Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets.We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP) technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO) annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology. An increase in the number and size of GO groups without any noticeable decrease of the link density within the groups indicated that this expansion significantly broadens the public GO annotation without diluting its quality. We revealed that functional GO annotation correlates mostly with clustering in a physical interaction protein network, while its overlap with indirect regulatory network communities is two to three times smaller.Protein functional annotations extracted by the NLP technology expand and enrich the existing GO annotation system. The GO functional modularity correlates mostly with the clustering in the physical interaction network, suggesting that the essential role of structural organization maintained by these interactions. Reciprocally, clustering of proteins in physical interaction networks can serve as an evidence for their functional similarity. Structurally, GO is a directed acyclic graph of terms (nodes) connected with links representing two types of term relations: "is-a" and "part-of." GO has three major branches covering corresponding aspects of protein functions: biological process, molecular function, and cellular components. The approaches for assigning GO terms to proteins can be grouped in two major classes.Modularity in biological networks was proposed more than a decade ago as a way for a cell to organize functional blocks and maintain specificity of cellular regulation . Since tThe first class uses predictive methods to transfer existing annotation to new or experimentally uncharacterized proteins. These methods utilize different learning techniques  to assign GO annotation (GOA) based on: i) similarity of sequence or sequence features, including amino acid and domain composition of proteins and 3D structure -16, ii) et al.  triplets. To compare public and MedScan annotation, we expanded both GOAs by adding every parent GO group of the original annotation to a protein. We found that 78,346 protein-GO associations were identical between expanded public and MedScan GO annotations. This constituted 20% of public expanded annotation and 24% of MedScan expanded annotation. We also determined that 6,986 proteins had at least one identical annotation and on average each protein had 43% of annotations identical between public and MedScan GOAs. The expanded public annotation had on average twenty GO groups per protein covering 19,946 proteins, while MedScan GOA had 39 GO groups per protein covering 8,246 proteins. The distributions of the GO annotations among proteins for public, MedScan, and combined GOAs are shown in Figure Significant portion of public GO annotation was created by human curators reading scientific articles. This allowed us to compare MedScan performance with results of human annotation using the same literature corpus. We have identified 2,864 references that were used by EBI curators to produce 14,544 protein-GO group associations annotating 6.694 proteins with 1,977 biological processes. MedScan has extracted 8,437 protein-GO group associations from the same abstracts annotating 2,655 proteins with 590 biological processes. The underperformance of MedScan is easy to explain because EBI curators used full-text articles while MedScan extracted annotation only from the abstract text. We have focused further comparison only on 512 GO groups that were recognized by both MedScan and EBI curators. This allowed us to correct for the absence of full article text during MedScan extraction as well as for the absence of some GO groups in MedScan dictionaries. We found that MedScan has extracted 4,649 unique protein-GO associations, while EBI curators extracted 1,138 protein-GO associations for the same set of GO groups. Thus, EBI curators extracted three times less protein-GO associations than MedScan did from the same set of articles and GO groups. This number surprises even more if we remember that EBI curators read the entire article while MedScan used only abstract text for extraction. To prove that human curators miss many true protein-GO associations we have identified 317 protein-GO associations that were identical between EBI and MedScan annotations and were extracted from the same set of 2,864 articles by both approaches. Again, 821 associations missing from MedScan annotation among 1,138 associations extracted by curators can be attributed in large to the fact that MedScan extracted information from abstract text only. Surprisingly however, only 180 out of 317 identical associations were derived from the same article by both MedScan and curator. Thus, EBI curators missed at least 137 true protein-GO associations when reading the 2,864 articles.the purpose of sub-task 2.2 resembled the typical human annotation procedure, in the sense that the participants had to return the annotations derivable from a given protein-article pair. The annotations which are contained within the articles should thus be automatically identified and the corresponding GO-term returned together with the supporting text passage"[The comparison with EBI manual curation has also allowed us to compare MedScan with other NLP methods for automatic