We use our method to provide a set of high-confidence interactions for human SH2 domains with known structure filtered on secondary structure and phosphorylation state. We validated the predictions using literature-derived SH2 interactions and a probabilistic score obtained from a naive Bayes integration of information on coexpression, conservation of the interaction in other species, shared interaction partners, and functions. We show how our predictions lead to a new hypothesis for the role of SH2 domains in signaling.Current experiments likely cover only a fraction of all protein-protein interactions. Here, we developed a method to predict SH2-mediated protein-protein interactions using the structure of SH2-phosphopeptide complexes and the FoldX algorithm. We show that our approach performs similarly to experimentally derived consensus sequences and substitution matrices at predicting known  Understanding the functional role of every protein in the cell is a long-standing goal of cellular biology. An important step in this direction is to discover how and when proteins interact inside the cell to accomplish their tasks. Many of the cellular functions depend on reversible protein modifications like phosphorylation. To sense these modifications, cells have protein domains capable of binding phosphorylated proteins such as the SH2 domain. In this work, we show that it is possible to use the three-dimensional structure of protein domains to predict its binding preferences. Using a computational tool called FoldX, we have predicted the binding specificity of several human SH2 domains. These predictions, based on the computational analysis of the 3-D structure, were shown to be of similar accuracy as those obtained from experimental binding assays. We show here that it is also possible to understand how a mutation changes the binding preference of protein binding domains, opening the way for better understanding of some disease causing mutations. The combination of this novel computational approach with other sources of information allowed us to provide a set of high-confidence novel interactions for the proteins here studied. The cell's ability to respond to internal and external cues depends largely on reversible post-translational modifications of proteins, such as phosphorylation, ubiquitylation, methylation or acetylation. These modifications often occur on short unstructured stretches of proteins and are read by domains that recognize the modified form in vivoCaenorhabditis elegansXenopus laevisExperiments using peptide libraries indicate that each SH2 domain binds a different spectrum of phosphopeptides in vivo SH2-mediated protein-protein interactions. However, the genome-wide determination of the binding specificity of SH2 domains using peptide libraries seems impractical given the more than one hundred human SH2 domains The in vitro binding specificity of SH2 domains is commonly determined using peptide libraries http://foldx.crg.es.Our group has developed FoldX, an empirical force field for the prediction of protein energetics in vitro SH2-phosphopeptide binding and in vivo SH2-mediated protein-protein interactions. Together with information on phosphorylation and secondary structure, FoldX can give accurate predictions of novel protein-protein interactions. We used the developed method to predict a high confidence SH2 interaction network and validated it using information on co-expression, conservation of the interaction in other species, shared interaction partners and shared GO functions, integrated using a naive Bayes network. The predicted interactions can be use to derive biologically relevant testable hypothesis.We have implemented the force field contributions of phosphorylated amino acids  into FoldX and used it to predict the binding specificity of nine human SH2 domains with known structure. Our calculations can reproduce experimental consensus target sequences. FoldX performs as well as experimentally derived consensus sequences or position-specific substitution matrices in the prediction of We have implemented phosphorylation of tyrosine, serine and threonine residues into FoldX Second, we have predicted the changes in the free energy of formation of 21 protein-phosphopeptide complexes upon mutation of protein residues close to a phosphorylated residue in vitro by exposing the domain to a synthetic phosphopeptide library in which several positions have been randomized. The preferred residues at each position and the consensus target sequence are identified by sequencing the pool of bound peptides Next, we tested the ability of FoldX to predict the binding specificity of phosphopeptide-binding domains. The binding specificity of a domain is commonly determined We have used position specific scoring matrices calculated with FoldX to compute the binding energy of 50,000 random sequences and 50,000 sequences matching the experimental consensus see . The avehttp://adan.embl.de, −10, indicating that FoldX can predict in vitro binding of phosphopeptides to SH2 domains.We have made a direct comparison between experimental SH2 domain binding specificity and FoldX predictions using experimental binding affinities of SH2 domains for non-randomized peptides. We have retrieved a list of 429 phosphopeptides tested for binding to the nine SH2 domains in −11). Experiments with randomized peptide libraries can also be used to generate position-specific scoring matrices −12) for the FoldX predictions and 0.70±0.03  for the predictions using experimental scoring matrices. Altogether, the performance of our structure-based calculations in the prediction of in vitro protein-phosphopeptide binding specificity is similar to experimental methods based on peptide libraries.We have made a direct comparison of FoldX and experimental consensus target sequences in the detection of protein-phosphopeptide complexes for the eight domains in in vitro binding specificity induced by the ThrEF1Trp mutation in the Src SH2 domain.The binding specificity of the Src SH2 domain changes from pYEEI-containing phosphopeptides to pYVNV-containing phosphopeptides upon mutation of threonine EF1 to tryptophan in vitro of phosphopeptides to a given SH2 domain for which high resolution structural data is available. Next, we used FoldX for the prediction of binding in vivo. We compiled a list of SH2-mediated protein-protein interactions in the following way: First, we extracted from the Human Protein Reference database all interactions for proteins containing the SH2 domains in −26), indicating that FoldX is able to predict in vivo SH2-mediated protein-protein interactions.We showed so far that FoldX can predict the binding in vivo SH2-mediated protein-protein interactions (−25). The results for the five domains with available Scansite matrices (88 positives) are shown in −21) for FoldX and 0.76±0.03  for the Scansite predictions. As observed for in vitro interactions, FoldX performs similar to experimental methods based on peptide libraries in the prediction of SH2-mediated in vivo interactions.We have compared the ability of FoldX, experimental consensus target sequences and Scansite matrices to identify ractions . There aractions , square ractions , circle −26) for the unfiltered FoldX predictions (blue line) to 0.93±0.02,  for the filtered predictions (red line). As a control, we tried to predict the same set of interactions using only the phosphorylation/secondary structure filter  or we found supporting information from homologous proteins. For example, the Lck SH2 domain (LCK_HUMAN) has been shown to bind IRS1 (IRS1_HUMAN) peptides in vitro Wiskott-Aldrich syndrome like protein (WASL_HUMAN) and SAP (SH21A_HUMAN) have been shown to interact with an activated form of Cdc42 in vitro WASL-Arp2/3 actin polymerization in synergy with Cdc42-WASL interaction in vivo by directing the activated form of Cdc42 to WASL.Some of the putative interacting proteins form complexes with common targets that might hint at the biological roles of the predicted interactions. For example, both This initial investigation of the possible biological functions of the predicted interactions further indicates that the predictions presented can be used to derive biologically relevant testable hypothesis.in vitro binding specificity . Second, we calculate the free energy of interaction between the protein and the phosphopeptide in the complex (“interaction energy”). Last, we add the two numbers to calculate the free energy for formation of the protein-phosphopeptide complex (“binding energy”).Substitution matrices are calculated as follows. First, the geometry of the wild-type complex was optimized. After this, we introduced all 20 residues at each phosphopeptide position. The “binding energy” for each residue at each position relative to the amino acid at the same position of the crystallized ligand was stored in a scoring matrix. The binding energy of a given sequence was calculated by summing over all positions of the matrix, which are taken to be independent.http://scansite.mit.edu/). It is not possible to obtain predicted binding scores covering the full dynamic range of the matrices from this web service so the lowest available threshold was selected. In the ROC curves calculated from Scansite predictions . These genomes were selected on basis of their availability and to cover a broad evolutionary time scale of divergence from human. The ortholog assignments were taken from the Inparanoid database We have previously shown that it is possible to improve the prediction of protein-interactions by combining the in-vitro binding specificity encoded in the form of linear motifs with additional information like conservation and secondary structure in vivo protein interactions, found in the Human Protein Reference Database We have used a naive Bayes predictor Figure S1Conservation as a filter for FoldX predictions of SH2-mediated protein-protein interactions. (A) ROC curves for FoldX predictions (AROC 0.79±0.02), filtered for conservation in one (AROC 0.81±0.02), two (AROC 0.82±0.02), three (AROC 0.82±0.02) and four genomes (AROC 0.77±0.03). (B) ROC curves for FoldX predictions filtered for phosphorylation/secondary structure (AROC 0.92±0.02), filtered also for conservation in one (AROC 0.92±0.02), two (AROC 0.92±0.02), three (AROC 0.91±0.02) and four genomes (AROC 0.86±0.02).(0.09 MB DOC)Click here for additional data file.Table S1Experimental and calculated changes in free energy for protein-phosphopeptide complex formation upon dephosphorylation.(0.12 MB DOC)Click here for additional data file.Table S2Experimental and calculated changes in free energy for protein-phosphopeptide complex formation for mutations in the environment of the phosphate group in protein-phosphopeptide complexes.(0.12 MB DOC)Click here for additional data file.Table S3Binding and non-binding phosphopeptides. For all SH2 domains with available x-ray structure we compiled a list of binding and non-binding peptides from the literature. We could not find significant number of known binding and non-binding peptides for the C-terminal SH2 domain of Syk.(0.50 MB DOC)Click here for additional data file.Table S4Area under the ROC curve (AROC) statistics for prediction of peptide binding and full protein targets for human SH2 domains using FoldX and the Scansite server.(0.08 MB DOC)Click here for additional data file.Table S5Known SH2-mediated protein-protein interactions and binding sites in human.(0.16 MB DOC)Click here for additional data file.Table S6High-confidence predictions of SH2-mediated protein-protein interactions.(0.27 MB DOC)Click here for additional data file.Text S1Supplementary methods.(0.18 MB DOC)Click here for additional data file.


