INhibitor of Growth (ING) family of type II tumor suppressors (ING1–ING5) is involved in many cellular processes such as cell aging, apoptosis, DNA repair and tumorigenesis. To expand our understanding of the proteins with which the ING proteins interact, we designed a method that did not depend upon large-scale proteomics-based methods, since they may fail to highlight transient or relatively weak interactions. Here we test a cross-species  bioinformatics-based approach to identify potential human ING-interacting proteins with higher probability and accuracy than approaches based on screens in a single species.The We confirm the validity of this screen and show that ING1 interacts specifically with three of the three proteins tested; p38MAPK, MEKK4 and RAD50. These novel ING-interacting proteins further link ING proteins to cell stress and DNA damage signaling, providing previously unknown upstream links to DNA damage response pathways in which ING1 participates. The bioinformatics approach we describe can be used to create an interaction prediction list for any human proteins with yeast homolog(s).None of the validated interactions were predicted by the conventional protein-protein interaction tools we tested. Validation of our approach by traditional laboratory techniques shows that we can extract value from the voluminous weak interaction data already elucidated in yeast and fly databases. We therefore propose that the weak  data from large-scale interaction datasets are currently underutilized. Protein-protein interactions play vital roles in regulating protein function and can provide valuable insight into the biological activity of proteins and biochemical pathways in which they function. The importance of protein interactions in biology has fueled intense efforts to identify such interactions and a vast repository of data has been accumulated over the years, particularly in relatively simple model organisms that are easier to manipulate genetically and biochemically. A number of bioinformatics-based approaches attempt to predict interactions using various techniques.Saccharomyces cerevisiae, is one of the most comprehensively studied eukaryotic organisms and a substantial amount of biochemical and genetic data has been accumulated. After the publication of the S. cerevisiae genome a decade ago [The budding yeast, cade ago , high thcade ago ,3. We suin vitro and tumor formation in vivo. Other ING genes  have been subsequently identified in various mammalian genomes [S. cerevisiae [The founding member of the ING family of type II tumor suppressors (ING1) was discovered using the method of subtractive hybridization aimed at identifying factors that were differentially expressed in normal mammary epithelial cells as opposed to breast cancer cell lines . Ectopic genomes ,7) and m genomes ). The ma genomes -11. This genomes -15. Bind genomes ,12,14. O genomes ,17 and c genomes . Therefoet al. (2006) have described the use of tandem affinity purification (TAP) tagging followed by two different mass spectrometry methods, namely MALDI-TOF and LC-MS/MS, with the aim of assigning interacting partners to each of the yeast proteins [Krogan proteins . An imprproteins , hoping . Additionally, CLUSTAL-W [The first member of the ING family ING1) was discovered in humans  and subsLUSTAL-W , T-COFFELUSTAL-W  and Gene was discLUSTAL-W  and alsoLUSTAL-W .Since members of the ING family of tumor suppressors show significant sequence conservation from yeast to humans , we prop  to identify proteins with domains closely related to the human INGs. All Uniprot proteins matching the human domain models were then added to the original model sequence to make them less species-specific and new HMMs were built based on the expanded list of sequences. This process was repeated until no new Uniprot matches were found. Because Uniprot contains data from many species, the iterative approach is a method to create domain models capturing sequence conservation amongst multiple species. The phylogenetic distribution and consensus sequences for the domains are illustrated in Figure Since conservation of interacting partners is often a function of conservation of domain structure within  or acrosD. melanogaster, C. elegans, and S. cerevisiae interactome data as these species have the most extensively consolidated lists of protein-protein interactions.Since protein-protein interactions are primarily based on specific domains, we tried to generate domain-specific models of amino acid substitutions for the various annotated domains of the ING family proteins. This allowed us to statistically examine the validation of the domain models across species. Results obtained from this method of analysis should be more sensitive and verifiable than the generalized substitution rates used by the current ortholog detection methods based on pairwise alignments. This improved sensitivity may be due to the accounting for the specific evolution of individual protein domains and/or the greater flexibility of HMMs over simple pairwise alignments. The domain structure models generated using this approach were then used to identify possible ING-like proteins in model species for which interaction data is readily available. We investigated C. elegans . Much better conservation of multiple ING domains was observed in S. cerevisiae and D. melanogaster. We therefore focused on identifying potential ING-interacting proteins in these two species. The domain conservation in these species is illustrated in Figure -4, PHD 10-3, NLS 10-6, PCR 10-3, and LZL 10-5. The thresholds represent the weakest e-value for any sequence used to create the domain model, i.e. the lowest score for a known positive example. The different thresholds are a consequence of the varying natures of the domain models . With the exception of PIM (10-2), all domain matches are well above these thresholds. PIM's somewhat weak score may reflect the fact that the domain model was built using only animal sequences , and only 3 of these showed a high degree of sequence conservation in humans. These 3 fly genes (having putative conserved interacting partners in yeast) have 5 potential homologs in human, namely: hRPC155, PAK1B, MAP3K4 (MEKK4), p38MAPKa, and GSPT1. The Venn diagram in Figure Using the taxonomic search tools of MAGPIE ,29, the In order to select candidates for biochemical validation of human ING interactors, we compared our data with experimentally validated ING interactions in human, as listed in the STRING database . Nine of-98, 10-45, and 10-26, respectively). We also selected another gene (RAD50) that does not fulfill all of the requirements of our method, but which is predicted by STRING. RAD50 has a weak yeast interaction score but an extremely strong homolog (10-156) in humans. We wanted to test if in such cases the yeast data could be used alone in successfully predicting human ING interactors.To restrict the list of 5 candidates further, we considered the biological relevance of the potential interactions to the known functions of INGs. Accordingly, the choice was amongst PAK1b, MAP3K4 (MEKK4) and p38MAPKa in descending order of homology among the 3 species , we chose to biochemically validate three putative interactions  using co-immunoprecipitation (Co-IP) followed by western blot analysis. As shown in Figure et al. marginal data and our prediction technique to the study of protein-protein interactions, we compared our results to those obtained from biochemical surveys, and other prediction algorithms [et al. [et al. that even the well-studied yeast interactome is only about 50% elucidated by existing filtered datasets.To evaluate the combined contribution of Krogan gorithms . Through [et al. , coverinet al., we strongly agree with Hart et al. that the research community would be much better served by the release of raw interaction datasets in general for comparison and consolidation. Additionally, some original datasets, such as those from Ito el al., were only available via the Internet Archive  as the original web links referenced in the papers no longer exist.Based on our success in identifying valid ING interacting proteins from the unfiltered dataset in Krogan Other bioinformatics-based approaches have been used to predict interactions between proteins . SequenIt must be noted that our core predictions  do not overlap with other ortholog-based techniques , which wet al's massive unfiltered dataset. This explains why our methodology is complementary to the existing techniques. We do not suggest that our technique will find all true positives, because interactions are not always shared between multiple species, and not all interactions have been elucidated. Rather, our technique provides guidance for researchers working on proteins whose interactions are not successfully predicted using existing techniques (such as the ING family presented here).The uniqueness of our core predictions suggests that the technique we have developed provides added value over a straightforward multi-species prediction tools. Given an unfiltered dataset, it is possible that some of the techniques used in Table in silico approach we have designed allowed us to predict new protein-protein interactions for the human INGs with a high degree of success and confirmed many previously elucidated interactions such as those with p21, Karyopherin, HAT/HDAC proteins and histone H3.In this study we have shown that a high degree of conservation of the ING proteins exists between human and yeast based on their interactions with analogous proteins across these species. This is consistent with previous reports showing conservation of ING protein sequence, particularly in specific domains , and conOur findings suggest that ING family proteins are involved in a more diverse array of biological processes than are presently suspected from the current literature and some of the interactions suggest possible additional mechanisms that might underlie their tumor suppressor capabilities. The three new interactions we have elucidated and biochemically confirmed here, RAD50, p38MAPK and MEKK4, further link ING1 to DNA damage/stress response pathways . ING intS. cerevisiae) Ssk2p protein are homologous, with MEKK4 being able to replace all of the known functions of Ssk2p in yeast. The stress-activated mitogen-activated protein kinase (SAPK) pathways are integral components of diverse stress signaling pathways such as UV, hypoxia, heat, osmotic shock, pH, oxidative damage, cytokines, pheromones and others [In an attempt to understand the connection between the ING, MEKK4, and p38MAPK protein interaction networks, we generated a merged interaction graph Figure . Severald others ,56. The d others ,57 and bd others . This obd others ,59 sinceThe overall philosophy of the prediction procedure outlined in Figure Our approach is semi-automatable (see Methods), but the researcher must: 1) assist in creating the multiple sequence alignments of domains, and 2) select biologically tenable interactions from the