Protein-protein interaction networks were used to analyze the functional evolution of duplicated genes in yeast. Pairs of paralogs can be grouped into 3 classes, which likely form part of a continuous scale of diversity. Studying the evolution of the function of duplicated genes usually implies an estimation of the extent of functional conservation/divergence between duplicates from comparison of actual sequences. This only reveals the possible molecular function of genes without taking into account their cellular function(s). We took into consideration this latter dimension of gene function to approach the functional evolution of duplicated genes by analyzing the protein-protein interaction network in which their products are involved. For this, we derived a functional classification of the proteins using PRODISTIN, a bioinformatics method allowing comparison of protein function. Our work focused on the duplicated yeast genes, remnants of an ancient whole-genome duplication.Starting from 4,143 interactions, we analyzed 41 duplicated protein pairs with the PRODISTIN method. We showed that duplicated pairs behaved differently in the classification with respect to their interactors. The different observed behaviors allowed us to propose a functional scale of conservation/divergence for the duplicated genes, based on interaction data. By comparing our results to the functional information carried by GO annotations and sequence comparisons, we showed that the interaction network analysis reveals functional subtleties, which are not discernible by other means. Finally, we interpreted our results in terms of evolutionary scenarios.Our analysis might provide a new way to analyse the functional evolution of duplicated genes and constitutes the first attempt of protein function evolutionary comparisons based on protein-protein interactions. Saccharomyces cerevisae genome, 40% that of Drosophila melanogaster, 50% that of Caenorhabditis elegans, and 38% of the human genome are composed of duplicated genes ,Dist and Terms(j) are the lists of their GO terms and Δ is the symmetrical difference between the two sets. This distance formula increases the weight of the shared GO terms by giving more weight to similarities than to differences. The GOToolBox website can be accessed at [in which, essed at .in vitro binding, far western, gel retardation and biochemical experiments).The protein-protein interaction dataset we investigated contains a total of 4,143 selected interactions involving 2,643 proteins. We updated our former dataset  with 1,2PRODISTIN, a computational method we recently proposed , was usePairwise sequence alignments were carried out on the set of 460 pairs of duplicated protein sequences using the Needleman-Wunsch  algorithm. The program used is available at . The choThe following additional data are available with the online version of this paper. Additional data file The expectation values for the distribution of functional distances based on the GO annotationsClick here for additional data fileDetails of the 123 PRODISTIN classes contained in the classification treeClick here for additional data file


Cellular processes require the interaction of many proteins across several cellular compartments. Determining the collective network of such interactions is an important aspect of understanding the role and regulation of individual proteins. The Gene Ontology (GO) is used by model organism databases and other bioinformatics resources to provide functional annotation of proteins. The annotation process provides a mechanism to document the binding of one protein with another. We have constructed protein interaction networks for mouse proteins utilizing the information encoded in the GO annotations. The work reported here presents a methodology for integrating and visualizing information on protein-protein interactions.GO annotation at Mouse Genome Informatics (MGI) captures 1318 curated, documented interactions. These include 129 binary interactions and 125 interaction involving three or more gene products. Three networks involve over 30 partners, the largest involving 109 proteins. Several tools are available at MGI to visualize and analyze these data.Curators at the MGI database annotate protein-protein interaction data from experimental reports from the literature. Integration of these data with the other types of data curated at MGI places protein binding data into the larger context of mouse biology and facilitates the generation of new biological hypotheses based on physical interactions among gene products. Cellular processes require the interaction of many proteins across several cellular compartments. Interactions can range in stability from persistent, such as between members of a stable complex, to transient, such as binding while being phosphorylated. Determining the collective network of such interactions should provide insight into which processes the individual members participate, and how they may be regulated.Understanding protein interaction networks requires two steps. First, the interacting proteins must be identified, usually through some experimental methods. Secondly, the significance of the interaction networks needs to be assessed. Recently, there has been a focus on devising large scale screening methods to collect data on interacting proteins -3. AdditIn addition to the large screening efforts, a significant amount of specific protein-protein interaction data has been reported in the literature over the years. Quite often, these studies report on only a few interacting proteins. It is difficult to place these isolated, yet specific reports in the larger biological context and interconnect them with other data. Recently, there have been efforts to extract such literature-based interaction information using text mining , or combe.g. [Model Organism Databases (MODs) have been collecting diverse types of data about the genes and proteins from their respective organisms since the early 1990s e.g. -13). The. Thee.g.The Mouse Genome Informatics system (MGI) is the MOD for the laboratory mouse . MGI intThe Gene Ontology Consortium provides the biological community a structured vocabulary with which to enable consistent functional annotation of genes and gene products. . Guidelimurine protein gene product is investigated. Many of the detailed annotations have been added on a paper-by-paper basis using the MGI literature collection that contains primary experimental information about mouse genes from over 90,000 references. The accumulation and use of these papers in annotation has been, for the most part, undirected. However, the structure of the GO and the relationships among terms allow grouping of the gene products that share common annotations. Such strategies may reveal hitherto unsuspected relationships between these proteins.In the course of over six years, curators at MGI have made 79690 annotations to 15231 gene products using 3742 GO terms (All database statistics used in this paper are from the MGI release as of 7/30/04). The curation policy focuses on experiments in which the "Protein binding" (GO:0005515), as used by the GO in the Molecular Function ontology, is defined as "interacting selectively with any protein or protein complex" . This teAger. In the case of Ager . A curator at MGI has captured this information in an MGI GO annotation for Ager. For completeness, a curator also annotated the gene product of Hmgb1 with "protein binding" with an IPI to SPTR:Q62151, the protein product of Ager, using the same reference. In this case, these are the only "protein binding" annotations for either of these proteins. These annotations represent an experimentally tested interaction of two proteins.An example of GO annotation that includes "protein-binding" is shown for the gene product of e et al.  have demHcph (hemopoietic cell phosphatase), has been shown to bind both the protein product of Jak2 (Janus kinase 2)   and Klrnase 2) . JAK2 nnase 2) , but alnase 2)  . KLRB1Bnase 2) , which nase 2)  of the gene product or the process it is involved in . A simple example is seen in the case of TIPIN (timeless interacting protein) Figure . It has ila gene . Howeverogenesis , and thunificant . These iTimeless .Cellular location may also be inferred from protein interactions. SOCS1  has "kinase inhibitor activity" (GO:0019210) and has been implemented in the "cytokine and chemokine mediated signaling pathway" (GO:0019221), and the JAK-STAT cascade (GO:0007259). However, its cellular location has not been documented in the available mouse literature. Analysis of the SOCS1 protein using predictive software such as Psort ) and SubThree networks involve over 30 partners, the largest involving 109 proteins Figure . Can we For the set of 109 proteins shown in figure Tools such as GO_Term_Finder  and its Finally, twelve of the proteins displayed in Figure The networks suggested by the collection of annotations to this GO term involve interactions that are more or less stable under experimental conditions. A gene product is shown to have protein binding activity by a variety of direct assays such as yeast two-hybrid screening , co-immuThe Mouse Genome Informatics system integrates not only data used for GO annotation, but also data on a variety of aspects of mouse biology including embryonic gene expression, alleles and their phenotypes, and chromosome location. The integration of these datasets allows for complex queries, such as "list all genes expressed in the liver at Tyler Stage 15, located on chromosome 12, annotated to "protein binding" AND "nucleus". The integration of protein-protein network visualization into such queries can aide in determining the significance of more complex interaction networks. By combining the above query with our graphical tools, it is possible to get a graphical view of all protein interaction networks in the nucleus of a 9.5 dpc mouse embryo. As annotation progresses and becomes more complete, these types of queries will become more and more informative.During the generation of the interaction sets, it was found that programs such as Graphviz, could easily visualize missing annotations based on the interaction of two proteins. When information about a protein comes from different sources, a curator that is curating a single reference may not necessarily record all of the information implied by a physical interaction, such as cellular location in the example above. Views such as Graphviz can help curators to spot missing data and they may at some point be useful in themselves to display annotations.Drosophila, and C. elegans interaction sets [MGI curators aggressively adopted the use of the "with" field when annotating to "protein binding" during the early stages of annotation efforts at the database. Similar networks may also be mined from the GO data sets available from the other model organism databases participating in the GO. Recently, Lehner and Fraser used GO annotation to analyze a human interaction set predicted from orthology to yeast, ion sets . The GO ion sets  and HPRDion sets  to guideWe have demonstrated that functional annotations curated via GO hierarchies can be used to obtain a summary set from independent annotations to "protein-binding" to form protein-protein interaction networks. The members of these protein-protein interaction sets can be further examined for additional shared GO annotations. Integration of these data with the other types of data curated at MGI places protein binding data into the larger context of mouse biology and will aid in the discovery of new biological knowledge based on physical interactions among gene products.Gene annotations for protein binding interactions are made by manual inspection of published literature. In every case, experimental evidence is supplied in the manuscript to support the interaction that is reported. Annotation of genes to other GO terms is made by a variety of methods including the conservative translation of functional information contained in SwissProt protein records, conservative inference from InterPro domains, and manual curation of the published literature.Data was obtained from the Mouse Genome Informatics system by use of custom SQL queries to collect all markers that had been annotated to "protein binding" or its children using the IPI evidence code. The protein sequence identifier in the "inferred from field" was matched to the appropriate gene in the database. The final output consisted of a two-column file with column 1 being the first protein, and column 2 the protein it binds. This formed the basic data set that was passed to Graphviz  for disp.The two column lists were also used as the basis for data files listing all unique genes in each network. These were then used for input files for GO_Slim Tool  and GO_TGraphViz on the Macintosh OS X platform is a product of Pixelglow . GraphViHJD conceived of and designed this study and implemented the graphical displays using Graphviz and analyzed the significance of the results. He has also been involved in contributing to the GO annotations. CH devised the Perl scripts used to parse out individual interaction sets and removal of redundancies. DPH helped to draft the manuscript and contributed to the annotations. JAB has been involved in the overall design of the GO project at MGI and of gene annotations in MGI overall, and has added critical revisions for important intellectual content to this paper.


