RPA phosphorylation in mitosis alters DNA binding and protein-protein interactions

The heterotrimeric DNA-binding protein, replication protein A (RPA), consists of 70-, 34-, and 14-kDa subunits and is involved in maintaining genomic stability by playing key roles in DNA replication, repair, and recombination. RPA participates in these processes through its interaction with other proteins and its strong affinity for single-stranded DNA (ssDNA). RPA-p34 is phosphorylated in a cell-cycle-dependent fashion primarily at Ser-29 and Ser-23, which are consensus sites for Cdc2 cyclin-dependent kinase. By systematically examining RPA-p34 phosphorylation throughout the cell cycle, we have found there are distinct phosphorylated forms of RPA-p34 in different cell-cycle stages. We have isolated and purified a unique phosphorylated form of RPA that is specifically associated with the mitotic phase of the cell cycle. The mitotic form of RPA (m-hRPA) shows no difference in ssDNA binding activity as compared with recombinant RPA (r-hRPA), yet binds less efficiently to double-stranded DNA (dsDNA). These data suggest that mitotic phosphorylation of RPA-p34 inhibits the destabilization of dsDNA by RPA complex, thereby decreasing the binding affinity for dsDNA. The m-hRPA also exhibits altered interactions with certain DNA replication and repair proteins. Using highly purified proteins, m-hRPA exhibited decreased binding to ATM, DNA pol alpha, and DNA-PK as compared to unphosphorylated recombinant RPA (r-hRPA). Dephosphorylation of m-hRPA was able to restore the interaction with each of these proteins. Interestingly, the interaction of RPA with XPA was not altered by RPA phosphorylation. These data suggest that phosphorylation of RPA-p34 plays an important role in regulating RPA functions in DNA metabolism by altering specific protein-protein interactions.     

Parkin stabilizes PINK1 through direct interaction

Parkinson disease (PD) is the most common movement disorder and is characterized by dopaminergic dysfunction. The majority of PD cases are sporadic; however, the discovery of genes linked to rare familial forms of the disease has provided crucial insight into the molecular mechanisms of disease pathogenesis. Multiple genes mediating familial forms of Parkinson’s disease (PD) have been identified, such as parkin (PARK2) and phosphatase and tensin homologue deleted on chromosome ten (PTEN)-induced putative kinase 1: PINK1 (PARK6). Here, we showed that Parkin directly interacts with PINK1, but did not bind to pathogenic PINK1 mutants. Parkin, but not its pathogenic mutants, stabilizes PINK1 by interfering with its degradation via the ubiquitin-mediated proteasomal pathway. In addition, the interaction between Parkin and PINK1 resulted in reciprocal reduction of their solubility. Our results indicate that Parkin regulates PINK1 stabilization via direct interaction with PINK1, and operates through a common pathway with PINK1 in the pathogenesis of early-onset PD.     

Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis

ABSTRACT: BACKGROUND: In this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease. RESULTS: We confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores. CONCLUSIONS: The applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.     

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.     

Condensin I stabilizes chromosomes mechanically through a dynamic interaction in live cells

BACKGROUND: Restructuring chromatin into morphologically distinct chromosomes is essential for cell division, but the molecular mechanisms underlying this process are poorly understood. Condensin complexes have been proposed as key factors, although controversial conclusions about their contribution to chromosome structure were reached by different experimental approaches in fixed cells or cell extracts. Their function under physiological conditions still needs to be defined. RESULTS: Here, we investigated the specific functions of condensin I and II in live cells by fluorescence microscopy and RNAi depletion. Photobleaching and quantitative time-lapse imaging showed that GFP-tagged condensin II bound stably to chromosomes throughout mitosis. By contrast, the canonical condensin I interacted dynamically with chromatin after completion of prophase compaction, reaching steady-state levels on chromosomes before congression. In condensin I-depleted cells, compaction was normal, but chromosomes were mechanically labile and unable to withstand spindle forces during alignment. However, normal levels of condensin II were not required for chromosome stability. CONCLUSIONS: We conclude that while condensin I seems dispensable for normal chromosome compaction, its dynamic binding after nuclear envelope breakdown locks already condensed chromatin in a rigid state required for mechanically stable spindle attachment.     

