Peeling the yeast protein network

A set of highly connected proteins (or hubs) plays an important role for the integrity of the protein interaction network of Saccharomyces cerevisae by connecting the network's intrinsic modules. The importance of the hubs' central placement is further confirmed by their propensity to be lethal. However, although highly emphasized, little is known about the topological coherence among the hubs. Applying a core decomposition method which allows us to identify the inherent layer structure of the protein interaction network, we find that the probability of nodes both being essential and evolutionary conserved successively increases toward the innermost cores. While connectivity alone is often not a sufficient criterion to assess a protein's functional, evolutionary and topological relevance, we classify nodes as globally and locally central depending on their appearance in the inner or outer cores. The observation that globally central proteins participate in a substantial number of protein complexes which display an elevated degree of evolutionary conservation allows us to hypothesize that globally central proteins serve as the evolutionary backbone of the proteome. Even though protein interaction data are extensively flawed, we find that our results are very robust against inaccurately determined protein interactions.     

Decomposing PPI networks for complex discovery

Background

Protein complexes are important for understanding principles of cellular organization and functions. With the availability of large amounts of high-throughput protein-protein interactions (PPI), many algorithms have been proposed to discover protein complexes from PPI networks. However, existing algorithms generally do not take into consideration the fact that not all the interactions in a PPI network take place at the same time. As a result, predicted complexes often contain many spuriously included proteins, precluding them from matching true complexes.

Results

We propose two methods to tackle this problem: (1) The localization GO term decomposition method: We utilize cellular component Gene Ontology (GO) terms to decompose PPI networks into several smaller networks such that the proteins in each decomposed network are annotated with the same cellular component GO term. (2) The hub removal method: This method is based on the observation that hub proteins are more likely to fuse clusters that correspond to different complexes. To avoid this, we remove hub proteins from PPI networks, and then apply a complex discovery algorithm on the remaining PPI network. The removed hub proteins are added back to the generated clusters afterwards. We tested the two methods on the yeast PPI network downloaded from BioGRID. Our results show that these methods can improve the performance of several complex discovery algorithms significantly. Further improvement in performance is achieved when we apply them in tandem.

Conclusions

The performance of complex discovery algorithms is hindered by the fact that not all the interactions in a PPI network take place at the same time. We tackle this problem by using localization GO terms or hubs to decompose a PPI network before complex discovery, which achieves considerable improvement.

     

Fast-dissociating complexes of estradiol and corticosterone with rat serum proteins

The dissociation of estradiol complexes with specific binding proteins of blood sera from 5-day-old and pregnant rats was studied. Two types of complexes differing essentially in their dissociation rates were obtained. The decomposition of fast-dissociating complexes was completed within 30 s. Two classes of corticosterone-binding sites, one of which could form highly labile complexes, were revealed.     

Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes

Absolute binding free energy calculations and free energy decompositions are presented for the protein-protein complexes H-Ras/C-Raf1 and H-Ras/RalGDS. Ras is a central switch in the regulation of cell proliferation and differentiation. In our study, we investigate the capability of the molecular mechanics (MM)-generalized Born surface area (GBSA) approach to estimate absolute binding free energies for the protein-protein complexes. Averaging gas-phase energies, solvation free energies, and entropic contributions over snapshots extracted from trajectories of the unbound proteins and the complexes, calculated binding free energies (Ras-Raf: -15.0(+/-6.3)kcal mol(-1); Ras-RalGDS: -19.5(+/-5.9)kcal mol(-1)) are in fair agreement with experimentally determined values (-9.6 kcal mol(-1); -8.4 kcal mol(-1)), if appropriate ionic strength is taken into account. Structural determinants of the binding affinity of Ras-Raf and Ras-RalGDS are identified by means of free energy decomposition. For the first time, computationally inexpensive generalized Born (GB) calculations are applied in this context to partition solvation free energies along with gas-phase energies between residues of both binding partners. For selected residues, in addition, entropic contributions are estimated by classical statistical mechanics. Comparison of the decomposition results with experimentally determined binding free energy differences for alanine mutants of interface residues yielded correlations with r(2)=0.55 and 0.46 for Ras-Raf and Ras-RalGDS, respectively. Extension of the decomposition reveals residues as far apart as 25A from the binding epitope that can contribute significantly to binding free energy. These "hotspots" are found to show large atomic fluctuations in the unbound proteins, indicating that they reside in structurally less stable regions. Furthermore, hotspot residues experience a significantly larger-than-average decrease in local fluctuations upon complex formation. Finally, by calculating a pair-wise decomposition of interactions, interaction pathways originating in the binding epitope of Raf are found that protrude through the protein structure towards the loop L1. This explains the finding of a conformational change in this region upon complex formation with Ras, and it may trigger a larger structural change in Raf, which is considered to be necessary for activation of the effector by Ras.     