It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity.In this paper we show that ProtoNet correctly captures functional and structural aspects of the protein world. Furthermore, a novel feature is an automatic procedure that reduces the tree to 12% its original size. This procedure utilizes only parameters intrinsic to the clustering process. Despite the substantial reduction in size, the system's predictive power concerning biological functions is hardly affected. We then carry out an automatic comparison with existing functional protein annotations. Consequently, 78% of the clusters in the compressed tree  get assigned a biological function with a high confidence. The clustering and compression processes are unsupervised, and robust.We present an automatically generated unbiased method that provides a hierarchical classification of all currently known proteins. The explosive growth in the number of sequenced proteins has created a glut of proteins that are sequenced but whose structure and function are as yet unknown. A common way to tackle this problem is to use database searches to find proteins similar to a newly discovered protein, thus inferring protein function. This method is generalized by protein clustering or classification where databases of proteins are organized into groups or families in a manner that attempts to capture protein similarity. Such classification into families is a critical component in structural and functional genomics -4. The nClassifying the entire protein space into families serves not only as a method for large-scale protein annotations but also to support functional and structural genomic initiatives . Some pr. For the SwissProt-based tree, there are 227,436 clusters (including the proteins as singletons). The classification provided by ProtoNet provides the full range of cluster granularity: from single proteins to huge protein clusters that carry no biological relevance (the root clusters). We test the biological validity of ProtoNet, by its examination from different perspectives, using external-defined protein keyword annotations. Four different annotation sources are used  in ordeAs mentioned, ProtoNet contains 227,436 clusters, which is obviously much more than the upper estimate of 30,000 protein families ,26. TherIn this paper we describe methods for the unsupervised compression of the ProtoNet tree, by using intrinsic tree-based parameters of the clusters that correlate well with biological validity. By preserving the unsupervised nature of the ProtoNet data, we prevent biasing towards previously discovered findings and better allow for future generalizations, in addition to maintaining the automation of the process.Finally, automatic functional annotation to proteins is of great importance. In ProtoNet, an automatic method for assignment of biological annotation to the protein clusters is used, yielding high-confidence functional assignments for a large majority of the proteins' clusters.In order to measure the correspondence between a given cluster and a specific annotation, and allow for supervised validation of the ProtoNet clusters, we define the notion of a correspondence score (CS). The CS for a certain cluster and a given keyword measures the correlation between the cluster and the keyword, using the well-known intersect-union ratio.c be the set of annotated proteins in cluster C; Let k be the set of proteins in the system annotated by keyword K; We define:Let C be a cluster in the ProtoNet tree, and K be a keyword (from a specific source) that annotates (some of) the proteins in the system; Let best cluster). The score for a given cluster on keyword K ranges from 0 (no correspondence) to 1 .The cluster receiving the maximal score for keyword K is considered the cluster that best represents K within the ProtoNet tree , GO (Molecular Function) and ENZYME . The results Table  show a h-100). Note that even for the SCOP fold level, which is associated with proteins that may be extremely remote in sequence, ProtoNet's relative success is extremely high .To avoid trivial correspondences between a keyword and a cluster, such as the assignment of a keyword that annotates only one protein to its singleton cluster, we tested our success only with keywords that annotated at least two proteins (for SCOP and ENZYME keywords). For InterPro and GO, we selected a threshold of 20 proteins per keyword, as the majority  of the annotations is included above this threshold, thus allowing the test to focus on the more significant keywords.In order to validate the hierarchical structure of ProtoNet, we compare it with the hierarchical structure of GO as described in Figure A total of 1577 GO terms were selected as described, with 1798 edges between them according to the GO hierarchy. 771 out of 1291 (60%) edges that were produced by the ProtoNet hierarchy appear in the GO hierarchy. This number is highly significant considering the fact that there exist over 1,200,000 possible edges between the 1577 vertices in the graph (considering it as a DAG). It should be noted that there are some terms in GO that are connected to many other vertices. These vertices may bias the results of this test. In order to confirm that ProtoNet performs well without these vertices as well, the vertices were removed manually and the test was repeated, with similarly significant results (33% of the edges were correct).In order to allow unsupervised automatic compression of the ProtoNet tree, we searched for an intrinsic parameter of the clustering process that would specify clusters of biological validity. By applying such a parameter one could dispose of clusters that do not pass a certain threshold value, remaining with clusters of high biological validity. Once we remain with a subset of biologically valid clusters, the new hierarchy amongst them can be reconstructed according to the original tree hierarchy.The agglomerative hierarchical clustering scheme defines a set of terms that are intrinsically associated with the process. In such a scheme, each cluster is created from smaller clusters, which are captured as its descendants in the clustering tree. The ProtoLevel (PL) ranges from 0-100 and is used as a standard quantitative measure of the relative height of a cluster in the merging tree. The PL of a cluster is defined as the arithmetic average of the BLAST E-score of the pairs of its proteins. The PL of the leaves of the tree is defined as 0, whereas the PL of a root equals 100. The larger the PL, the later the merging that created the cluster took place. Therefore, the PL scale is considered as an internal monotonic timer of merging, during the clustering process. As mentioned above, a cluster is said to be created when the merging of its two children clusters forms it. The PL at this point is said to be the birthtime of this cluster. The deathtime of a cluster is defined as the PL at its termination, i.e. the point at which it merges into its parent cluster (or 100 if it has no parent). The lifetime (LT) of a cluster is defined as:LT = deathtime - birthtimeTherefore, the LT of a cluster reflects its remoteness from the clusters in its "vicinity" in protein sequence space.We examined the LT distribution of the set of InterPro best clusters in comparison with the LT distribution of all clusters in ProtoNet Figure . The resIn order to search for a reasonable LT threshold value , several threshold values were examined Figure . The resThe mean best CS was examined for all four external sources Table . The resThe following scheme was used to annotate the protein clusters: For each cluster C and keyword K we define the following score:TP is the amount of true positives (proteins in C that have the keyword K), FN is the amount of false negatives (proteins not in C that have the keyword K) and FP is the amount of false positives (proteins in C that do not have the keyword K).Where For each cluster, we search against all keywords of GO and InterPro for the keyword with the highest AS. If the AS of the cluster is greater than 0.25, the cluster is assigned that keyword as its annotation. The logic behind the score and the threshold is as follows: the score is determined by two parameters, the specificity and the sensitivity; let us consider the two worst-case limit cases. In the first case, specificity>0.5 and sensitivity = 1: a majority of the proteins of the cluster share the keyword, and there exist no other known proteins that have the keyword. In the second case, specificity = 1 and sensitivity>0.25: all proteins of the cluster share the keyword and they constitute more than 1/4 of the total proteins known to have this keyword. In both cases, the keyword can be assigned to the protein cluster with a high degree of confidence. All other clusters fall in between these cases.Using this method, all 6,879 clusters that contain 20 or more proteins and that remain after the compression were tested. 5,355 (77.8%) clusters passed the high confidence threshold and were therefore given an annotation. Figure Figure The challenge of protein classification by using sequence similarity has been addressed extensively by several different methods. In order to assign function to proteins, advanced methods (such as Hidden Markov Models implemented in Pfam) have been used to learn sequence-based patterns on "seeds", manually validated alignments of known protein families. The widely-used BLAST algorithm is considered to be a reliable tool for sequence alignment, but has been shown to lack sensitivity for weak similarities that may be detected by signature detection methods. We show here that by using an unsupervised bottom-up clustering method based on BLAST E-values, we have been able to construct a global hierarchy of the SwissProt proteins that can be validated by external biological sources, merely by undertaking a global view of the protein space.The four different external sources that were used for validation reflect different aspects of the protein space: InterPro annotation is predictive and is based on various signature detection methods; GO annotation assignments are both based on prediction and from known research, while the GO hierarchy was constructed completely manually; SCOP is a semi-manual classification of structures that is not necessarily reflected in sequence; the ENZYME database supplies Enzyme Commissions, which constitute a hierarchy that is based on the enzymes' chemical reactions. ProtoNet successfully constructs clusters that correspond highly to all four of the sources. Even high levels of SCOP (such as the Fold classification), which are considered to have no detectable sequence similarity, are partially matched . NotablAfter validating the biological relevance of the ProtoNet clusters by using external sources, we examined the