final shortlist generated. The procedure focuses on one gene (or set of closely related genes) at a time, and is not specific to ING proteins, but rather can be applied to any human gene with equivalents in yeast. We expect researchers who concentrate on a specific gene can beneficially use this focused approach to interaction prediction when generalized, large-scale prediction services do not yield good results.in vivo and contribute to the activities of, in this case, the ING proteins. This cross-species  bioinformatics-based approach was used to identify potential human ING1 interacting proteins with higher probability and accuracy than approaches based on screens in a single species. We confirm the validity of this screen and show that ING1 interacts specifically with three proteins tested: p38MAPK, MEKK4, and RAD50. These novel ING-interacting proteins further link ING proteins to cell stress and the DNA damage signaling, providing previously unknown upstream links to DNA damage response pathways in which ING1 participates.We have developed a method using comparisons in different organisms in which homologs exist, to predict with a high degree of certainty what particular protein interactions found in unfiltered data may occur of S. cerevisiae genes downloaded from the yeast genome database (SGD) [ was used to build and calibrate Hidden Markov Models (HMMs) from multiple, distinct conserved regions, i.e. potential domains, of the MSA. In step 4, these HMMs were used to search for proteins in other species with the same domains, using a DeCypher hardware-based HMM search , although HMMER software could also be used. The database searched against was Uniprot [-5, and few large gaps) were incorporated into the HMM. This searching and extra sequence incorporation was done iteratively  until no new matches were found in Uniprot. The end result of step 4 was that for each domain we had an HMM representing the domain's very particular evolution across eukaryotic species. Step 5 was to compare the HMMs against the complete protein sets from model organisms with large amounts of interaction data, namely, worm [The process of creating a list of protein interaction predictions consists of 8 broad steps  . Given gse (SGD) , and adj Uniprot , which ply, worm , fly [30ly, worm  or 2) thly, worm  and asseWe have focused on the use of thorough methods in our approach to maximize the sensitivity of our results. It would be possible to substitute certain methods, such as BLAST  for Smit2 at 37°C. Cells were seeded in 10 cm or 15 cm dishes 24 hrs prior to transfection.HEK293 (ATCC CRL-1573) cells were maintained in Dulbecco's Modified Eagle's medium  supplemented with 10% fetal bovine serum and 100 units/ml of penicillin and 100 mg/ml of streptomycin (Gibco-BRL) in an incubator with 5% COhis tag), MEKK4 and p38MAPK constructs were kind gifts from Drs. Tanya Paull (Rad50), Steve Pellech (p38MAPK), Richard Vaillancourt (MEKK4) and James Woodgett (p38MAPK).The pCI-ING1b plasmid has been described in , and the2 of UV radiation or left untreated. Fresh media was added and cells were incubated for 2 hrs before they were harvested.HEK293 cells were transiently transfected with the plasmids mentioned above at 60–70% confluence using a standard calcium phosphate protocol. Media was removed after 24 hrs, cells were washed with PBS and either exposed to 40 J/mg at 4°C for 15 min. The supernatants thus obtained were precleared by incubation with 20 μl 1:1 slurry of protein G-Sepharose (Amersham) for 30 min at 4°C and then incubated with 5 μg of specific antibody and 40 μl of protein G-Sepharose (1:1 slurry) or an equivalent amount of mouse anti-ING1 preconjugated with 40 μl of protein G-Sepharose (1:1 slurry) at 4°C for 3 hrs on a roller system. The immunocomplexes recovered on beads were washed two times for 5 min with 1 ml of RIPA buffer before the addition of Laemmli sample buffer. Proteins were resolved by sodium dodecyl sulfate-PAGE and transferred to nitrocellulose membranes . Immunoblotting was performed with a cocktail of four mouse anti-ING1 monoclonal antibodies or with rabbit anti-RAD50 polyclonal (Abcam), anti-p38MAPK (Zymed) or anti-MEKK4 (a gift from Dr. Richard Vaillancourt). Immunoreactive bands were visualized using an enhanced chemiluminescence reagent (Amersham Biosciences).Transfected and untransfected HEK293 cells were harvested and lysed under non-denaturing conditions in ice-cold RIPA buffer containing protease inhibitors . Cell lysates were sonicated on ice and centrifuged at 14,000 × PG devised the interspecies mining algorithm. PG and MS wrote the initial version of the manuscript. MS and PB reviewed the predictions and performed the lab experiments. QT assisted in the pairwise and multiple sequence alignments. KR conceived of the study and CS and KR provided guidance on the biological relevance of the method and manuscript revisions. All authors read and approved the final manuscript.Pairwise similarity of ING family proteins in yeast and human. Using various alignment algorithms, we found that YNG1 is the ortholog of human ING1/2, YNG2 is the closest homolog to human ING4/5, and PHO23 (YNG3) is similar to human ING3.Click here for file-35, yielding 381 potential conserved interactions in human.Potential yeast ING-interacting proteins with human homologs. Using the taxonomic tool in MAGPIE, we filtered the list of 1075 yeast ING-interacting proteins to only those having human homologs with e-value < 10Click here for fileEvidence for potential ING-like proteins and their interactors in worm, fly, human and yeast. In order to increase the confidence in our predictions, we filtered the human-yeast common ING interactors to only those interactions conserved in fly (worm had poor homologs). We found 36 fly ING-interacting proteins with either yeast or human homologs, and only 5 showed conservation amongst the three species.Click here for file


Analysis of the phosphoproteomes and the gene interaction networks of divergent yeast species defines the relative contribution of changes in protein phosphorylation pathways to the generation of phenotypic diversity. Saccharomyces cerevisiae, Candida albicans, and Schizosaccharomyces pombe) in order to quantify the evolutionary rate of change of phosphorylation. We estimate that kinase–substrate interactions change, at most, two orders of magnitude more slowly than transcription factor (TF)–promoter interactions. Our computational analysis linking kinases to putative substrates recapitulates known phosphoregulation events and provides putative evolutionary histories for the kinase regulation of protein complexes across 11 yeast species. To validate these trends, we used the E-MAP approach to analyze over 2,000 quantitative genetic interactions in S. cerevisiae and Sc. pombe, which demonstrated that protein kinases, and to a greater extent TFs, show lower than average conservation of genetic interactions. We propose therefore that protein kinases are an important source of phenotypic diversity.The extent by which different cellular components generate phenotypic diversity is an ongoing debate in evolutionary biology that is yet to be addressed by quantitative comparative studies. We conducted an in vivo mass-spectrometry study of the phosphoproteomes of three yeast species –promoter interactions, whereas TFs and kinases both show a faster than average rate of functional divergence estimated by the cross-species analysis of genetic interactions. Our data provide a quantitative estimate of the relative frequencies of different kinds of functionally relevant mutations and demonstrate that, like mutations in gene regulatory regions, mutations that result in changes in kinase–substrate interactions are an important source of phenotypic diversity.Natural selection at a population level requires phenotypic diversity, which at the molecular level arises by mutation of the genome of each individual. What kinds of changes at the level of the DNA are most important for the generation of phenotypic differences remains a fundamental question in evolutionary biology. One well-studied source of phenotypic diversity is mutation in gene regulatory regions that results in changes in gene expression, but what proportion of phenotypic diversity is due to such mutations is not entirely clear. We investigated the relative contribution to phenotypic diversity of mutations in protein-coding regions compared to mutations in gene regulatory sequences. Given the important regulatory role played by phosphorylation across biological systems, we focused on mutations in protein-coding regions that alter protein–protein interactions involved in the binding of kinases to their substrate proteins. We studied the evolution of this “phosphoregulation” by analyzing the in vivo complement of phosphorylated proteins (the “phosphoproteome”) in three highly diverged yeast species—the budding yeast  Genetic variation, in the form of point mutations, gene duplication/loss, and recombination serves as the raw material upon which natural selection acts during the evolution of a species. To understand this evolutionary process, we must in turn be able to understand how this variation translates into phenotypic changes that have a measurable impact on fitness. The great advances in DNA sequencing and comparative genomic analysis have brought us tremendous insight into the organization of genomes and the extent of genomic variation across species Saccharomyces cerevisiae and Candida albicans are phenotypically similar (both controlled by a conserved MAT locus), the regulatory arrangements that specify the mating types are different. These authors were able to trace mutations in one of the proteins involved  that have contributed to the changes in regulation. Similarly, Moses and colleagues have shown that regulation of the nuclear localization of the MCM complex by Cdk phosphorylation of Mcm3 was acquired in the Saccharomyces lineage but does not occur in C. albicansRecently, comparison of protein interaction networks in different species has been used to propose that protein–protein interactions change at a fast evolutionary rate after gene duplication Protein phosphorylation is a ubiquitous and reversible modification that is crucial for the regulation of cellular events S. cerevisiae, C. albicans, and Schizosaccharomyces pombe), and we used these data to generate a cross-species analysis of phosphoregulation. We quantified the rate of evolutionary change of protein phosphorylation and analyzed the divergence of kinase–substrate interactions for particular protein complexes. Finally, we tested and validated the observed evolutionary trends through comparative genetic interaction studies.We have carried out a mass spectrometry (MS) analysis of the in vivo phosphoproteome of three fungal species  varying from 1.3–1.7% , this difference is small compared to the eight orders of magnitude spanned by the abundance of all proteins. In fact, the known phosphoproteins also span similar orders of