immunohistochemical staining revealed nuclear expression of PP1A only in GBM samples. Thus, CSK21 and PP1A, whose functions are intimately associated with cell cycle regulation, might play key role in gliomagenesis. Cancer Res; 70(16); 6437-47. (C)2010 AACR.

Enhanced J-protein interaction and compromised protein stability of mtHsp70 variants lead to mitochondrial dysfunction in Parkinsons disease

Parkinsons disease (PD) is the second most prevalent progressive neurological disorder commonly associated with impaired mitochondrial function in dopaminergic neurons. Although familial PD is multifactorial in nature, a recent genetic screen involving PD patients identified two mitochondrial Hsp70 variants (P509S and R126W) that are suggested in PD pathogenesis. However, molecular mechanisms underlying how mtHsp70 PD variants are centrally involved in PD progression is totally elusive. In this article, we provide mechanistic insights into the mitochondrial dysfunction associated with human mtHsp70 PD variants. Biochemically, the R126W variant showed severely compromised protein stability and was found highly susceptible to aggregation at physiological conditions. Strikingly, on the other hand, the P509S variant exhibits significantly enhanced interaction with J-protein cochaperones involved in folding and import machinery, thus altering the overall regulation of chaperone-mediated folding cycle and protein homeostasis. To assess the impact of mtHsp70 PD mutations at the cellular level, we developed yeast as a model system by making analogous mutations in Ssc1 ortholog. Interestingly, PD mutations in yeast (R103W and P486S) exhibit multiple in vivo phenotypes, which are associated with omitochondrial dysfunction', including compromised growth, impairment in protein translocation, reduced functional mitochondrial mass, mitochondrial DNA loss, respiratory incompetency and increased susceptibility to oxidative stress. In addition to that, R103W protein is prone to aggregate in vivo due to reduced stability, whereas P486S showed enhanced interaction with J-proteins, thus remarkably recapitulating the cellular defects that are observed in human PD variants. Taken together, our findings provide evidence in favor of direct involvement of mtHsp70 as a susceptibility factor in PD.

Development of a New Generation of Vectors for Gene Expression, Gene Replacement, and Protein-Protein Interaction Studies in Mycobacteria

Escherichia coli-mycobacterium shuttle vectors are important tools for gene expression and gene replacement in mycobacteria. However, most of the currently available vectors are limited in their use because of the lack of extended multiple cloning sites (MCSs) and convenience of appending an epitope tag(s) to the cloned open reading frames (ORFs). Here we report a new series of vectors that allow for the constitutive and regulatable expression of proteins, appended with peptide tag sequences at their N and C termini, respectively. The applicability of these vectors is demonstrated by the constitutive and induced expression of the Mycobacterium tuberculosis pknK gene, coding for protein kinase K, a serine-threonine protein kinase. Furthermore, a suicide plasmid with expanded MCS for creating gene replacements, a plasmid for chromosomal integrations at the commonly used L5 attB site, and a hypoxia-responsive vector, for expression of a gene(s) under hypoxic conditions that mimic latency, have also been created. Additionally, we have created a vector for the coexpression of two proteins controlled by two independent promoters, with each protein being in fusion with a different tag. The shuttle vectors developed in the present study are excellent tools for the analysis of gene function in mycobacteria and are a valuable addition to the existing repertoire of vectors for mycobacterial research.

Human la protein interaction with GCAC near the initiator AUG enhances hepatitis C virus RNA replication by promoting linkage between 5 ` and 3 ` untranslated regions

Human La protein has been implicated in facilitating the internal initiation of translation as well as replication of hepatitis C virus (HCV) RNA. Previously, we demonstrated that La interacts with the HCV internal ribosome entry site (IRES) around the GCAC motif near the initiator AUG within stem-loop IV by its RNA recognition motif (RRM) (residues 112 to 184) and influences HCV translation. In this study, we have deciphered the role of this interaction in HCV replication in a hepatocellular carcinoma cell culture system. We incorporated mutation of the GCAC motif in an HCV monocistronic subgenomic replicon and a pJFH1 construct which altered the binding of La and checked HCV RNA replication by reverse transcriptase PCR (RT-PCR). The mutation drastically affected HCV replication. Furthermore, to address whether the decrease in replication is a consequence of translation inhibition or not, we incorporated the same mutation into a bicistronic replicon and observed a substantial decrease in HCV RNA levels. Interestingly, La overexpression rescued this inhibition of replication. More importantly, we observed that the mutation reduced the association between La and NS5B. The effect of the GCAC mutation on the translation-to-replication switch, which is regulated by the interplay between NS3 and La, was further investigated. Additionally, our analyses of point mutations in the GCAC motif revealed distinct roles of each nucleotide in HCV replication and translation. Finally, we showed that a specific interaction of the GCAC motif with human La protein is crucial for linking 5' and 3' ends of the HCV genome. Taken together, our results demonstrate the mechanism of regulation of HCV replication by interaction of the cis-acting element GCAC within the HCV IRES with human La protein.

Predicting protein interaction interfaces from protein sequences: Case studies of subtilisin and phycocyanin

Identification of protein interaction interfaces is very important for understanding the molecular mechanisms underlying biological phenomena. Here, we present a novel method for predicting protein interaction interfaces from sequences by using PAM matrix (PIFPAM). Sequence alignments for interacting proteins were constructed and parsed into segments using sliding windows. By calculating distance matrix for each segment, the correlation coefficients between segments were estimated. The interaction interfaces were predicted by extracting highly correlated segment pairs from the correlation map. The predictions achieved an accuracy 0.41-0.71 for eight intraprotein interaction examples, and 0.07-0.60 for four interprotein interaction examples. Compared with three previously published methods, PIFPAM predicted more contacting site pairs for 11 out of the 12 example proteins, and predicted at least 34% more contacting site pairs for eight proteins of them. The factors affecting the predictions were also analyzed. Since PIFPAM uses only the alignments of the two interacting proteins as input, it is especially useful when no three-dimensional protein structure data are available.

Drug-protein interaction studied by technique of microdialysis combined with high performance liquid chromatography

Drug-protein binding is an important process in determining the activity and fate of a pharmaceutical agent once it has entered the body. This review examines the method of microdialysis combined with high-performance liquid chromatography (HPLC) that has been developed;by ours to study such interactions, in which the microdialysis was applied to sample the free drug in the mixed solution of drug with protein, and HPLC to quantify the concentration of free drug in the microdialysate. This technique has successfully been used for determining various types of binding interactions between the low affinity drugs, high affinity drugs and enantiomers to HSA. For the case of competitive binding of two drugs to a protein in solution, a displacement equation has been derived and examined with four nonsteroidal anti-inflammatory drugs and HSA as model drugs and protein, respectively. Microdialysis with HPLC was adopted to determine simultaneously the free solute and displacing agent in drug-protein solutions. The method is able to locate the binding site and determine affinity constants even up to 10(7) L/mol accurately.