extraction of protein-GO associations that were evaluated in BioCreAtIvE task 2.2. According to the original article " passage". Assumin passage" using thParticipant: Daraselia et al.; Run: 1; Evaluated results: 317; Perfect prediction: 180 (56.8%).Because these numbers assume the worst case scenario they give the lower estimate for MedScan performance relative to other NLP methods. Using this numbers we position MedScan relative to other methods for automatic GO annotation on Figure Measuring identical records, however, is not adequate for comparing two hierarchical annotations due to ambiguity in the annotation process (see the Discussion section for more details). We therefore developed a hierarchical evaluation measure as described in the Methods section. This method evaluates the similarity between two hierarchical annotations by a score ranging between zero and one. Following the guidelines provided by the GO Consortium, 44,254 unique GO annotation records were split in three groups, reflecting the quality of annotation according to their evidence code:1) A high-confidence group containing the manual annotation with evidence codes TAS, IDA, and IC;2) An average-confidence group containing mainly the annotation derived from high-throughput experiments with evidence codes IMP, IGI, IPI, ISS, IEP, and NAS;3) A low-confidence group containing sequence-similarity-based "electronic" annotation with evidence codes IEA, ND, and NR.Table To investigate network clustering within the functional modules and to further validate MedScan GO annotation, we have constructed several collections of pathways for three GOAs: public biological processes GOA (public GOA), MedScan biological processes GOA (MedScan GOA), biological processes GOA combining MedScan and public annotation (combined GOA). A pathway contains only those proteins from a GO group that interact with each other; the protein-protein interactions were taken from the ResNet 4.0 database described previously . FurtherMore than half of GO groups did not contain any proteins. Upon inspection we found that the majority of empty GO groups were irrelevant for the mammalian species. Because we have used only the mammalian protein names dictionary for extraction of protein-GO associations, while GO terms have been identified in the text regardless organism specificity, we believe that this observation provides additional support for the validity of annotations extracted by MedScan.Summary of the pathways' characteristics is shown in Table To check the hypothesis that cellular functional modularity is achieved by the increased link density in the molecular interaction network and to further study MedScan extraction accuracy, we then investigated whether proteins within a GO group had an increased probability to interact with each other than with arbitrary network proteins. Since the experimentally obtained physical interaction networks have been already shown to cluster within GO groups, we have analyzed the interaction networks extracted from the literature. Networks of both physical and regulatory protein interactions were taken from our ResNet 4.0 database, described previously . Unlike We also found that the addition of MedScan GOA to the public GOA did not affect the fraction of the GO groups that are densely linked, keeping it around 60% for the physical interaction network. Thus, the addition of MedScan GOA does not affect the average quality of GO groups as judged by the physical interaction link density. This further validates the GO annotation extracted by MedScan.Regulation network has on average 2.2 times fewer GO groups with high link density than the Physical interaction network. For example, in the biological process GOA obtained by combining public and MedScan annotations, 59.2% of the GO groups had a number of relations significantly higher than expected in the randomized physical interaction network. For the Regulation relations this number was only 21.5%. The number of statistically significant densely linked GO groups is even smaller for the other types  of regulatory networks.We investigated how the GO functional annotation modules clustered in each of the five types of relations in ResNet 4.0 and for ResNet 4.0 as a whole. The interactions in ResNet 4.0 can be divided into direct physical (binding and protein modification) and the remaining indirect regulatory links. Table Regulation link density is almost three times higher than that of the MedScan . A tWe used the hierarchical evaluating measure (HM) suggested in  to compaThe limited similarities between different GO annotations were documented at the BioCreAtIvE inter-annotator experiment  . Their r1) Automatic extraction methods are developed by teams of scientists who have their own standards in regards to how to interpret statements in scientific texts for GO annotation. Since the inter-annotator agreement test revealed only a 40% concordance between two human annotators, the 14% recall rate can be adjusted upwards by the inter-annotator accuracy. This adjustment makes the actual accuracy of NLP methods closer to 22%.2) In our case, the human annotator was the developer of the NLP algorithm for the GO annotation. Therefore, the concordance between MedScan results and human evaluation is much closer. The MedScan GO annotation recovery rate adjusted downwards by the results of