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.     

A nonhistone protein-protein interaction required for assembly of the SIR complex and silent chromatin

Budding yeast silent chromatin, or heterochromatin, is composed of histones and the Sir2, Sir3, and Sir4 proteins. Their assembly into silent chromatin is believed to require the deacetylation of histones by the NAD-dependent deacetylase Sir2 and the subsequent interaction of Sir3 and Sir4 with these hypoacetylated regions of chromatin. Here we explore the role of interactions among the Sir proteins in the assembly of the SIR complex and the formation of silent chromatin. We show that significant fractions of Sir2, Sir3, and Sir4 are associated together in a stable complex. When the assembly of Sir3 into this complex is disrupted by a specific mutation on the surface of the C-terminal coiled-coil domain of Sir4, Sir3 is no longer recruited to chromatin and silencing is disrupted. Because in sir4 mutant cells the association of Sir3 with chromatin is greatly reduced despite the partial Sir2-dependent deacetylation of histones near silencers, we conclude that histone deacetylation is not sufficient for the full recruitment of silencing proteins to chromatin and that Sir-Sir interactions are essential for the assembly of heterochromatin.     

The crystal structure of the complex of replication protein A subunits RPA32 and RPA14 reveals a mechanism for single-stranded DNA binding.

Replication protein A (RPA), the eukaryote single-stranded DNA-binding protein (SSB), is a heterotrimer. The largest subunit, RPA70, which harbours the major DNA-binding activity, has two DNA-binding domains that each adopt an OB-fold. The complex of the two smaller subunits, RPA32 and RPA14, has weak DNA-binding activity but the mechanism of DNA binding is unknown. We have determined the crystal structure of the proteolytic core of RPA32 and RPA14, which consists of the central two-thirds of RPA32 and the entire RPA14 subunit. The structure revealed that RPA14 and the central part of RPA32 are structural homologues. Each subunit contains a central OB-fold domain, which also resembles the DNA-binding domains in RPA70; an N-terminal extension that interacts with the central OB-fold domain; and a C-terminal helix that mediate heterodimerization via a helix-helix interaction. The OB-fold of RPA32, but not RPA14, possesses additional similarity to the RPA70 DNA-binding domains, supporting a DNA-binding role for RPA32. The discovery of a third and fourth OB-fold in RPA suggests that the quaternary structure of SSBs, which in Bacteria and Archaea are also tetramers of OB-folds, is conserved in evolution. The structure also suggests a mechanism for RPA trimer formation.     

Genome-wide studies of protein-protein interaction

Recent large-scale studies of protein complexes in yeast have demonstrated that the wide majority of proteins exist in the cell as parts of multicomponent assemblies, mostly novel and of unknown function. The structural and functional analysis of these complexes should be a priority for structural biologists in coming years. In silico methods such as docking simulations, which may contribute to this analysis, are being tested in the CAPRI community-wide experiment, which assesses blind predictions of the structure of protein-protein complexes.     

Development of high-molar-mass cellobiase complex by spontaneous protein-protein interaction in the culture filtrate ofTermitomyces clypeatus