hierarchy of ProtoNet. The test showed that the hierarchy presented by ProtoNet significantly corresponds to the manually-constructed biological hierarchy of GO. It is important to note that the method used by ProtoNet is not expected to fully recapture the GO hierarchy due to the fact that ProtoNet is structured as a collection of trees while GO is structured as a DAG. In this sense, the approach of ProtoNet may be limited in the portrayal of evolutionary complexity (as in cases of multiple domains). However by using a domain-based clustering approach, allowing multiple entities of each protein in the hierarchy, a substantial improvement in the CS quality measure may be achieved (unpublished results).An intrinsic parameter that reflects the stability of clusters during the clustering process was used in order to compress the cluster sets, leaving 16.4% of the clusters; removing the singletons clusters as well leaves 12.2% of the clusters. As mentioned above (see Methods), the entire ProtoNet scaffold contains 227,436 clusters that are represented by 630 roots; following this condensation, there are only 27,823 clusters that are represented by 2,236 roots. We show that although a massive portion of the clusters is discarded, very little performance is lost by this condensation process. It is obvious that prior to the condensation process, ProtoNet holds within it both clusters that correctly represent biological groups and clusters that are irrelevant artifacts of the clustering process (e.g. the large root clusters that are constituted of tens of thousands of proteins). Therefore, by allowing a major reduction without significant loss of biological coherence ProtoNet seems to present a more correct view of the protein world.An automatic unsupervised method for the classification of proteins has some important advantages over supervised methods : First, an unsupervised method is unbiased in automatic assignment of function to proteins, a major goal in bioinformatics. Also, it allows high-throughput analysis of whole genomes and enhances understanding of global biological systems without the need for the manual annotation of every protein. Using an automatic annotation method, we are able to successfully annotate 77.8% of the major protein clusters (of size 20 or more) that remain after the compression of the ProtoNet tree. The annotation uses a relatively conservative threshold and therefore yields high-confidence annotations. This further suggests that the clusters remaining after the condensation process are relevant biological clusters and not mere artifacts.One aspect that we have rigorously examined is the robustness of the ProtoNet tree: given a different set of proteins to cluster or a different clustering method, would the resulting tree be significantly different, or are its properties maintained? Interestingly, changing the underlying protein databases , the substitution matrices used for the preliminary BLAST, or the merging strategy  producedProtoNet version 2.4 which was used for the analyses described in this paper is based on classification of the SwissProt database (version 40.28) that contains 114,033 proteins. The entire ProtoNet scaffold contains 227,436 clusters that are contained in 630 trees. Most trees (611) are singletons and only one contains most (>99%) of the proteins. For more details on the construction of the ProtoNet hierarchy see . ProtoNeSeveral external sources were used as a biological reference for validation of the ProtoNet tree: InterPro  is an exWe have used EBI mappings of InterPro and GO to SwissProt proteins.Annotation Score (AS), Correspondence Score (CS), Directed Acyclic Graph (DAG), Enzyme Commission (EC), Gene Ontology (GO), Lifetime (LT).ML conceived of the compression of the ProtoNet tree. NK, MF and MF participated in implementation of the condensation and in the design and implementation of the various tests. All authors participated in the analysis of the results. All authors read and approved the final manuscript.


Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas.Here we report a new approach, named CLAN, which clusters proteins according to both annotation and sequence similarity. This approach is extremely fast, clustering the complete SwissProt database within minutes. It is also accurate, recovering consistent protein families agreeing on average in more than 97% with sequence-based protein families from Pfam. Discrepancies between sequence- and annotation-based clusters were scrutinized and the reasons reported. We demonstrate examples for each of these cases, and thoroughly discuss an example of a propagated error in SwissProt: a vacuolar ATPase subunit M9.2 erroneously annotated as vacuolar ATP synthase subunit H. CLAN algorithm is available from the authors and the CLAN database is accessible at CLAN creates refined function-and-sequence specific protein families that can be used for identification and annotation of unknown family members. It also allows easy identification of erroneous annotations by spotting inconsistencies between similarities on annotation and sequence levels. To achieve high quality of annotation, curators are using direct evidence from additional experiments or infer functional roles by sequence similarity to experimentally characterized genes or proteins. However, erroneous annotations generated at early stages may propagate to new homologous sequences, ultimately leading to erroneous annotation of entire families. The number of database errors is known to grow with time, as the number of entries and errors tend to accumulate. A recent analysis suggested that as databases grow annotation errors may propagate at an exponential rate .One of the most accurate and consistently annotated databases is SwissProt  – a manuThis impressive collection of textual information provides excellent opportunities for natural language processing in computational biology. Text mining of the free text in biomedical literature is well established  function, according to the functional descriptions of the corresponding database entries. To group pairwise hits between these proteins, clustering of connected components was used, and sequence similarity within the clusters was considered. Using CLAN, an exemplar error was identified in the annotation of the M9.2 (M9.7) subunit of a vacuolar H+ ATPase (V-ATPase).-2 and 10-6. Depending on the threshold, the output contained between 2,070,221 (for e ≤ 10-6) and 3,980,763 (e ≤ 10-2) pairwise annotation similarities. The output of CLAN was subjected to clustering of connected components, resulting in 43,416 (e ≤ 10-2) up to 47,344 (e ≤ 10-4) clusters, including singletons. When a lower score threshold was used, the number of clusters in the output decreased, as more proteins were excluded from any clusters (see below).We used CLAN [see Methods] to cluster SwissProt entries based on annotation strings. We experimented with score cut-offs [see Figure A sample of few hundred of the annotation clusters was examined manually to assess whether they contained proteins annotated as functionally unrelated. At the low score cut-offs, we failed to find any such examples, thus suggesting a high degree of specificity. Also, hypothetical proteins were correctly not grouped together, which reflects their singularity in the protein function space. However, at these cut-off values, proteins annotated with a single word (i.e. Plastocyanin) failed to pass the threshold of meaningful annotation. This effect is due to the fact that no single term has a sufficiently low frequency to provide an score below the threshold. In total, 29,845 entries were not even defined as singletons  as their annotation strings are non-descriptive, resulting in high scores above the threshold.-2, the biggest cluster contains proteins annotated as "Hypothetical X kDa protein in Y intergenic region", where X is the calculated protein molecular weight and Y is the genomic location. These proteins are most probably unrelated, perform different functions or may not be expressed at all, and thus should not be clustered together. In fact, this cluster is eliminated at score cut-off of 10-3.On the other hand, at the highest score threshold the method succeeds in clustering together many single-word annotations. However, this increase in sensitivity is counter-balanced by a decrease in specificity. At the score of 10e ≤ 10-2) to the Pfam-A database [To estimate how well annotation clusters correspond to the sequence-based protein families, we compared the CLAN clusters  family from Pfam is divided by CLAN to distinct annotation families, thus providing a finer distinction between family members that can be used for more accurate annotation of functional specificity [As many of the CLAN clusters which correspond to several Pfam families contain multi-subunit complexes or multidomain proteins, a rigorous domain detection procedure is expected to drastically improve this result. For the results of the combination of CLAN annotation clusters with sequence-based clustering, see the section describing the CLAN database.Though SwissProt annotation is not independent from Pfam assignments and often depend on them, a few clusters were found that did not correspond to Pfam families. Thus, CLAN system might facilitate identification and rapid incorporation of new families to curated protein family databases such as Pfam.We aimed to determine whether similarity of the annotation corresponds to the sequence similarity of the proteins. For this, we compared protein sequences within annotation clusters using BlastP program . OverallAnalysis of sequence similarity within annotation-based families identified the following exceptions to the consistent similarity patterns between all family members.1. Fragments of proteins. The fragments often fail to produce significant Blast scores, thus appearing as singletons on the similarity graph.2. Taxa – specific forms of enzymes that form separate protein families, such as Bacterial and Archaeal adenylate kinases, forming distinctive sequence-based families.3. Non-orthologous gene displacements, such as malate dehydrogenases.4. Protein complexes. Annotation – derived families often included multiple chains/subunits of protein complexes, such as Glutamyl-tRNA(Gln) amidotransferase subunits A, C, D and E.5. False positives of CLAN. The only case found so far involves the 'hypothetical protein' cluster, appearing at the permissive score cut-off 0.01 (see previous section).6. SwissProt annotation errors.Vibrio species documented in SwissProt (LUXE_VIBHA) contains 378 residues. There are at least 71 entries annotated as "fragment", whose sequences are less then 10 residues long. As their sequence often contains a subset from full-length close homologs, the information from