magnitude . Assuming that the gain or loss of a phosphoprotein corresponds to the gain or loss of up to five kinase–substrate protein–protein interactions, we estimate that kinase–substrate interactions change at a rate of approximately 1×10−6 to 1×10−5 interactions per protein pair per My . We considered that a phosphosite in an orthologous protein had diverged when no phosphosite was observed in the S. cerevisiae protein within an alignment window ranging from 20 to 200 alignment positions centered on the phosphosite of the orthologous protein. The rate of change of kinase–substrate regulation calculated in this way is 5 to 7 times faster  than the same calculations based on the phosphorylation status of the full proteins.This value likely represents a lower bound estimate, because changes of kinase–substrate interactions can occur without changing the total number of phosphoproteins. We next considered that evolutionary changes in phosphosite position should also be considered a change of kinase regulation. To estimate the rate of change in kinase–substrate interactions considering also changes in phosphosite locations, we aligned −4 to 3×10−4 per TF–gene interaction per My, at most two orders of magnitude faster than kinase-substrate turnover  to promoter regions for different yeast species turnover .S. cerevisiae to other species using orthology assignments. In this way, we defined, for each species, sets of proteins grouped according to their functional categories or protein complex membership. We then calculated the number of phosphosites per protein within each group, normalized by the average number of phosphosites per protein in the proteome. We observed a generally high correlation of the number of phosphosites per protein across different functions for all three species studied , and vice-versa .A potential pitfall of analyzing phosphorylation levels as the number of phosphosites per functional group is that it may miss cases where phosphorylation levels within that group of proteins remain the same across species, but the exact proteins that are phosphorylated have diverged. One striking example of this is the phosphorylation of the pre-replication complex. Although the level of phosphorylation of this complex is conserved, the proteins that are phosphorylated have changed. For this complex, phosphorylation of the S. cerevisiae to the other fungal species do not necessarily have the same number of proteins in all species. Because of this, gene duplication could account for some of the observed changes in the average number of phosphosites per protein across species. To examine this, we analyzed the functions and complexes showing significant differences in phosphorylation levels that also show significant differences in the number of proteins assigned to them  curve  as a measure of the method's performance. We obtained an AROC value of 0.69 for the motif enrichment method and 0.73 using GPS 2.0. For each protein complex, we used the prediction method that would best predict the phosphoproteins experimentally determined for S. cerevisiae, C. albicans, and Sc. pombe.To predict the phosphorylation propensity from protein sequence, we used two different approaches: (1) likelihood ratios (LRs) for kinase motif enrichment and spatial clustering following the method of Moses and colleagues S. cerevisiae. We used a set of features that include the number of shared  interaction partners between a kinase and a putative substrate, the existence of a phosphosite matching the substrate recognition motif of the kinase, etc.  we retain the top five kinases and for each we predict the phosphoproteins observed in the three species  using their substrate recognition motif and the motif enrichment method; (3) we then assume that the kinase that best predicts the phosphoproteins would be the most likely regulator.For each divergent complex identified above, we first calculated the predicted phosphorylation propensity for the orthologous group across 11 ascomycota species. In addition, we tried to determine the most likely kinase(s) responsible for the observed phosphorylation of each complex across the three species in a three-step process: (1) we use the kinase–substrate predictor to rank all 116 S. cerevisiae can be found in We present below the results obtained for the pre-replication complex and for the clathrin-associated complex. The analysis of the remaining complexes as well as individual kinase–substrate predictions for The evolution of cell-cycle control has previously been studied by analyzing gene expression data for multiple species S. cerevisiae , Cdc28p, a well-known regulator of these complexes S. cerevisiae we correctly predicted phosphorylation by Cdc28p of Mcm3p and Mcm4p Saccharomyces lineage that it is not observed in more divergent species S. cerevisiae by masking nuclear localization and export sequences that work in coordination with localization signals in Mcm2 C. albicans' Mcm2, which overlaps with an experimentally observed phosphorylation and shows strong homology to a conserved nuclear localization sequence. Therefore we postulate that in C. albicans, the localization of the MCM complex might be regulated via phosphorylation of Mcm2p instead of Mcm3p as occurs in the Saccharomyces lineage.The MCM and ORC complexes are a part of pre-replication complex and are among the few examples were evolutionary studies of phosphoregulation have been conducted However, there are known regulatory events that we fail to predict. We do not correctly predict the known Cdc28p regulation of Orc2p S. cerevisiae complexes were Cka1p, Yck1p, Yck2p, Cka2p, and Cdc7. Contrary to the example above, the observed phosphorylations could be explained equally well by the binding specificity of the five kinases so we selected the top kinase associated with the complex in S. cerevisiae, casein kinase type I (both isoforms Yck1 and Yck2) as the most likely kinase responsible for the observed phosphorylations .The results presented above show that the changes of phosphorylation during evolution might contribute significantly to evolutionary divergence, possibly at levels similar to transcriptional regulation. One could postulate that, if a large fraction of the phosphorylation sites played no significant functional role, then the observed changes in phosphorylation could represent mostly neutral variation with no impact on species fitness. In contrast, if most changes in phosphorylation observed here have an impact on fitness, then we would expect also to see significant divergence of protein kinase function. In order to test for functional changes, we decided to study the genetic interactions of protein kinases in two different yeast species  were conserved in Sc. pombe, whereas only 8% (38 out of 472) of genetic interactions with protein kinases and 4% (6 out of 141) of genetic interactions with TFs are conserved. This shows that indeed the functional roles of protein kinases and TFs are less conserved than average genes .We used quantitative genetic interaction screening to ask whether protein kinases do indeed evolve new functions more rapidly than average genes. We excluded from this analysis kinases that phosphorylate cellular components other than proteins . We assembled genetic interaction maps for ombe see . We obsep-value = 0.007 with hypergeometric distribution). This trend is stronger for genetic interactions among transient physical interactions partners . Interactions were defined as transient based on the experimental methods used  or somewhat slower  than TFs. Therefore we suggest that protein kinases, given their high regulatory potential and rapid divergence in their interactions, are an important source of phenotypic diversity.Comparing cellular interaction networks across different species is crucial for understanding how DNA variability drives functional potential. Changes in the regulation of gene expression have, to date, been seen as a prime mechanism leading to phenotypic divergence. This stems from the early studies of molecular evolution and a large body of work on the study of the evolution of morphology S. cerevisiae, C. albicans, and Sc. pombe) spanning 400 to 600 million years of evolution. We have used this information to estimate the global rates of change of phosphoproteins. Based on these rates, our estimated kinase–substrate interaction changes are within an order of magnitude of previous estimates for gain or loss of interaction after gene duplication. Furthermore, kinase–substrate interaction evolution is at most two orders of magnitude slower than TF–promoter interactions. These observations are further supported by the comparative analysis of quantitative genetic interactions between S. cerevisiae and Sc. pombe genes. We observed a lower-than-average conservation of genetic interactions for protein kinases and TFs, suggesting that the observed divergence of phosphorylation correlates with functional changes of protein kinases. Interestingly the level of conservation of genetic interactions between kinases and their interaction partners is similar to that observed for TFs and the genes they bind to. However, it should be noted that the current overlap between genetic interactions and physical interactions for kinases and TFs is still small. Also, the different nature of physical interaction (protein–DNA versus protein–protein) could potentially result in differences in the genetic interactions observed between interacting partners. For these reasons, further studies are needed to determine the exact relative functional divergence rate.This apparent discrepancy can be explained by noting that transient interactions of lower specificity, like interactions mediated by short peptide motifs, are much more likely to change than stable interactions are Our results indicate that there is a high level of conservation of phosphorylation for different functional groups across the broad time scale studied. This would mean that even if individual kinase–substrate interactions differ, the overall phosphorylation levels of a given functional group might be strongly predicted by homology. It is conceivable that this conservation of phosphorylation levels is maintained by physical proximity of kinases and substrates due to shared interaction partners or sub-cellular localization. Given that the in vivo targets of a protein kinase are determined, in large part, by factors other than its own substrate recognition  In this study, we have combined experimental phosphorylation information with computational methods to predict kinase–substrate interactions and their evolution. We used this approach to study eight protein complexes that show significant changes in phosphorylation and we predict putative kinase regulators responsible for these observed changes. Analysis of well-studied pre-replication complexes showed that we predict known examples of conserved and divergent phosphoregulation. In addition to our analysis, the study of human phosphorylation sites has recently