Protein-inhibitor complexes analyzed by alkaline capillary LC-MS

Liquid chromatography-mass spectrometry (LC-MS) has been used extensively in determination of the molecular weights of proteins, as well as covalent protein-ligand complexes. We have successfully developed LC-MS method for protein molecular weight measurement using small-bore and capillary LC-MS under acidic and basic conditions. A high pH method was critical in studying complexes that were unstable under acidic conditions. Microgram sensitivity was achieved using both methods. A protocol to study the binding mode of protein-ligand complexes under denaturing conditions was developed. These methods were applied to CP88 (a proprietary cysteine protease) inhibitors and revealed different binding modes of inhibitors to proteins that had similar non-reversible behavior in biochemical activity assays. The method also confirmed that one inhibitor studied binds to CP88 in a reversible covalent manner.     

Ceramic hydroxyapatite high-performance liquid chromatography of complexes membrane protein and sodium dodecylsulfate

Ceramic hydroxyapatite high-performance liquid chromatography was examined as a chromatographic method by which complexes of whole membrane proteins and sodium dodecyl sulfate could be analyzed. The chromatographic conditions were optimized using the erythrocyte membrane as a model. Whole proteins, including membrane proteins larger than 100 kDa, were eluted as sharp peaks from the column and separated well from each other under optimum conditions. This method gave better resolution of protein-SDS complexes than other chromatographic methods reported so far. The sodium dodecyl sulfate complexes of 24 well characterized proteins were analyzed by this method and their retention times were examined. The positive correlation of the retention time with log (molecular mass) and log sigma (hydrophobicity of amino acids) but not with the isoelectric point, was observed. Based on these results, the mechanism underlying the interaction of protein-SDS complexes with ceramic hydroxyapatite was discussed.     

Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.     

Fluorescent proteins as proteomic probes

Protein complexes mediate the majority of cellular processes. Knowledge of the localization and composition of such complexes provides key insights into their functions. Although green fluorescent protein (GFP) has been widely applied for in vivo visualization of proteins, it has been relatively little used as a tool for the isolation of protein complexes. Here we describe the use of the standard GFP tag to both visualize proteins in living cells and capture their interactions via a simple immunoaffinity purification procedure. We applied this method to the analysis of a variety of endogenous protein complexes from different eukaryotic cells. We show that efficient isolations can be achieved in 5-60 min. This rapid purification helps preserve protein complexes close to their original state in the cell and minimizes nonspecific interactions. Given the wide use and availability of GFP-tagged protein reagents, the present method should greatly facilitate the elucidation of many cellular processes.     

Identification of protein-protein interactions using in vivo cross-linking and mass spectrometry

The purification of protein complexes can be accomplished by different types of affinity chromatography. In a typical immunoaffinity experiment, protein complexes are captured from a cell lysate by an immobilized antibody that recognizes an epitope on one of the known components of the complex. After extensive washing to remove unspecifically bound proteins, the complexes are eluted and analyzed by mass spectrometry (MS). Transient complexes, which are characterized by high dissociation constants, are typically lost by this approach. In the present study, we describe a novel method for identifying transient protein-protein interactions using in vivo cross-linking and MS-based protein identification. Live cells are treated with formaldehyde, which rapidly permeates the cell membrane and generates protein-protein cross-links. Proteins cross-linked to a Myc-tagged protein of interest are copurified by immunoaffinity chromatography and subjected to a procedure which dissociates the cross-linked complexes. After separation by SDS-PAGE, proteins are identified by tandem mass spectrometry. Application of this method enabled the identification of numerous proteins that copurified with a constitutively active form of M-Ras (M-Ras(Q71L)). Among these, we identified the RasGAP-related protein IQGAP1 to be a novel interaction partner of M-Ras(Q71L). This method is applicable to many proteins and will aid in the study of protein-protein interactions.     

Protein complex compositions predicted by structural similarity

Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (http://salilab.org/pibase). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain-porcine alpha-amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE (http://salilab.org/modbase).     

