Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac physiology. The advent of massive mass spectrometry datasets has in turn led to increasing intersection between proteomics and big data science. Here we review new frontiers in technological developments and their applications to cardiovascular medicine. The impact of big data science on cardiovascular proteomics investigations and translation to medicine is highlighted. The heart is in many ways an exceptional organ. Proteins at the sarcolemma, sarcomere, mitochondrion, and other cardiac organelles must orchestrate vital functions seamlessly on a beat-by-beat basis, while dynamically adjusting energetic and contractile outputs to environmental cues within seconds. Heart diseases including cardiac hypertrophy and failure are characterized by complex remodeling of various protein signaling networks and subcellular components, which often involve a multitude of collaborating proteins. Therefore, understanding how multiple protein species interact to carry out higher physiological phenotypes and regulation has been an important objective of cardiovascular research. The power of proteomics to simultaneously provide information on the panoply of expressed proteins has made it uniquely suitable for resolving complex signaling conundrums and revealing disease mechanisms in the heart.Advances in genome sequencing are often celebrated to have outpaced even the vaunted Moore’s law of computing power . Lesser Cardiovascular proteomics has grown rapidly in the intervening period, with >400 studies now being published yearly Fig.  3]. To . To 3]. 6 proteolytic peptides per sample. With the addition of post-translational modifications (PTMs)—e.g., the four histone proteins alone have identified PTMs on at least 105 different residues in myriad combinations [An early hurdle that bedeviled cardiovascular proteomics was the limitation in the sensitivity and dynamic range of protein detection, which skewed results towards few high-abundance proteins  and masked low-abundance species. This is due to the complexity of proteomes. The mammalian heart is known to express at least ~8000 genes at significant levels , and at In the past decade, great strides have been made to improve proteome coverage, from how protein samples are extracted to mass spectrometry instrumentation. To perform proteomics analysis, it follows that the proteins must be effectively extracted and released from biological samples. This is typically achieved via mechanical homogenization or chemical surfactants. Protein solubilization techniques in early proteomics protocols were at times ineffectual in extracting hydrophobic or membrane proteins, which often aggregated out of the sample and led to their non-detection. Development in this area in recent years have led to commercially available, mass spectrometry compatible surfactants , size exAdvances in separation sciences have had a particularly tremendous impact on reducing the complexity of peptides prior to mass spectrometry signal acquisition. High-performance shotgun proteomics using mass spectrometry has supplanted two-dimensional 2D) electrophoresis to become the de facto standard for large-scale analysis of cardiac proteins , sensitivity , and mass resolution (increasing power to differentiate similar peptide species). Recent proteome profiling experiments of the mammalian heart using the latest and greatest LC–MS combinations routinely achieve 5000 or more proteins identified in an experiment Fig. b. In a rTaken together, these advances have helped solve a principal challenge to proteomics applications, namely how to successfully detect the maximal number of peptides inside the overwhelmingly complex mixture that is the cardiac proteome. Although each technological development is incremental, over time they accrued into a qualitative transformation on the power and utility of proteomics, when proteins of biomedical interest gradually became measurable and discoverable in large-scale experiments. Catalogs of so-called “complete proteomes”  have now been described for many human tissues and organs, including the heart , 20. TheEarly applications were also plagued by the variability of quantification results, which limited power to discover significant changes between disease model and control samples. A major source of variability in proteomics quantification originated from the variable detectability of peptides with different amino acid compositions. Two equimolar peptide sequences can show rather different intensities in MS signals. Accurate prediction of peptide intensity based on sequence information remains an unsolved issue in computational proteomics due to the large number of combinatorial variables that contribute to signal behaviors. Several methods have been developed to normalize peptide intensity and achieve accurate quantification. Targeted MS methods, such as Multiple Reaction Monitoring (MRM) , allow uWith advances in data acquisition methods, non-targeted label-free techniques can also reliably deduce accurate protein intensity from shotgun experiments directly through bioinformatics analysis. Label-free quantification is analogous to deducing transcript abundance from read counts in next-generation sequencing. Existing approaches largely fall into two categories Fig. . SpectraThe massive amount of MS data generated in proteomics experiments requires computational aid for effective data processing and analysis. A growing number of open-access computational tools concerning all steps of proteomics data analysis are now freely available to users, a subset of which are listed in Table A major computational task in shotgun proteomics is to efficiently interpret the mass and intensity information within mass spectral data to identify proteins. The computational task can be formulated thus: given a particular tandem mass spectrum, identify the peptide sequences most likely to have given rise to the set of observed parent molecular mass and fragment ion patterns in a reasonable time frame. A general solution to this problem is “database search”, which involves generating theoretical spectra based on in silico fragmentation of peptide sequences contained in a protein database, and then systematically comparing the experimental MS spectra against the theoretical spectra to find the best peptide-spectrum matches. The SEQUEST algorithm was the first proposed to solve this peptide spectrum matching problem in 1994  and its Means to distinguish true and false positives are critical to all large-scale approaches. The “two-peptide rule” was once commonly adopted to decrease false positives at the protein level by requiring each protein to be identified by at least two independent peptides. However, this rather conservative rule could inflate false negatives, as some short or protease-incompatible proteins may only produce maximally one identifiable peptide. More recent conventions involve foregoing the two-peptide rule and instead estimating the false discovery rate (FDR) of identification through statistical models, often with the aid of decoy databases. The use of decoy databases/sequences (reversed or scrambled peptide sequences), allows a quick estimation of the number of false positive proteins, by assuming identical distribution in protein identification scores for false positive hits and the decoy hits. A maximum acceptable FDR can then be specified  to determine which protein identifications are accepted in the final result. To explicitly reveal the posterior probability of any particular identification being correct , a mixture model has been used that assumes that the peptide identification result is a mixture of correct and incorrect peptides with two distinct Poisson distributions of identification scores . AuxiliaWith the increase in data size and multiplexity (number of sample compared) in proteomics experiments, statistical approaches to analyze data have also evolved to tackle high-dimensionality data. Whereas early studies utilized mostly confirmatory statistics, modern proteomics datasets typically contain thousands of features  over a handful of observations, hence simply testing whether each protein is significantly altered across experimental conditions can result in under-analysis and failure to distinguish latent structures across multiple dimensions, e.g., whether there exists a subproteome of co-regulated proteins across multiple treatment categories. To gain biological insights, quantitative proteomics datasets are now routinely mined using statistical learning strategies that comprise feature selection , dimensionality reduction , and both supervised and unsupervised learning  to discern significant protein signatures, disease-implicated pathways, or interconnected co-expression networks Fig. .Fig. 4PrImprovements to computational methods that allow more robust results from label-free quantification are an area of active research. For example, recent works  have attempted to resolve the respective quantities of multiple proteins that share common peptide sequences in spectral counting, either using weighted average methods or more statistically motivated models , 37. In With the proliferation of inter-compatible tools, an ongoing trend is to daisy-chain individual tools into user-friendly pipelines that provide complete solutions to a set of related data analysis problems. An ideal proteomics pipeline may combine identification, quantification, and validation tools in a modular organization accessible from a single location. Computation may be performed on the cloud to avoid the need to repeatedly copy, transfer, and store large files. Researchers can carry out computational tasks remotely from the browser on any computer system, obviating the need for redundant infrastructure investments. Currently, the Trans-Proteomics Pipeline  and the Not unlike other omics approaches, the success of proteomics experiments relies heavily on having adequate and up-to-date resources to analyze large-scale data. To wit, for protein database search to succeed, it follows that the protein being identified must first be documented on a sequence database. Fortunately, there have been tremendous advances on the completeness (number of true positive sequences recorded) and precision  of sequence databases such as UniProt and RefSeq. Some databases are manually curated to contain precise information, whilst others strive to be more comprehensive; but many now list precise “complete proteomes” for commonly studied laboratory species. Databases for human and popular laboratory model organisms have seen particular progress in the completeness of annotation in recent years, such that proteomics studies can now be performed similarly well in mice, drosophila, rats, and other organisms to interrogate cardiac physiology. On the horizon, one can foresee an influx of sequence information on protein polymorphism and alternative splicing isoforms. Although current databases primarily originate from genomic translation or cDNA library of specific cell types or genetic backgrounds, “proteogenomics” efforts are underway to translate additional sequences for proteomics studies, which will expand the scope and precision of protein identification. For non-model organisms, alternative search methods have been devised, such as against a custom database generated by manual six-frame translation of genomic sequences.Data interpretability problems arise when the complex results comprising changes of many proteins could not be easily digested and summarized in terms that are relatable and of value to biomedical and clinical researchers. The biological significance of the implicated protein targets may be interpreted and connected to the growing corpus of biomedical knowledge through functional annotations. Commonly used annotation resources include Gene Ontology for biological functions , ReactomTo map molecular data to curated annotations, a class discovery approach is commonly utilized, which looks for annotated properties that are preferentially shared amongst a subset of proteins with interesting quantitative features over the proteome-wide background, e.g., through sequence motif analysis or annotation term enrichment. The principle behind such analyses is to infer biologically relevant processes based on