BioCreAtIvE inter-annotator agreement experiment is about 31%. We anticipate that a group of independent curators will report this rate upon inspection of MedScan results. This downward correction makes MedScan recovery rates closer but still superior to other NLP methods.In light of the 40% concordance in the BioCreAtIvE inter-annotator experiments, one should not expect that the objective recall of any NLP method including MedScan will exceed 40% as well if measured against a the same literature corpus. We have confirmed this estimate by comparing MedScan performance with the performance of Gene Ontology curators from EBI. We found 28% overlap  between protein-GO associations extracted by MedScan and associations found by human curators for the same set of GO groups in the same articles. We also showed that human curators miss at least 10% of the true facts described in the articles .Speed is a major advantage of the NLP methods. They can process a vast amount of literature that cannot be read by human annotators in a timely way. Our experience with MedScan shows that the estimates based on the processing of a limited number of articles can significantly underestimate the "global" recovery rate of the NLP technology. This happens due to the high redundancy of scientific literature that helps to overcome the incomplete "local" recovery rates . A stateOne more possible reason for superior MedScan performance over human curation is MedScan's protein names dictionaries. The efficiency of extracting protein-GO association depends not only on the correct recognition of GO terms in the text but also on the efficient protein name recognition. MedScan benefits from the four years of development that have produced highly accurate and comprehensive dictionaries of protein names and aliases. The MedScan performance to recognize and assign protein names far exceeds capabilities of any human curator because its dictionaries are manually curated and contain hundreds of thousands of protein names synonyms . A humanThe maturity of MedScan technology also explains its apparent superiority over other methods for automatic extraction Figure . All sixCurrently, the correlation between protein annotation and network clustering was demonstrated only for the physical interaction networks generated by high-throughput two-hybrid screens and mass-spectrometry experiments ,37,38. WRegulation network. Such a low correlation for the Molecular function classification is expected since this type of annotation does not relate to cellular processes and is based purely on protein sequence similarity. Still, the observed non-negligible correlation indicates that homologous proteins tend to participate in similar biological processes. Additionally, it may be due to the fact that protein paralogs tend to interact with each other more often than the evolutionary non-related proteins [The correlation between physical interaction clusters and GO groups was observed for all three branches of GO classification: biological processes, cellular component, and molecular function. The cellular component annotation provided the highest correlation. This suggests that the integrity of cellular organelles and complexes described by this GO branch, in addition to the specific "structural" interactions that define organelle topology, is maintained by the increased density of protein-protein interactions within an organelle. The network clustering within biological processes groups supports the view that the existence of functional modularity in a cell is primarily maintained by means of protein-protein physical interactions. We demonstrate this correlation by using both the complete GO classification as well as solely the annotation automatically extracted from the PubMed database. The Molecular function GO classification has the smallest number of densely linked GO groups: 54% in the physical interaction network and 44% in the proteins .We found that clusters in all regulatory networks have a smaller correlation with GOA than the physical interaction clusters. Thus, it appears that the functional modules exchange regulatory signals between each other almost as often as signals propagate inside the functional modules themselves. Faster signal propagation within a module, as compared to the signaling between modules, is the only apparent reason of having functional clusters or modules in the physical interaction network without regulatory clustering. This ensures that an execution of a function happens first within the module and then the higher-level information exchange between modules takes place. The information exchange between modules can be viewed as an integration of signals from several processes by the cell.Proteins in a pathway built from a Biological process GO annotation are involved in the same cellular process and are linked to each other by physical interactions and regulatory relations that are described in the literature and therefore have been thoroughly measured by dedicated experiments. Even though some of these relations can be conditional upon time, tissue and cellular localization, the majority of the interactions occur at all times and therefore represent the plausible mechanism for the regulatory and physical interaction events that mediate corresponding biological process. The fact that proteins in a pathway both belong