Xanthomonas oryzae pathovar oryzae (Xoo) that is an important pathogenic bacterium that causes bacterial blight (BB) in rice. We developed a detection protocol to find possibly interacting proteins in its host using whole genome PPI prediction algorithms. The second aim was to build a DB server and a bioinformatic procedure for finding target proteins in Xoo for developing pesticides that block host-pathogen protein interactions within critical biochemical pathways.Protein-protein interactions (PPIs) play key roles in various cellular functions. In addition, some critical inter-species interactions such as host-pathogen interactions and pathogenicity occur through PPIs. Phytopathogenic bacteria infect hosts through attachment to host tissue, enzyme secretion, exopolysaccharides production, toxins release, iron acquisition, and effector proteins secretion. Many such mechanisms involve some kind of protein-protein interaction in hosts. Our first aim was to predict the whole protein interaction pairs (interactome) of A PPI network in Xoo proteome was predicted by bioinformatics algorithms: PSIMAP, PEIMAP, and iPfam. We present the resultant species specific interaction network and host-pathogen interaction, XooNET. It is a comprehensive predicted initial PPI data for Xoo. XooNET can be used by experimentalists to pick up protein targets for blocking pathological interactions. XooNET uses most of the major types of PPI algorithms. They are: 1) Protein Structural Interactome MAP (PSIMAP), a method using structural domain of SCOP, 2) Protein Experimental Interactome MAP (PEIMAP), a common method using public resources of experimental protein interaction information such as HPRD, BIND, DIP, MINT, IntAct, and BioGrid, and 3) Domain-domain interactions, a method using Pfam domains such as iPfam. Additionally, XooNET provides information on network properties of the Xoo interactome..XooNET is an open and free public database server for protein interaction information for Xoo. It contains 4,538 proteins and 26,932 possible interactions consisting of 18,503 (PSIMAP), 3,118 (PEIMAP), and 8,938 (iPfam) pairs. In addition, XooNET provides 3,407 possible interaction pairs between two sets of proteins; 141 Xoo proteins that are predicted as membrane proteins and rice proteomes. The resultant interacting partners of a query protein can be easily retrieved by users as well as the interaction networks in graphical web interfaces. XooNET is freely available from  Saccharomyces cerevisiae   E013598] .Although there was a report showing that some Xoo insertion mutants of unknown or hypothetical protein genes had shown changed pathogenicity , it is aIn XooNET, predicted PPI information involving Hrp proteins can give us additional function information. For example, Xa21, the resistance gene of rice, has been reported . However4,538 proteins of Xoo were retrieved from NCBI and were aligned with SCOP  domains The same 4,538 proteins of Xoo were aligned with proteins in PEIMAP using the BLASTP  algorithPfam  domains As a filter, we used the 'combined score' between any pair of proteins which were predicted by PEIMAP, PSIMAP, and iPfam algorithms. As a result, we selected 684 Xoo proteins participating in 2,494 high-confidence PPIs (> 0.6) that were commonly found in all the three databases encompassing PSIMAP, PEIMAP, and iPfam. Those were further rescaled into the confidence range from 0.0 to 1.0 combining all the scores .Oryza sativa is known as the sole host of Xoo. Therefore, we added 3,407 PPI interaction predictions between Xoo and rice (Oryza sativa japonica and Oryza sativa indica). We chose 354 proteins expected to be membrane proteins and extra cellular proteins in Xoo using GO-Slim [Oryza sativa japonica , or Oryza sativa indica (18/118). As a result, we predicted that 141 Xoo proteins have 3,407 interaction pairs with rice . We evaluated many different thresholds of psi-Blast and hmmpfam for domain assignment, and the most adequate one was 10e-4 for PSIMAP, 40% identity and 70% coverage for PEIMAP, and 10e-2 for iPfam. GO-Slim . With thOryza sativa. Users can also input amino acid sequences. In addition to giving users the functional category of gene sets, XooNET provides the tree of gene ontology annotation using GO-Slim. Figure XooNET can be accessed by gene symbols, gene descriptions, locus tags, and NCBI gi numbers to find gene information and interacting partners not only of Xoo but also of The public interaction databases such as BIND and DIP at this time are limited for the PPIs of Xoo. Therefore, PEIMAP, which is an integrated resource of experimental PPI data, covers only about 14% of the total Xoo proteins. We found that some PPI pairs reported in experiments  were not predicted by XooNET by using the updated PEIMAP algorithm. The cases include: interactions between HrpB1 and RecA, HrpB2 and RecA, HrpB5 and XorII, Hpa2 and RecA; AvrBs2 and HpaP, and AvrBs2 and HrcQ. This shows that the prediction capability of XooNET is still limited for newly discovered protein interactions. By contrast, XooNET predicted that AvrBs2 is interacts with itself. However, a yeast two-hybrid assay showed no self interaction . Thus, to increase the prediction boundary of XooNET, we expanded it by providing a field for users to add newly confirmed experimental interaction information.Xanthomonas oryzae pathovar oryzae.Avr proteins are known to be crucial effectors that make many bacterial species pathogenic. We found 15 annotated AvrBs3 homologues in Xoo that fall on to three groups according to the interaction promiscuity in protein protein interaction: group 1, zero or one; group 2, more than 60; and group 3, around 10 partners. The highly interactive protein group showed that their numerous partners are functionally related to pathogenicity and can be subdivided. This shows that PPI analysis can assist researchers in discovering new targets and in designing more systematic experiments. One such highly interacting protein, Xoo1125, a hypothetical protein which has over 60 interaction partners including the Avr proteins, caused the loss of pathogenicity when transposon insertion mutation was carried out in a separate experiment. This suggests that XooNET approach is useful in investigating the functions of unknown or hypothetical proteins in XooNET is an integrated database of mutually complementary protein-protein interaction databases: PSIMAP, PEIMAP, and iPfam. The XooNET server is the first specialized Xoo PPI database which provides information of possibly interacting partners against query proteins. In particular, as only one third of the Xoo proteome are fully annotated, there are still many hypothetical and unknown proteins. XooNET provides a platform for biologists to annotate them by predicting their interaction partners and looking into their pathways.The basic procedure of PSIMAP is to infer interactions between proteins by using their homologs. Interactions among domains or proteins for known PDB (Protein Data Bank) structures are the basis for the prediction. If an unknown protein has a homolog to a domain, PSIMAP assumes that the query tends to interact with its homolog's partners. Its concept is called 'homologous interaction'. The original interaction between two proteins or domains is based on the euclidean distance. Therefore, PSIMAP gives a structure based interaction prediction .PEIMAP  has been constructed by combining several experimental protein-protein interaction databases. We carried out redundancy check to remove identical protein sequences from the source interaction databases. At present, it contains 116,773 proteins and 229,799 interactions.JGK started this project, wrote the manuscript, manually validated the interaction lists and tested part of the interaction with yeast two-hybrid system. DP designed the system and wrote the manuscript. BCK constructed the database. SWC developed the website. YTK, YJP and HJC manually validated the web, and performed pathogenicity tests for the Xoo insertion mutants including Xoo1125. HP and KBK constructed automatic graph building modules. KOY and SJP developed pre-search basic modules, gene ontology mapping. BML directed the study. JB conceived and directed the study and helped to draft the manuscript.


Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties . We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles (“meta-features”) in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integrated assessment framework can be readily extended to other correlational analyses at the genome scale.Rates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein evolutionary rate. However, dissecting the correlations among evolutionary rate and these genomic features remains a major challenge. Here, we use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in  Proteins encoded within a given genome are known to evolve at drastically different rates. Through recent large-scale studies, researchers have measured a wide variety of properties for all proteins in yeast. We are interested to know how these properties relate to one another and to what extent they explain evolutionary rate variation. Protein properties are a heterogeneous mix, a factor which complicates research in this area. For example, some properties  are numerical, while others  are descriptive; protein properties may also suffer from noise and hidden redundancies. We have addressed these issues within a flexible and robust statistical framework. We first ranked a large list of protein properties by the strength of their relationships with evolutionary rate; this confirms many known evolutionary relationships and also highlights several new ones. Similar protein properties were then grouped and applied to predict slowly evolving proteins. Some of these groups were as effective as paired species comparison in making correct predictions, although in both cases a great deal of evolutionary rate variation remained to be explained. Our work has helped to refine the set of protein properties that researchers should consider as they investigate the mechanisms underlying protein evolution. Different proteins evolve at drastically different rates Saccharomyces cerevisiae (baker's yeast), it is now possible to rigorously assess the functional genomic correlates of protein evolutionary rate. Many studies have focused on calculating the correlation between protein evolutionary rate and a single protein feature that can be determined for a large fraction of yeast proteins, followed by statistical hypothesis testing of the observed correlation. This method has been successful in identifying a number of key correlates of protein evolutionary rate, such as protein abundance With the advent of reliable high-throughput functional genomic measurements, particularly in the model organism Assessing the relative strengths, synergistic effects, and redundancy among such correlations requires more sophisticated statistical methods. Multivariate techniques have already been applied in a number of studies aimed at simultaneously dissecting multiple correlates of evolutionary rate S. cerevisiae). Moreover, a reduction in genome coverage may be accompanied by the introduction of specific biases. For example, stringent demands on gene orthology automatically bias a dataset toward more slowly evolving proteins. Coverage will tend to be further limited—and the dataset further biased—as more genomes and more protein features are added to an analysis.Analyses of evolutionary rate correlation have been historically limited by less-than-complete coverage of the genome—often far less. Consider the calculation of evolutionary rate itself. A commonly used reference dataset was produced by Wall et al., where a set of evolutionary rate calculations were meticulously performed in yeast S. cerevisiae and assessed their correlations with an approximate, high coverage measure of protein evolutionary rate. To manage potential outliers, noise, and non-linear relationships, we employed robust measures of correlation, such as rank correlation and mutual information. By considering many protein attributes simultaneously, it was possible to rank them according to their degrees of association with evolutionary rate. Our high-coverage framework allows us to re-assess known genomic correlates of evolutionary rate, while simultaneously identifying new, statistically significant correlates.In this study, we used an integrated probabilistic modeling approach to assess genomic correlates of protein evolutionary rate for 5,537 proteins in the yeast genome . We assembled a list of diverse protein sequence, physicochemical, and functional genomic features with high coverage of the proteins in S. cerevisiae protein evolutionary rate are comparable to those based on paired species comparisons, and approaching those based on the lineage-independent component of evolutionary rate.In addition, we employed a logistic regression framework on binned data to assess the information contribution of sets of features in the task of predicting slowly evolving proteins. Our framework is flexible and robust, and is able to account for intercorrelation, non-linearity, and heterogeneity within features. Using this framework, we were able to group overlapping and interrelated features into natural ensembles (“meta-features”), and quantitatively assess their combined predictive power. Next, natural ensembles were evaluated in progressively larger groups to measure the independent significance of their contributions. Finally, we show that our optimal predictions of S. cerevisiae possessed an annotated ortholog in at least one of these species. Evolutionary rates were calculated for pairs of orthologous sequences following previously established procedures . These rates were then ranked and normalized within a given paired species comparison. The evolutionary rate of a given protein is the average of its ranked, normalized rates across all paired species comparisons in which an ortholog was present.We employed an approximate, high coverage method for calculating yeast protein evolutionary rate based on multiple paired species comparisons. S. cerevisiae serves to enhance the signal-to-noise ratio for evolutionary rate along the S. cerevisiae lineage. As our measure of evolutionary rate is approximate, we rely on relative rankings and binning to limit the influence of error. Our rankings were tested in comparison with the well established Wall et al. dataset of yeast dN/dS values We operate under the initial assumption that the ranked evolutionary rate of a protein is constant over time, and should therefore be approximately equal when estimated using different yeast species pairs. Averaging over multiple paired species comparisons involving We collected a list of 42 high coverage protein sequence, structure, and functional genomic attributes that potentially correlate with evolutionary rate . We rankIn addition, to deal with categorical variables and potential non-linear relationships between genomic features and evolutionary rate, we converted continuous variables into discrete variables through binning and then ranked genomic features according to their mutual information with evolutionary rate. The top twenty correlates under this scheme are listed in z-scores>6, mutual information z-scores>40; all p-values≪0.001).Statistical significance was determined from the distribution of correlation measures resulting from 100 randomizations of the feature data annotations. All correlations discussed here and listed in We selected the slowest evolving 20% of the proteins and asked which features best distinguish them from the remainder of the genome using a fold enrichment analysis . In agreThe methods described so far have helped us to overcome some of the difficulties inherent to analyzing the relationships between protein feature data and evolutionary rate. We have been able to rank the importance of the various features in a robust statistical framework with full genome coverage, while compensating for non-linear relationships, mixed data types, and to some extent noise. We next sought to address two additional issues—joint information contribution and contribution independence—without losing the gains that our approach had already made. In order to accomplish this, we applied a logistic regression model to study the information contribution of features (and sets of features) in the task of predicting slowly evolving proteins. Logistic regression has been used in the past for predicting protein-protein interactions correct classification rates—i.e., when evaluating the model using the test data, the percentage of the proteins that were correctly assigned to their respective classes (slowly evolving versus not slowly evolving). Since all datasets were balanced prior to training and testing, a random classifier would produce correct predictions 50% of the time. This is a lower bound to which the feature-based classifications can be compared. Mathematical details of the logistic regression procedure can be found in the We constructed a positive dataset consisting of the slowest evolving 20% of the proteins, and a negative dataset of the same size consisting of a random sampling of the remaining 80% of the proteins. The positive and negative datasets were then divided into five partitions. Using five-fold cross validation, we trained a logistic regression classifier using four of the five partitions, and then evaluated our model using the remaining partition as a test set. Results take the form of The top panel of In some cases, the protein features that we consider serve as proxies to some well-defined (but difficult-to-measure) property of a protein. If there are multiple proxies for a general protein property, then we expect them to be highly redundant. Other features are important in their own right, but are more tractable when considered together . Features like these may also possess hidden interdependencies that we would like to model. We address these issues by grouping related features into natural ensembles, which we call meta-features. The logistic regression classifier can be trained and tested based on a meta-feature in order to assess the joint information contribution of its constituent features. Working with meta-features has several advantages: (i) it compensates for redundancy and interrelations among features, (ii) it averages out noise present in individual features, and (iii) it summarizes the many individual features into a handful of highly relevant general protein properties. Note that in all analyses based on subsets of features , we rely on prior expert knowledge to form the subsets, as there are too many possible subsets to exhaustively enumerate.We pooled our 42 individual features into seven natural meta-features and then evaluated the information contribution of each using the logistic regression model  and perfect (100%) classification. Here, we evaluate the significance of this performance in comparison with predictions based on other methods of estimating evolutionary rate.S. cerevisiae versus one of the other five yeasts) to predict the slowest evolving 20% of the proteins among the average rankings of the four remaining paired species comparisons . The magnitude of the conserved component of protein evolutionary rate is an important quantity, as it defines the upper limit for evolutionary rate prediction using only broad, lineage-insensitive genomic features. The intuition here is simple: genomic features that do not vary