significant overlaps between data features and the data annotation classes. Computational and statistical approaches are used to determine whether particular annotations are over-represented in a particular subset results than would be expected by chance. This allows both bias-free discovery and specific questions to be asked of a dataset, e.g., whether the down-regulated proteins in a heart failure patient are preferentially involved in fatty acid metabolism, etc. Enrichment analyses can be easily carried out using online tools that perform binomial or hypergeometric tests on the over-representation of functional annotations, including NCBI DAVID, WebGestalt, and Pantherdb –51. For We note that many proteomics functional analysis strategies were originally developed for microarray datasets. Although the analytical goals between proteomics and microarray experiments often overlap, i.e., identify functional associations from numerical molecular expression data, several methodological differences merit considerations. The stochastic nature of shotgun proteomics can lead to missing values and high variability in the data for low-abundance proteins. To address this, several proteomics-focused enrichment analysis tools have recently been developed to address specific features of proteomics datasets, e.g., using weighted sampling, which should account for relative abundance and variability of observations and further improve quantification performance. Monte Carlo sampling approaches have also been used to counter the bias of annotations on high abundance proteins when calculating enrichment significance .As in the case for protein identification, the success of functional analysis is contingent upon the completeness and accuracy of annotations in knowledgebases. Commonly used knowledgebases such as Uniprot and Reactome have steadily improved in size and richness of functional information. Nevertheless, it sometimes remains the case that some classes of annotations are more complete, either because they are easily computable from sequence information  or can be derived from popular experimental designs , and as a result are more likely to turn up in functional enrichment analyses. On the other hand, annotations on higher-level pathophysiology and tissue-specific regulations are more challenging, because a majority of such information is hidden in unstructured free text in the literature. With >2 million cardiovascular related articles alone on PubMed , howeverThe aforementioned analytical and computational advances have enabled novel and noteworthy applications in cardiovascular proteomics. An exciting trend is to venture beyond simply inventorying which proteins are present in the heart or the blood, and into quantifying their dynamic and spatiotemporal properties. Protein complexity necessitates that many parameters are needed to sufficiently describe the overall proteome in a particular physiological state. New methodologies continually arise that enable new insights into protein–protein interactions , proteinWith increased experimental power to detect peptides, rare and hard-to-detect peptides are increasingly analyzable, including many modified by PTMs. Because translational modifications are attached to proteins following synthesis, their chemical identity, position, and fractional quantity cannot be easily predicted from transcripts, necessitating proteomics studies. PTMs have been the focus of proteomics studies for over a decade and these efforts have increasingly begun to bear fruit in various biomedical investigations. In a recent notable example, Lee et al.  used a gEvidence suggests that the current investigations into PTMs have barely scratched the surface of their complexity. Over 380,000 PTM events are documented on the PhosphoSitePlus database , includiThe function and functionality of a protein are modulated to a great extent by the spatial milieu in which it is situated, which in turn dictates the substrates and interacting partners with which it comes across. Ongoing studies into the spatial distributions of cardiac proteins are inventorying the protein compositions in different cardiac organelles, with particular successes in cardiac mitochondria and nucleus proteomes, as elucidated via targeted enrichment of specific organelles. More recent studies in other organs are suggesting the possibility that proteins are actively and constantly translocalizing between organelles in response to cues, which can be measured by combining differential centrifugation, isotope labeling, and machine learning techniques. Recent advances in data analysis have allowed such differential centrifugation techniques to be used for pan-organellar mapping. Instead of enriching only for a pure sample of a particular organelle type, a centrifugation gradient here is matched to a supervised classification algorithm to classify proteins based on their sedimentation behaviors. The average intracellular position of many proteins can therefore be discovered by their grouping with known organellar markers. These approaches can be readily adopted to understand dynamic protein translocalization from one organelle to another, using new analytical frameworks that can quantify protein translocation in differential centrifugation experiments , 72. ThrSecondly, the synthesis rates of proteins have also proven important to tracing the progression of cardiac hypertrophy preceding heart failure. Interests in classical physiology to understand skeletal and cardiac muscle mass gain have propelled technological and software developments aimed at understanding protein turnover, which can be applied to other fields. There have been particular developments in isotope labeling and kinetic modeling methods, which have elucidated the half-life of proteins in many various cellular compartments in the heart –77. DeveThere have been marked improvements in experimental protocols in affinity purification, as well as statistical and data science methods to filter out false positives in pulldown experiments. Chiang et al. recently elucidated the interactome of the protein phosphtase 1 catalytic subunit (PP1c), identifying 78 interacting partners in human heart. The proteomics results found increased binding to PDE5A in paroxysmal atrial fibrillation patients to impair proteins involved in electrical and calcium remodeling, a result that has implications in the understanding and treatment of atrial fibrillation . WaldronAt the same time, there is renewed interest to perform crosslinker studies on a large scale, which in addition to identifying protein–protein interaction partners, can provide information on the topology and protein domains involved in the interactions. Again we note that the development of new proteomics methodologies now necessitates hand-in-hand advances of novel data science solutions almost without exception. An example is the application of chemical cross-linkers in proteomics, which allows the linking of proximal proteins to quantify the degrees and likelihood of protein–protein interactions in their native cellular environment. Cross-linking proteomics experiments are however infeasible without specialized search engines that can consider the combinatorics of crosslinked peptide sequences, and identify interacting proteins whilst controlling for the FDR that result from the quadratic increase of search space , 84.Quantitative shotgun proteomics has developed into a remarkably powerful technology that enables sophisticated questions on cellular physiology to be asked. The total volume of proteomics data generated per year now ranges in the petabytes. This is paralleled by an increasing number of available proteomics datasets in the public domain that can be reused and reanalyzed, with as many as 100 new datasets being made available per month on the proteomics data repository PRIDE . Hence jDespite impressive gains, improvement of protein identification will likely continue to be an area of active research. It is estimated that up to 75–85 % of mass spectra generated in a proteomics experiment can remain unidentified by current data analysis workflows , 86, thuSecondly, advances in protein quantification techniques, via both experimental and computational developments, will likely continue unabated. Many quantification techniques do not take into account the peptides that may become post-translationally modified or otherwise lost in a biological state. Currently, decreases in label-free measured quantity may be confounded by differences in protein modifications, digestion, or ionization, or matrix effects from different samples. For instance, the acquired spectral counts may be inflated by the existence of shared peptides among multiple (documented or undocumented) protein forms  as well Lastly, the identification of unknown or unspecified PTMs will likely see continued progress. Because the multiplicity of the possible modification types on a peptide can shift peptide fragment masses combinatorially, they can greatly inflate the number of possible matches. Efforts are underway to develop custom sequence databases and devise new algorithms to extract information from existing cardiovascular proteomics datasets that is currently “hiding in plain sight”. For instance, algorithms can be used to predict peptide fragment intensity to improve peptide identification . To makeTo summarize, we recall an apt analogy provided by Loscalzo to compare the understanding of cardiac proteome with that of building a house and the genome with that of its floor plan . Genomic


In recent years, protein-protein interaction (PPI) networks have been well recognized as important resources to elucidate various biological processes and cellular mechanisms. In this paper, we address the problem of predicting protein complexes from a PPI network. This problem has two difficulties. One is related to small complexes, which contains two or three components. It is relatively difficult to identify them due to their simpler internal structure, but unfortunately complexes of such sizes are dominant in major protein complex databases, such as CYC2008. Another difficulty is how to model overlaps between predicted complexes, that is, how to evaluate different predicted complexes sharing common proteins because CYC2008 and other databases include such protein complexes. Thus, it is critical how to model overlaps between predicted complexes to identify them simultaneously.http://imi.kyushu-u.ac.jp/~om/software/RocSampler/.In this paper, we propose a sampling-based protein complex prediction method, RocSampler (Regularizing Overlapping Complexes), which exploits, as part of the whole scoring function, a regularization term for the overlaps of predicted complexes and that for the distribution of sizes of predicted complexes. We have implemented RocSampler in MATLAB and its executable file for Windows is available at the site, We have applied RocSampler to five yeast PPI networks and shown that it is superior to other existing methods. This implies that the design of scoring functions including regularization terms is an effective approach for protein complex prediction. In recent years, protein-protein interaction (PPI) datasets have been recognized as important resources to elucidate various biological processes and cellular mechanisms. The prediction of protein complexes from PPIs  is onFor protein complex prediction, many computational methods have been proposed, which were directly or indirectly designed based on the observation that densely connected subgraphs, or clusters of proteins, of a whole PPI network often overlap with known complexes. This observation is often valid for relatively large protein complexes. However, small complexes, consisting of two or three proteins, form a major category of the known complexes of an organism , 5. For To resolve this issue, we have proposed a sampling-based method for predicting protein complexes, PPSampler2 . The conpartition of all proteins in a given PPI network, and every element with two or more proteins is taken as a predicted complex. Thus, any two predicted complexes are exclusive, namely, they never share any common proteins due to the structure of partition. This partition model is also adopted by the Markov cluster algorithm (MCL), which is a popular node-clustering