shown that highly conserved phosphorylation networks are associated to disease .These results highlight the importance of studying the evolution of kinase regulation and our work offers a starting point for further studies.Selection pressure acts on the preservation or acquisition of phenotypes, rather than the mechanisms by which these phenotypes are implemented. A picture is emerging of highly conserved modules  that are regulated and organized in different ways in different species. For instance, the conservation of timed assembly of cell-cycle complexes, regulation of mating, or co-expression of ribosome subunits may be conserved, although details of the implementation diverges in different species There has been a long-standing debate, in particular in the field of developmental biology, as to the types of adaptive mutations that contribute most to phenotypic changes Proteins were precipitated from yeast lysates using TCA on ice and washed once with acetone at 4°C. Protein pellets (approximately 24 mg protein) were resuspended in 3 ml of freshly deionized 8 M urea. Samples were incubated for 1 h at 57°C with 2 mM Tris(2-carboxyethyl)phosphine hydrochloride to reduce cysteine side chains, these side chains were then alkylated with 4.2 mM iodoacetamide in the dark for 45 min at 21°C. The mixture was diluted 8-fold with 25 mM ammonium bicarbonate and 1% (w/w) modified trypsin (Promega) was added. The pH was adjusted to 8.0 and the mixture was digested for 12 h at 37°C. The digests were desalted using a C18 Sep Pak cartridge (Waters) and lyophilized to dryness using a SpeedVac concentrator (Thermo Electron).Phosphorylated peptides were enriched using an ÄKTA Purifier. Peptides run over an analytical guard column (Upchurch Scientific) loaded with 5-µm titanium dioxide beads (GL Sciences). Peptides were re-suspended in 750-µl wash solution , and the enrichment was done on three separate 250-µl aliquots. Each aliquot was injected over the titanium dioxide column, with an additional 3.9 ml wash solution to remove non-phosphorylated peptides. This was then followed by 3.5 ml of rinse solution . Phosphorylated peptides were eluted from the titanium dioxide column using 1 ml of elution solution (1 M KH2PO4).High-pH reverse-phase chromatography was performed using an ÄKTA Purifier  equipped with a 250-×4.60-mm column packed with 3-µm Gemini C18 resin (Phenomenex). Phosphopeptide-enriched fractions were loaded onto the column in 2 mM ammonium trifluoroacetic acid, pH 9.5 (buffer A). Buffer B consisted of 2 mM ammonium trifluoroacetic acid in acetonitrile. The gradient went from 1% B to 60% B over 20 ml, and from 60% B to 100% B over 5 ml. Between 30 and 40 fractions were collected and dried down using a SpeedVac concentrator. Samples were desalted using C18 ziptips (Millipore).Individual fractions were separated using a 75-µm×15-cm reverse-phase C18 column (LC Packings) at a flow rate of 350 nl/min, running a 3–32% acetonitrile gradient in 0.1% formic acid over 1 h on an Agilent 1100 series HPLC equipped with an autosampler (Agilent Technologies). The LC eluent was coupled to a micro-ionspray source attached to a QSTAR Elite mass spectrometer (Applied Biosystems). Peptides were analyzed in positive ion mode. MS spectra were acquired for 1 s. For each MS spectrum, the two most intense multiple charged peaks were selected for generation of subsequent collision-induced dissociation MS. For precursor ion selection, the quadrapole resolution was set to “low,” which allows for transmission of ions within approximately 2 mass to charge (m/z) units of the monoisotopic mass. The collision-induced dissociation energy was automatically adjusted based upon peptide charge and m/z ratio. A dynamic exclusion window was applied which prevented the same m/z from being selected for three minutes after its initial acquisition.Data were analyzed using Analyst QS software (version 1.1) and MS/MS centroid peak lists were generated using the Mascot.dll script (version 1.6b18). The MS/MS spectra were searched against the entire Uniprot database of the respective species (downloaded 19 April 2007) using the following parameters. Initial peptide tolerances in MS and MS/MS modes were 200 ppm and 0.2 Da, respectively. Trypsin was designated as the enzyme and up to two missed cleavages were allowed. Carbamidomethylation was searched as a fixed modification. Oxidation of methionine, protein N-terminal acetylation, pyro-glutamine formation, and phosphorylation of serine/threonine/tyrosine residues were allowed as variable modifications. All high-scoring peptide matches  from individual LC-MS/MS runs were then used to internally recalibrate MS parent ion m/z values within that run. Recalibrated data files were then searched with a peptide tolerance in MS mode of 50 ppm. The false-positive rates were estimated by conducting the search using a concatenated database containing the original Uniprot database as well as a version of each original entry where the sequence has been randomized.S. cerevisiae were defined using the gene ontology mapping provided by SGD (http://www.yeastgenome.org/). The complexes definitions for S. cerevisiae were obtained from the MIPS database (http://mips.gsf.de/). For the other fungal species studied, complexes and functional groups were defined by transferring these annotations using the orthology definitions from the Synergy algorithm Functional groups for S. cerevisiae with respect to another species, we considered only the set of orthologous proteins between species i and S. cerevisiae (denomined as ortProteins and ortKinases). We assumed that the coverage (c) of our compiled set of S. cerevisiae phosphoproteins is 92%, the largest value obtained from leaving out one of the previously published sets. We define the number of expected phosphoproteins (“expPhospho”) the number of orthologous phosphoproteins in species i and the conserved phosphoproteins (“consPhospho”) the number of ortologous phosphoproteins in species i detected as phosphorylated in S. cerevisiae. The number of divergent phosphoproteins (“divPhospho”) was thus defined as the difference: (expPhospho×c)−consPhospho. We defined the rate of change of S. cerevisiae phosphoproteins in reference to species i as:S. cerevisiae and species i. Similarly, we defined the rate of change of kinase–substrate interactions as:N is the assumed number of kinase–substrate interactions changed with every change in total phosphoproteins. We calculated similar rates for the change of TF–gene interactions using available information from the literature In order to calculate the global rate of change of phosphoproteins in S. cerevisiae and Sc. pombe growing in exponential phase For each species and for each functional group defined above, we determined the average number of phosphosites per protein. For this analysis, we used the phosphosites determined in this study and additional studies for i:i, as defined above, and n the set of three yeast species studied here.To search for significant cross-species differences in the average number of phosphosites per protein, we defined for each functional group and each species a measure of comparative phosphorylation (compPhos) as the relative contribution to the sum across the three species. For species p-value <0.05). z-scores for each functional group are provided in Defined in this way, functional groups with the same average fraction of phosphosites per protein, in the three species, would have a comparative phosphorylation value matching exactly 1/3 in the three species. As expected from the high-cross species correlation shown in In order to find complexes with significant differences in average number of phosphosites, we considered only 28 complexes that had at least ten protein subunits to discard large variations due to small complex sizes.We used two different approaches to predict phosphorylation from sequence for all fungal proteins studied: (1) LRs for kinase motif enrichment and spatial clustering; (2) phospho-site propensity predictions from GPS 2.0 S. cerevisiae, S. bayanus, S. paradoxus, S. castellii, Kluyveromyces lactis, K. waltii, Debaryomyces hansenii, C. albicans, Yarrowia lipolytica and Sc. pombe. The prediction scores were benchmarked using the known phosphoproteins of S. cerevisiae. We plotted the ROC curve and determined the area under the ROC (AROC) curve for both methods  the number of shared physical or genetic interactions partners in common between the kinase and the putative target. These four indicators were integrated using a naïve Bayes algorithm, and its performance was evaluated by AROC using a set of 472 kinase–substrate interactions curated from the literature In order to predict kinase–substrate interactions for S. cerevisiae kinase–substrate prediction score across all the complex subunits. For each complex, we selected (from the 116 S. cerevisiae protein kinases) the top five kinases predicted to regulate the complex for further analysis. These five kinases were then ranked on how well their substrate specificity explains the phosphorylation pattern of the complex subunits across the three species with available phosphorylation data. The ranking was done on the AROC value for phosphorylation prediction using the kinase–substrate LRs predicted from their binding motifs as described above. Detailed results for the complexes studied are provided in To predict the kinases most likely responsible for the phosphoregulation of a protein complex, we defined the kinase-complex association score as the sum of the S. cerevisiae and Sc. pombe were compiled from different quantitative high-throughput studies Genetic interaction information for S. cerevisiae and tested an additional 1,293 gene pairs in Sc. pombe using the E-MAP method as previously described S. cerevisiae that were also tested in Sc. pombe. This set is provided in Sc. pombe also genetically interact according to the definition defined above (S-score >2 or S-score <−2.5) having a similar phenotypic effect (suppression or enhancement) in both species.To increase the overlap available for cross-species analysis, we determined 634 novel strong genetic interactions in http://www.yeastgenome.org). TF–promoter interactions were obtained from Harbison and colleagues Physical protein–protein interactions were obtained from BioGRID database Dataset S1Experimentally determined phosphorylation sites for S. cerevisiae, C. albicans, and Sc. pombe.(2.63 MB XLS)Click here for additional data file.Dataset S2Probability scores for kinase-target naïve Bayes predictions for S. cerevisiae trained on available phosphorylation information as well as known physical and genetic interactions data.(3.43 MB TAR)Click here for additional data file.Dataset S3Genetic interaction for S. cerevisiae and Sc. pombe determined in this study and references for additional interactions collected from other sources.(1.16 MB XLS)Click here for additional data file.Protocol S1Supporting methods and results.(0.63 MB PDF)Click here for additional data file.