across lineages cannot distinguish the fine details of lineage-specific evolutionary rate variation. We directly estimated the conserved component of evolutionary rate that is common to all yeasts by averaging over all paired species comparisons that do not involve ications ; this isS. cerevisiae. The average correlation coefficient is high at 0.917, suggesting that amino acid composition is generally well conserved among yeasts, yet still subject to some degree of lineage-specific variation. As for abundance, experimental expression data for other yeast species are limited, condition-specific, and susceptible to noise. Here, we use codon bias as a proxy for expression level, and compare S. cerevisiae-specific values to values averaged over the other yeast species. Here the correlation coefficient is high at 0.886, again indicative of general conservation with elements of lineage-specific variation. The genomic features most likely to be variable among yeasts are network-based features, since transcriptional regulation and protein-protein interaction are known to vary between yeasts The value of 92.8% is the predictive upper limit only when the integration is restricted to lineage-insensitive genomic features. How conserved are the genomic features that we consider here? Gross structural properties and broad functional assignments are likely to be conserved for homologous proteins This study focused on identifying genomic features which contribute to the task of predicting evolutionary rate. While the purpose and relevance of many prediction tasks is immediately clear—for example, predicting gene essentiality in order to avoid the difficulty and expense of experimental determination The answer is that we are not interested so much in the predictions themselves, but rather the features which provide them. Biologists have long been interested in understanding the forces that drive evolution at various scales of life. However, our knowledge of the causal forces which underlie evolution at the molecular scale remains limited. By ranking the degree of correlation between various protein features and evolutionary rate, we hope to highlight those features which best dictate the selective constraint on a given protein. From the careful dissection of these individual correlations, one stands to gain a deeper understanding of their underlying biological significance. For example, the observed correlation between protein abundance and evolutionary rate led to biological insights regarding the evolution of translational robustness Protein abundance, biological function, and amino acid composition consistently appeared in our analyses as dominant correlates of evolutionary rate. As we have mentioned before, the significance (if not dominance) of abundance is generally accepted; the significance of function has also been previously described The previous section highlighted the importance of understanding within-feature correlation, specifically that between protein abundance and amino acid composition, in the search for determinants of evolutionary rate. sr = −0.142 and −0.146, respectively; p≪0.001 in both cases). However, as previously noted sr = −0.104, p≪0.001). The observed non-transitive correlations are statistically significant (p≪0.001), although we note that the correlations are rather weak and account for 1.1% to 2.1% of the variance. This seemingly counter-intuitive observation can be explained in the following way. Slowly evolving proteins can be divided into two largely non-overlapping groups: (i) those that are important under all conditions, meaning that they are essential, but not necessarily highly regulated, and (ii) those that are important only under specific conditions, which may experience sophisticated regulation, but are not necessarily annotated as essential. Proteins in the first group drive the negative correlation between essentiality and evolutionary rate, while proteins in the second group drive the negative correlation between number of regulators and evolutionary rate. This explains the observed non-transitive correlations among evolutionary rate, number of transcriptional regulators, and marginal essentiality.The patterns of correlation among features in The integrated probabilistic approach we have taken in this study has both drawbacks and advantages. Like other correlational approaches, our approach is not able to distinguish correlation from causation, nor is it able to isolate cause from effect. We do not explicitly model the noise within the feature data as some other methods do To our surprise, we found that integrating a diverse collection of single-genome features was roughly equivalent to paired species comparison for identifying slowly evolving proteins, but still worse than what lineage-insensitive features can in principle predict. Our conclusion from this finding is that the dominant, independent correlates of evolutionary rate are likely known, even though other significant and interesting correlates may remain to be found  in the S. cerevisiae genome, 324 had no annotated orthologs S. cerevisiae versus one other yeast) were then treated as follows: (i) dN/dS was first adjusted according to the method of low, medium low, medium, medium high, and high evolutionary rate. This procedure is summarized in S. kluyveri were obtained from We based our measure of protein evolutionary rate on comparisons between Saccharomyces Genome Database high, medium high, medium, medium low, low, and missing. These “completed” features are then correlated with evolutionary rate. Here, we assume that missing data bins such as “unknown biological process” or “missing mRNA expression” can be correlated with evolutionary rate just as we would correlate regular feature bins, such as “constituent of the ribosome,” or “high mRNA expression level.” This approach involves fewer assumptions about the nature of missing data than alternative strategies, such as listwise deletion, mean substitution, and imputation.Basic protein information about each ORF was downloaded from the N pairs of quantities , i = 1,…,N, the Spearman rank correlation coefficient sr is computed in the following way:iR is the rank of ix among the other x's, iS is the rank of iy among the other y's.Given I between two discrete random variables X and Y is computed in the following way:The mutual information iy  by integrating genomic features F. There are m categorical features, 1F, …, mF, where each feature jF can take on jr different values, j1f, j2f, …, (i)F, (i)y); i = 1,…,n}, contains n samples. Logistic regression can be expressed as the following weighted voting scheme:I is the indicator function—I(X) is 1 when statement X is true, and 0 otherwise. jkw are weights associated with each piece of evidence. p(y = 1|F) is the probability that the protein evolves slowly given the features. The protein is predicted to evolve slowly if and only if p(y = 1|F) is larger than 0.5.For a given protein, we want to predict the class label y and the predictions p(y|F).All weights are chosen to optimize the following log-likelihood function for the training set, i.e. the log-probability of observing the data given the weights:Figure S1Noise reduction and independent contribution during feature integration. When integrating abundance features in various meta-feature combinations, predictive power increases and gradually levels off due to noise reduction. Addition of the amino acid composition meta-feature results in a marked jump in predictive power, indicating an independent effect.(0.42 MB PDF)Click here for additional data file.Table S1Rankings and associated bins for yeast protein evolutionary rate.(0.44 MB XLS)Click here for additional data file.


Public proteomics databases such as PeptideAtlas contain peptides and proteins identified in mass spectrometry experiments. However, these databases lack information about human disease for researchers studying disease-related proteins. We have developed mspecLINE, a tool that combines knowledge about human disease in MEDLINE with empirical data about the detectable human proteome in PeptideAtlas. mspecLINE associates diseases with proteins by calculating the semantic distance between annotated terms from a controlled biomedical vocabulary. We used an established semantic distance measure that is based on the co-occurrence of disease and protein terms in the MEDLINE bibliographic database.The mspecLINE web application allows researchers to explore relationships between human diseases and parts of the proteome that are detectable using a mass spectrometer. Given a disease, the tool will display proteins and peptides from PeptideAtlas that may be associated with the disease. It will also display relevant literature from MEDLINE. Furthermore, mspecLINE allows researchers to select proteotypic peptides for specific protein targets in a mass spectrometry assay.Although mspecLINE applies an information retrieval technique to the MEDLINE database, it is distinct from previous MEDLINE query tools in that it combines the knowledge expressed in scientific literature with empirical proteomics data. The tool provides valuable information about candidate protein targets to researchers studying human disease and is freely available on a public web server. Public databases of mass spectrometry experiments contain an enormous amount of data about the human proteome. Repositories such as PeptideAtlas , PRIDE , and PepStreptococcus pyogenes bacteria [e.g., multiple reaction monitoring) can now detect and quantify proteins of very low abundance relative to traditional shotgun approaches  [Mass spectrometry has significant potential in studying proteins that are involved in human disease. For example, researchers have identified virulence factors of bacteria , candidabacteria , and canbacteria  using marometry) , allowinUnfortunately, mass spectrometry experiment databases do not currently support searching for disease-related proteins and peptides. Although further integration with genomic databases as in PeptideAtlas  could alBy combining knowledge about disease with empirical data about the proteome, mspecLINE offers a unique new interface to assist proteomics researchers who are studying proteins that are involved in human disease. We provide an overview of the data sources used by mspecLINE and discuss relevant work on biomedical information retrieval below.mspecLINE incorporates data from a number of sources including the Medical Subject Headings vocabulary, MEDLINE, and PeptideAtlas.mspecLINE uses the Medical Subject Headings (MeSH) vocabulary to identify semantic relationships between diseases and proteins. MeSH is a controlled vocabulary of biomedical descriptors arranged in a hierarchical structure that can be modeled as a set of trees . MeSH deEach reference in the MEDLINE bibliographic database  is manuaAdditionally, mspecLINE uses data from PeptideAtlas, a publicly accessible repository that currently contains empirical data pertaining to more than 130,000 human peptides identified in mass spectrometry experiments. The database also maps peptides to their parent proteins and to their locations in the human genome . Proteinet al. [et al. used LSI to identify novel gene function annotations by analyzing previous annotations in the