these fragments is often redundant. Also, these short fragments fail to produce significant scores in any sequence similarity search procedure, and thus are usually irrelevant for sequence search engines. Moreover, fragments often create noise in sequence alignments and HMM analyses creating pseudo-conserved domains, and pseudo-gaps. In summary, we question the value of the presence of short protein fragments in a manually curated database, aiming for maximum precision rather then maximum coverage.SwissProt contains 7,232 entries annotated as "fragment". These are normally short fragments of larger protein molecules. An extreme example is LUXE_VIBFI (3 residues only), a fragment of Long-chain-fatty-acid-luciferin-component ligase. The complete sequence from a related In some taxa, proteins performing identical functions might diverge to a point where they are defined as separate sequence-based families, sharing marginal similarity. One such example is the bacterial and archaeal adenylate kinases. Though similar at the 3-dimensional level , the seqEnzymes belonging to different structural families may perform identical cellular function. In this case, annotation does not provide enough information about the type of the family a particular enzyme belongs to. One such case is represented by glucokinases, which belong to different families but perform identical function. For example, GNTK_BACSU and GNTK_ECOLI are both glucokinases and are annotated as such, were thus clustered together by CLAN but are unrelated to each other .Subunits in protein complexes often have almost identical annotation. For example, Glutamyl-tRNA(Gln) amidotransferase subunits A, C, D, and E differ only in the chain identifier. The annotation of the subunits is somewhat misleading, as subunits A, B and C form one form of a rather ubiquitous complex , and subFinally, CLAN can serve as a powerful tool to identify annotation errors. Overall, we found SwissProt annotations very consistent and robust. Most of the annotation-based sets resulted in tight sequence-based similarity clusters. A few cases where no similarity was found between a protein and the rest of the cluster were normally due to the limits in sensitivity by BlastP, and the homology was clear when multiple alignments, profile- or HMM-based search methods were used. Nevertheless, there are cases where annotation is problematic. Below, we discuss in detail an example of inconsistency found in the annotation of a cluster containing vacuolar H-ATPase.-6, we found an interesting example of a propagated error. The protein VAOH_CAEEL (Q20591) from Caenorhabditis elegans has been annotated as "Probable vacuolar ATP synthase subunit H (EC 3.6.3.14) (V-ATPase H subunit) (Vacuolar proton pump H subunit)". This protein is clustered by CLAN with other vacuolar ATP synthase subunits. However, BlastP alignment failed to produce evidence of similarity between this protein and other members of the cluster annotated as vacuolar ATP synthase subunit H. Also, this protein is much shorter then other members of the annotation cluster, comprising only of 96 residues, while other members of the cluster have more then 400 residues. In fact, C. elegans was reported to contain another two V-ATPase H subunits, with length of 451 and 470 residues that share substantial sequence similarity with other members of the family. Multiple alignments of the family suggested that VA0H_CAEEL is a distant protein not related to the rest of the family. The SwissProt entry does not contain a link to a published work. A PSI-Blast search against the NCBI NR database allowed to identify other members of the family. The family was first described in the literature as "M9.2 V-ATPase subunit" in bovine [-6, which was unified with other members of "vacuolar ATP synthase subunit" cluster at lower score cut-offs.When analyzing the output of CLAN with score threshold 10n bovine . InteresC. elegans [Mesembryanthemum crystallinum (gi 26986112) [Arabidopsis thaliana, Anopheles gambiae and a few paralogs in Drosophila melanogaster. The multiple alignment of these proteins is provided as supplementary data.The M9.2 V-ATPase subunits were identified in mammals ,12, inse elegans . The alt elegans ,15 and A6986112) . Using PM. sexta protein indicates that the C-terminus is exposed to the extracellular surface, whereas the C-terminus of Vma21p appears to be localized on the cytosolic side of the membrane [The M9.2 protein was suggested to be a homolog  of yeastmembrane . To idenmembrane , and promembrane  search mThe error in the annotation of M9.2 subunit of V-ATPase was reported to the SwissProt developers, and is likely to be corrected at the time of publication of this work.Another form of annotation error results in a false negative case for CLAN. It occurs when a protein clearly belongs to a certain family but is not annotated as such. This event can be detected when several CLAN families map to a single sequence similarity-based family. One example is SUCD_BACSU, annotated as " Succinyl-CoA synthetase alpha chain (EC 6.2.1.5) ". This protein belongs to the SUCD family of proteins, and should cluster with its other members. However, its annotation contains synonyms " (Vegetative protein 239) (VEG239)", which makes this protein virtually unique in annotation space. Another well-known example is the initial characterization of some general stress proteins in bacteria, later shown to be ribosomal proteins   – the in-2. The CLAN database contains two levels of clustering. The first level contains clusters based on comparison of protein annotations, as described in the Methods section. To ensure completeness and reliability of the results, any protein annotated as 'fragment' was excluded from further processing. The second level in the database is generated by applying sequence-similarity-based grouping to each of the clusters obtained in the first stage. All members within annotation clusters are aligned using BlastP. The output of BlastP is subjected to various clustering procedures. In order to distinguish between the annotation-based clusters of the first database level, and the sequence-based sub-clusters of the second level, we call these 'clusters' and 'sub-clusters' accordingly. When referring to both these categories simultaneously, we use the notation (sub)clusters.Based on the results described above, we designed a database using the CLAN output with score threshold of 10To estimate the contribution from the second level of clustering, we repeated the comparison to Pfam database, using sub-clusters instead of clusters. We found that on average 97% of CLAN sub-clusters members belong to a single Pfam family, (compared to the 91% for clusters). The gain in selectivity was accompanied by gain in sensitivity, and 66% of Pfam family members corresponded to a single sub-cluster (compared to 51% for clusters). Compared to annotation-based clustering alone, the dramatic increase in the sensitivity reflects the fact that in the second level of clustering, non-homologous sequences are removed and form separate sub-clusters.. To facilitate database navigation, we introduced (sub)cluster names, computed as a minimal consensus string for all (sub)cluster members. The MySQL-based storage of data allows fast searches based on sequence or cluster name, SwissProt accession or cluster identifier. The sequence similarity within clusters can be examined visually using the BioLayout software [The CLAN database has a web-based interactive interface, located at software . To facisoftware , and theWe have presented CLAN, a rapid and powerful method to build consistent annotation-based families, and assessed the consistency of annotation in protein databases. We have also presented the CLAN database, as a collection of protein families grouped by function. We believe that these tools can be used to improve the quality of database curation and assist annotation of newly sequenced proteins.The double-step clustering presented here has resulted in the creation of a reliable database of protein families, with include proteins similar on both annotation and sequence levels. One of the potential uses of this database is an accurate assignment of function to newly sequenced proteins by protein – to – profile alignments or HMMs. Our method has the potential to distinguish between subtle changes that might reflect diversification of protein function. For example, homologous Malate and Lactate dehydrogenases are found in different (sub)clusters, allowing their characterization as separate protein families. As most protein family databases group proteins by homology, as opposed to function, to the best of our knowledge, our approach is unique in its ability to build protein sequence families distinguishing between functions. In the future, other such comparisons could include other structural classifications such as CATH  or TRIBEWe used SwissProt release of Feb 13, 2003 that contained 121,744 protein sequences. We aimed to find the similarity between the database entries solely based on the annotation and without any reference to sequence similarity. The annotation corpus considered here consisted of description line (protein name) and synonyms from the SwissProt database. We interchangeably use the term 'function' or 'annotation' to denote the contents of this corpus, implying the use of the corresponding annotation strings from the database.φ of each word as the number of database entries containing the word N divided by the total number of entries in the database D (Equation 1). The word frequencies served as weights, because the words describing specific functions (such as "plastocyanin") occur less frequently than non-specific words (such as "protein").To obtain a distance measure, we first calculated the frequency p between protein functions may be computed as the product of frequencies of n terms shared between the two annotations (Equation 2).We aimed to devise a scoring scheme that would produce highly significant scores for proteins with related function, while being able to distinguish between proteins with different functions. The score for the common terms However, sharing only part of the total number of significant words is insufficient: proteins may use similar substrates for different reactions, or perform similar chemical reactions on different substrates. There may be three proteins with annotations AB, BC, and CD respectively, and clustering by similarity only could lead to clustering together AB and CD. For example, "lactate dehydrogenase" should be differentiated from "lactate permease" or "alcohol dehydrogenase".q is the product of frequencies of m terms unique to any of the two annotations (Equation 3).To achieve this distinction, we computed the difference between the two annotations, as above, for the case of the similarity measure. The score for the unique terms e between the two annotations is defined as the fraction between the score for common terms p and the score for unique terms q for the two annotation strings (Equation 4). We refer to this scoring function as 'score' throughout the manuscript.Finally, the distance An example of calculation of the score is shown on Figure The procedure calculating pairwise distances between protein annotations described above was implemented in a perl program. Its sole input consists of SwissProt identifiers and annotation lines, and the output contains pairs of proteins with the score of annotation similarity lower than a given threshold. Compared to sequence-similarity clustering, this procedure is very rapid: an all-against-all comparison of SwissProt database containing 121,744 entries took about 26 minutes on a single processor of a Sun-Fire-480R server with 2 GB of RAM.et al., submitted). To evaluate the ability of CLAN to detect genuine protein families on the basis of annotation alone, the output (e ≤ 10-2) was compared to the Pfam-A database release 9.0 [The result of the pairwise comparisons between protein annotations were then subjected to clustering of connected components. The protein sequences of the clusters obtained were subsequently analyzed with the BlastP program  and the ease 9.0 . For eac