The 450 kDa cellobiase fromTermitomyces clypeatus which migrates as a single band on IEF, PAGE and SDS-PAGE, was found to possess appreciable sucrase activity. The fungus produced sucrase and cellobiase constitutively in different media but with different activity ratios. The kinetics of secretion of the two enzymes was similar underin vivo andin vitro conditions. HPGPLC analysis of the culture filtrates indicated the presence of both sucrase and cellobiase in the same protein fractions of different molar mass, even in the 30-kDa protein fraction. No free sucrase or cellobiase could be detected in the culture filtrates. It was also observed that fractionation of cellobiase by (NH₄)₂SO₄ precipitation was different with different amounts of associated sucrase activity present in the culture filtrate. The (NH₄)₂SO₄-precipitated cellobiase fraction also contained cellobiases in proteins of widely varied molar mass ranges. However, none of the low-molar mass proteins other than the 450-kDa enzyme could be purified, as all low-molar-mass fractions spontaneously aggregated to the 450-kDa enzyme. Hydrophobic chromatography of the (NH₄)₂SO₄-precipitated fractions followed by HPGPLC of the eluted active fraction yielded both cellobiase-free sucrase and a very low sucrase-containing cellobiase fraction. The cellobiase fraction, homogeneous in PAGE, was also a high-molar-mass protein complex dissociating into a number of protein bands on SDS-PAGE. It was suggested that the 450-kDa cellobiase was not liberated by the fungus as a preformed enzyme complex but that the complex developed through interaction of cellobiase with sucrase underin vitro conditions and the possibility of the involvement of other proteins in the aggregation cannot be excluded.     

Arc1p organizes the yeast aminoacyl-tRNA synthetase complex and stabilizes its interaction with the cognate tRNAs

Eukaryotic aminoacyl-tRNA synthetases, in contrast to their prokaryotic counterparts, are often part of high molecular weight complexes. In yeast, two enzymes, the methionyl- and glutamyl-tRNA synthetases associate in vivo with the tRNA-binding protein Arc1p. To study the assembly and function of this complex, we have reconstituted it in vitro from individually purified recombinant proteins. Our results show that Arc1p can readily bind to either or both of the two enzymes, mediating the formation of the respective binary or ternary complexes. Under competition conditions, Arc1p alone exhibits broad specificity and interacts with a defined set of tRNA species. Nevertheless, the in vitro reconstituted Arc1p-containing enzyme complexes can bind only to their cognate tRNAs and tighter than the corresponding monomeric enzymes. These results demonstrate that the organization of aminoacyl-tRNA synthetases with general tRNA-binding proteins into multimeric complexes can stimulate their catalytic efficiency and, therefore, offer a significant advantage to the eukaryotic cell.     

Grafting of protein-protein interaction epitope

Transferring the biological function of one protein to another is a key issue in understanding the structure and function relationship of proteins. We have developed a strategy for grafting protein-protein interaction epitopes. As a first step, residues at the interface of the ligand protein which strongly interact with the receptor protein were identified. Then protein scaffolds were docked onto receptor protein based on geometric complementarity. Only high docking score matches were saved. For each saved match, the scaffold protein was accepted if it had suitable positions for grafting key interaction residues of the ligand protein. These candidate residues were mutated to corresponding residues in the ligand protein at each relevant position and the mutated scaffold protein was co-minimized with receptor protein. Finally, the minimized complexes were evaluated by a scoring function deduced from statistical analysis of rigid binding data sets. As a test case, the binding epitope of barstar, the inhibitor of barnase, was grafted onto smaller proteins. Pheromone Er-1 (PDB entry 1erc) has been found to be a good scaffold. The calculated binding free energy for mutated Pheromone Er-1 is equivalent to that of barstar.     

Structural mechanism of RPA loading on DNA during activation of a simple pre-replication complex

We report that during activation of the simian virus 40 (SV40) pre-replication complex, SV40 T antigen (Tag) helicase actively loads replication protein A (RPA) on emerging single-stranded DNA (ssDNA). This novel loading process requires physical interaction of Tag origin DNA-binding domain (OBD) with the RPA high-affinity ssDNA-binding domains (RPA70AB). Heteronuclear NMR chemical shift mapping revealed that Tag-OBD binds to RPA70AB at a site distal from the ssDNA-binding sites and that RPA70AB, Tag-OBD, and an 8-nucleotide ssDNA form a stable ternary complex. Intact RPA and Tag also interact stably in the presence of an 8-mer, but Tag dissociates from the complex when RPA binds to longer oligonucleotides. Together, our results imply that an allosteric change in RPA quaternary structure completes the loading reaction. A mechanistic model is proposed in which the ternary complex is a key intermediate that directly couples origin DNA unwinding to RPA loading on emerging ssDNA.     

Computational approaches to protein-protein interaction

The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.