to the same GO group and interact with each other increases the confidence of both GO annotation and protein-protein relations in a pathway. Generally, a GOA pathway contains fewer proteins than the corresponding GO group as it includes only the proteins linked into a connected network. Therefore, annotation of the proteins included into a pathway can be considered of a higher confidence than the annotation of the proteins that were left outside due to the absence of interactions.The automatically constructed pathways can be used directly for the analysis of high-throughput data such as microarray gene expression. That in principle could produce results different from the standard GO group analysis and of a higher confidence. In addition, GOA pathways can be used as initial data for further manual curation, significantly simplifying this procedure. Similar GO pathways can be built for other type of protein annotations such as Protein Disease Ontology, Gene Ontology for other organisms, or proteins identified in whole genome genetic association studies.The GO annotation extracted by the NLP technology described here expands and enriches the existing manual GO annotation. Two principal attributes of a protein's cellular role, its functional annotation and its place in the topology of a cellular protein-protein network, are significantly related to each other and, therefore, should complement each other when used for the interpretation of high-throughput experiments. The GO functional modularity correlates most strongly with the clustering in the physical interaction network, which is another manifestation of the fact that any biological process requires a certain supporting structural molecular organization in a cell.In future, we plan to improve the precision and recall rates of the GO extraction by NLP by thoroughly analyzing the cases of its incorrect interpretation or ignoring of the relevant parts of the text. Redundancy of scientific literature and its effect on global NLP performance is another possible topic of our future research. We also plan to use the correlation between the GOA and protein network clustering to improve the quality of both the GO and protein interaction datasets by automatically rectifying strong mutual contradictions.As a first step, we constructed the dictionary of GO term descriptors suitable for efficient identification in text. We narrowed the set of terms from the "Biological processes" of GO to a reasonably informative and consistent subset of cellular-level biological processes by removing some terms that we considered inappropriate, too general, or too detailed. In particular, the following terms were removed from the dictionary:• GO terms representing positive or negative regulation of another GO term; GO terms referencing individual proteins; GO terms representing organism-level behavioral processes• GO terms representing steps of protein metabolism• High-level GO terms with high frequency of occurrence and low informational content • GO terms for highly specific biochemical processes.In addition, we preprocessed the GO terms containing chemical names using the MedScan preprocessing algorithm for tagging chemical names in text . IdentifSimilarly, we have created a small dictionary of word synonyms, containing names of tissues and cell types, frequently used names of cellular components, and notations of high-level processes often used in GO terms, e.g., transport, synthesis/degradation, movement, and assembly/disassembly. Finally, we have manually added commonly used synonyms to our dictionary of GO term descriptors. The synonyms were taken from the existing MedScan dictionaries as well as by adding adjective forms of the noun synonyms such as "mitosis-mitotic," for example. In the end, the dictionary contained 6,318 descriptors representing 5,764 unique GO terms from the "biological process" category.Our detection of GO terms in the scientific text involves the following steps.1. Input text is scanned for the names of mammalian proteins [proteins  and chem2. The GO term dictionary is loaded. During loading, it is normalized in a manner described in step 5 of this process, and each word in each GO term descriptor is replaced with the corresponding numerical identifier. This identifier is first searched for in the synonym dictionaries for chemicals, tissues, cell types, and compartments. If a word is not found in the synonym dictionaries, a new identifier is assigned to each unique word. The main purpose of this step is to normalize terminological variations in text.3. Input text is split into individual sentences, and each word in a sentence is replaced with the corresponding numerical identifier the same way as it is done for the GO term dictionary in the previous step. The algorithm splitting text into sentences uses the following token as a separator: a period followed by any number of whitespaces and then a capital letter.4. The minimal noun phrases (MNPs) are detected in every sentence, using regular expression over the syntactic categories assigned by the dedicated Part of Speech (POS)-tagging program. We used the following regular expression for MNP detection:(DET|Pron|D)* ((COMMA|AND|OR|BUT_NOT)? J)* N N*DET is determiner, Pron is pronoun, J is adjective, D is adverb, and N is noun. Other symbols are introduced in Additional file where 5. MNPs separated