The authors present a method to identify modules within protein-interaction networks. Phylogenetic profiles are used to determine the biological relevance of the modules. By applying a graph-based algorithm to yeast protein-interaction networks we have extracted modular structures and show that they can be validated using information from the phylogenetic conservation of the network components. We show that the module cores, the parts with the highest intramodular connectivity, are biologically relevant components of the networks. These constituents correlate only weakly with other levels of organization. We also discuss how such structures could be used for finding targets for antimicrobial drugs. There is a strong belief underpinning systems biology that between the individual molecules and an organism's phenotype there exist intermediary levels of organization . The lowGiven that we could define such modules for a particular data source, for example, protein-protein interactions, there exists the further problem of understanding how modules relate to other forms of organization. Do for example, the proteins in a given module within a protein-protein interaction network show evidence of being coexpressed? Are they regulated by the same transcription factors and do they have the same level of dispensability?Whether we can define modules in a stringent biologically relevant fashion is not just important for our understanding of the organization of biological systems. Many authors have conjectured that if modules are real they may also be more likely to contain proteins that are essential to viability. Hence, a network approach could be imagined to hone down potential drug targets such as, for instance, candidate targets for antimicrobials.Here we ask whether phylogenetic information could be used to verify putative interaction-based modules. The assumption we make is that if a set of proteins belongs to the same module and that module has some biological relevance, then such a set should be generally conserved to act as an integrated functional unit ,19. HencL, between any given node and the rest of the nodes in the graph would exhibit a similar pattern for those nodes belonging to the same module. Alternatively, modularity could also imply that proteins within a module would interact more frequently with each other than with proteins of different modules, a property characterized by high values of a generalized clustering coefficient, C .Several network-clustering algorithms have been developed recently that make use of the local and global properties of networks -11. To tWe introduce here a simple algorithm that makes use of both sources of information. The basic steps of the so-called overlap algorithm are as follows  (discarding those ones containing just a single protein). In the next step we calculated an average overlap between both modular structures. A -value with significantly high maximal overlap was then chosen.e metric . We obtaC-based and L-based modules with a -value selected as previously described, we calculated the overlap of each C-based module with all those obtained with the L-based method. An L-based method less efficiently discriminates modular structures in small-world networks [C-based technique into a unique module. The C-based method is more robust but is weak at discriminating modules when organization levels are high. Therefore we used the C-based results as a template and the L-based method as a filter in the extraction of modular structure. In the C-based modular structure we kept in each module only those components which also appeared in the corresponding L-based module with which the selected C-module had the greatest overlap. In those cases with more than one module with maximal overlap, we selected one of them at random. Although finding the optimal classification choice is a common problem of clustering analysis, this simple algorithm allows one to select a -value with a high average maximal overlap and low overlap ratios between both methods, a measure of the reliability of the obtained modules .Having obtained networks , collapsThe overlap method was applied to the yeast protein-interaction network; that is, yeast would act as an imaginary 'poorly' characterized system where we can, however, check the relevance of our findings. This was derived from two public databases  and would be, more generally, the result of high-throughput experiments. In any case, these data are probably incomplete and no doubt contain false interactions . Should -values with significantly high average maximal overlap, that is, overlap equal to or greater than 0.8, and low ratios, characterizing the reliability of the proposed modular organizations. For an analysis of the performance of the algorithm as a function of -see Additional datafile 1. Note that these results extend the presence of modularity found previously in some yeast networks [-values in the regime described above were chosen such that the average module size is around  equal to 5 to 25 proteins, the so-called meso scale of biological networks [We therefore opted to analyze networks consisting of proteins belonging to different Munich Information Center for Protein Sequences (MIPS) protein functional categories . This alnetworks ,10,24 tonetworks   are obtained from each member of the population [To verify this initial suggestion, we examined the corresponding null hypothesis, that there is no phylogenetic correlation of the proposed structures, which is based on a completely uncorrelated distribution of phylogenetic profiles with respect to the modular organization. We made use of a class of statistical methods termed multi-response permutation procedure (MRPP). MRPPs are commonly used in ecological and environmental studies to compare an pulation . In contξ =  -  . Significance was tested by randomization; that is, we randomly permute the proteins within the modules while keeping the global modular organization fixed  to 1/2. We then sum these distances for the relevant cores in each network and estimate statistical significance by randomization Figure . We findAssuming that the putative functional group of a protein can be assigned blind to genes, this method then has the potential to narrow down the possible drug targets in poorly described species. Perhaps as expected, cell-cycle, protein synthesis and transcription-related modules have the most significant tendency to amass lethal genes. Could we apply the knowledge of validated network structures in a therapeutical context, for instance to identify targets for antimicrobials? In principle, identifying candidate proteins as antimicrobial targets is straightforward: the protein needs to be in the microbe and not the host and to be essential to the microbe. To this end, we calculated the probability of finding lethal genes in the set of proteins without human homolog belonging to the significant cores. We compared this with the probability of finding lethal genes in those yeast proteins not found in humans which are part of the full network. While the data on which genes are essential is questionable, owing to condition-dependent lethality , the ratWe have shown that by combining protein-protein data and phylogenetic information it is possible to systematically describe biologically relevant modules in protein networks which partially correlate with other types of organization. The analysis also suggests, however, that not all core modules within the functional network are equally vital for the organism's survival. This may just reflect condition-dependent lethality . Indeed,We used two databases as of July 2003: MIPS , contribL-based clustering, where L is referred to the shortest path length between two nodes in the network. From the interaction network, a matrix of distances is computed and transformed into an 'association' matrix by taking 1/L2 [C-based clustering, where C is a generalized local connectivity coefficient measuring common interactors of any two proteins in the interaction graph [Network clustering can be based on a global property, that is, ing 1/L2 . A seconon graph ,9,11 givAdj(i) the adjacency matrix, that is, the set of proteins interacting with protein i. Local properties tend to be more robust [Here |...| denotes the size of the set, ∩ the intersection and e robust .iM, jM, we considered the following overlap [Given two different modules,  overlap :) is given by:with |...