Weak protein–protein interactions revealed by immiscible filtration assisted by surface tension

Biological mechanisms are often mediated by transient interactions between multiple proteins. The isolation of intact protein complexes is essential to understanding biochemical processes and an important prerequisite for identifying new drug targets and biomarkers. However, low-affinity interactions are often difficult to detect. Here, we use a newly described method called immiscible filtration assisted by surface tension (IFAST) to isolate proteins under defined binding conditions. This method, which gives a near-instantaneous isolation, enables significantly higher recovery of transient complexes compared to current wash-based protocols, which require reequilibration at each of several wash steps, resulting in protein loss. The method moves proteins, or protein complexes, captured on a solid phase through one or more immiscible-phase barriers that efficiently exclude the passage of nonspecific material in a single operation. We use a previously described polyol-responsive monoclonal antibody to investigate the potential of this new method to study protein binding. In addition, difficult-to-isolate complexes involving the biologically and clinically important Wnt signaling pathway were isolated. We anticipate that this simple, rapid method to isolate intact, transient complexes will enable the discoveries of new signaling pathways, biomarkers, and drug targets.     

Single-molecule approach to immunoprecipitated protein complexes: insights into miRNA uridylation

Single-molecule techniques have been used for only a subset of biological problems because of difficulties in studying proteins that require cofactors or post-translational modifications. Here, we present a new method integrating single-molecule fluorescence microscopy and immunopurification to study protein complexes. We used this method to investigate Lin28-mediated microRNA uridylation by TUT4 (terminal uridylyl transferase 4, polyU polymerase), which regulates let-7 microRNA biogenesis. Our real-time analysis of the uridylation by the TUT4 immunoprecipitates suggests that Lin28 functions as a processivity factor of TUT4. Our new technique, SIMPlex (single-molecule approach to immunoprecipitated protein complexes), provides a universal tool to analyse complex proteins at the single-molecule level.     

In Vitro Reconstitution of Light-harvesting Complexes of Plants and Green Algae

In plants and green algae, light is captured by the light-harvesting complexes (LHCs), a family of integral membrane proteins that coordinate chlorophylls and carotenoids. In vivo, these proteins are folded with pigments to form complexes which are inserted in the thylakoid membrane of the chloroplast. The high similarity in the chemical and physical properties of the members of the family, together with the fact that they can easily lose pigments during isolation, makes their purification in a native state challenging. An alternative approach to obtain homogeneous preparations of LHCs was developed by Plumley and Schmidt in 1987¹, who showed that it was possible to reconstitute these complexes in vitro starting from purified pigments and unfolded apoproteins, resulting in complexes with properties very similar to that of native complexes. This opened the way to the use of bacterial expressed recombinant proteins for in vitro reconstitution. The reconstitution method is powerful for various reasons: (1) pure preparations of individual complexes can be obtained, (2) pigment composition can be controlled to assess their contribution to structure and function, (3) recombinant proteins can be mutated to study the functional role of the individual residues (e.g., pigment binding sites) or protein domain (e.g., protein-protein interaction, folding). This method has been optimized in several laboratories and applied to most of the light-harvesting complexes. The protocol described here details the method of reconstituting light-harvesting complexes in vitro currently used in our laboratory,and examples describing applications of the method are provided.     

A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein‐complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core‐attachment–based method named CO‐DPC to detect dynamic protein complexes is presented. First, CO‐DPC selects active proteins according to gene expression profiles and the 3‐sigma principle, and constructs dynamic PPI networks based on the co‐expression principle and PPI networks. Second, CO‐DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO‐DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.     

Whole Cell Formaldehyde Cross-Linking Simplifies Purification of Mitochondrial Nucleoids and Associated Proteins Involved in Mitochondrial Gene Expression

Mitochondrial DNA/protein complexes (nucleoids) appear as discrete entities inside the mitochondrial network when observed by live-cell imaging and immunofluorescence. This somewhat trivial observation in recent years has spurred research towards isolation of these complexes and the identification of nucleoid-associated proteins. Here we show that whole cell formaldehyde crosslinking combined with affinity purification and tandem mass-spectrometry provides a simple and reproducible method to identify potential nucleoid associated proteins. The method avoids spurious mitochondrial isolation and subsequent multifarious nucleoid enrichment protocols and can be implemented to allow for label-free quantification (LFQ) by mass-spectrometry. Using expression of a Flag-tagged Twinkle helicase and appropriate controls we show that this method identifies many previously identified nucleoid associated proteins. Using LFQ to compare HEK293 cells with and without mtDNA, but both expressing Twinkle-FLAG, identifies many proteins that are reduced or absent in the absence of mtDNA. This set not only includes established mtDNA maintenance proteins but also many proteins involved in mitochondrial RNA metabolism and translation and therefore represents what can be considered an mtDNA gene expression proteome. Our data provides a very valuable resource for both basic mitochondrial researchers as well as clinical geneticists working to identify novel disease genes on the basis of exome sequence data.     