by prepositions are normalized. The main purpose of this step is to disregard prepositional variations in a noun phrase structure. Normalization is done by flipping the order of MNPs and removing prepositions separating MNPs. The MNP order is changed so the phrase "MNP1 preposition MNP2 preposition MNP3" becomes "MNP3 MNP2 MNP1". For example:"rapid de-differentiation of hepatocytes from human liver into fat cells"is normalized into"fat cells human liver hepatocyte rapid de-differentiation"de-differentiation", "liver", "hepatocytes" and "cells" are marked as head words.During the normalization, the information about "head" word from each MNP is preserved. In the preceding example, the words "6. Finally, normalized GO term descriptors are linearly matched over a noun phrase by sequentially comparing the numerical identifier of each word in a GO descriptor with numerical identifiers of words in the normalized noun phrase. A successful match is allowed to contain any number of gaps between the words in the GO descriptor, but all words of the descriptor have to be matched. In addition, the following two constraints must be satisfied:• The entire match must occur within the borders of a single MNP, or• If a match spans more than one MNP within a normalized NP, the "head" words of all spanned minimal NPs must be matched.L is the number of words in a GO term, G is the total length of gaps between GO term words, and H is the distance (in words) from the end of a match to the "head" of a normalized noun phrase. In the end, the set of the highest-scoring non-intersecting matches is selected and matches in the text are tagged with the corresponding GO identifier.Initially, all possible matches of different GO terms within each normalized NP are considered and each match is assigned a simple score: S = L - G - H, where Associations between proteins and GO terms are extracted using a small set of linguistic patterns (defined using regular expression-like notation) matched over a linear sequence of sentence words. The matching is done by automatically constructed deterministic finite automaton (DFA), which will be described in detail elsewhere. Following are some of its features that are relevant for GO annotation. First, our DFA recognizes protein names and GO terms tagged in the text by the MedScan preprocessor and treats them as variables. Second, it can match words in all grammatical forms using morphological word-form reduction . The DFAet al. [To compare public and automatically extracted annotations, we chose the method described in Kiritchenko et al. . BrieflyA contains terms {A1, A2, A3... An} terms and the second annotation B for the same protein contains terms {B1, B2, B3... Bm}. The similarity between two annotations can be defined as an average similarity among N pairs of GO terms, where each pair contains one term from the set {A1, A2, A3... An} and its most similar GO term among B1, B2, B3... Bm:Given the local similarity between two GO terms, we can define the asymmetric similarity between two GO annotations. Let the first annotation Finally, we define the global similarity between two annotations sharing many common but differently annotated proteins as an average similarity between two alternative sets of GO terms from every protein present in both annotations.The protein networks used in this work were extracted from the ResNet 4.0 database of mammalian protein relations, described in detail in . The numMolTransport-indicating regulation of molecular translocation, Expression and PromoterBinding-both indicating regulation of a target via gene expression; and Regulation-indicating the regulatory event of an unknown mechanism. If a protein could not be connected to any protein in the pathway by any of those relations, such protein was removed from the pathway at the end of the algorithm run. This order of connecting proteins does not effect the protein selection and was designed to give a priority to direct physical interactions over indirect regulatory relations for labeling links with dual identity. A collection of automatically generated pathways for the GO Biological Processes annotation is freely available from the Ariadne Genomics Webpage and it can be accessed by downloading the free demo version of Pathway Studio software.The biological process pathways were built by connecting all proteins that belong to the same GO group with relations from the ResNet 4.0 database. All proteins annotated by the child GO group were included into the parent GO group recursively for all child groups. Types of relations between proteins and their abundance in ResNet 4.0 are listed in Table spin) is assigned to each network vertex; linked pairs of vertices with spins in the same state are energetically favored. Initially, spins are assigned at random, the number of possible spin states q is usually of order of the number of vertices N, we used q = N/5. The model is allowed to evolve by changing spins of randomly selected vertices according to the Metropolis algorithm with a gradually lowered temperature. Usually, each vertex is allowed to sample all q spin states several times after which the temperature is decreased by a small fraction (1%). To prevent condensation of all vertices into the same state, an antiferromagnetic energy penalty that depends on the number of vertices ni in each state state is