| denoting the size of the set and ∩ the intersection. The average overlap used to determine the number of branches present in the clustering tree , the ratio between the number of interactions found among the m proteins connected to a given one, say j, and the maximal potential number of such interactions, which equals m(m - 1)/2 for a undirected graph. We obtained high values of such clustering coefficient and small characteristic path length for all cases, reflecting the small-worldness of the networks. To assess the statistical significance of these values, we generated 100 randomly rewired graphs for each functional network with the algorithm described in [P = 0.01), that is, , and L ≥ random L(we obtained P < 0.05 for L in the case of the energy network).To characterize the small-world property of the networks, we first calculated the clustering coefficient, ribed in . All casE-values bigger than a given threshold, thE, were considered absent [E and the absent ones receive a score of -1/logthE. For the binary case, profiles take the value 1 or 0 when the E-values are below or above the threshold, respectively. Finally, note that E-values were corrected to account for the different database sizes. Results in the main text are for the case of binary phylogenetic profiles and a threshold value of th E= 1e-6.We calculated binary and continuous phylogenetic profiles  for diffd absent . A partiNon-parametric randomization methods, such as MRPP, have several advantages compared to more well-known parametric procedures. In particular, if the assumption of normally distributed populations is not reasonable, the datasets have multiple measurements and if multivariate comparisons are desired .i, and j, we considered the following matching coefficient as a simple similarity measure: Sij = (x + w)/(x + y + z + w), where x is the number of homologs present in both phylogenetic profiles, y is the number present in profile i only and z is the number present in profile j only. Finally, w is the number of absent homologs in both profiles.Given two binary phylogenetic profiles corresponding to proteins Within similaritym cis the ratio between the number of components of module m, mn, and the number of components of all modules, MN, that is, m c= mn/MN, m Wis the mean of similarities between proteins belonging to module m, and M is the total number of modules.Here, Between similarity:m,s cis the ratio between the product of the number of components of modules m and s, m-nsn, and the total number of components squared, N2M, that is m,s c= mnsn/N2M. m,s Wis the mean of similarities between proteins of modules m and s, and M is the total number of modules. Results for all discussed tests were robust to the use of Euclidean distances with continuous profiles instead of similarities with binary profiles, as it is argued in the main text.Here, P-values for a set of k individual tests that are being performed and then rank them from smallest to largest. While Bonferroni would compare all null hypothesis to the same value α, the Holm test compares the smallest to α/k and, in case of rejection of the null case, to decreasing values α/(k - 1),... until failing to reject the null.The Holm test  is a metm W-  (see above) and determined the unadjusted P-value for each module by means of a permutation test with 10,000 randomizations. Suppose that we have M modules. We assemble an ordered vector of size M whose components are the uncorrected P-values in increasing order, that is, P1 is the smallest uncorrected P-value and PM is the largest. To adjust a particular vector component i Pwe multiply this component by Ai = (M - i + 1), thus generating a vector P for adjusted P-values. The added power of the Holm test can then be seen in a simple example. Imagine the case of three modules, that is, M = 3. The uncorrected P-values of the corresponding MRPP tests are: ν P= . A Bonferroni procedure for multiple testing would consider only the first test as significant according to a 0.05 significancy threshold. However, the adjusted P-values obtained with the Holm test would imply that all tests are significant, that is,  = ν P×  = .To perform the MRPP Holm test, we computed the branch length, that is, To obtain the core component of the modules, we selected for each module those components with more than two interactions, for the case of a module whose component with maximal number of interactions (MNI) is less than ten, or those components with more than four interactions for the case of a module whose component with MNI is equal to or greater than 10. Slight modifications to these rules produced similar results.ξm≥ 0.1, we calculated the mean of pairwise Euclidean distances between expression vectors of proteins belonging to a given module core. In the case of the 5' motifs, the statistic measures the number of regulatory motifs common to at least more than half of the core size. Finally, for each significant core, we simply measured the number of components that are lethal. The overall statistic for all cases is the sum of each corresponding measure in each core weighted by the ratio of the core size vs network size. P values are obtained with 10,000 randomizations.For each of the significant module cores, Additional data file A discussion on the network clustering algorithm, the list of species and lineages for the phylogenetic profiles, and a list of phylogenetically distinct module core components and their biological characterizationClick here for additional data file


Computational protein annotation methods occasionally introduce errors. False-positive (FP) errors are annotations that are mistakenly associated with a protein. Such false annotations introduce errors that may spread into databases through similarity with other proteins. Generally, methods used to minimize the chance for FPs result in decreased sensitivity or low throughput. We present a novel protein-clustering method that enables automatic separation of FP from true hits. The method quantifies the biological similarity between pairs of proteins by examining each protein's annotations, and then proceeds by clustering sets of proteins that received similar annotation into biological groups.Using a test set of all PROSITE signatures that are marked as FPs, we show that the method successfully separates FPs in 69% of the 327 test cases supplied by PROSITE. Furthermore, we constructed an extensive random FP simulation test and show a high degree of success in detecting FP, indicating that the method is not specifically tuned for PROSITE and performs well on larger scales. We also suggest some means of predicting in which cases this approach would be successful.Automatic detection of FPs may greatly facilitate the manual validation process and increase annotation sensitivity. With the increasing number of automatic annotations, the tendency of biological properties to be clustered, once a biological similarity measure is introduced, may become exceedingly helpful in the development of such automatic methods. Computational protein annotation is a major goal of bioinformatics and annotation methods are widely used. A wide variety of annotation methods exist, many of which rely on some kind of scoring. Typically, when testing whether a protein should be given a certain annotation, a score threshold is set, and proteins that score higher than the threshold are given the annotation. Obviously, some annotation mistakes may occur. Such mistakes can be divided into false positives (FPs) and false negatives (FNs). FPs  are annotations that were mistakenly assigned to a protein (type I error). FNs (or misses) are annotations that should have been assigned to a protein but were not (type II error). Adjustment of score thresholds allows tradeoff between these two types of mistakes. FPs annotations are considered to be of graver consequence than FNs. This is partially due to the fact that introduction of a false positive annotation into a protein database may cause other proteins to become incorrectly annotated on the basis of sequence similarity ,2. A sysDue to the importance of minimizing the amount of false annotations and maintaining highly reliable protein databases, three methods are generally used to avoid false annotations. The first method is manual validation of the annotation of each protein, which creates a serious bottleneck in the addition of new proteins and annotations to the database. The second method is using high score thresholds, thus lowering the rate of FPs but also increasing the rate of FNs (resulting in a loss of sensitivity). The third method is requirement for hits from different detection methods, eliminating advantages that are unique to some methods. Thus it would be beneficial to develop means by which FP annotations could be detected automatically, allowing both high throughput and high sensitivity.Here we present such a method that uses clustering of protein functional groups to separate true positives (TPs) from FPs automatically. Our method is based on the following notions: (a) protein annotations represent biological properties; (b) protein functional groups share specific combinations of biological properties, essentially constituting "property clusters"; (c) if two proteins have very different combinations of annotations, they are unlikely to share a single functional annotation and therefore there is a high chance that one of them was given that annotation incorrectly. These notions are not obvious, but were shown to correctly indicate false annotations in some individual cases tested manually using the graphical annotation-analysis tool of PANDORA . We aim Using these ideas, the method attempts to separate a group of proteins into "property clusters", by introducing a measure that quantifies the similarity between the annotation combinations of two proteins. According to our basic notions, these clusters are likely to be in accordance with false and true hits.all the TPs were clustered together without any FPs. We applied a stringent scoring, where a partial success is considered failure.We tested our method on the PROSITE protein signature database . The datFurthermore, we constructed a random FP simulation test in order to provide a more extensive test. In this test, all 5,551 InterPro  annotatiWe begin by describing the method of property-based clustering. Given a set P of all proteins that were given a certain annotation, and that there are both FPs and TPs in P, we would like to separate the set into disjoint subsets, so that one of the subsets will include all TPs and no FPs (leaving one or more subsets containing the FPs).Annotation-based clustering is used to detect these subsets. We define an annotation as a binary property assigned to a protein (each protein may or may not have a "hit"). At the first stage, annotations from GO (Gene Ontology) , InterPrp1 and p2 is defined as:The clustering works in the following way: between each two proteins we define a similarity score that tries to quantify how much do the two proteins have in common from a biological perspective. The score between two proteins A1 and A2 are the set of annotations of proteins p1 and p2 respectively, i is the current annotation, and f(i) is the frequency of i in the database. This score uses the following logic: if two proteins share an annotation, they are biologically similar in some manner. The more annotations these proteins share, the more cause we have to believe that they are similar biologically. However, two proteins sharing an annotation like "Enzyme"  should receive a worse similarity score than two proteins that share a much uncommon annotation like "Heat Shock Protein" (that appears only 832 times). This is taken into account by using log(f(i)). Obviously, one