The Analysis of Participation of Individual Proteins in the Protein Interactome Formation

It becomes increasingly clear that most proteins of living systems exist as components of various protein complexes rather than individual molecules. The use of various proteomic techniques significantly extended our knowledge not only about functioning of individual complexes but also formed a basis for systemic analysis of protein-protein interactions. In this study gel-filtration chromatography accompanied by mass spectrometry was used for the interactome analysis of human liver proteins. In six fractions (with average molecular masses of 45 kDa, 60 kDa, 85 kDa, 150 kDa, 250 kDa, and 440 kDa) 797 proteins were identified. In dependence of their distribution profiles in the fractions, these proteins could be subdivided into four groups: (1) single monomeric proteins that are not involved in formation of stable protein complexes; (2) proteins existing as homodimers or heterodimers with comparable partners; (3) proteins that are partially exist as monomers and partially as components of protein complexes; (4) proteins that do not exist in the monomolecular state, but also exist within protein complexes containing three or more subunits. Application of this approach to known isatin-binding proteins resulted in identification of proteins involved in formation of the homo- and heterodimers and mixed protein complexes.     

A method of spectral subband decomposition by simultaneous fitting the initial spectrum and a set of its derivatives

An improved method for spectral subband decomposition based on simultaneous fitting of the initial spectrum and a set of its derivatives is introduced. Additionally, a procedure for finding an optimal smoothing filter to obtain undistorted derivatives is suggested. The proposed method is demonstrated with a model spectrum as well as with experimental absorption spectra of the photosynthetic antenna complexes, peridinin-chlorophyll a-protein (PCP) and the main light-harvesting complex of higher plants (LHC II).     

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

Background

Effectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.

Results

In this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained.We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.

Conclusion

CPredictor3.0 can serve as a promising tool of protein complex prediction.

     

Decomposition of protein tryptophan fluorescence spectra into log-normal components. II. The statistical proof of discreteness of tryptophan classes in proteins

The physical causes for wide variation of Stokes shift values in emission spectra of tryptophan fluorophores in proteins have been proposed in the model of discrete states (Burstein, E. A., N. S. Vedenkina, and M. N. Ivkova. 1973. Photochem. Photobiol. 18:263-279; Burstein, E. A. 1977a. Intrinsic Protein Luminescence (The Nature and Application). In Advances in Science and Technology (Itogi Nauki i Tekhniki), Biophysics Vol. 7. VINITI, Moscow [In Russian]; Burstein, E. A. 1983. Molecular Biology (Moscow) 17:455-467 [In Russian; English translation]). It was assumed that the existence of the five most probable spectral classes of emitting tryptophan residues and differences among the classes were analyzed in terms of various combinations of specific and universal interactions of excited fluorophores with their environment. The development of stable algorithms of decomposition of tryptophan fluorescence spectra into log-normal components gave us an opportunity to apply two mathematically different algorithms, SImple fitting with Mean-Square criterion (SIMS) and PHase-plot-based REsolving with Quenchers (PHREQ) for the decomposition of a representative set of emission spectra of proteins. Here we present the results of decomposition of tryptophan emission spectra of >100 different proteins, some in various structural states (native and denatured, in complexes with ions or organic ligands, in various pH-induced conformations, etc.). Analysis of the histograms of occurrence of >300 spectral log-normal components with various maximum positions confirmed the statistical discreteness of several states of emitting tryptophan fluorophores in proteins.     

Exploration of the binding mode of α/β-type small acid soluble proteins (SASPs) with DNA

The α/β-type small acid soluble proteins (SASPs) are a major factor in protecting the spores from being killed in bacteria. In this article, we perform a systematic phylogenetic analysis of the α/β-type SASP in the genus of Geobacillus, which indicates that the whole family can be divided into three groups. We choose one protein from each group as a representative and construct the tertiary structure of these proteins. In order to explore the mechanism of protecting DNA from damage, 15 ns molecular dynamics simulation for the four complexes of Gsy3 with DNA are performed. The sequence alignment, model structure and binding energy analysis indicate that the helix2 region of SASPs is more conserved and plays a more crucial role in protecting DNA. Pairwise decomposition of residue interaction energies calculation demonstrate that amino acids of Asn10, Lys24, Asn49, Ile52, Ile56, Thr57, Lys58, Arg59 and Val61 take major effect in the binding interaction. The differences of energy contribution of the amino acids between different complexes make us conclude that the protein structure conformation has a slight change upon more proteins binding to DNA and consequently there occur protein-protein cooperation interactions.