human genome [Investigators have applied numerous information retrieval techniques to extract relationships from biomedical knowledge bases. Many of these techniques involve semantic analysis using Latent Semantic Indexing (LSI) . For exaet al.  successfn genome .et al. developed a method for extracting gene-function relationships from MEDLINE using co-occurrence data [Other investigators have applied co-occurrence analysis techniques to extract relationships from biomedical literature. The Associative Concept Space method, for instance, extracts associations between concepts in literature using co-occurrence data as input . Additionce data . Others nce data  as well nce data  from MEDmspecLINE associates diseases with proteins by calculating the semantic distance between MeSH annotations in MEDLINE using Normalized Google Distance (NGD). NGD is grounded in the well-established theory of Kolmogorov complexity  and the NGD is defined as a value between 0 and 1 that indicates the semantic distance between two terms in a set of documents. It uses the co-occurrence of two terms in a document as evidence of their semantic relatedness. If two terms occur frequently in the same documents, those terms are said to be highly related and have a low NGD value. Likewise, if two terms rarely occur in the same documents, those terms are said to be unrelated and have a high NGD value. We apply the NGD measure to the MEDLINE database, so we shall refer to it as Normalized MEDLINE Distance (NMD) throughout this paper. We describe our calculation of NMD in detail in the following section.mspecLINE builds sets of MeSH disease descriptors and MeSH protein descriptors, then calculates the semantic distances between each pair of descriptors using NMD. We describe our method and provide an overview of the tool's architecture below.D consisted of 4,323 MeSH descriptors.First, we used the 2009 MeSH distribution from the National Library of Medicine (NLM) to find all descriptors in the 'Diseases' tree. In total, our set of diseases P containing 2,585 MeSH protein descriptors to represent all unique annotations for proteins in PeptideAtlas.We then extracted a list of all unique protein accessions in PeptideAtlas. We used BioThesaurus  to find D and each MeSH protein descriptor in P by analyzing article annotations in MEDLINE. We ran our calculations using a local copy of the 2008 MEDLINE database on lease from NLM. For each MeSH disease descriptor d ∈ D and each MeSH protein descriptor p ∈ P, we calculated NMD as:To associate the diseases described in MeSH with the proteins in PeptideAtlas, we calculated pairwise semantic distances between each MeSH disease descriptor in f  as the number of articles in MEDLINE that are annotated with both d and p, and M as the total number of articles in MEDLINE. Further, we define gmax and gmin as:We define f (d) as the number of articles in MEDLINE that are annotated with d, and f (p) as the number of articles that are annotated with p.We define As shown in Figure Our software consists of four main components: the NMD Data Store, the MEDLINE Data Store, the mspecLINE Data Service, and the mspecLINE web user interface (UI). Figure The NMD Data Store has a denormalized schema that encodes the associations depicted in Figure We developed a web service on top of PeptideAtlas that operates on the Cancer Biomedical Informatics Grid with caGrid infrastructure . The PepResearchers interact with mspecLINE by entering a disease in the UI and viewing associated proteins, peptides, and MEDLINE literature. We provide a walkthrough below using Creuztfeldt-Jakob Syndrome as an example disease. Figure First, the researcher enters a proper MeSH descriptor name for a disease. He may click on a link to open the NLM MeSH browser and find the proper name. In our example, the MeSH disease name is 'Creutzfeldt-Jakob Syndrome' and mspecLINE finds all matching entries in the NMD Data Store. The UI displays MeSH protein descriptors that are semantically related to Creutzfeldt-Jakob Syndrome in the left panel along with their NMD values: Prions (0.17), 14-3-3 Proteins (0.35), and PrPC Proteins (0.36). The researcher may select or de-select MeSH protein descriptors in the left panel and the other views will filter and update accordingly. It is important to note that mspecLINE only retrieves semantically related protein descriptors below a threshold NMD value. In our example, we have chosen an arbitrary NMD threshold of 0.5, although the UI allows researchers to adjust the threshold.mspecLINE displays all proteins annotated with one or more of the MeSH protein descriptors in the center panel. The center panel also lists peptides from PeptideAtlas for each protein, the amino acid sequence for each peptide, and the annotated MeSH descriptors for each protein. In our example, mspecLINE found a total of 305 peptides from PeptideAtlas for proteins that may be related to Creutzfeldt-Jakob Syndrome.The center panel allows the researcher to sort and group peptides by different fields. For example, she may want to find all peptides detectable for a specific target protein related to Creutzfeldt-Jakob Syndrome, therefore she groups peptides by the 'Protein' field.As previously discussed, selecting proteotypic peptides for a protein is an essential step in developing a targeted mass spectrometry assay. mspecLINE displays the PeptideAtlas Empirical Observability Score (EOS) for each peptide in the center panel, which indicates the empirical likelihood that a researcher would observe a specific peptide if its parent protein is detected in the sample . mspecLIThe center panel in the mspecLINE UI displays only minimal information about disease-related proteins and peptides. The researcher may click in the table to open a protein details tab that contains additional information about a protein and its peptides from PeptideAtlas. The information in the tab includes the name of the gene that encodes the protein and a detailed description of the protein. The tab also provides additional empirical information about the protein's peptides including the isoelectric point, molecular mass, and hydrophobicity of each peptide.mspecLINE displays all MEDLINE references that contribute to the association of a disease with a set of proteins in the lower panel. The UI queries the MEDLINE Data Store to find all article references that are annotated with both the input MeSH disease descriptor and one or more of the associated MeSH protein descriptors. In our example, mspecLINE found 118 articles in the MEDLINE database that are annotated with the MeSH descriptors 'Creutzfeldt-Jakob Syndrome' and '14-3-3 Proteins'. The researcher can use the MEDLINE references to explore literature that discusses disease-related proteins. She may also click on a specific reference and open the PubMed article entry in a separate browser window. In our example, the MEDLINE references include numerous articles discussing the 14-3-3 brain protein as a clinical marker for Creutzfeldt-Jakob Syndrome in cerebrospinal fluid.In addition to the tabular view in the center panel of the UI, mspecLINE provides a visualization of associations between a disease, MeSH protein descriptors, as well as proteins and peptides from PeptideAtlas. The researcher can click the 'Visualize' button to visualize these associations in a network using Cytoscape . The visInvestigators have developed numerous tools for querying MEDLINE. For example, GoPubMed allows researchers to query and browse MEDLINE using terms from the Gene Ontology . Other tmspecLINE is unique in that it combines knowledge about human disease from MEDLINE with empirical data about the detectable human proteome from PeptideAtlas. It is intended for researchers who study specific diseases and wish to explore candidate proteins for targeting in mass spectrometry experiments. For example, researchers seeking proteins that may be markers for disease could use mspecLINE to identify candidate protein targets and proteotypic peptides for those targets. They could subsequently use analytical tools such as Corra  to identWe have identified a number of future enhancements for mspecLINE. First, we are exploring procedures to empirically validate our method for associating diseases with proteins. Second, we are investigating techniques to automatically determine an appropriate threshold for Normalized MEDLINE Distance when a researcher enters a MeSH disease descriptor. Last, we are researching data management systems that would allow us to more effectively store and process the data that we use in our method. Specifically, we are looking at integrating mspecLINE with the Addama infrastructure  and deveMass spectrometry is a promising technology to study proteins involved in human disease. We have presented mspecLINE, a new tool that bridges empirical proteomics data with knowledge about human disease. As we have demonstrated using an example disease, mspecLINE allows researchers to explore potential disease-related protein targets based on semantic distances between disease and protein annotations in MEDLINE. The tool also assists researchers in identifying proteotypic peptides for mass spectrometry assays. mspecLINE is open source software and is freely available for use on a public web server.Project name: mspecLINE• Project home page: http://informatics.systemsbiology.net/informatics/mspecLINE• Operating system(s): Platform independent• Programming languages: Ruby, Java, Javascript• License: Apache License, Version 2.0• The authors declare that they have no competing interests.JH developed the mspecLINE software, the PeptideAtlas caGrid service, performed data collection and analysis, and drafted the manuscript. ED participated in development of the PeptideAtlas caGrid service. JB initiated and guided the investigation, conceived the application of Normalized Google Distance in our method, and participated in designing the user interface. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/3/7/prepub


Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.PNPRD features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new  The need to analyse the massive accumulation of biological data generated by high-throughput human genome projects has stimulated the development of new and rapid computational methods. Computational approaches for predicting and classifying protein functions are essential in determining the functions of unknown proteins in a faster and more cost-effective manner, because experimentally determining protein function is both costly and time-consuming. Approaches based on sequence and structure comparisons play an important role in predicting and classifying the function of unknown proteins. Generally, if an unknown gene or protein sequence is identified, researchers may carry out a sequence similarity search using BLAST , PSI-BLARecently, several researchers have developed methods for classifying and predicting protein function independent of sequence or structural alignment -9,20-50.et al.  consists of 129 amino acids with five negatively charged residues and eight positively charged residues. However, NNR and PPR were 0 and 2.32, respectively. This indicates that although the protein includes five negatively charged residues, the positively and negatively charged residues in the sequence always alternate among the neutral residues.Figure et al. [Distx, y)  was 1.29 ± 0.42 for the positive classification samples and 0.72 ± 0.53 for the negative samples. These findings indicate that alternating positively and negatively charged residues occur more frequently in the local region from 61% to 70% in the guanine nucleotide-releasing factor sequence than in the negative protein samples. Therefore, local information on the distribution of negatively and positively charged residues in the interval was informative. Because of these essential differences, guanine nucleotide-releasing factor proteins can be predicted with a high level of accuracy.Previous studies in this field have analysed only the number or percentage of positively and negatively charged residues; however, the positions or regions of the charged residues in the sequence are very important in determining protein function and structure -82. For et al.  analysedPNPR for the positive classification samples was 12.04 ± 3.56, while for the negative samples, PNPR was 7.84 ± 3.03. These results indicate that continuous changes from a positively charged residue to the next negatively charged residue or vice versa occurred more frequently over the full sequence of transcription proteins than in the negative samples.Figure PNPRDist for the gluconate utilisation sequences was 1.03 ± 0.3, while PNPRDist for the negative samples was 1.3 ± 0.6. These results indicate that there were fewer continuous changes from a positively charged residue to the next negatively charged residue or vice versa in the local region from 11% to 20% of the gluconate utilisation sequences than in the negative samples.Figure There are two ways of using positively charged residues in classification and prediction of protein function. One process uses the positively charged residues arginine (R), histidine (H), and lysine (K) , while tThe following is a summary of the benchmark comparisons and important findings of this study for prediction of protein function over a broad range of cellular components, molecular functions, and biological processes. Analyses were conducted by SVM and the random forest method with and without feature selection, based on many traditional and proposed features extracted from the sequences.• Using a larger number of features to predict protein function does not always result in improved performance. In terms of accuracy and AUC, SVM with feature selection has distinct performance advantages with this type of data, indicating that removal of the many redundant and irrelevant features by feature selection can improve prediction performance. However, there was no significant difference in prediction performance for the random forest method with and without feature selection.• The use of a particular classifier does not always result in improved performance. There is no single method that is optimal for all conditions because the performance of a method depends on the type of data involved, the size of the dataset, the number of features involved, the type of extracted features, and whether feature selection is used, among other things. Therefore, selection of the optimal classifier for a given dataset depends on an understanding of machine-learning algorithms, feature selection processes, and biological background information relevant to the dataset.• Features useful for predicting a specific protein function in a given dataset are not always useful for predicting another protein function – discriminative and informative features differ according to protein function. Therefore, identifying discriminative features applicable to a broad range of protein classes is difficult.• Although many methods have recently been proposed for predicting protein function, most methods are not suitable for function prediction under high-throughput conditions, because they require information on protein structure. Currently, there is much more data available on protein sequences than on protein structures; thus, the methods developed in this study focused on predicting protein function based solely on features extracted from the protein sequence. This reduces the effort required to extract useful features, as the predictive or experimental work required to acquire structural information is both costly and time-consuming. In the experiments undertaken in this study, we found that sequence-based classifiers can also generate very good results.• Local information regarding the protein sequence is meaningful in predicting protein function; several examples have been presented to demonstrate its usefulness. Although identifying local information for a sequence is difficult and the information does not always correspond to striking difference in protein function, unique features extracted from specific positions or local regions can be predicted with a high level of accuracy.PPR and NNR were extracted from sequences based on the presence of negatively and positively charged residues. These novel features include information on the existence of negatively and positively charged residues as well as the manner in which the two charged residue types co-exist in a sequence. PPR and NNR were found to be selected more frequently for function prediction than were the number of negatively and positively charged residues. Thus, these features appear to be highly correlated with protein class and have a low inter-correlation with each other.• The numbers or percentages of positively and negatively charged residues are some of the most important and well-known features used for function prediction. The above results indicate that it is possible to generate accurate predictions for a broad range of protein functions without the use of sequence or structural similarities. Feature selection improves predictions for a variety of protein functions, but does not always ensure improved performance, depending on the dataset and the method used. Finally, local information for protein sequences is meaningful for predicting protein function, and a feature set with good performance and dimensional reduction was identified, as many features initially included in this study were removed by CFS.Many previous studies have attempted to biologically and computationally determine meaningful and accurate features that assist in predicting protein function. Features that show an obvious propensity for predicting many different protein functions have not yet been reported, and this provides a motivation for discovering the relationship between features and protein function.PPR, NNR, PNPR, and Distx, y)  was achieved and predictive features for several protein classes were effectively identified.This paper described a highly accurate prediction method capable of identifying protein function by using features extracted solely from protein sequences, irrespective of sequence and structural similarities. In this study, the The results presented here suggest that our new features, developed in the course of this study, will be useful in predicting many protein class functions. We believe that prediction performance can be improved by combining sequence-based features and additional features, such as predicted secondary structure, surface area, and subcellular location. Accordingly, further insight into feature analysis and biological understanding is needed. In future studies, we will apply this method to predict the functions of proteins that have not been identified by sequence alignment.The authors declare that they have no competing interests.PPR, NNR, PNPR, and Distx, y)(, and wrote the manuscript. MSS and YJO assisted in developing the method and revising the manuscript. KHR and HSO supervised the work, provided useful suggestions to improve performance, and revised the manuscript. All authors read and approved the manuscript.BJL conducted the experiments and analysis, conceived the concepts of


Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This “regulatory complexity” causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as “black boxes”, we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology. ad hoc approaches that emphasize the regulatory logic of interactions, can reduce the number of parameters required to model experimental observations. We also demonstrate how the application of our framework gives insights into the assembly of macromolecular complexes and increases the predictive power of a standard model of G protein-coupled receptors. These benefits are possible in many systems, given the ubiquity of allostery in biochemical networks. Our research delineates a fundamental relationship between allostery, modularity, and complexity in biochemical networks.The complexity of biochemical networks challenges our ability to create quantitative and predictive models of cellular responses to extracellular changes. In these networks, the regulation of allosteric receptors and proteins by multiple drugs or endogenous ligands introduces “regulatory complexity” because a large number of parameters is required to describe such interactions. Protein interactions also give rise to “combinatorial complexity” by generating large numbers of protein complexes and covalent modification states. To address these twin problems, we propose a modelling framework that combines a modular description of protein structure and function with a rule-based description of protein interactions. We define the input-output function of an allosteric protein through its thermodynamic properties and structural components. We show that our “biomolecule-centric” methodology, in contrast to  A goal of biology is to understand the structure and function of the biochemical networks that underpin cellular decision-making. One organizing principle is that these networks are inherently modular Efforts to tackle complexity in biochemical networks should also exploit the modularity of protein structure. Protein structure is hierarchical, and a given protein often has domains also present in other proteins or repeated subunits. For example, many signalling proteins contain SH2 or PDZ domains, and many receptors, ion channels and enzymes are multimers. In genetic networks, transcription factors are also often multimers or have a common DNA-binding domain, such as a zinc finger or homeobox. The re-use of protein domains is both a simplifying and confounding feature: once a domain has been characterized, that characterization can be used again, but it is also necessary to model molecular cross-talk between signalling pathways that contain proteins with similar structures.In vivo, protein interactions can generate both combinatorial and regulatory complexity. Combinatorial complexity is an “explosion” in the number of possible species in a system as the number of proteins and interactions in the system increases. It arises because the number of states of a module dramatically increases as its proteins bind ligands as well as each other and as different residues are covalently modified 37 states parameters required to describe the regulatory interactions within a system as the number of interactions increase. This complexity arises because the strength of protein interactions depends on the state of a module, and each state