could think of different scoring schemes that would quantify this differently. For a specific example of how the score is calculated see Table where The similarity score is calculated between every two proteins in P. Next, we define the similarity score between two clusters as the arithmetic average of scores of all inter-cluster protein pairs:C1 and C2 are clusters of proteins. Starting with clusters of 1 protein each, the method begins by an initial one-step clustering which merges all clusters that have the exact same combination of annotations. Following this the primary clustering commences: At each clustering step the two clusters that have the highest similarity score are merged. At each step the contents of the clusters are evaluated, and if all TP proteins appear in one cluster without any FPs, we say that the clustering process successfully separated the TPs from the FPs. Note that we do not require all the FPs to be grouped into one cluster, due to the fact that they cannot be expected to share biological similarity amongst themselves.where Out of 327 sets of proteins that share a PROSITE signature, the method showed successful separation (as defined previously) in 227 sets, i.e. 69% of the cases. The average size of the protein sets was 156.1 and the median 76. Altogether 58,254 proteins were used for this test. The average and median FP rates (FP rate is defined as: FP/(TP+FP)) of the sets were 0.12 and 0.07 respectively. These general statistics about the test set indicate that the sets were large enough and had a high enough amount of TPs and FPs so that the chance of random success would be minimal.In order to demonstrate the method's performance in this test, we provide the following example of testing a single protein set. The set presented here is the set of all 37 proteins that matched the PROSITE "Serum albumin family" signature. Each protein in the set contains an average of 18.2 annotations . First, the score between every pair of proteins is calculated, based on their mutual annotations. Next, the proteins undergo a preliminary clustering step in which all proteins that have the exact same combination of annotations are merged into clusters. Following this, the proteins are clustered together based on their mutual similarity score. Finally, once the clustering has finished we examine the tree to see if the true positives were separated from the false positives. In the given example, there are 5 proteins that were incorrectly assigned the PROSITE annotation (FPs), and in Figure 5,551 sets of proteins were tested 15 times each and showed successful separation in 74% of the cases. Altogether 99,076 proteins were used for this test. This can be subdivided into 78% success for the sets that had 1 random protein added, 74% success for the sets that had 5 random proteins added and 68% for the sets that had 10 random proteins added. The average set size was 78 proteins. The drop in the performance by increasing the level of FPs is due to the fact that there is a higher chance that one of the randomly selected proteins will be biologically similar to the TPs. Since we consider only cases in which all FPs are detected, then there would be a higher chance of failure as the number of randomly-generated FPs increases.While the simulation of FP errors randomly provides endless amounts of test sets, which is a clear advantage over the limited test sets provided by a real database such as PROSITE, the simulation has its own limitations. The hidden assumption made by this approach is that the FP hits are independent of each other. This assumption is not necessarily true: for example, if annotation is done by means of sequence similarity, false hits may be more likely to be biologically similar to each other (e.g. belong to the same family). In fact, in many cases in the PROSITE test we find that the correct separation separates the TP proteins into one cluster and the FP into one or two clusters, suggesting that the FPs share some degree of biological similarity . This difference in the way that FP annotations are generated may also account for the difference in success rates between the PROSITE test set and the simulated test set. The way FP annotations are introduced into databases is impossible to model, but the combined success of the method on both a real database test set and on an extensive simulated test set seems promising.A further issue which concerns the simulation method is determining the amount of FPs to add to each set. Here we chose to add 1, 5 or 10 proteins to each set. This does not necessarily reflect the amount of FPs in real databases. Understandably, each database's average FP rate depends on its specific characteristics. However, the PROSITE database's average FP rate of 0.12 (median of 0.08) might give an indication as to what a typical rate is. In comparison, the average FP rate for our random simulation set was 0.11 (median of 0.07), which suggests that our choice was reasonable.We call a clustering process successful if it managed at any step to separate the false annotations. However, this step must be somehow determined automatically. There are two approaches to this: one is to use an intrinsic parameter of the clustering process that would indicate where the correct halting step is located; the other is selecting a predetermined step of the process. We chose the similarity score at each merging step as an intrinsic process parameter. When plotting the score against the progression of the clustering Figure , a knee Interestingly, we found that with certain sets the method tended to be more successful than with other sets, probably indicating that these sets are more coherent biologically. This might suggest exploring an approach in which for each annotation one could predict the level of success provided by this method. Furthermore, we used the InterPro categorization of annotations into types in order to check success in specific annotation types. InterPro divides its annotations into different categories, such as "domain", "repeat" and "family". Understandably, "family" type annotations had a ~30% higher success rate than the other annotation types, primarily due to the fact that the "family" annotations often represent protein sets that are biologically coherent whereas other types such as "repeat" or "domain" annotations are biologically diverse. This result would be expected by a method that performs a clustering based on biological similarity. This indicates that this approach should be aimed primarily at functional family annotations.However, functional families can be defined at different resolutions: an alcohol dehydrogenase belongs to the enzyme family, the dehydrogenase family and the alcohol dehydrogenase family. The test sets of the PROSITE and InterPro databases mainly represent mid-level and low-level annotations, with a typical size of tens or a few hundreds of proteins (see the statistics given previously). In order to further our understanding of the resolution in which this method is successful, we divided the protein groups into size categories and studied the relative amount of success in every size category. Figure Because multiple annotation sources were used, concerns arose regarding interdependencies amongst them. For example, InterPro is highly dependent on PROSITE, so proteins that have a PROSITE annotation will very likely be assigned an InterPro annotation as well automatically. In order to minimize this effect, we did not allow the algorithm to use the InterPro annotations that matched the PROSITE annotation which was being tested. Furthermore, in order to increase reliability of the random FP simulation test, all known PROSITE FPs were removed from InterPro prior to the test. Still, there is some concern that the results are partially biased due to annotation source interdependencies. Furthermore, it is difficult to determine whether these dependencies represent true biologically dependent properties, or simply a duplication of the same property in different sources. Keeping this difficulty in mind, our results which show different levels of success for different types of annotations (see "Prediction of success") indicate that the success of the method is more likely due to biological dependency rather than artificial duplication.It should be stressed that the clustering process is based on sufficient annotation. Therefore, it may be difficult to apply this method to proteins that are poorly annotated. Still, these cases should be relatively rare: Nearly 77% of the ~1,600,000 proteins in TrEMBL  have at It is worthwhile noting that amongst the 58,254 proteins used in these sets there were 3,587 (6%) proteins annotated by SwissProt as "hypothetical proteins". 18% of the sets that were successfully separated contained such hypothetical proteins, with an average of 8% hypothetical proteins for each such set. These results suggest that the method is capable of handling to some extent hypothetical proteins of unknown function.Another helpful approach to the problem of insufficient annotation could be the introduction of quantitative protein properties that are easily determined and show some correlation with function  into this method. Preliminary testing showed some positive correlation between protein length and Isoelectric point with function in certain cases (not shown).Introduction of FP annotations into protein databases can be harmful. It has been shown that once a mistaken annotation is introduced into a database, it often transfers to other proteins that are sequentially similar causing a propagation of false annotation . Due to Based on the notion that protein functional groups share specific combinations of annotations, we have introduced a method that by separating a set of proteins into biological "property clusters" shows successful separation of incorrectly annotated proteins from correctly annotated proteins. We test the method both with a manually validated test set and with a randomly constructed test set, and in both cases show a high degree of success. These results suggest that this tendency of certain annotations to appear in groups may be used as a basis of automatic methods that detect FPs. Naturally, different computer learning methods can be used to take advantage of these interdependencies of biological properties (for example see ).We created a database that includes all proteins from SwissProt 40.28  . The datAnnotation source and the number of annotation for each (in parenthesis) are: SwissProt version 40.28 (865 keywords), InterPro version 5.2 , GO as of July 2002 , PROSITE version 17.5 .NK and ML conceived of the study. NK designed the method. NK implemented and developed the method. NK designed the tests and analyzed the results. NK and ML wrote the manuscript.