introduced To find clusters in the physical interaction network, we used annealing of the network-based ferromagnetic Potts model with an adjustable antiferromagnetic term . This claaa@3E97@. An aliga priori unknown number of possibly overlapping mesoscopic clusters in a sparse network with a low link density contrast.When the evolution stops at a sufficiently low temperature, linked vertices in the same state are declared network communities. This method allows one to find an ND designed and implemented algorithm for protein annotation using MedScan technology and for statistical comparison of different GOA,AY developed an algorithm for pathway building using GO groups and wrote the manuscript,SE implemented algorithm for protein annotation using MedScan technology,IM was the general manager and contributed to the manuscript,II developed the modification of Potts model clustering method, preformed statistical evaluation of the correlation between GOAs and network clustering and wrote the manuscript.All authors have read and approved the final manuscript.Regular expression patterns used for automatic extraction of protein associations with GO terms by MedScan deterministic finite automaton (DFA). The file contains the complete list of linguistic patterns used for extraction of GO annotation by MedScan.Click here for file


A new method for studying signal transmission between functional sites by decomposing protein structures into modules demonstrates that protein domains consist of modules interconnected by residues that mediate signaling through the shortest pathways. Allosteric communications are vital for cellular signaling. Here we explore a relationship between protein architectural organization and shortcuts in signaling pathways.s subunit and P450 cytochromes) to illustrate that the modular architecture of active sites is linked to their functional specialization.We show that protein domains consist of modules interconnected by residues that mediate signaling through the shortest pathways. These mediating residues tend to be located at the inter-modular boundaries, which are more rigid and display a larger number of long-range interactions than intra-modular regions. The inter-modular boundaries contain most of the residues centrally conserved in the protein fold, which may be crucial for information transfer between amino acids. Our approach to modular decomposition relies on a representation of protein structures as residue-interacting networks, and removal of the most central residue contacts, which are assumed to be crucial for allosteric communications. The modular decomposition of 100 multi-domain protein structures indicates that modules constitute the building blocks of domains. The analysis of 13 allosteric proteins revealed that modules characterize experimentally identified functional regions. Based on the study of an additional functionally annotated dataset of 115 proteins, we propose that high-modularity modules include functional sites and are the basic functional units. We provide examples (the GαOur method decomposes protein structures into modules, allowing the study of signal transmission between functional sites. A modular configuration might be advantageous: it allows signaling proteins to expand their regulatory linkages and may elicit a broader range of control mechanisms either via modular combinations or through modulation of inter-modular linkages. Allosteric communications play crucial roles in many cellular signaling processes. Perturbations caused by factors such as ligand binding at one functional site affect a distant site, thereby regulating binding affinity and catalytic activity ,2. SinceDynamic models have been proposed to explain the conformational changes involved in signal transmission between functional sites ,5. In paThe rationale for modular organization of proteins in allosteric behavior has been discussed previously -18. Modus subunit and P450 cytochromes are examples of functional sites shared between modules. Interestingly, the modular decomposition of the Gαs subunit reflects binding site partitioning into regions involved in different sub-functional specialization, general binding and information transfer regions [The small-world topology of protein structures suggests that the key amino acids for signal transmission should lie in the shortcuts linking different regions of the structure. The removal of the most central contacts forming these shortcuts divides the structure into modules. We characterize these modules from a structural point of view. Our results, derived from a non-redundant dataset of multi-domain proteins, reveal that, in the vast majority of the cases, modules tend to be located within rather than across domains. Therefore, modules can be considered as sub-domains. Further analysis shows that the percentage of long-range interactions at the modular boundaries is much higher than that in non-boundary regions. Residues forming inter-modular contacts fluctuate less than those participating only in the intra-modular interactions. One possible explanation of this finding is that most central residues, which have been shown to be important for the allosteric communications, are located at the inter-modular interfaces and, therefore, tend to be more rigid to maintain their contacts. Inspection of 13 allosteric proteins shows that