of the module potentially requires a unique set of parameters to characterize interactions within the module, with other modules in the network, and with molecules external to the network. Measuring this number of parameters in vivo is challenging.Rule-based modelling addresses combinatorial complexity and allows biologists to specify the regulatory logic of a system L different drugs or endogenous ligands and one of G different G proteins, then in principle we require LG pair-wise cooperativity parameters to describe how each ligand regulates the GPCR's affinity for each G protein. Thus, the number of regulatory parameters scales with LG, and the number of rules also scales with LG because each parameter is part of a rule with distinct contextual constraints. Promiscuous allosteric proteins can therefore require a large number of rules and parameters to characterize their interactions.Rule-based formalisms can describe complex biochemical systems, but inherently offer little guidance on avoiding a number of methodological problems. First, using rules to specify the regulatory logic of a system does not address the system's regulatory complexity. Consider G protein-coupled receptors (GPCRs), which allosterically couple an extracellular ligand-binding site to an intracellular G protein-binding site ad hoc (system-specific) conditions that no longer apply when the proteins interact with different partners. These ad hoc rules obfuscate the mechanism underlying allosteric regulation because they do not show explicitly how the intrinsic structural and thermodynamic properties of allosteric proteins generate their functional properties. In contrast, a “biomolecule-centric” approach would encode regulatory logic in the proteins themselves. Fewer changes to rules would then be required to define how a new set of interaction partners regulates the protein's activity. If a model includes protein domains and subunits, re-use of these components would also be simplified.Second, a module should have a well-described function and be easily re-used and “portable” between systems, but most rule-based formalisms are not inherently modular. Modellers typically treat proteins as “black boxes” and define interactions using biochemical equations. In such “interaction-centric” approaches, the regulation of proteins is encoded by rules with Finally, models generated by rule-based methods should be thermodynamically correct. In biochemical networks, there are often sets of reversible reactions that connect into a closed loop, forming a thermodynamic cycle. In many of these cycles no free energy is consumed: for example, when proteins bind multiple ligands, when ligands bind several conformations of a protein, or when ion channels bind multiple agonists and have closed, open, and desensitized states. Thermodynamics imposes a mathematical relationship between the equilibrium constants for all the reactions involved in such cycles: their product must be unity. Equilibrium constants cannot therefore be assigned independently. A thermodynamically correct methodology should ensure that a model satisfies this constraint, ideally by construction.http://swainlab.bio.ed.ac.uk/anc.Here, we present a modular and scalable modelling methodology that alleviates the regulatory as well as the combinatorial complexity of biochemical networks. We first describe our modelling framework, which uses a thermodynamically grounded treatment of allostery in which ligands distinguish only the conformational state of allosteric proteins. We also introduce a rule-based modelling tool that implements our methodology: the Allosteric Network Compiler (ANC). We use ANC to examine how allostery can make macromolecular assembly more efficacious. We then show how our modelling framework describes common mechanisms of allostery by mapping the regulatory properties of a protein onto conformational changes in the protein itself and demonstrate how we can ease the analysis of multiple ligands interacting through an allosteric protein. Next, we discuss how our approach reduces regulatory complexity and thereby increases a model's modularity. Finally, we use our framework to develop a model of G protein-coupled receptors whose regulatory complexity scales with (L+G) instead of LG and consequently has greater predictive power. While our major goal is to introduce a new modular modelling methodology rather than its implementation, we have made ANC and the models we discuss available at: Our method is based on the Monod-Wyman-Changeux (MWC) paradigm of allostery Thus, an allosteric protein can be seen as a modular and dynamic computational device, and we can define the input and output of each allosteric component. The input is a “modifier”, a molecule that binds to and locally perturbs the structure of the component; the output is the fraction of time the component spends in each conformation when the allosteric transition is at equilibrium see Methods.structure, which captures the true structure of a protein in terms of its components  is the differential affinity of the X (or Y) to each conformation of A. The cooperativity increases as the degree of bias (ΓX and ΓY) that X and Y exert on the conformational transitions of A increase. We can also define the apparent affinity of X and Y to this coarse-grained A:X of X to A and the affinity θKX of X to A when A is bound by Y are measurable.In the model, the binding of X and Y to A is cooperative because binding of X to A changes the affinity of A for Y by a factor θ and likewise the binding of Y to A changes the affinity of A for X also by a factor θ. By coarse-graining over the conformations of A , inset, Counter-intuitively, an excess of some components of a macromolecular complex can inhibit formation of the complex 4, and the maximal amount of complex formed increases by a factor of 5.7 −1/2 and thus assembly of the XAY trimer could in principle be controlled through the binding of a cofactor or a covalent modification that changes the allosteric equilibrium constant of A from a value far from its optimum to a value near the optimum (or vice versa).That the efficacy of macromolecular assembly depends strongly on the value of the cooperativity parameter θ suggests that assembly could be modulated by changing θ. concerted, reversible, and quaternary-level transition between two conformational states. Ligand-binding to each conformation is non-cooperative, but each conformation differs in its affinity for ligands and this difference gives rise to cooperative effects. Subsequently, Koshland, Nemethy and Filmer lifted the assumption of concerted transitions with their sequential model, in which each subunit transitions individually between two conformational states induced fit, in the structure of the subunit. Both the concerted and induced fit assumptions can be relaxed and are special cases of the general allosteric model of Herzfeld and Stanley There are two well-known mechanisms for generating cooperative behaviour in proteins: concerted and sequential allostery. In their seminal paper, Monod, Wyman and Changeux introduced a two-state model to explain cooperative interactions in oligomeric enzymes and proteins R and T, is concerted because a single allosteric component contains all subunits. Cooperativity will arise if a ligand has a higher affinity for one state, say the R state, and if the unligated protein is mostly in the alternate state T. Then, once bound by ligand, the protein spends more time in the R state and so favours the binding of additional ligands. In contrast, a sequential model has an ANC-structure with four allosteric components, each with r and t states. et al.r state of an individual subunit, and this subunit when in its r state favours the r state in those subunits to which it is allosterically coupled and so generates a cooperative response. Finally, we illustrate the general approach with the tertiary two-state model shown in R↔T and tertiary, r↔t, allosteric transitions r at the bound subunit because of the ligand's higher affinity for r. Cooperativity arises because the R state of the quaternary structure reciprocally favours the r state of the tertiary subunits. Thus, a subunit in the r state favours R at the quaternary level and so favours subunits not yet bound by ligand to also be in the r state, promoting binding of additional ligands. Our ANC implementation of the tertiary two-state model correctly reproduced the Henry et al. model, which has 252 molecular species ANC-structures can be used to implement these models of allosteric regulation. A concerted model of a generic, homotetrameric protein is shown in An advantage of ANC is its ability to easily formulate and simulate mathematically complex models. For example, we will show that the cooperativity of an allosteric protein binding a ligand, such as a transcription factor binding an inducer, can be substantially changed through adding a competing ligand. Although a mathematical analysis of various allosteric models with two competing ligands exists In addition to ligand binding, our methodology also describes other mechanisms for allosteric regulation that are ubiquitous in cellular signalling. Phosphorylation or other post-translational modifications, dimerization, receptor clustering and point mutations can also regulate or change protein function. Our thermodynamic framework see Methods intensive parameters and extensive parameters. Intensive parameters describe the conformational transitions and intramolecular interactions of a protein and, as such, are modular because they are inherent to the protein and independent of the protein's interaction partners. Therefore, we associate intensive parameters with a protein's ANC-structure. In contrast, extensive parameters describe the interactions of a protein with other biomolecules and increase in number as the number of these interactions increase. Extensive parameters, contained in rules for interaction, are the “wiring” between modules and are non-modular because they depend on the system in which the protein functions. Regulatory complexity occurs when the number of extensive parameters describing a system scales combinatorially with the number of interactions in the system.We can distinguish two types of parameters that affect modularity in different ways: N-valent, two-state protein where each of the N binding sites is unique and binds exactly L ligands . GPCRs are a common target for pharmaceutical drugs Although several allosteric models have been proposed et al.'s assumption that the receptor has only two conformational states. However, it does not capture the cooperative binding of a ligand to either the inactive or active states of the receptor because such binding is incompatible with the paradigm that each modifier contributes independently to the equilibrium constant of the allosteric transition et al.'s model through the cooperativity parameters γ and δ. However, these parameters are extensive and specific to each combination of ligand and G protein. They therefore introduce regulatory complexity.A naive implementation of the cubic ternary complex model in our framework uses a divalent ANC-structure with a single allosteric component . This imboth allosteric components. They therefore “see” four possible