algorithm for an edge-weighted undirected graph based on the simulation of stochastic flow in the graph . Additionally, we suppose that, for e={u,v}∉E, w(e)=0. We suppose that any self-loops, {u,u} where u∈V, are not included in E. If self-loops are included in a given data set, they are removed in a preprocessing step. For a subset, x, of V, we define w(x) as the sum of the weights of the interactions included in x, that is, A PPI network is represented as an undirected, edge-weighted graph, u∈V and x⊆V, we denote by w the sum of weights of interactions between u and proteins in x, that is, u is outside of x and the other in which it is not.Furthermore, for V as a predicted complex, and call it a predicted cluster to clearly distinguish it from a known complex. We denote a set of predicted clusters by We consider a subset of xi∈X, should have two or more components as it models a protein complex. Note that, in this model, clusters are allowed to overlap with each other.Every predicted cluster, V, x and x′, which is defined as x, and a known complex, x′, match with each other, and in evaluating dissimilarity between x and x′, which is explained in the next section.The Jaccard index between subsets of f, which is a linear combination of various terms, In this section, we describe our scoring function, Smax is the upper bound on the size of a predicted cluster. The default value is simply set to be 100, and cclu−discclu−size,chy, and cpro−num, are the coefficients of the corresponding terms.where b(X), checks the minimum requirements for the predicted cluster of X. Whenever there is a cluster in X violating at least one of them, the resulting probability of X is zero. The second term, hclu−den(X), calculates the negative of the sum of a generalized density of a predicted cluster in X. The effectiveness of these two terms for protein complex prediction is empirically shown in our previous works . Thus, the scaling exponent is 2.02. The root-mean-square error is 1.75. Furthermore, a human protein complex database, CORUM . The probability of cluster size, s, in the power-law distribution with a scaling exponent, γ, is formulated as s=2,3,…,Smax. We denote by ψX(s) the fraction of predicted clusters with s components in X, that is, hclu−size,s(X) as the square error between ψX(s) and ψγ(s), that is, Thus, we regularize the distribution of the sizes of predicted clusters in hhy(γ) is a prior distribution of γ, which is defined as a quadratic loss function, that is, The term γ0 is set to be 2.5, the median of the interval, , which is the typical range of a scaling exponent of power-law distributions in physics, biology, and the social sciences [γ is introduced in this work, although γ was fixed to be 2 in the previous work [The parameter sciences . Note thous work , which ihpro−num(X), we also control the total number of proteins over all predicted clusters in X. The term is simply formulated as the square of that number, that is,Using the term X. Thus, it can be expected that this term contributes to form more reliable predicted clusters.λ is a parameter representing a target number of proteins over all clusters. Thus, we do not need to specify that parameter in our new method.This term is simpler than the corresponding term, ous work , 12, wheαa,c,αa,p,αr,c, and αr,p, respectively : X,randomly add a new cluster with two components to a set of predicted clusters, X,randomly add a new protein to a cluster in X, andrandomly remove a cluster with two components in X.randomly remove a protein from a cluster in In general, a proposal function of the Metropolis-Hastings algorithm provides a candidate state of the next iteration that is slightly and randomly modified from the current state. The proposal function used in Algorithm ?? first randomly chooses one of the following four procedures with probabilities, γ, which is max{10−10,γ+ε} where μ, and variance parameter, σ2. The minimum value of 10−10 is used to avoid the value γ being negative.Details of the four procedures are explained in the subsequent sections. After executing one of the above four options, the proposal function subsequently proposes a new candidate value of e∈E, is randomly chosen with the probability proportional to the weight, w(e). Let xe be the cluster formed with the two proteins of e. Then, xe is added to X. As a result, a candidate state X′ is given as X∪{xe}. The total probability of this proposal, denoted by Qa,c(X′|X), is X, X′ is set to be X.In this option, an interaction, x, we denote by N(x) the set of neighboring proteins to x, i.e., X is as follows: x, is uniformly chosen at random from X.A cluster, u, is randomly chosen from N(x) with probability proportional to w, which is the sum of the weights of the interactions between u and all components of x.A protein, u, is added to x.The chosen protein, For a cluster of proteins, X′. The resulting probability of this proposal is N(x) is empty, X′ is the same as X.The resulting state is X. It chooses a cluster, x, of size two from X at random with probability proportional to the inverse of the weight of the unique interaction of x. The probability of this proposal is given asThis procedure removes a cluster with two components from x does not exist, X′ is equal to X.x, is uniformly chosen at random from the clusters with three or more components in X.A cluster, u, in x is randomly chosen with probability proportional to 1/w, representing the inverse of the strength of the connectivity between u and x.A protein, u, is removed from x.The chosen protein, The last option removes a protein from a cluster by the following procedure. X does not include any clusters with three or more components, X′ becomes X.Thus, the resulting probability is ℓ-th iteration of Algorithm 1 by Tℓ, which is simply formulated as follows. Let T0 be the initial temperature. It is gradually reduced from T0(=1) by We denote the value of the temperature parameter of the x matches k with matching threshold η if J≥η. Let X be a set of all clusters predicted by a method, and K be a set of all known complexes. For subsets, We use the same performance measure as in , 17, whiη. The latter is the subset of η. For an integer i (≥2), we denote by X|i the subset of X whose elements have i components, that is, X|i={x∈X||x|=i}, and by X|i≥ the subset of X whose elements have i or more components, that is, X|i≥={x∈X||x|≥i}. Similarly, we introduce the notations of K|i and K|i≥ for K. We then formulate the precision and recall as follows:The former represents the subset of η=0.5. On the other hand, the matching criterion for predicted clusters and known complexes with two or three components is an exact match as η=1. The reason for this is as follows. In many works on the problem of protein complex prediction, the degree of overlap between a predicted cluster, x, and a known complex, x′, is measured by the Jaccard index, x and x′ to the geometric mean of |x| and |x′|, that is, x and x′ with |x|=|x′|=2 share exactly one protein. Note that this situation is easily realized by randomly predicting clusters with two components because there are many known complexes with two components in protein complex datasets. In this case, we see that J=1/3 and the other ratio is 1/2. Thus, x and x′ are determined to match with each other by both measures if the threshold is set to be less than or equal to 1/3. We avoid this issue by setting the threshold to be one for small clusters and complexes. The F-measure of X to K is the harmonic mean of the corresponding precision and recall, that is, Notice that the matching threshold for predicted clusters and known complexes with four or more components is set to be A set of PPIs with weights is given as input to a protein complex prediction method. Our main PPI dataset is the WI-PHI database . Every PIn addition to the WI-PHI dataset, we also use four different datasets of PPIs with weights, which are denoted by Collins , Gavin , Krogan All protein complexes in the yeast protein complex database, CYC2008 , are use5, 106, and 107. The target number of proteins used in that term, λ, is selected from 1,000, 2,000, and 3,000. The four coefficients of the scoring function of RocSampler are optimized over the ranges: β∈{0.2,0.3,0.4}, cclu−size∈{200,300,400,500}, chy∈{5,10,15,20}, cpro−num∈{5×10−5,10−4,1.5×10−4}, and cclu−dis∈{70,90,110,130,150,170}. The repeat count, L, is fixed to 5,000,000.To evaluate how RocSampler works well, we carry out a performance comparison with existing methods, MCL , SPICi , ClusterNote that MCL, SPICi, and PPSampler2 do not allow predicted clusters to overlap with each other.The selected parameter values on the WI-PHI PPI dataset are shown in Table We here compare the performances of PPSampler2 and RocSampler intensively, because, RocSampler is an improved version of PPSampler2. The precision scores of PPSampler2 and RocSampler are 145/396 = 0.37 and 147/281 = 0.52, respectively. On the other hand, their recall scores are, as mentioned, the same, 156/408 = 0.38. Thus, RocSampler improves the precision score without reducing the recall score. As a result, the F-measure score of RocSampler, 0.44, is 19% higher than that of PPSampler2, 0.37.We furthermore compare details of the predictions by PPSampler2 and RocSampler. Figure Surprisingly, no predicted clusters of RocSampler overlap with others, although we had expected that some would overlap with each other. A relatively sparse set of predicted clusters might be a good approximation to the current gold standard protein complexes, although further investigation of this issue is required.γ is 1.91, which is quite similar to the true value.We have mentioned that the scaling exponent of the power-law regression curve in Fig. The prediction performances of the methods on the four remaining PPI datasets are given in Figs. RocSampler has succeeded in predicting overlapping clusters only from the Collins PPI dataset. We here give an example of such overlapping clusters, which are good predictions of known complexes.x1 and x2, which surround their component proteins. As we can see, they share the four proteins, Smb1p, Smd1p, Smd2p, and Smd3p. These four proteins are known to be part of the heteroheptameric complex with Sme1p, Smx3p, and Smx2p, which are also shown in Fig. x1 and x2 match some of the spliceosomal snRNPs. Actually, as shown in Fig. x1 matches the U1 snRNP complex [x1 overlaps more with the commitment complex with Jaccard index 0.81, indicated by a brown solid curve. The commitment complex is known as an ATP-independent complex that commits hnRNAs to the splicing pathway [x2 matches the U4/U6.U5 tri-snRNP complex [Figure  complex  with Jac pathway . Further complex , 25 whosOn the other hand, PPSampler2 found the cluster with Mud1p, Luc7p, Prp42p, Snu56p, Snu71p, Nam8p, Snp1p, Prp40p, Yhc1p, Prp39p, Sto1p, Cbc2p, and Smx3p. This cluster includes only Smx3p among the seven components of the heteroheptameric complex. Although it matches the commitment complex and U1 snRNP complex, the Jaccard indexes are 0.61 and 0.58, lower than the corresponding ones of RocSampler. It can be expected that all or most of the remaining components of the heteroheptameric complex are included in another cluster which matches the U4/U6.U5 tri-snRNP complex, but PPSampler2 failed to find such a cluster. Thus, we can say that, by allowing predicted clusters to overlap with each other, more refined predictions are obtained.In this work, we have proposed a novel sampling-based protein complex prediction method, RocSampler, which is a successor to PPSampler2. The major difference between them is that RocSampler exploits a regularization term for controlling overlaps of predicted clusters and PPSampler2 does not allow predicted clusters to overlap with each other. RocSampler also introduced a new proposal function for generating overlapping clusters and regularization terms refined from those of PPSampler2. We have shown that RocSampler outperforms five other methods on five different PPI datasets. RocSampler has succeeded in finding overlapping clusters from the Collins PPI dataset, but it has not done so from the other PPI datasets. Future work is required to identify the reason for this and to devise a new scoring function to attain higher performance and simultaneously to find overlapping clusters of proteins.


Effectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.In this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained.We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.CPredictor3.0 can serve as a promising tool of protein complex prediction. Proteins, as the material basis of life, are the ultimate controller and direct performer of life activities, participate almost all of biological functions. Most proteins do not perform biological functions alone, but form protein complexes with others . So to hAlthough biological techniques such as Tandem Affinity Purification with Mass Spectrometry (TAP-MS)  can deteWith the development of high-throughput experimental technologies, PPI data rapidly increase, which provides chance for using computational methods to detect protein complexes. Moreover, computational methods can overcome drawbacks of experimental technologies. PPI networks can be constructed by using PPI data, where nodes and edges represent proteins and interactions between proteins respectively. Empirical studies on PPI networks indicate that there are modular components in these networks . From thSo far, there have been many researches that put forward different graph clustering methods to detect local dense subgraphs to detect protein complexes from PPI networks –9. TheseIn this paper, based on our previous works CPredictor  and CPreSo far, a variety of computational methods for complex prediction have been proposed. Here, we present a brief survey on the related works by roughly classifying the existing methods into the following types: methods based on local dense subgraphs, methods based on the Core-Attachment Model, methods based on dynamic PPI networks, methods based on supervised learning. Among them, methods based on local dense subgraphs constitute the most part of existing works. Note that this method hierarchy only reflects our view of existing works. There may be other hierarchies of existing works in the literature. And a brief survey, we cannot cover all existing works, but we try our best to present the major existing works.p) with the largest weight is selected to be a seed node of a primary complex, which is expanded by including other proteins whose weights exceed a pre-set threshold recursively, till there are no more nodes to be added. If there are unprocessed nodes, new complexes will be generated in the way above. Finally, the neighbors of each complexes generated above are included into the complexes if their weights is higher than a pre-set “fluff” parameter. MCL [As one of earliest computational methods of complex prediction, MCODE  first weter. MCL  predictster. MCL  uses a nAs there exists high false-positives and false-negatives in PPI networks. Some methods weight edges of PPI network using PPI network topology, gene expression data and protein function to improve the reliability of PPI networks. DPClus  weights In general, proteins that form functional groups have similar gene expression, so some methods weight edges of PPI networks using expression data. MATISEE  measuresExcept for expression data, protein function provides important clue for protein complex detection. SWEMODE  proposedIn our previous works CPredictor  and CPreGavin et al.  studied Following the core-attachment structure, two methods CORE  and COACEarlier methods detect complexes from static PPI networks. Actually, the interactions among proteins are dynamic and change over time at different biological stages. In recent year, there are some works on detecting protein complexes from dynamic PPI networks. Tang et al.  applied Some works use supervised learning to detect protein complexes. Qi et al.  classifiHere, we first give an overview of CPredictor3.0, then describe the major components of CPredictor3.0 in detail.Figure The rationale behind our method is that proteins of a complex performs some function(s) by interacting with each other at the same time and the same place in cellular systems . CPredicGene expression data reveal the dynamic properties of proteins in their lifetime. As a protein is not always active, its expression level changes with its activity degree. Concretely, higher gene expression level means higher activity. To get the active time points of each protein, Tang et al.  set a gln time points. For a protein p, Vk(p) represents protein p’s expression value at time point k, μ(p) and σ(p) are the mean and the standard deviation of expression values over the period from 1 to n. The active threshold of protein p is evaluated as follows: Suppose the expression data are measured at β is an adjustable parameter that helps us to get the most optimal threshold. Usually, we set β=0, 1, 2, 3.Above, p at time point i , if its expression value is no less than Active(p), then it is an active protein at time point i. In such a way, we can get the set of active proteins APi for each time point i. Thus, we have a series of active protein sets { APi }.After obtaining the active thresholds for all proteins, we can collect all active proteins at each time point. That is, for each protein Here, we cluster proteins by functional annotations. First, we compute the functional similarity of any two proteins using the method proposed by Wang et al. , then weA can be described as DAGA= where TA consists of term A and all its ancestors in DAG, EA is composed of all edges (relationships) connecting A to all terms in TA. As defined in Wang’s method, the semantic content of a term is the sum of semantic contributions of all its ancestors in DAGA to A. The semantic contribution of term t to A is as follows: The similarity between any two proteins is computed by the GO terms annotated on the two proteins. GO includes a series of biological terms to describe gene and gene products such as protein, and covers three aspects: biological process (BP), cellular component (CC), and molecular function (MF). Here, we use only BP data. GO can be represented as a directed acyclic graph (DAG), in which nodes and edges represent terms and their relationships (e.g. ‘is-a’ and ‘part-of’) between two terms. A GO term childrenof(t) returns the children of t in DAGA, and we as the weight on the edge between t and t′, which depends on the relationship type between the two terms. For example, the weight is 0.8 for ‘is-a’ and 0.6 for ‘part-of’.where function SV(A) of term A is evaluated as follows: So the semantic value SGO between term A and B is evaluated as follows: The semantic similarity P1 and P2, which are annotated by { go11, go12, ⋯, gom1} and { go21, go22, ⋯, gon2} respectively, their similarity can be evaluated as follows: Generally, one protein may participate one or more biological functions, so one protein may be annotated by multiple terms. For two proteins K disjointed clusters PC={ PC1, PC2, ⋯, PCK} where K is an adjustable parameter to control the number of protein clusters.After getting the similarity matrix for all proteins, where each element represents the semantic similarity of two proteins. Then, we apply the spectral clustering algorithm  to the mAPi|i=1,…,n} and the set of protein clusters of similar function {PCj|j=1,…,K}, here we go to compute the active protein clusters of similar function. For time point i, the set of active protein clusters of similar function is APCi=APi∩{PCj|j=1,…,K}={APi∩PCj|j=1,…,K}. Thus, we can get all active protein clusters of similar function as follows: With the sets of active proteins {G= where V and E represent proteins and interactions respectively, to get connected subgraphs induced by each cluster on G. Concretely, given the active protein cluster of similar function APCij, we map APCij onto G and get the induced graph Gij= by APCij. That is, Vij=APCij and Eij are the set of edges in G that connect proteins in APCij. Gij may be not a connected graph, i.e., it may consist of several connected subgraphs. We treat each resulting subgraph of size > 1 as a candidate complex. Thus, from Gij we get a set of candidate complexes CCij. Similarly, by mapping other active protein clusters of similar function onto G, we obtain other candidate complexes. We denote the set of all candidate complexes as CC ={CCij|i=1,⋯,n;j=1,…,K}.We have already gotten the set of active protein clusters of similar function, considering that complexes consist of interacting proteins, we map all active protein clusters of similar function onto a PPI network G. Consider a candidate complex c∈CC, its corresponding graph is Gc=. First, we search the set of neighbor nodes of candidate complex c in G, which is denoted as NG(c). We have Here, we try to expand each candidate complex on p is any protein not in Vc and E is the set of interactions between protein p and any protein in Gc. For any protein p∈NG(c), if the following condition holds, we add p and its interactions with proteins in c to c: where α is a pre-specified threshold. In this paper, it is set to 0.6 by experience. α is in the range 0 to 1. When α is set to 0, all neighbors of Vc will be added into Vc. Otherwise, if α is set to 1, no nodes will be added into Vc. Usually, if one node interacts with more than half nodes of Vc, the node will be added [c. We do expansion to all candidate complexes in CC, and denote the set of candidate complexes after expansion as CCexp.Above, be added , 19. OurCCexp. For two overlapping candidate complexes, if their overlapping score is larger than a predefined threshold, we merge them to one complex. Concretely, given two candidate complexes cA and cB, their overlapping score is evaluate as follows: There may be overlapping between candidate complexes in OS≥γ, we merge cA and cB. Here, γ is a pre-specified parameter. By experiments, we set γ=0.8. When there are no more candidate complexes that can be merged, the resulting and remaining candidate complexes constitute the final set of predicted complexes.If The algorithm of CPredictor3.0 is presented in Algorithm 1. Here, Lines 5-10 are for computing active protein clusters of similar function, Lines 11-24 are for candidate complexes extraction, Lines 25-35 are for candidate complexes expansion, and Lines 36-40 are for candidate complexes merging.We downloaded gene expression data GSE3413  from GenIn addition to gene expression data, we used three PPI datasets of yeast, which are referred to as Krogan , CollinsP = and R=  are a predicted complex and a known complex, respectively. The affinity score (AS) of the two complexes is defined as follows: To measure the quality of predicted protein complexes, predicted complexes are checked against with reference complexes. Let P and R are considered matched when AS≥0.2. This criterion was widely used in the literature [Vp and Vr is 2, then we have Usually, terature , 38–43. recall, precision and F1-measure are used to measure the prediction performance. Let PS = { ps1, ⋯, psm} and RS={ rs1, ⋯, rsn} are the predicted complex set and the benchmark complex set respectively, the three performance metrics are evaluated as follows: In our method, Nr is the number of reference complexes that match at least one predicted complex, Np is the number of predicted complexes that match at least one reference complex. |RS| and |PS| are