functionally annotated regions exhibit a modular architecture, with modules interconnected by FCCRs, which are responsible for mediating the shortest pathways between all amino acids and, thus, play crucial roles in allosteric communications [ regions . The P45Our results lead us to propose that the modular architecture of protein structures yields a more efficient performance of the functional activity. Modules may possess certain functional independence; and, they are interconnected through amino acids previously shown to mediate signaling in proteins. Modules consist of groups of highly cooperative residues. Evolution has organized proteins as systems consisting of modules linked by amino acids that maintain the shortest pathways between all amino acids and are, thus, crucial for signal transmission, leading to robust and efficient communication networks. This organization is advantageous and, as such, has been conserved by evolution.Here we propose a novel way to decompose protein structures into modules based on their representation as residue interacting networks . Our approach relies on the edge-betweenness clustering algorithm presented by Newman and Girvan ,21. ModuWe carried out the modular decomposition of protein structures of a non-redundant dataset of 100 multi-domain proteins . Results show that the majority of the modules have most of their residues in one domain Figure . This fiA detailed analysis of 115 proteins  with available structures in different conformational states and temperature B-factors showed that residues with inter-modular contacts fluctuate less than those forming exclusively intra-modular contacts. Figure The modular decomposition of protein structures provides information about functional sites and signal transmission. We selected a dataset of 13 allosteric proteins based on previously analyzed examples [s subunit and Cytochrome P450eryF and P450cam examples are discussed in detail below.Functional sites can be decomposed into modules. In some cases, the modules are located in different domains. An illustrative example of this situation is the pyruvate kinase . The catalytic site is divided into two modules belonging to different domains and exhibiting different degrees of flexibility   shows that the adenylyl cyclase-binding site is divided into two modules: one of the modules contains the switch I and switch II regions and the other module comprises the α 3-β5 loop  [The camphor monoxygenase P450cam catalyzes the 5-exo hydroxylation of camphor . Its actsite II) . Allostesite II) . The modThese examples suggest that the modular design of functional sites might be related to their sub-functional specialization. Each module contains a portion of the active site and is mainly involved in a specific sub-function, such as the binding of the substrate, the product or an allosteric ligand.Analysis of the previously studied dataset of 115 proteins with functional site annotations  indicates that modules exhibiting high modularity values tend to comprise functional sites. The analysis of all modules illustrates that a large percentage of modules comprising functional regions exhibit above average modularity values Figure . Figure s subunit and of Cytochromes P450eryF and P450cam illustrate that the modular architecture of the active site may relate to its sub-functions. Modules containing functional sites display high modularity, suggesting that modularity can be used to identify functional modules.In signaling proteins, modular domains can act as switches mediating activation, repression and integration of diverse input functions. Experimental studies confirm that inter-domain linker regions are crucial for the domain coupling required for the information transfer . Our appTo conclude, our approach decomposes protein domains into modules. Mapping annotated functional regions onto the decomposed structures illustrates that the modules characterize functional sites. We observe that most inter-modular boundary residues provide the shortcuts in the communication wires. These residues maintain the shortest pathways between all amino acids, leading to robust and efficient signal transmission communication networks. Functional specificity and regulation relies on the communication between modules. This advantageous organization has been conserved by evolution. Furthermore, due to the possible functional independence of modules, changes in boundary residues may lead to new functions or to functional alterations as might be needed in a changing environment. Therefore, a modular configuration might allow signaling proteins to increase their regulatory links, and to expand the range of control mechanisms either via new modular combinations or through modulation of inter-modular linkages. Since our results indicate that boundary residues are crucial in efficient short communication pathways, both mechanisms appear possible.A non-redundant dataset of 100 multi-domain proteins was selected from NCBI . The domEach protein structure was modeled as an undirected graph, where amino acid residues corresponded to vertices, and their contacts were represented as edges. Residues i and j were considered to be in contact if at least one atom corresponding to residue i was at a distance of less than or equal to 5.0 Å from an atom from residue j. This value approximates the upper limit for attractive