conformations of the receptor instead of two. These conformations are implied in the cubic ternary complex model because each ligand has four distinct affinities to the receptor. However, none of the extensive parameters in our model are cooperativity parameters specific to a ligand-G protein pair, thus eliminating regulatory complexity.To resolve this difficulty, we propose a sequential allosteric model of the GPCR with two coupled allosteric components: an extracellular allosteric component, which binds a ligand, and an intracellular allosteric component, which binds a G protein . The ligs and t states, which in turn uniquely modulates the affinity of the active GPCR for the G protein.Our quartic ternary complex model can be projected onto the cubic model by defining coarse-grained variables that sum over the conformations of the extracellular allosteric component . The “inad hoc rules with extensive parameters. Our “refactored” model has 11 parameters (3 of which are intensive) compared to the 7 parameters of the cubic model (1 of which is intensive) and double the number of states. This initial cost for increased modularity and “portability” becomes a benefit as the number of types of ligands and G proteins increases. The number of extensive parameters in our model scales linearly with the number of interactions; in the cubic model, the number of extensive parameters scales combinatorially. For example, suppose we wish to model 4 different ligands that activate the thyroid-stimulating hormone receptor. In human thyroid membranes, this GPCR can activate at least 10 different G proteins Our quartic model for the GPCR is more modular and parsimonious than the cubic model because it includes a structurally and biophysically plausible mechanism for how ligands and G proteins interact cooperatively with the GPCR. We encode the logic of these regulatory interactions in the protein's ANC-structure using intensive parameters, rather than in The quartic model also has more predictive power than the cubic model and therefore can be more rigorously tested. For each pair of ligands and G proteins, the cubic model requires the specification of two cooperativity parameters, δ and γ, specific to that pair. It is therefore limited in the predictions it can make. For example, for each new G protein added to the system, new cooperativity parameters are needed for all previously characterized ligands to be able to predict the new G protein's GPCR-mediated response to these ligands. In contrast, the quartic model is completely characterized for the new target pathway by measuring four extensive parameters – one for each conformation of the GPCR – and we can then predict the GPCR-mediated response to all ligands. In particular, we can predict the rank order of potency of the ligands to activate the new pathway, a standard means to compare agonists in pharmacology, and detect functional selectivity inactive states of the receptor activation of two different G proteins . Ligand receptor .in vivo, for instance by forming tetramers The quartic model is modular and therefore is easily extended to include additional signalling interactions such as the regulation of the receptor by allosteric ligands ad hoc rules encoding the regulatory logic specific to each pathway.Biochemical networks are complex yet modular: networks exhibit both combinatorial and regulatory complexity, but individual proteins have intrinsic functional properties that determine how they detect and process information. Complexity is also reduced because similar proteins or similar protein domains appear in many signalling pathways and often interact with similar protein partners. We propose a modelling methodology, embodied by ANC, that exploits the modularity of proteins to reduce the complexity of modelling biochemical networks. Given modular ANC-structures, which encode a protein's regulatory properties, adding new interactions to an ANC model usually requires substantially fewer parameters than with other rule-based models, particularly as the promiscuity of binding of proteins, and hence the complexity of the network, increases. ANC-structures are also portable because different signalling pathways are modeled by simply “re-wiring” proteins rather than through writing new in vivo. Indeed, our modelling framework reflects a natural division between two classes of parameters: “intensive” parameters describe the allosteric transitions and intramolecular interactions of a particular protein and are attributes of ANC-structures; “extensive” parameters describe the interactions of the protein with other biomolecules and are associated with rules. In different biochemical networks, only the extensive parameters of a protein change. Through its assumption that a ligand or substrate distinguishes only the conformation of a protein or enzyme and not its occupancy or state of covalent modification at distant sites, our modelling framework substantially reduces the number of extensive parameters required: their number scales linearly, rather than combinatorially, with the number of interactions. This reduction in extensive parameters allows a model for the interaction of a protein with individual ligands to also predict the response to mixtures of ligands with no additional parameters In our methodology, models are structured to minimize regulatory complexity both to avoid over-fitting data and because large numbers of biochemical parameters are difficult to measure Our methodology reduces the regulatory complexity but increases the combinatorial complexity of a system because each conformation of an allosteric protein introduces a new state. Thus, a reduction in regulatory complexity incurs the computational cost of modelling additional species. Nevertheless, recent advances in rule-based modeling have introduced new methods that allow fast simulation of systems with large numbers of chemical species and reactions We also make a first step at integrating free energy-based constraints into a rule-based modelling framework, adding to earlier work on imposing detailed balance in models of biochemical networks Two other advantages of our modelling framework are significant. First, ANC-structures enable a coarse-grained hierarchical description of physical structure by requiring the specification of protein domains and if desired tertiary and quaternary structure, including oligomeric receptor clusters. ANC-structures can also model the internal geometry of a protein by describing those domains of the protein that interact allosterically and those that do not . Second,Our modelling framework encourages the modeller to develop a mechanism to explain the regulatory properties of a protein and hence to build models that have predictive power and so can be experimentally tested. For example, an ANC model of the activation of GPCRs suggests that the well-known cubic ternary complex model has implicitly coarse-grained some conformations of the GPCR. By including these conformations in an ANC-structure, our new quartic model prevents over-fitting and has the potential to predict the rank order of potency and efficacy of ligands acting through a GPCR. This model of the GPCR has two linked allosteric components, each with just two conformational states that interact independently with other molecules. These mechanistic assumptions do not, however, apply to the GPCR as a whole, which has four conformational states. Thus, while the two-state assumption may not hold for all proteins, other mechanistic models can be accommodated within our framework.Having allostery at its centre, our framework can suggest simple mechanisms through which the cell might regulate and increase the efficacy of cellular processes. For example, the assembly of macromolecular complexes can be considerably undermined through the prozone effect when linker proteins are over-expressed A challenge in designing synthetic biological systems is to have predictive modelling tools. Here, ANC has several potential advantages. First, the modularity of ANC-structures allows models of synthetic systems to be straightforwardly extended: for example, as different synthetic subsystems are combined to generate more complex behaviour Faced with the complexity of cellular signalling and genetic networks, researchers are developing new computational methods to quantitatively model and predict cellular behaviour despite that complexity. In this spirit, we have identified and discussed a distinct form of complexity – regulatory complexity – which arises from the allosteric regulation of proteins. Combining and extending established biophysical principles with more recent rule-based methods, we propose a modular and scalable methodology, exemplified by our Allosteric Network Compiler, to describe the complexity of cellular signalling. By emphasizing the allosteric control of proteins, we capture the inherent modularity of protein structure and function exploited by cells themselves. Our method is a general, principled and simplifying addition to any modeling framework.R and the alternate conformation as T. If we let R and the T conformations in the absence of any modifiers, then the difference in free energy in the presence of N modifiers is, quite generally To compute how multiple modifiers collectively bias the conformational equilibrium of an allosteric component, we use thermodynamics R state of the component given by T state by i. Consequently, the free energy required to apply a modifier to each conformation does not depend on the presence or absence of other modifiers – a modifier can only distinguish the conformational state of the component. Therefore we need consider just the first order terms of equation (1) and ignore higher order interactions:R and the T states of the component, between the modifier i being applied to the R state  with each conformational state of the protein component, with the energy of interaction to the i, which describes the effect of an input modifier i on the allosteric equilibrium. If the modifier is a ligand, then Γi is the ratio of the ligand's affinity to each conformation. If the modifier is a covalent modification such as a phosphorylation, the regulatory factor is an independent parameter related to the free energies required to phosphorylate each conformation. If the modifier is another allosteric component to which the component is allosterically coupled  describes the input-output function of an allosteric component, which may embody a domain, a subunit, or an entire protein. The output led e.g. , then ΓiC and that modifiers contribute independently to a change in the free energy of the transition state  and each conformational state i = Φj implies the existence of a linear free energy relationship for two modifiers i and j  The reaction network is enumerated, so ANC's performance may degrade significantly if the compiled network is large. 2) Only rules for binding and Michealis-Menten interactions can be created. 3) While ANC supports unimolecular association and dissociation, detailed balance is enforced only for cycles comprising purely bimolecular associations.Text S1Supplementary information. This file contains all supplementary information for the article.(4.94 MB PDF)Click here for additional data file.