One of these methods is Domain Fusion, which has proven to be effective in predicting functional links between proteins.Several Saccharomyces cerevisiae proteins.Analyzing the structures of multi-domain single-chain peptides, we found that domain pairs located less than 30 residues apart on a chain are almost certain to share a physical interface. The majority of these interactions are also conserved across separate chains. We make use of this observation to improve domain fusion based protein interaction predictions, and demonstrate this by implementing it on a set of We show that existing structural data supports the domain fusion hypothesis. Empirical information from structural data also enables us to refine and assess domain fusion based protein interaction predictions. These interactions can then be integrated with downstream biochemical and genetic assays to generate more reliable protein interaction data sets. Networks of interacting molecules drive every process in biological cells. Proteins dominate these networks, some of which involve transient interactions such as signal transduction cascades and ligand-receptor interactions, while others form more permanent molecular machineries such as ribosomes and polymerases. Unraveling these networks and interactions will not only help us better understand complex cellular processes, but also enable us to make inferences about the function of individual proteins through 'guilt-by-association' .Saccharomyces cerevisiae, in which networks of its interacting proteome where constructed using genome-wide screens. [Over the last few years, high-throughput interaction detection assays have been introduced and refined to complement the traditional genetic and biochemical techniques. High-throughput mass spectrometry protein complex identification (reviewed by Pandey and Mann ), and yescreens. -7in silico methods have recently been developed to predict associations from these data.The wealth of genomic and protein sequences, the increase of 3D structures of protein complexes, together with the deluge of microarray expression data, has provided researchers with an overwhelming body of information that can be used to infer both functional as well as interaction linkages. Clearly, bioinformatics and computational biology are necessary tools for delineating this information. In response to the data explosion, several Phylogenetic profiles focus on the co-occurrences of genes across several organisms. By studying the pattern of evolutionary conservation between sets of genes in different organisms (phylogenetic distribution), it has been shown that these phylogenetic profiles can be successfully used to infer both localization as well as functional association between proteins -10. Protab initio categorization and prediction of protein interactions [Structural data has also been mined and analyzed for residue patterns within interfaces between pairs of interacting proteins. These are then used to train learning models for ractions ,14. Jansractions  illustrain silico experiments [A number of groups have compared and reported on the protein interaction datasets that are emerging from the various genome-scale biochemical, genetic and eriments -18. All in silico methods have higher coverage and higher accuracy than the majority of biochemical/genetics methods, second only to high-throughput mass spectrometry. The use of sensible strategies and filters has allowed in silico analyses to provide better performance. On top of that, these methods are less biased towards abundant proteins. In silico analyses are indispensable, and further improvements of these methods to make them more accurate will provide a cleaner set of data for downstream biochemical/genetic studies.The study by von Mering and co-workers  showed tIn this study, we make use of an empirical observation that domain pairs, which lie in close proximity on a protein chain tend to interact, to refine the domain fusion analysis. This way, we aim to improve the accuracy of the domain fusion analysis.The basis for domain fusion (or gene fusion) is the observation that certain proteins (termed the Rosetta stones) in a given species are found to consist of a fusion between two separate proteins in another species. Through fusion, the entropy of dissociation between the two proteins is reduced, and it is hypothesized that in all likelihood, these two separate proteins share a functional association, if not a physical interaction ,12.Domains have been described as the primary building blocks of proteins , recombiIn this paper, we make use of existing structural data to support the domain fusion hypothesis. We interrogated known 3D structures for evidence of inter-domain physical interactions on the same chain. We investigated and concluded that there was an association between the distances at which domains are spaced apart on the chain, and the propensity for a domain-pair to interact.We also show that domain pairs, located in close proximity on a protein chain, are likely to interact even when found residing on different chains, hence proving that the domain fusion hypothesis is valid.Finally, we demonstrate that peptide chains with closely spaced domains are likely to make better Rosetta stones, and we make use of this observation to improve domain fusion based protein interaction predictions.The available structural data indicate that intra-chain domain pairs, which lie in close proximity on a peptide chain, tend to physically interact with one another. The mean distances of interacting intra-chain domain pairs are smaller than ones which do not interact; interacting pairs are on the average 50 residues apart, while non-interacting pairs have a mean distance of 166 residues between them.H0): Domains pairs separated by no greater than a predefined distance and their tendency to interact were independent. The p-values indicate the probability of having the chi-squared test statistic as extreme as, or larger than observed when H0 is true.In order to verify the correlation between distance and interaction, we made use of contingency tables and the chi-squared test statistic. For a set of inter-domain distances ranging from 5 residues to 200 residues, we constructed 2 × 2 contingency tables that classified domain pairs according to two criterions; 1) whether or not they are separated by a distance no greater than a threshold, and 2) whether or not the domain pair is interacting. The chi-squared value of each table was used as a statistic to test the null hypothesis . In other words, there is a correlation between domain-pairs spaced less than 30 residues apart on a single peptide chain and their tendency to interact across separate chains of a polypeptide complex.S. cerevisiae was downloaded from Swiss-Prot, and domain fusion based protein interactions were predicted as described in the Methods section. After filtering for promiscuous domains, a total of 9279 protein interactions remained, of which 28% or 2629 were supported by a Rosetta stone with no more than 30 residues between the fused domains.We wanted to verify that the 30 residues criteria could be used as a measure to filter and improve predictions made using the domain fusion methodology. A set of proteins for the budding yeast The functional category assigned to each protein in an interacting pair was used to gauge the plausibility of the interaction; if two different proteins were found physically interacting, one would expect the two proteins to have overlapping functional categories. 62% of the interacting protein pairs, supported by a 30 residue Rosetta stone, have both partners belonging to the same functional category. The same proportion for interacting pairs not supported by a 30 residue Rosetta stone is 48%. This 14% difference is significant with a p-value of less than 0.001, using a two-sample t-test.In silico methods for predicting protein interactions are not only able to match the accuracy of the other genetic, biochemical and biophysical techniques, but also have the added advantage of providing higher coverage [in silico methods, domain fusion is an attractive technique because it enables a functional link to be drawn between two proteins based solely on their primary sequence. Still, large-scale sets of high-throughput protein interaction data available today are spurious, more than half of them proving to be false positives [coverage . Among tositives , the chaProtein interactions can be classified as either permanent or transient interactions. The data from this study were taken from the PDB, where most of the submitted structures are results from x-ray crystallography experiments. Consequently, we believe that the vast majority of our deduced domain and protein interactions are physical, permanent interactions.Our study of multi-domain, single and multiple-chain protein structures in the PDB gave us two results. First of all, it supports the domain fusion hypothesis suggested by Marcotte and Enright. Secondly, it allows us to conclude that single chain peptides with closely spaced domain pairs make better Rosetta stones, and hence better predictors of protein interactions.Evident from the set of PDB structures we studied, is a correlation between the distance separating a pair of domains on a protein chain, and their tendency to physically interact with one another. As described by Marcotte and co-workers when they constructed the domain fusion hypothesis for evolution of protein interactions, affinity between interacting pairs of domains may be enhanced when the domains are fused together on the same chain . ConsequPreviously, Park and co-workers had obserThe data from multi-chain PDB structures provide additional support to the domain fusion hypothesis, by showing that most of the intra-chain domain interactions are similarly represented across separate chains of a complex. This provides additional mechanistic evidence that the interaction between the two domains is most probably functional and conserved.To our knowledge, this is the first time structural data has been used to support the domain fusion hypothesis.We tried to uncover a pattern within the set of closely spaced, yet non-interacting domain pairs. We wanted to detect if there was an over-representation of domains from a specific molecular functional category in this non-interacting list. This list is displayed in Table Furthermore, since the interactions we can detect from structural data are more likely to be permanent interactions, it is possible that the reason no physical contact is witnessed between these proximal domains in structural data is because the domains form transient interactions that are not captured in the x-ray crystallography data.We also looked for a relation between protein disorder and interacting domain pairs. We wanted to see if protein domain pairs which interact on the same chain, tend to be linked by a disordered region. To this effect, we used DisEMBLto do theS. cerevisiae proteins indicate that Rosetta stones with domains separated by less than 30 residues do indeed make better domain interaction (and hence protein interaction) predictors.Our results from predicting interactions among The set of protein interactions inferred from these Rosetta stones are enriched with more reliable interactions, as judged by using similar function as a criteria. The total number of interactions is reduced to nearly a quarter when employing this method. This allows us to conclude that the number of false positives is reduced, increasing the accuracy of the prediction. Without needing to employ a hard filter, protein interactions predicted using the domain fusion methodology may be ranked according to the quality of the Rosetta stones each interaction is inferred from, allowing one to identify a much smaller subset of more reliable interactions, and use them for downstream analyses.We have successfully demonstrated the use of current structural data as a resource for refining current protein interaction predictions, in particular domain fusion predictions. Our data strongly suggests that domain pairs separated by less than 30 residues on a peptide chain are almost certainly to physically interact, and this criterion is useful in accessing protein interactions predicted from Rosetta stone proteins.Going forward, the availability of a large number of structures through structural genomics programs will facilitate a larger sampling of the domain structure space. New patterns may emerge as use of this data becomes available, allowing better predictions to be made.We used domain models from the Protein Family database (PFAM)  which weWe used the coordinates within the PDB data files to calculate the distances between domains, and to determine if they are interacting. Two domains are judged to be interacting if they share at least five contacting residue pairs, where contacting residues are residue pairs with less than 6Å between their respective alpha-carbon atoms.Using a similar approach to the above, we obtained a set of multi-chain PDB structures in which the previously determined domain-domain interactions can be observed across separate peptide chains within a complex. The ASALIST from the PQS server was used PFAM domains were categorized into Gene Ontology (GO) molecular functions and cellular processes using the PFAM2GO data provided by the GO consortium .Saccharomyces cerevisiae found within the Swiss-Prot database, and gauged the quality of the interactions by looking at the function of each interacting protein.In order to assess how distance between domains could be used to improve the domain fusion based protein interaction predictions, we predicted interactions between 6918 proteins from the organism The steps taken to predict protein interactions based on domain fusion are as follows. Swiss-Prot (release 42.9) and Trembl (release 25.9)  protein S. cerevisiae proteins, by looking at the complement of PFAM domains between each and every pair of proteins, and seeing if there were any Rosetta stone determined domain interactions between the domains of each protein. The protein interactions were sorted into two groups; one group inferred from domain interactions supported by the existence of a Rosetta stone protein with no more than 30 residues between the domain pair, and the other group with no support from a 30 residue Rosetta stone.This domain interaction set was then used to predict pairwise protein interactions between the To validate these protein interactions, we mapped the proteins to the MIPS comprehensive yeast genome database , and looAll the data was stored in a relational database schema implemented in MySQL, with a set of perl modules written for data transaction and manipulation. The Bioperl bioinformatics tool kit  was usedJMC participated in the design of the study, performed the data and statistical analysis, as well as drafted the manuscript. PRK conceived of the study, participated in its design and coordination, and edited the final draft of the manuscript. Both authors read and approved the manuscript.