The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS  procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age. Proteins are inherently dynamic molecules of marginal stability. Many marvelous biological functions of proteins are realized through their internal motions Traditional methods of measuring protein's metabolic stability rely on either pulse-chase metabolic labeling or administration of protein synthesis inhibitors followed by half-life biochemical analysis of the abundance of the protein concerned at multiple time points during the chase period. Highly regulated proteins tend to be present in low amounts. Since even mass spectrometry plus failed to detect low-abundance proteins, study about protein's metabolic stability remains far from complete yet although it is of critical importance for drug development. Recently, it was reported that high-throughput systematic approaches for the analysis of global metabolic stability were taken by using a fluorescence-based system to monitor metabolic stability at the single-cell level http://horfdb.dfci.harvard.edu/), and translated ORFs to protein sequences using transeq in Emboss As elucidated in a recent review In order to formulate protein samples of different sequence lengths with vectors of a uniform dimension, let us adopt the concept of pseudo amino acid composition (PseAAC) Of the above seven types of properties, except for AAC , N , or H (hydrophobic). Thus, for a protein sequence, say, “MSDKPDMAEIEKFSKETIEQEKQAGESTQEKNPLPMLLPATDKSKLKKTE”, it can be coded as “HNPPNPHNPHPPHNPPNHPPPPPNNPNNPPPPNHNHHHNNNPPNPHPPNP”.For the above coded sequence, the following three extensive quantities can be derived: Take the above coded sequence of 50 letters as an example. It is composed of 10 Hs, 16 Ns and 24 Ps, as shown in 21 elements in a similarly way as described above for the case of “hydrophobicity”.For the “secondary structure”, “polarizability”, “normalized van der Waals volume” and “polarity”, each of them is also classified into three categories and hence would also generate For the “solvent accessibility”, since it is classified into two categories, the combination of Now for the “AAC” we have 20 elements The function of a protein is closely correlated with its subcellular location 10 of the p value generated by hypergeometric test on KEGG pathway. The larger enrichment score means more overrepresentation. There were 220 KEGG enrichment scores for each of the proteins investigated here.The simplest and most direct method for predicting the function of a query protein based on the training dataset of function-known proteins is the immediate neighborhood method If a protein can form a complex with other proteins, it will be more stable and have longer half-life. Therefore, the number of this kind of complexes a protein can form is a feature relevant to its stability, and should be counted in prediction as well. We downloaded the protein complex dataset from CORUM Table S1.As mentioned above, the 7 types of biochemical and physicochemical properties would contribute 132 components to describe a protein. In addition, its length could also be counted as a component, its occurrences in the 22 subcellular location sites as 22 components, its 220 KEGG enrichment scores as 220 components, and its number in forming protein-protein complexes as a component, the total components used in this study to represent a protein sample would be (132+1+22+220+1) = 376 components. For the list of the 376 feature components, see the Thus, the Note that before performing prediction, each of the 376 components in Eq.1 should undergo the following standard conversion procedure:The “maximum relevance & minimum redundancy” (mRMR) method was originally developed by Peng et al. Suppose For a feature set with In our study, the Nearest Neighbor (NN) algorithm or NNA is used to classify a protein as either labile or a stable one. NNA makes its decision by calculating the “distances” of a query protein with all the proteins in the training dataset one-by-one. Varieties of distance scales can be used for this purpose, such as Euclidean distance st equation of Eq.9), nd equation of Eq.9); rd equation of Eq.9).In biological literatures, the independent dataset test, subsampling or K-fold (such as 5-fold and 10-fold) test, and jackknife test are the three cross-validation methods often used to examine the accuracy of a statistical predictor Although the mRMR step could arrange the feature components according to some sort of ranks, it is not sufficient for us to determine which feature components should be selected to optimize the performance of our predictor. To solve the problem, the IFS  method is adopted as illustrated below.i-th set given byi in Eq.11 as its X-axis and the corresponding jackknife success rate as its Y-axis. The feature set, say Based on the ranked features obtained from the mRMR step, we can construct 376 feature component sets by adding one component at a time in an ascending order, with the We predicted the stability of 170 proteins targeted by 332 drugs with known half-life. The drug-target pairs and half-life of drugs were downloaded from DrugBank http://penglab.janelia.org/proj/mRMR/. We set the parameter The mRMR program in this study was downloaded from S obtained in the mRMR step. Accordingly, 376 prediction models were constructed and tested as described above. Shown in st equation of Eq.9), (B) only the “short” and “medium” half-life proteins (cf. the 2nd equation of Eq.9), (C) only the “long” and “extra-long” half-life proteins (cf. the 3rd equation of Eq.9). As shown in Table S2 (A), (B), and (C), respectively.In the IFS procedure, we built 376 feature sets based on the ordered feature set To investigate what kinds of features are critical for protein stability, we extracted the optimal feature components and counted the numbers of each kind of features. Shown in Table S2), which is quite sensible as well.In a recent work Yen et al. Proteins bound with ligands or proteins not prone to be denatured, are usually more stable. This would logically require them to have proper fold patterns or microenvironments. The reason why membrane proteins are relatively more stable is that their folding process involves binding with, transmembrane helix insertion into  was shorter than the half-life of drugs targeted to proteins with predicted “long or extra-long half-life” (with median of 709 minutes). The median of the half-life of drugs targeted to proteins with predicted “short half-life”, “medium half-life”, “long half-life” and “extra-long half-life” were 303, 510, 540 and 1080 minutes, respectively.It is interesting to predict the metabolic stability of drug target proteins and compare the results with the half-life spans of the corresponding drugs. Although there were many factors that can affect the half-life of a drug, we found that the stability of its target protein is a quite important one. For demonstration, the predicted metabolic stability outcomes for some drug target proteins and the real half-life spans of their corresponding drugs are given in the For instance, Dinoprostone (DrugBank accession number DB00917) is a prescription drug used, as a vaginal suppository, to prepare the cervix for labour and to induce labour. The half-life of Dinoprostone is less than 5 minutes. The predicted stability results for its target proteins PTGER1 (UniProtKB/Swiss-Prot ID P34995) and PTGER2 (UniProtKB/Swiss-Prot ID P43116) were both “short” half-life. Again, Clorazepate (DrugBank accession number DB00628) is for treating anxiety. It also has the function for muscle relaxant and anticonvulsant. Its half-life is about 2 days , and the predicted stability for its target proteins BZRP (UniProtKB/Swiss-Prot ID P30536) and GABRA1 (UniProtKB/Swiss-Prot ID P14867) were “long” and “extra-long”, respectively, fully consistent with the sense that the more stable a protein is, the longer half-life drug is needed for effectively targeting it; and vice versa.We have developed a new method for predicting the metabolic stability of proteins by integrating their various biochemical and physicochemical features. It is indicated by the rigorous jackknife cross-validation test that the predictor can achieve an overall success rate of 72.8%. With the feature selection approach based on the mRMR method and IFS procedure, we found that the following seven features would play the major roles in determining the stability of proteins: KEGG enrichment scores, subcellular locations, polarity, amino acids composition, hydrophobicity, secondary structure propensity, and the number of protein complexes. These findings might provide useful information for drug development. The method presented in this paper might also become a high throughput tool for large-scale annotating the metabolic stability of proteins.Dataset S1The sequences of benchmark dataset.(0.71 MB TXT)Click here for additional data file.Table S1List of the 376 feature components.(0.10 MB XLS)Click here for additional data file.Table S2The optimal feature components.(0.05 MB XLS)Click here for additional data file.Table S3The predicted metabolic stability for drug target proteins.(0.10 MB XLS)Click here for additional data file.


Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies.. The HAPPI database was created by extracting and integrating publicly available protein interaction databases, including HPRD, BIND, MINT, STRING, and OPHID, using database integration techniques. We designed a unified entity-relationship data model to resolve semantic level differences of diverse concepts involved in PPI data integration. We applied a unified scoring model to give each PPI a measure of its reliability that can place each PPI at one of the five star rank levels from 1 to 5. We assessed the quality of PPIs contained in the new HAPPI database, using evolutionary conserved co-expression pairs called "MetaGene" pairs to measure the extent of MetaGene pair and PPI pair overlaps. While the overall quality of the HAPPI database across all star ranks is comparable to the overall qualities of HPRD or IntNetDB, the subset of the HAPPI database with star ranks between 3 and 5 has a much higher average quality than all other human PPI databases. As of summer 2008, the database contains 142,956 non-redundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins. The HAPPI database web application also provides …” should be “The HAPPI database web application also provides hyperlinked information of genes, pathways, protein domains, protein structure displays, and sequence feature maps for interactive exploration of PPI data in the database.We developed a new web-based resource, the Human Annotated and Predicted Protein Interaction (HAPPI) database, located at HAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies. Saccharomyces cerevisiae,  [Drosophila melanogaster , Caenorhabditis elegans , and Mus musculus [et al. first reported the generation of 13,656 high-throughput human protein interactions in homogenized human brain using a random yeast two-hybrid platform [et al. identified 3,186 mostly novel interactions among 1,705 human proteins [et al. reported the mapping of ~2,800 proteins in a human protein-protein interaction network [et al. reported a large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach, producing a data set of 6,463 interactions among 2,235 distinct human proteins [Protein-protein interactions (PPIs) is  an important foundation for understanding how biological processes take place in cells, how cellular signals are modulated, and how molecules orchestrate in response to external environmental stimuli . High-throteins) , Drosophmusculus -5. In 20S. cerevisiae, S. pombe, D. melanogastor, C. elegans, M. musculus, and H. sapiens [These high-throughput experimental determinations of PPIs have led to an influx of PPI experimental data. By early 2008, BioGrid reported a comprehensive collection of 198,000 protein and genetic interactions from major organisms, including  sapiens . However sapiens , there aWhile it remains an open question how many measurable human protein interactions there are, the use of PPI data in building disease-relevant molecular interaction network models has already emerged as a major theme for "translational bioinformatics", studies that aim to facilitate the transformation of bioinformatics discoveries from "Omics" experiments into biomedical applications via bi-directional information exchange ,13. Receet al. applied a probabilistic model and integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict human PPI networks [Several human PPI databases have begun to expand experimental human PPI data coverage that is bottlenecked by experimental data throughput and cost. There are four common approaches for PPI data expansions: 1) manual curation from the biomedical literature by experts; 2) automated PPI data extraction from biomedical literature with text mining methods; 3) computational inference based on interacting protein domains or co-regulation relationships, often derived from data in model organisms; and 4) data integration from various experimental or computational sources. Partly due to the difficulty of evaluating qualities for PPI data, a majority of widely-used PPI databases, including DIP, BIND, MINT, HPRD, and IntAct ,19-22, tnetworks . The chanetworks ,30.. As of early 2008, HAPPI (version 1.1) contains 142,956 non-redundant, medium to high-confidence human protein interaction pairs among 10,592 human proteins identified by UniProt protein names. The HAPPI database aims to become the most comprehensive public compilation of human protein interaction information. The protein interactions are integrated from multiple data sources including both experimental and computationally-derived PPI. Each protein interaction in HAPPI is assigned a PPI confidence grade of 1, 2, 3, 4, or 5 to help users evaluate the reliability and confidence of reported interactions. Each interaction is computationally annotated with information including biological pathways, gene functions, protein families, protein structures, sequence features, and literature sources. These database capabilities will enable both biomedical researchers and network biology users to evaluate the biological significance of specific protein interactions, from which they can build network models for future translational bioinformatics research.In this work, we describe a new PPI web database resource, Human Annotated Protein-Protein Interactions (HAPPI), located at g database server. To take into account of PPIs derived from different data sources, we adopted the data source naming standard from the OPHID database. In particular, for human PPI data from HPRD, BIND, and MINT, we directly used these original database names as the data source names. For data integrated from the STRING database, we used eSTR to represent the "experimentally derived subset of STRING interactions", and pSTR to represent the "predicted/computationally-derived subset of STRING interactions".Human protein interaction data were collected, extracted, and integrated from the HPRD , BIND 2, MINT 2, STRING We represented the semantic relationships among different concepts involved in protein interactions as an Entity-Relationship (ER) data model shown in Figure Si (between 0 and 1) to each interaction pair, based on the type of its experimental/computational derivation method and the database source. Si provided an estimate of the degree of reliability of user confidence in the interaction data. Therefore, the more trustworthy the experimental or computational protocols were, the higher the confidence score (Si) was. Second, to combine the individual confidence scores from different sources into a final hscore for the interaction, we used the following formula:We developed a unified scoring scheme to assess the reliability of integrated human protein-protein interactions from the public domain. First, an interaction scoring system for each individual data source is either preserved  or created . In the later case, we assigned a heuristic confidence score N represented the count of different data sources and conditions, for each of which an independent assessment of protein interaction reliability score, Si, exists. The hscore ranges in value between 0 and 1. Third, to convert hscore to ranks, we use a ranking method that works in principle by clustering the interactions with closely-related hscore values for all interactions managed in the HAPPI database . Then, a five-star ranking model was developed to set the cut-off threshold at the hscore distribution cluster boundary. The results are summarized in Table hscore values for both high-throughput experimental data (default is 0.75) and curated experimental data from BIND, HPRD, and MINT (default is 0.80) are above 0.75, we therefore selected a combined score of hscore >= 0.75, or a final star rank of 4 or 5, as the minimal criteria for reporting interactions and their statistics for HAPPI. A complete initial scoring scheme to assess the reliability of human protein-protein interactions is shown in Additional file where g data warehouse from UniProt [All interacting proteins in the HAPPI database were annotated with gene function, pathway, protein domain, protein structure, and sequence feature map data. The data were separately imported into the Oracle 10 UniProt , GenBank UniProt , HUGO No UniProt , Ensembl UniProt , PubMed  UniProt , PDB [38 UniProt , and KEG UniProt  databasetransient protein interactions. Furthermore, comprehensive expression profiles are difficult to compile for all cellular conditions. To improve the development of a co-expression based confidence measure for interacting proteins, Tirosh and Barkai showed that a method using co-expression of orthologs of interacting partners performed quite well [In this study, we chose to apply evolutionarily conserved co-expression pairs to the assessment and comparisons of PPI data qualities for different sources, including the HAPPI database. High-quality conserved gene co-expression profiles were used to assess protein interaction quality. Many protein interaction data sets were cross-validated with human gene co-expression profiles such as . While iite well . Their mite well .et al [H. sapiens, 979 from C. elegans,155 from D. melanogastor, and 643 from S. cerevisiae. The relative quality of each PPI database, including HAPPI, OPHID [U0 set) of all the known human PPI databases, including HAPPI, OPHID, IntNetDB, ProNet, UniHI, and HPRD; furthermore, 6,145 of the 6,297 MetaGene pairs form a large connected MetaGene co-expression association network that showed the scale-free property commonly observed of most molecular interaction networks. Therefore, we regarded 6,145 Metagene pairs (M0 Set) to be most relevant high-quality subset of U0 and could be used as a gold standard for evaluating unknown PPIs from large databases. To facilitate comparisons of overlaps for different databases with MetaGene, we also developed an artificially synthesized protein-protein "random interaction" set (R0 Set) of 37,000 PPIs , by randomly reconnecting proteins observed in U0. Therefore, the lower-bound of any protein interaction data set derived from U0 could be given by counting the overlap between R0 and M0. To adapt to the different sizes of PPI databases, we took a random sample of 1000 PPIs each time from each database in comparison (including R0), and repeated this random sampling process 1000 times to obtain a distribution of normalized overlap counts with M0.Our computational method was based on the degree of overlap between protein interactions and the use of an evolutionarily conserved co-expressed gene data set called MetaGene. MetaGene consists of 22,163 evolutionary conserved co-expression relationships from humans, flies, worms, and yeast, based on the analysis of over 3182 published DNA microarray experiments by Stuart et al . It is aI, OPHID , IntNetDI, OPHID , ProNet I, OPHID , UniHI [I, OPHID , and HPRI, OPHID , was est. In the current release, HAPPI contains 13,601 proteins and 1,209,463 PPIs integrated from five databases collected with both experimental and computationally methods as described in the previous section. Users of the HAPPI web application software can search for PPIs using common protein identifiers. Typical web query results display all HAPPI PPIs at a default quality grade (star rank 3 and above). Users can drill down to explore annotations of the protein interaction or proteins involved.HAPPI was developed as a web-based PPIs database application and is freely accessible to the public at While there are several methods for validating PPI data, including those based on interacting domains, gene co-expression profiles, or gene ontology (GO) annotation semantic distances ,45-49, wIn Figure Figure In Figure We also analyzed PPI overlaps between HAPPI database subsets of different quality grades and two reference PPI databases. In Figure HAPPI enables users to retrieve human PPI data through multiple types of protein identifiers, such as UniProt IDs, Swiss-Prot