the size of benchmark complex set and the size predicted complex set respectively.Above, K and β on prediction performance of our method. Finally, we compare our method with major existing methods in terms of recall, precision and F1-measure.We present the experimental results from three aspects. Firstly, we count the size distribution of predicted protein complexes of different algorithms. Secondly, we check the impact of two parameters As our method employs function and expression constraints to filter complexes, which may tend to produce small complex candidates. However, our method also use cluster expansion and merging strategies to generate the final predictions. To check the effectiveness of the expansion and merging strategies, here we present the size distribution of predicted protein complexes for different methods on different PPI datasets against different complex benchmark sets in Fig. K and β which can impact prediction performance. Here, we present the results of how F1-measure changes with the values of the two parameters, which are shown in Fig. In our method, there are two adjustable parameters K to from 1 to 100. The parameter β is used to set the threshold for filtering active proteins. According to three sigma(SD) model, we set the largest value of β to 3, and change it from 0 to 3. As shown in Fig. K is greater than 20. For different K values, the best F1-measure is achieved when β is set to 0. So in the comparison experiments, we set K =30, β=0 for Collins PPI data, K=35, β=0 for Krogan PPI data, and K=17, β=1 for WI-PHI PPI data.By checking the complexes in the reference sets, we can see that the size of most protein complexes is less than 30. In experiments, we set the value of K is set small. This is reasonable. As parameter K indicates the number of clusters that the proteins are to be divided. So, small K will lead to large clusters, i,e, large complexes, and vice versa.By checking the predicted complexes further, we can see that there are some large complexes of size > 100 when β, which is the threshold for filtering active proteins from gene expression data. A larger β will results in more proteins being filtered as inactive proteins. In Fig. β is set to 0, i.e., we set β to the mean of expression values over all time points, we get the best performance on Collins and Krogan PPI networks, while the best performance is achieved on WI-PHI network with β=1.0.As for parameter Here, we compare our method CPreditor3.0 with eight existing protein complex prediction methods, including MCODE , DPClus The experimental results are shown in Fig. From Fig. To give a detailed explanation, we compute the ratio of the number of overlapping proteins between each PPI dataset and each complexs reference set over the number of proteins contained in the PPI dataset. We call it “overlapping ratio” in short. The results are presented in Table This paper introduced a new method CPredictor3.0 to boost complex prediction performance from PPI networks by using both expression data and functional annotations. Experiments on three commonly used PPI datasets and two benchmark complexes sets show that CPreditor3.0 performs best in overall. It is well recognized that complexes consist of proteins that have similar function and are active at the same time and place in cellular systems. Our method considers all these aspects, including function and dynamic interaction by using PPI data, functional annotations and expression data. This may explain the best performance of our method.As for future work, on the one hand, we are considering more advanced models to extract complexes from PPI networks, such as graph sparsity models  and temp


Among these variants, non-synonymous ones can result in amino-acid changes that are called single amino-acid polymorphisms (SAPs). Although some studies have tried to investigate the SAPs, only a small fraction of SAPs have been identified due to inadequately inferred protein variation database and the low coverage of mass spectrometry (MS) experiments. Here, we present the dbSAP database for conveniently accessing the comprehensive information and relationships of spectra, peptides and proteins of SAPs, as well as related genes, pathways, diseases and drug targets. In order to fully explore human SAPs, we built a customized protein database that contained comprehensive variant proteins by integrating and annotating the human SNPs and mutations from eight distinct databases . After a series of quality controls, a total of 16 854 SAP peptides involving in 439 537 spectra were identified with large scale MS datasets from various human tissues and cell lines. dbSAP is freely available at  Genetic variations can be divided into diverse categories including single nucleotide polymorphisms (SNPs), insertions, deletions, inversions, translocations and duplications , and theTechnologies for protein identification are in fast revolution. Shotgun proteomics uses bottom-up proteomics techniques to identify proteins  in complet al. have tried to use the Jurkat human cell lines to describe various characteristics of the detected SAP peptides, including their transcriptional abundance (An efficient way to identify the spectra that harbor single amino-acid polymorphisms (SAPs) is to improve the reference protein database by adding amino acid changes resulted from corresponding non-synonymous genomic variants , or custbundance . Althougbundance , the Eurbundance . Howeverbundance . Since tHere we present a comprehensive SAP database named dbSAP that catalogs the spectra harboring variants and related abundant information we collected. Firstly, we constructed a customized protein sequence database that contains a large number of corresponding amino acid changes derived from known SNPs and mutations. The comprehensive set of variations was collected from eight SNP and mutation public databases with redundancy removed according to their sequence comparison. Secondly, we built a refined workflow for variant peptide identification based on Mascot database searching software (V2.3.0) . FinallyTo build the protein database that contains human variants, we integrated the variant data from UniProt (16 April 2014) , PMD 26, HPMD 2, MS-CanPThe MS data we used for SAPs detection were from three different resources Figure : the NCIAll MS data were then searched against the aforementioned non-redundant variant protein database  using Mascot database search algorithm , which pq-value of peptides to control the global FDR (criteria: ≤0.01) at protein level and obtained 238 071 peptides and 130 377 proteins  posWe first conducted the identification of SAPs based on the MS data of 14 709 LC MS/MS experiments from diverse human tissues, cell lines and body fluids Figure . Then wehttp://genome.ucsc.edu/), which allows users to easily access any interested part of the genome. This webpage is also connected to related variation searching page with ‘Location’ link.We collected gene-disease association information from various databases, including OMIM (current free available version) , PMD 2222 and HPIn the variation query page, if users perform the searching with dbSNP or COSMIC ID, dbSAP will return the associated gene, reference nucleotide and altered nucleotide. When user clicks the ‘dbSAP Accession’ and ‘Variation Peptide’ displayed in the ‘Single Amino Acid Polymorphism (SAP)’ table, a new webpage with corresponding protein and peptide information will be returned.The users can get the information of variants and variant related proteins and peptides in the peptide query webpage. When users enter the peptide sequence, dbSAP will return three tables: Peptide table, Spectrum table and Experiment table. In the Peptide table, users can get variant protein, peptide, reference Aa, altered Aa and SAP position in the peptide sequences. In the Spectrum table, dbSAP will retrieve the spectra of entry peptide when users click the spectrum icon. Users can also download all the spectra of the entry peptide through clicking ‘Download all’ button, or a single spectrum by clicking each ‘Spectrum ID’. In the experiment table, users can get the tissue name and experiment methods. When users put the mouse over the upper right corner of each table, the webpage will show the function of each button, such as ‘Hide/Show pagination’, ‘Refresh’, ‘Toggle’, ‘Columns’ and ‘Export data’. Users can download each table by clicking the ‘Export data’ button.Users can also retrieve the information of SAPs and variant proteins from the protein searching webpage, including wild-type protein ID , dbSAP accession, SAP peptide in protein, variation position on the protein sequence, protein description and the source of the variation.When users click ‘dbSAP Accession’, dbSAP will return three tables: protein table, protein sequence table and protein–protein interaction (PPI) table. In the ‘Protein Sequence’ table, the variant peptide and variant position in the protein sequence will be colored in red and light green and the Domain region in the protein sequence will be marked in yellow. Specifically, while users put the mouse on the SAP position in protein sequence, a picture of the stick structure of reference and altered Aa will be shown. Users can also get the PPIs derived from BioGRID database (3.4.138)  in the ‘Post-translational modifications on amino acid residues may result in mass shift to the peptide spectra, such as Asn to Asp. We also collected those spectra and showed them in ‘PTM Page’. Users can view the potential PTM peptide and spectra by clicking the ‘Wild-type Peptide’ and ‘Variation Peptide’.Database searching is the most widely used method for protein identification, but has suffered from ‘the streetlight effect’ that only those peptides contained in the reference database are detectable. This effect may result in missing the meaningful information between SAPs and diseases, and hindering the understanding of the functional consequences of those protein-coding variants. However, the application of shotgun proteomics to identify the variant proteins remains a big challenge. Protein variants derived from nsSNPs and mutations largely depend on the known genomic variants, especially when the identified genomic variants and proteomics experiments are not matched.To facilitate the functional study of variant proteins, we constructed dbSAP based on the large amount of MS data and a comprehensive set of human nsSNPs and mutations integrated from eight distinct databases. To better assess FDR, we introduced a strict QC workflow including global and group FDR strategies and we also reassembled the protein from peptides through QC criterion using a python script. Importantly, the associations between variant peptides and diseases were established according to the known associations between genes and diseases derived from OMIM, PMD and HPMD, which enables us to better understand the function of variant proteins. dbSAP enables related research communities to easily use the comprehensive reference dataset of variant proteins to conduct corresponding analyses.We will continue to update dbSAP by collecting more MS data and SNPs/mutations to identify more SAPs. One of the important MS data resources is from the China Human Proteome Project (CNHPP) we are participating in ,36, whic