accession numbers, RefSeq IDs, or IPI accession numbers, at its query home page. Query results that contain protein interaction data and quality rank are shown in a single web page as a data table. The query result is available for download either in a Molecular Interaction (MI) format recommended by the Proteomics Standard Initiatives (PSI) or in a Graph Markup Language (GML) format recommended by the International Molecular Exchange Consortium. Additional annotation details of the protein or protein interaction can be queried and retrieved online by selecting the hyperlinks in the protein interaction result page.HAPPI users can retrieve a list of protein interactions showing the following fields in a table: the query protein, a relationship symbol , the data source of the interaction, and a confidence rating of 1 to 5 stars. Figure We created two interactive components in the protein interaction details page: one to explore interacting protein 3D structures and the other to explore interaction protein feature alignments. In Figure HAPPI is by far the most comprehensive public compilation of human protein interaction data that come with a unified framework of interaction data reliability scores. In its current release, the HAPPI database contains 13,601 proteins and 1,209,463 PPIs integrated from several databases derived either experimentally or computationally. By comparing the degree of overlap between PPIs of varying quality grades and evolutionarily conserved co-expressed gene pairs, we assessed the quality of HAPPI. While the overall quality of HAPPI is comparable to that of the HPRD database, HAPPI PPIs with 3-5 star rank levels have a higher average quality than all other human PPI databases considered in this study, which include ProNet, UniHI, IntNetDB, OPHID, HPRD, and BioGrid.For future HAPPI database releases, we have three plans. First, we wish to continue integrating and linking valuable annotation data into the HAPPI database. Protein interaction data from high-precision text mining projects could be used to improve the validation of high-quality protein interactions as "re-discovered" compared to the findings reported in past literature. Gene co-expression and Gene Ontology data are also candidates for data import next, since they both can help define common functional context in which protein interactions may take place. Second, we plan on applying database customization techniques to improve the user querying experience with HAPPI. For example, we will add control buttons for users to customize interaction data quality filter thresholds, and to select a subset of retrieved protein interactions for downloading into spreadsheet programs. Third, we wish to improve existing PPI data investigation features. For example, we hope to run molecular docking programs and show computationally predicted protein binding constants and binding sites between two proteins. We also plan to improve the interplay between JMOL and Safmap Java Applets so that a highlight of sequence segments in one program may also be highlighted in the other program. With these improvements, we expect the database to play essential roles for biomedical researchers to retrieve trustworthy information on plausible human protein interaction data and for bioinformatics scientists to conduct network biology modeling studies.The authors declare that they have no competing interests.JYC conceived the initial idea, designed the method for the database construction, and drafted the manuscript. SM implemented the design, developed the database from integrated data sets, and implemented the web-based database interface. TH performed database comparisons and evaluations of the database. All authors are involved in the revisions of the manuscript.A unified scoring model to assess the reliability of human protein-protein interactions integrated from public protein interaction databases.Click here for file


Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data.in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach.In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application. Thereet al.  suggests [et al.  observedad hoc because there is no clear separation between the 'sticky' (highly connected) and 'non-sticky' (sparsely connected) proteins. Moreover, biological networks are scale-free in nature [et al. [Various approaches have been proposed to analyze the proteomics data by extracting the subset of valid interactions from their background noise. In some original high-throughput experiments ,7,13,14,n nature -19, whic [et al.  and Bade [et al.  studied et al. [et al. [It is also possible to explore the relationship between protein-protein interaction data and other types of data to assess the quality. Mrowka et al.  comparedet al.  as the ret al. ,23,24. Net al. . Even wo [et al.  noticed et al. [Besides expression data, sequence homology between two proteins and their corresponding interaction partners has been adopted to verify high-throughput protein-protein interactions . Howeveret al.  and Goldet al.  exploiteet al. -37 and pet al. -43. DespRecently, there has been a growing interest in data integration. In a study on the yeast signal transduction pathway for amino acid transport, Chen and Xu  demonstrin silico model to assess confidence of two proteins to interact in a target organism (e.g. human) on the basis of information entirely extracted from other model organisms . A cross-organism computational system for protein interaction prediction is attractive and needed, mainly because model organisms are well studied and have a tremendous amount of experimental data, while there may be little information about the target organism, especially with newly sequenced proteins . Among protein interaction studies using data from model organisms, data from target organism is employed in addition to data from other organisms [et al. [in silico model, we assess the overall quality of the protein-protein interaction data obtained from high-throughput screens for yeast.In this study, we describe a novel Bayesian network-based integrative model that assigns a likelihood score to every interaction. The main contributions we make are as follows. First, we establish a cross-species rganisms ,49. In eP1, P2) in a target organism, genome-wide orthologous mapping between the target organism and model organisms can be obtained from the InParanoid database [C is then provided to evaluate how closely related two orthologs are.The proposed cross-organism predictive system is illustrated in Figure database . The InPP1, P2), we determine their orthologs in the model organisms. Second, features are extracted for each ortholog pair from gene expression profiles and GO annotations of model organisms (details are discussed in next section). Finally, the heterogeneous data features are integrated to describe the protein pair  of the target organism using a Bayesian network-based model (details are in Methods) that assigns likelihood ratios for interaction.Our strategy is as follows. First, for a pair of proteins  in the target organism, we identify its ortholog pairs (Ri)12 in three model organisms . From each model organism, we download three microarray datasets. For each ortholog pair (Ri)12, Pearson correlation coefficients (PCC) are calculated from the gene expression profiles. A 4-level uniform quantization is used and each PCC is discretized into one of four states: high, medium high, medium low, and low. Rather than assuming the PCCs extracted from different microarray data are independent, we model the three PCCs from individual model organism jointly with one node in our Bayesian network model 12 of the protein pair . Three unique features are derived from each of the "molecular function", "biological process", and "cellular component" annotations in GO. The first feature checks whether two proteins share annotation terms: if the two proteins share at least one common term, the feature value is one; otherwise, it is zero. The second feature is called correlation ratio. In GO, gene products can be associated with more than one term. Therefore, the correlation between two GO terms is defined as the number of gene products in common. The larger the correlation value is, the closer the two GO terms are. We examine all possible pairs of GO terms between the two proteins and identify two GO terms (we refer them as "term_1" and "term_2") with the largest correlation value. The correlation ratio is then defined as n/(n1+ n2- n), where n is the correlation between term_1 and term_2, n1 and n2 are the numbers of gene products with term_1 and term_2, respectively. The correlation ratio is also quantized into two levels with a threshold of 0.5: high and low. The third feature is based on the minimum GO distance d between two proteins. Since GO is organized as a directed acyclic graph where each node represents a GO term, distance between two terms is described as the least number of nodes separating them in the graph. Again, to identify the two GO terms ("term_3" and "term_4") with the minimum distance, we examine all possible pairs of GO terms between the two proteins. For the third feature, incorporating the graph structures, we define eight states: 0 if d is zero; 1 if d is one  should have higher overlap with the previously established interactions. To evaluate our integrative method, we use both specificity and sensitivity. The specificity is defined as the percentage of matched non-interactions between the predicted set and the observed set over the total number of observed non-interactions. The sensitivity is defined as the percentage of matched interactions over the total number of observed interactions.First of all, with a specificity of 95%, our method can achieve a sensitivity of about 44%, and if the specificity reduces to 50%, the sensitivity increases to 80%. These results clearly demonstrate that model organisms certainly provide significant information for the prediction of PPIs in the target organisms. Secondly, we compare our method to the commonly-used naïve Bayesian method ,49. In tFigure in silico methods can (1) utilize existing biological knowledge; (2) predict large-scale PPIs; and (3) produce the confidence levels of interactions for each protein pairs.While high-throughput technologies generate thousands of protein-protein interactions (PPI) data and allow for genome-wide analysis, they tend to produce a large number of false positives. On the other hand, low-throughput methods can yield reliable results but are typically labor intensive, time consuming, and on a small scale basis. Computational methods provide an ideal tool for evaluating experimentally detected PPIs, as in silico model to evaluate high-throughput yeast PPI data and detect the spurious interactions. Our model is ideal for this type of application, as we do not use the direct PPI data from model organisms in our training process (features are extracted from microarray data and GO only). The current available yeast interaction pairs in databases may be determined by various experiments; therefore, the more experiments confirming it, the more confident we are in the interaction.We apply our cross-organism integrative We collected the yeast interaction data from the General Repository for Interaction Datasets (BioGRID) . The depet al. [Each PPI pair can be ranked by the likelihood ratio (positive versus negative). The larger the ratio is, the higher confidence we have in the interactions. We consider a protein pair as interacting if its likelihood ratio is larger than one . Figure et al. .et al. [Moreover, we analyze some PPIs detected by high-throughput experiments but predicted as negatives by our model. To assess the data, we consider the shortest distance of two proteins in GO cellular components, molecular function, and biological process. As discussed by Sprinzak et al. , for