Accurate prediction of cancer prognosis based on gene expression data is generally difficult, and identifying robust prognostic markers for cancer remains a challenging problem. Recent studies have shown that modular markers, such as pathway markers and subnetwork markers, can provide better snapshots of the underlying biological mechanisms by incorporating additional biological information, thereby leading to more accurate cancer classification.In this paper, we propose a novel method for simultaneously identifying robust synergistic subnetwork markers that can accurately predict cancer prognosis. The proposed method utilizes an efficient message-passing algorithm called affinity propagation, based on which we identify groups – or subnetworks – of discriminative and synergistic genes, whose protein products are closely located in the protein-protein interaction (PPI) network. Unlike other existing subnetwork marker identification methods, our proposed method can simultaneously identify multiple nonoverlapping subnetwork markers that can synergistically predict cancer prognosis.Evaluation results based on multiple breast cancer datasets demonstrate that the proposed message-passing approach can identify robust subnetwork markers in the human PPI network, which have higher discriminative power and better reproducibility compared to those identified by previous methods. The identified subnetwork makers can lead to better cancer classifiers with improved overall performance and consistency across independent cancer datasets.The online version of this article (doi:10.1186/s13637-014-0019-9) contains supplementary material, which is available to authorized users. The utilization of modular markers allows us to interpret and analyze the gene expression data in a more system-oriented way, which may facilitate the prediction of system-level properties based on the markers.Identifying disease-related biological markers is an important problem in translational genomics, and there have been significant research efforts to find robust markers for disease diagnosis and prognosis from gene expression data obtained from microarrays or next-generation sequencing (NGS). However, the small sample size and the high dimensionality of the typical genomic data makes the prediction of such biomarkers very challenging. A large number of approaches have been proposed so far to deal with these issues, where it has been recently shown that the concept of ‘modular markers’ have potentials for detecting better disease markers that are more robust and reproducible across independent datasets. In the past, it has been a common practice to look for the so-called ‘key genes’ that show significant differential expression under different conditions or between distinct phenotypes to discover gene markers that may be used for discriminating between different classes of biological/clinical samples. Unlike these traditional gene markers, where each gene is viewed as a potential biomarker, a Examples of such modular markers include the pathway markers -5] and  and 5] aThe concept of subnetwork markers has been originally proposed to address the weakness of pathway markers ,8]. The. The8]. z-score of the member genes and the class label. More recently, Su et al.  have havez-scIn this paper, we propose a novel method for identifying effective subnetwork markers for predicting cancer prognosis. The proposed method is based on an efficient message-passing algorithm, called affinity propagation, which can be used to efficiently identify clusters of discriminative and synergistic genes whose protein products are either connected or closely located in the PPI network. Unlike previous subnetwork marker identification methods, the proposed method can simultaneously predict multiple subnetwork markers, which are mutually exclusive and have the potential to accurately predict cancer prognosis in a synergistic manner. Based on several independent breast cancer datasets, we demonstrate that the proposed method can identify better prognostic markers that have improved reproducibility and higher discriminative power compared to the markers identified by previous methods.We obtained four independent breast cancer microarray gene expression datasets from previous studies, which we refer to as the USA dataset (GEO:GSE2034) , Netherlaffinity propagation is used to control the penalty term. According to the above definition, gene gi regards gene gk as being ‘similar’ to itself: gk has high discriminative power (first term);if if combining the two genes increases the overall discriminative power;if both genes have similar discriminative power.As shown in , the sims is to indicate the direction of similarity. Based on our asymmetric definition, the exemplars of the identified clusters tend to have higher discriminative power compared to other non-exemplars. Intuitively, the gene similarity defined in +1⌋. After clustering, the genes in the group with the lowest average t-test score were removed from the subnetwork.Although the affinity propagation algorithm can effectively identify subnetworks that consist of discriminative and synergistic genes, the clustering process does not completely rule genes with relatively lower discriminative power out of those subnetworks. As a result, the initial subnetworks that are predicted by affinity propagation may still contain genes with relatively lower discriminative power compared to other genes in the same subnetwork. In order to improve the overall discriminative power of the potential subnetwork markers, we post-processed the initial subnetworks as follows. First, we clustered the genes in a given subnetwork into n member genes x={x1,x2,…,xn} of a patient, where xi is the expression level of the gene gi, the activity level of the subnetwork is computed by: For estimating the activity level of a subnetwork based on the gene expression profile of a patient, we adopted the probabilistic pathway activity inference method introduced in . Given aλi(xi) is the log-likelihood ratio between the two class labels . This is given by where xi under phenotype j. We assume that the gene expression level of gi under phenotype j follows a Gaussian distribution.where t-test statistics score of the subnetwork activity. Three different values of α  were used in our experiments to investigate the effect of the penalty term in  which is the same as in . For. For4]. K, and the vertical axis corresponds to the mean absolute t-test score of the top K subnetwork markers. We compared the discriminative power of the subnetwork markers predicted by the proposed method with the discriminative power of the subnetworks predicted by the greedy method proposed in ..7].Considering that our ultimate goal is to identify effective subnetwork markers that can be used for building robust classifiers that can accurately predict breast cancer prognosis, it is important to verify whether the predicted markers can actually lead to better classifiers whose performance can be reproduced on independent datasets. For this purpose, we performed the following cross-dataset experiments.First of all, we selected one of the four breast cancer datasets just for identifying the potential subnetwork markers and selecting the optimal feature set . To select the optimal set of features, we randomly divided the chosen dataset into three folds, where two folds  were used for evaluating the discriminative power of the subnetwork markers and the remaining one fold (feature-selection set) was used for selecting the features to be used in the classifier. We used the entire set for estimating the class conditional probability density functions that are needed for the pathway activity inference .t-test score in a descending order. Initially, we built a classifier based on linear discriminant analysis (LDA), where only the top subnetwork marker was included in the feature set. The classifier was trained on the marker-evaluation set, and its classification performance was assessed by measuring the area under ROC curve (AUC) on the feature-selection set. Subsequently, we added the next best subnetwork marker to the feature set, re-trained and re-evaluated the classifier, and kept the subnetwork marker only if the AUC increased. We repeated this process for the top 50 subnetwork markers.We evaluated the discriminative power of all potential subnetwork markers based on the marker-evaluation set, selected the top 50 markers, and sorted them according to their absolute Next, we chose a different dataset to train an LDA classifier (using the markers selected from the first dataset) and evaluate its performance. For this, the second dataset was randomly divided into five folds, where four folds were used for training (without reselecting the features) and the rest was used for computing the AUC. The entire process was repeated for 100 random partitions, and we report the average AUC as the performance measure. Similar experiments have been performed to evaluate the classification performance of previous methods, including the greedy subnetwork marker identification method  as well Finally, we also performed within-dataset experiments to investigate the performance of the proposed method and compare it with previous subnetwork and pathway-based methods. In these experiments, the classifiers were trained and evaluated on different folds of the same dataset, where a similar five-fold cross-validation set-up was used as before. We first selected a dataset and then randomly divided it into five folds. Four out of the five folds were used as a training set for building the classifier. The remaining one fold was used as a test set for evaluating the classification performance. The subnetwork markers were identified using the entire dataset, and not just the four fold training set, due to the high computational burden for re-identifying the subnetwork markers every time for a large number of random partitions. The results are depicted in Additional file In this paper, we proposed a novel method for identifying robust and synergistic subnetwork markers that can be used to accurately predict breast cancer prognosis. Our proposed method utilizes an efficient message-passing algorithm called affinity propagation  to identSupplementary material. This supplement contains figures for additional experimental results. Figure S1. shows the discriminative power of the subnetwork markers identified by the proposed method with and without the post-processing step. Figure S2. contains charts showing the discriminative power of the subnetwork and pathway markers across different datasets. Figure S3. shows the classification results of the within-dataset experiments. (PDF 999 KB)Additional file 1:Authors’ original file for figure 1Authors’ original file for figure 2Authors’ original file for figure 3Authors’ original file for figure 4Authors’ original file for figure 5Authors’ original file for figure 6Authors’ original file for figure 7Below are the links to the authors’ original submitted files for images.