truSimilar observation can be made regarding protein pairs supported by two biological experiments but predicted as non-interacting. For example, the protein pair, YEL061C and YNL147W, has several pairs of GO cellular component terms that are closest to each other between the two proteins: , , , and   [C. elegans, 33,518 D. melanogaster, and 111,611 S. cerevisiae interaction pairs. The human interacting protein pairs are obtained from the Human Protein Reference Database (HPRD) [The interaction data for BioGRID) . In totae (HPRD) ,54 whereGenome-wide orthologous mapping between the target organism and model organisms is obtained from the InParanoid database . InParanMicroarray gene expression data are collected from NCBI Gene Expression Omnibus (GEO) ,56. OnlyH. sapiens by integrating information from three model organisms  as shown in Figure C from the InParanoid, respectively. Thus, the confidence scores of mapping for each ortholog set are explicitly incorporated into our model.The heterogeneous data from different organisms are integrated using a Bayesian network (BN) model as shown in Figure Mi, Fi, Pi, and Ci represent features extracted from Microarray data, molecular Function, biological Process, and cellular Component from model organism i , respectively. For each model organism, we compute Pearson Correlation Coefficient (PCC) from three microarray datasets and each PCC is discretized into 4 levels . Unlike the commonly used naïve Bayes model, we do not assume that microarray datasets are conditionally independent. We model them jointly using the node Mi, a node with 20 states. For example, Mi = 1 or  indicates that the PCC values calculated from the three microarray data sets are all low; Mi = 2 or  means that PCC values are low in two microarray data sets and medium low in one microarray data set. Note that high-high-low, low-high-high and high-low-high etc. are considered as the same state. In other words, we only consider the PCC levels regardless of which microarray data set is used.From each model organism, we extract microarray features and GO features as discussed above. The nodes Fi, Pi, and Ci represent the combination of three features extracted from GO for each organism. For example, the variable F1 is a vector of  and has 32 states . We summarize the information for each node in Table Similarly, The BN model integrates heterogeneous data from three model organisms to predict PPIs in a target organism. For each model organism, features extracted from multiple microarray data or GO terms are modelled jointly without assuming conditional independence. Features of different model organisms are conditionally independent giving the interaction information of a protein pair in the target organism and model organisms. This cross-organism conditional independence allows us to derive a simple solution for PPI prediction, as we detail next.Ei = , i = 1, 2, 3 asThe Bayesian approach to classify a test sample is to assign the most probable class or the class with a larger posterior probability for a two-class problem. Based on Bayes theorem, we can write the posterior probability of PPI given all the evidence For the model shown in Figure The ratio of the posterior probability for two classes iswhere  and P(PPI = 0) can be computed empirically. In application, we compute probability ratio L for a pair of proteins and predict the two proteins as an interacting pair if L > 1 and non-interacting pair otherwise. ROC curves are created by varying this decision threshold, which is equivalent to adjusting the priors. The individual likelihood can be computed from training data.The prior PPI: Protein-Protein Interactions; GO: Gene Ontology; MIPS: Munich Information Center for Protein Sequences; Co-IP: coimmunoprecipitated protein complex; ROC: Receiver Operating Characteristic; BioGRID: General Repository for Interaction Datasets; HPRD: Human Protein Reference Database; DAG: Directed Acyclic Graph; PCC: Pearson Correlation Coefficient; BN: Bayesian NetworkThe authors declare that they have no competing interests.XTL carried out the experiments and participated in acquisition and preparation of the microarray data. ML participated in acquisition of protein interaction data and carried out feature extraction of the gene ontology data. XWC conceived the study and designed the experiments. All authors helped in drafting the manuscript and approved the final manuscript.


Structural phylogenomics refers to the combined use of evolutionary and structural information in a bioinformatics analysis. The term phylogenomics refers to two distinct tasks: reconstructing a species phylogeny using multiple genes —allowing a kind of mix-and-match grab-bag of function and structure. The result of these evolutionary innovations is a multiplicity of biological functions and structures that contribute to the diversity of life forms. Consider G protein–coupled receptors (seven-transmembrane receptors found in many eukaryotic species); over 800 have been found in the human genome alone. These genes have diverged from their common ancestor through repeated duplication events, allowing them to recognize hundreds of different ligands and to participate in distinct biological processes. Tracking the evolution of a protein superfamily within and across different species and overlaying the phylogenetic tree with experimental data can allow highly nuanced predictions of function and structure for uncharacterized proteins.Protein superfamilies evolve novel functions and structures through mutations at key positions, gene duplication, internal repeats, and gene fusion and fission events. Many proteins are composed of multiple structural A phylogenomic analysis, as originally defined semi-global  clustering techniques and alignments are also used to construct hidden Markov models  for use in identifying important functional or structural domains in novel sequences. While internal nodes (evolutionary branch points) of protein superfamily phylogenies typically represent speciation and duplication events, in cases where a structural domain-based phylogeny includes sequences with different domain architecture, internal nodes of the tree may also represent gene fusion and fission events; this complicates a phylogenetic analysis, but can yield very powerful insights into the functional roles of individual domains.Structural information can be useful at various points in a phylogenomic inference of function. For instance, most annotation transfer protocols do not differentiate between homologs having only local similarity and those aligning along their entire lengths, and can thus result in errors in annotation. Incorporating domain architecture information , or using a structural phylogenomic clustering method such as FlowerPower A fundamental tenet of phylogenomic inference of function is that the evolutionary tree is correct. Is this assumption reasonable? In our experience estimating phylogenetic trees for protein superfamilies in the PhyloFacts Phylogenomic Encyclopedias positional homology). However, numerous studies have shown that sequence alignment accuracy drops sharply with evolutionary divergence Many factors can affect phylogenetic tree topology accuracy. First, dataset selection (how homologs are selected) can affect phylogenetic accuracy. It is not uncommon to see phylogenetic trees restricted to sequences from whole (fully sequenced) genomes, or from a subset of available species. These restrictions in dataset selection may be convenient, but can cause problems in the estimated phylogeny due to sparse taxon sampling genome trees or gene matrix approaches To mitigate the impact of errors in a multiple sequence alignment, alignment masking is typically performed to remove columns of uncertain homology prior to phylogenetic tree estimation. However, alignment masking protocols appropriate for species phylogeny estimation, where the input is a concatenated alignment of many genes/proteins , or in which different phylogenetic methods may disagree on the branching order. Given these possible problems, how do we evaluate phylogenetic tree methods for use in analysis of protein superfamilies?Phylogenetic tree estimation methods have traditionally been evaluated in two ways: using simulation studies and comparing inferred trees and trusted phylogenies based on fossil data and morphological characteristics. Phylogenetic simulation protocols do not currently model gene duplication events and structural and functional changes, but could presumably be modified to do so. However, while we do not  have the equivalent of fossil evidence for gene families, we do have the rough equivalent of morphological data: abundant 3D protein structures; assays for biochemical function; experimental data indicative of biological process and pathway association, cellular localization, protein–protein interaction, and so on. We propose that these data could be used to evaluate protein superfamily phylogenies estimated from sequence information. Our fundamental assumption in proposing the use of these experimental data to evaluate protein superfamily phylogenies is the following: since evolution is primarily conservative of function and structure, a phylogenetic tree that clusters functionally and structurally similar proteins ought to be more accurate  than one that does not. Unfortunately, this approach has two fundamental limitations. First, functional similarity is not easily quantifiable. Second, evaluating phylogenies based on agreement with annotated function is problematic because of the prevalence of annotation errors and the paucity of experimentally supported annotations.Fortunately, protein structure has several attributes that make it an appealing basis for evaluation of phylogenetic trees. First, structural similarity correlates closely with evolutionary distance, with closely related proteins having higher structural similarity than more distantly related proteins true tree may not be known (or even knowable), but we can assess the predictive power of phylogenetic methods for their actual use in practice.In this article, we are clearly advocating a pragmatic approach to evaluating the effectiveness and utility of phylogenetic tree methods, rather than one that is based on some theoretical or ideological agenda. Simply put, we need a concrete measure of the utility of phylogenetic methods for functional inference. Whether these estimated phylogenies correspond to the If phylogenetic methods have limitations with respect to protein superfamily analysis, how can we improve them? In particular, is it appropriate to use protein structure or other experimental data to improve phylogenetic reconstruction accuracy? For instance, it has been shown that inclusion of phylogenetic information improves the specificity and sensitivity of bioinformatics methods for numerous tasks, including enzyme active site identification In our experience, orthologous groups are typically clustered correctly by most phylogenetic estimation methods. Where methods disagree is at the coarse branching order between paralogous genes.  At these evolutionary branch points, sequence information may be insufficient for phylogenetic resolution, and the use of structural data might prove helpful. If solved structures are available for some of these subtrees, we could use these data to bias the tree topology estimation . This joint analysis of sequence and structural characters is enabled by MrBayes (as shown in complete solution to the problem of protein function prediction, it has been shown to provide significantly higher precision in functional annotation than many other approaches to this task, and provides a unique framework for investigating the changes in protein function and structure explored by evolution.While structural phylogenomics is just one of many methods for predicting protein function (see