S. cerevisiae with no functional interaction reported, denoted as non-interacting pairs, and compared their 3D structures to available experimental complexes. We identified some non-interacting pairs with significant structural similarity with experimental complexes, indicating that, even though they do not form functional interactions, they have compatible structures. We estimate that up to 8.7% of non-interacting protein pairs could have compatible structures. This number of interactions exceeds the number of functional interactions  by a factor 40. Network analysis suggests that the interactions formed by non-interacting pairs with compatible structures could be particularly hazardous to the protein-protein interaction network. From a structural point of view, these interactions display no aberrant structural characteristics, and are even predicted as relatively stable and enriched in potential physical interactors, suggesting a major role of regulation to prevent them.The vast majority of proteins do not form functional interactions in physiological conditions. We have considered several sets of protein pairs from  Saccharomyces cerevisiae, is remarkably sparse: about 0.2% of the possible protein-protein interactions actually occur, with an estimation of 37,600 direct interactions between about 6000 proteinsProteins do not act alone, but in interaction with other macromolecules: DNA, membranes and other proteins. At the genomic scale, the collection of all protein-protein interactions of an organism, its interactome, can be modeled as a network that provides a framework to understand, interpret, and question the biology of this organism12via charge complementarity, hydrophobic patches, hydrogen bonds and salt bridges56789What about the 99.8% pair interactions that do not take place? How are they prevented in the dense milieu of a living cell? The fate of proteins is indeed regulated at many levels: expression, translation, and localization of proteins are controlled, and post-translational modifications of the proteins modulate their interactions. In addition, the physical interaction between two proteins relies on their structural properties: shape complementarity and favorable atomic contacts Logically then, 3D structures have been exploited as a source of information for the prediction of protein-protein interactions1112131415161718192021Aloy and Russell1115161715per se, still relies on homology, since they use interface similarity or conservation measures. In parallel with these explicit comparisons of candidate proteins to known complexes, one machine learning method has been proposed so far, in which the trends of known complexes are used to train a support vector machine classifierIn the absence of detectable homology, interaction can be inferred from the comparison with experimental complexes at the structural level, as in the PrePPI algorithm141927ab initio docking to generate interaction models, based solely on the 3D structures of the potential partners202122ab initio docking algorithms always generate acceptable models, as judged by their scoring functions.Until now, few methods have tackled the challenge of interaction prediction using intrinsic structural properties, without comparison with available complexes. Different groups used versus regulation of protein fate in the existence or absence of protein-protein interactions in vivo? To answer this question, we focused on non-interacting proteins, i.e., protein pairs for which no functional interaction is reported, and their 3D structures, and we quantified how frequently they can be mistaken for interacting proteins, by comparison with experimental structures.There is no unanimity at the end, on the decisive contribution of 3D structures in the prediction task. A majority of methods use them in the context of homology, and supplement the structural information with other sources of functional information, which are proxies of regulation. So what is the weight of intrinsic structural properties S cerevisiae might be similar to interacting pairs, a deluge of potential interactions in comparison with the less than 1% of known functional ones. The non-interacting pairs that are similar to experimental complexes turn out to be significant destabilizers of the native protein-protein interaction network, being more central than other non-interacting pairs. Interestingly, we found no intrinsic structural determinant that could distinguish between interacting and non-interacting pairs, even with a sophisticated physics-based method of binding affinity prediction. Two orthogonal approaches, based on threading scores and interologue detection, allow us to estimate that 9 to 50% of non-interacting pairs that are similar to interacting pairs could interact in vitro. On the practical side, we conclude that current tools cannot distinguish low-resolution models of interacting from non-interacting protein pairs. On the theoretical side, we propose that this lack of distinction reflects a real resemblance between interacting and some non-interacting pairs, implying a major role of biological regulation to repress deleterious interactions between proteins with compatible structures.Using global structure comparison, we found that up to 8.7% of non-interacting protein pairs in S. cerevisiae to a set of experimental dimers from the PDBstructural precedent”. Protein interactions happen through interfaces with specific properties, which are not taken into account by the global structural comparison. Additional criteria are thus used to ensure the existence of a potential interface between monomers (see Methods). When a structural precedent is found, it can be used to derive an “interaction model,” by superimposition of the monomers onto the structural precedent. We use the interaction models thus formed to perform additional tests on the interface properties, namely conventional structural features, affinity prediction and contact conservation.In this study, we compare the 3D structures of non-interacting protein pairs of Here, we perform the structural comparison in order to evaluate the extent of the similarity between interacting and non-interacting protein pairs and to assess the weight of the structural information in the prediction task. We do not imply that the existence of a structural precedent indicates an interaction. It only indicates a global structural compatibility between proteins.To evaluate the current coverage of the protein-protein interaction network by the set of experimental dimers, we did the same comparison using datasets of reported interactions. Note that, in predictive studies, it is customary to remove homologous proteins from the reference set in order to test the performance of the methodA random sampling among unobserved interactionsA balanced sampling among unobserved interactions, which takes into account the number of connections of the proteins in the native networkProteins with different subcellular localization,Pairs with the most dissimilar functional annotationsPairs selected after two-hybrid data post-processing, in order to reduce the false negative rateThere is no consensus yet on a gold standard for non-interacting datasets in the literature, but rather several alternatives based on different selection strategies323334353637For the interacting proteins, we considered four different datasets: one set of high confidence physical interactions (positive BRS) from BioGridpositive KUPS”, and the “negative GO” datasets. Note that they were retrieved from the same resourceFor each pair of proteins from our different interaction data sets, 3D models of monomers are used as input to identify structural precedents in the reference set of experimental dimers. The identification is thus contingent on the availability of 3D models. The fraction of proteins with available 3D models is similar across all the data sets: around 76% for single proteins and 60% for protein pairs see . Two datThe structural similarity between 3D models and experimental structures is assessed by the TM-scoreThe threshold value has a clear impact on the rate of detection of structural precedents. Initially, the value of 0.5 was proposed to detect proteins with identical fold42versus 2% to 3% for non-interacting pairs. In the rest of the study we will use the 0.6 threshold.Here, a threshold of 0.4 yields rates of structural precedents in overlapping ranges for non-interacting and interacting proteins: 40% to 50% for non-interacting pairs and 45 to 65% for interacting pairs. This means that roughly half of the protein pairs are similar to a known experimental dimer, even when the proteins do not interact. As expected, higher thresholds lead to a decrease in the rate of structural precedents, but also to a clear distinction between non-interacting and interacting pairs. At TMmin threshold equal to 0.6, the rate of structural precedent ranges from 15% to 23% for interacting pairs, positive BRS direct” and the “positive KUPS” datasets both contain only direct interactions and they have consistent rates of structural precedents (23 and 21%). By contrast, the “positive BRS” dataset, which contains direct and indirect interactions, has a lower rate of 17% of structural precedents. However, this factor does not explain why the “positive Ito” dataset, obtained by large-scale yeast-two hybrid, has a lower rate of 15%. This illustrates the recurrent problem of the interaction data qualityInterestingly, the rate of structural precedents is more variable across the datasets of interacting pairs than across the non-interacting pairs. A possible factor is the disparity of experimental techniques. The “We now focus on the numbers obtained at the 0.6 threshold and what they mean. One could object that the low rates of structural precedents in the non-interacting datasets are merely noise due to false negatives . But let us recall that the ratio between effective and potential interactions in yeast is about 0.2% , and even more if we take into account the incompleteness of the PDB. Indeed, using the rate of structural precedents of interacting pairs as a proxy of the PDB completeness (23% in the most optimistic scenario) and the lowest observed rate of structural precedents (2% at least), we obtain 2/23*100 = 8.7% of non-interacting pairs with structural precedents. This is the expected rate if the PDB were complete in terms of interactions . Of course, this estimation is based on our observations for available 3D models, i.e. roughly 60% of interactions. In addition, in doing so, we postulate that the growth of the PDB from 23 to 100% is equally affecting the rate of structural precedents for interacting and non-interacting pairs. The PDB is not an unbiased sample of the protein structural space and one could object that the new structures might be more likely to match the interacting pairs than the non-interacting ones. If that were the case, our current estimation would indeed underestimate the rate of structural precedents of non-interacting pairs.Here, we have found that a substantial fraction of non-interacting protein pairs indeed resemble interacting proteins. What would be the biological impact of these extra interactions if they were to occur? In particular are they more dangerous than other interactions? To answer these questions, we analyzed the localization of the non-interacting pairs in the native protein-protein interaction network. The structure of the network is here used as a proxy of the biological system integrity. Each non-interacting pair was added separately to the native network, and the centrality of the extra edge was measured by its betweenness value, which is the count of the shortest paths in the graph that go through that edge1positive BRS” dataset. Protein-protein interaction networks typically contain permanent but also transient interactions that are difficult to detect. In our case, the ‘positive BRS’ dataset gathers interactions detected by several methods. Notably, one third of the 5621 interactions are detected by yeast-two hybrid, which is well-suited to detect transient interactions“negative balanced” dataset, restricted to the 5128 interactions between the proteins present in the native network. The enrichment in bottleneck edges is expressed by the log-odd score of the observed versus expected number of bottlenecks in each category. As shown in As a physiological network, we considered the network formed by the 5621 interactions from the “Our results thus show that non-interacting pairs with structural precedents correspond to key extra connections in the network. They are more central than other non-interacting pairs, meaning that they create more shortcuts when added to the native network. This suggests that they would have more dramatic biological consequences.We also analyzed the functional annotations of the proteins involved in pairs with high TM scores (>0.6). The following annotations were over-represented for non-interacting pairs but not for interacting pairs: “biological regulation” , “regulation of cellular process” , “signal transduction” , “intracellular signaling pathway” , “signal transmission” , “regulation of biological process” , “signaling process” , “signaling pathway”  and “signaling” . These pairs are thus enriched in signaling proteins.via low protein abundance. Let us note that the control of protein abundance is one mechanism of control among several others. The global regulation results from the juxtaposition of these different mechanisms. The relative contribution of each mechanism is unknown, and probably difficult to assess. It is likely that protein abundance alone has a mild effect, as suggested by the moderate correlation between protein abundance and propensity for promiscuous interactions found by othersWe analyzed the abundance of the proteins involved in non-interacting and interacting pairs, on the dataset used for the network analysis. We found that negative pairs are slightly but significantly depleted in highly abundant proteins compared to positive pairs see . However“BRS direct” and “KUPS” datasets, and 118 models of non-interacting pairs from the union of all negative datasets  are predicted as potential physical interactors by threading scores and network analysis.This estimate should, of course, be considered with caution, as any estimate obtained by indirect, predictive methods. The ultimate confirmation of physical interaction would require the use of experimental methods like isothermal titration calorimetry (ITC). The most probable scenario is that only a fraction of the potential physical interactors identified here would indeed produce a measurable interaction using ITC. However, we want to emphasize that, more important than the estimate fraction itself, we observed an enrichment of potential physical interactors in non-interacting pairs with structural support compared to non-interacting pairs without support. Altogether, our findings support the view of protein-protein interaction as a property to repress.Saccharomyces cerevisiae. The different datasets of interacting and non-interacting protein pairs are presented in http://www.uniprot.org/uniprot.All the data used in this study are for the model organism et al. studyhttp://fbs3pcu112.leeds.ac.uk/BRS-nonint/PPI_RandomBalance.html:Positive BRS: high confidence physical interactions, obtained after filtering out the “HC-BIOGRID-2.0.31.tab” dataset to remove proteins without PFAM domains. The experimental methods supporting each interaction were retrieved from BIOGRID.Negative random: random sampling of non-interacting pairs within the “positive BRS” list.Negative balanced: sampling of non-interacting pairs within the “positive BRS” list, while preserving the number of connections of each gene.We derived two additional datasets:Positive BRS-direct: by filtering the “Positive BRS” list using BioGrid dataNegative non-colocalized: by filtering the “Negative random” list using Uniprot annotationsThree data sets from the Yu http://www.ittc.ku.edu/chenlab/kups/), using the following input parameters: size of the data set = 1500, species = Saccharomyces cerevisiae, for the positive set: interactions = direct interaction, detection methods = all, for the negative set: interactions = all, detection methods = all, restricted = yes, selection strategy = functionally dissimilar pairs:Positive KUPS: by removing self-interactions from the resulting positive list.Negative GO: by removing self-interactions from the resulting negative list. These pairs are chosen in order to minimize functional similarity between proteins using Gene Ontology annotations (GO)et al.Two data sets were derived from the study by Trabuco Positive Ito-core: we retrieved the interaction data described in ref. http://www.ebi.ac.uk/intact/). Only interactions annotated as “core” (high confidence pairs with interaction observed at least three times in the experiment) were retained; self-interactions were removed.Negative FNR reduction: we retrieved an initial list of 214,696 pairs constructed by Trabuco et al.http://www.russelllab.org/negatives/. This list contains interactions obtained after post-processing of the Ito core data set, in order to reduce the false negative rate (FNR). Interactions in this set were likely tested . As described in ref. Two datasets were retrieved from the KUPS databasehttp://pax-db.org/). We considered the ‘integrated’ values, which are the weighted averages over several dataset. Thanks to abundance values, we defined three abundance categories: ‘low abundance’ for proteins annotated as bottom 25% or less by PaxDb, ‘high abundance’ for proteins annotated as top 25% or more, ‘medium abundance’ for the others. We then defined abundance for a pair of proteins as the lowest abundance in the pair. The rationale is that the low abundance of one protein is a limiting factor for the formation of an interaction.Protein abundance values were retrieved from the PaxDb4 database (modbase.compbio.ucsf.edu). Some ORFs have several 3D models. Models covering less than 40% of the full length of the target proteins were excluded.Pre-computed homology 3D models of yeast ORFs were retrieved from ModBase  to remove crystallographic interfaces. Some of these dimers are parts of higher order complexes; all are classified as homo/heterodimers and as obligate/non-obligate complexes by the InterEvol resourceWe derived a non-redundant list of 12,379 PDB dimers from the InterEvol resourceversus X and B versus Y . In case of sufficient similarity (TMmin higher than a given cut-off), the PDB dimer X/Y is termed the structural precedent of the pair. The interaction model produced by superimposition of the yeast models onto the structural precedent must have an interface of at least 20 residues (at the 5 Å cutoff) and less than three clashes between Cαs. If several 3D models are available for a candidate protein, we considered every possible pair of models, ranked them by decreasing TMmin value, and retained the first case with a valid interface.The similarity between 3D models of yeast proteins and PDB dimers was measured using the TM-score, which ranges from 0 (no similarity) to 1 (perfect similarity) using TM-alignWe used the igraph R packageThe over-representation of functional annotations for a set of proteins was assessed using the BINGO plugin for CytoscapeConventional structural descriptors of protein-protein interfaces were computed for the interaction models: interface size (number of residues below a 5Å distance threshold), interface hydrophobicity  from the total energy of the individual partners in their bound conformations (EA + EB), and scaled as follows:The binding affinity of the interaction models was predicted with the PaLaCe coarse-grained modelThis correction is based on the high correlation  between a representative subset of experimental binding affinitiesInterfaces were compared using the following distance:C1 and MC2 are NxN matrices counting the number of interface contacts (less than 12 Å between Cα) in complexes C1 and C2. We tested various amino-acid groupings and opted for N = 7 groups: {V, I, M, C, L}, {A, S, P, T}, {G}, {Y, F, W}, {K, R, H}, {D, E}, {N, Q}. D is equal to 0 when the interfaces are identical in terms of amino-acid contact types and 1 when they have no contact type in common.where N denotes the number of amino-acid classes and Mhttp://groups.csail.mit.edu/cb/struct2net/webserver/. The Struct2Net approach relies on structure-based threading and is independent of functional genomic information such as co-expression or cellular localization.Pre-computed predictions of interactions for yeast proteins by the Struct2Net algorithmInterologues for protein pairs were retrieved with the following protocol.In a first step, for each protein sequence of the dataset, a maximum of 500 homologues were extracted from the uniprot database (release 2016_08) using the blastpgp softwareIn the second step, protein-protein interaction data for all the proteins (queries and homologs) were obtained from the IntactHow to cite this article: Launay, G. et al. Non-interacting proteins may resemble interacting proteins: prevalence and implications. Sci. Rep.7, 40419; doi: 10.1038/srep40419 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.