London-van-der-Waals forces ,37.et al. [Qm as follows:FCCRs were calculated as in del Sol et al. . Proteinet al.  based onet al.  of the Get al. , we defiL is the number of edges in the network, lm is the number of edges between nodes in module m, and dm is the sum of the degrees of the nodes in module m. The rationale for this modularity measure is as follows: modules with high modularity values must contain many within module links and as few as possible between-module links. The equation above imposes Qm = 0 in cases when the module comprises the whole network or if nodes are placed randomly into modules.where The analysis was carried out over the dataset of 115 proteins with conformers in two ways. We first calculated the averaged main chain residue RMSD considering all pairs of structurally aligned conformers. The structural alignments were obtained using MultiProt ,47. We aThe following additional data are available with the online version of this paper. Additional data file Additional examples of protein modularity and the datasets used for the analyses.Click here for file


Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites.We generate descriptors for 740 classified types of protein-protein binding sites and for more than 3,000 protein-ligand binding sites. Cross validation reveals that two thirds of the PPI descriptors are sufficiently conserved and significant enough to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally identify the interface residues. Finally we test ligand-binding descriptors for the case of ATP. From sequences with Swiss-Prot annotation "ATP-binding", we achieve a recall of 25% with a precision of 89%, whereas Prosite's P-loop motif recognizes an equal amount of hits at the expense of a much higher number of false positives (precision: 57%). Our method yields 771 hits with a precision of 96% that were not previously picked up by any Prosite-pattern.The automatically generated descriptors are a useful complement to known Prosite/InterPro motifs. They serve to predict protein-protein as well as protein-ligand interactions along with their binding site residues for proteins where merely sequence information is available. Exhaustive knowledge about protein interactions is a prerequisite to understanding the molecular machinery of the cell. While comprehensive protein sequence databases are available, the number of known PPIs is still small. In addition, experimentally proven PPIs often do not reveal the binding sites involved. The implications of the discovery of binding sites are manifold: the discovery of patterns in amino acid arrangements is of general importance in the study of protein-protein interactions. Furthermore, docking algorithms benefit greatly from the correct prediction of binding sites. Finally, interaction prediction is the key to mapping global interaction networks and signalling pathways, and may help elucidate the functions of individual proteins. Complementary to experimental techniques are computational approaches that analyze and predict protein-protein interactions. Sequence-based methods include gene context conservation , synthetFunctional characterization of novel genes and their proteins remains an important and challenging task. It often is improved by the identification of novel interaction partners. It has been observed that structural and functional features of proteins like catalytic sites are often well conserved . In contAs pointed out by Bailey and Gribskov , the siget al. [Many approaches for interaction prediction and function annotation require structural knowledge about the protein of interest -23. By wet al.  prove thWe compiled a library that comprises profile HMM descriptors for 740 protein-protein and 3000 protein-ligand binding sites in the Protein Data Bank PDB . Each deet al., even homologous domain pairs can associate in geometrically different ways by employing different sets of residues to form interfaces [Based on the family level of the Structural Classification of Proteins, SCOP , we can terfaces ,20. Consterfaces . Often sterfaces , MAST [1terfaces  and Metaterfaces  to repreAs large protein complexes are often difficult to crystallize, knowledge about protein-protein interactions can be drawn from the more abundant protein-peptide interactions. The descriptor construction method can be extended to these cases in particular and to PLI in general. We construct HMM profiles for faces that bind to small molecules and peptides. To this end we scanned PDB for most frequently occuring co-crystallized ligands. We identify the residues surrounding the ligand incl. possible cofactors. A profile is solely built from one structural template and aligned sequences utilizing HSSP .In order to assess the significance of HMM scores, a number of comparisons to shuffled databases were conducted. Figure et al. [Expectation values provided by the HMMer software are a means for assessing the significance of HMM hits. As demonstrated by Li et al. , the staWe evaluate ligand-binding descriptors for the case of ATP. The descriptors for ADP-binding and ATP-binding were run against 205,000 Swiss-Prot-annotated sequences and could extract 10,349 hits whereof 9,255 were true positives and 1,094 false positives. Given that 36,774 sequences were annotated as ATP-binding, this amounts to 25.2% recall and a precision of 89.4%. Prosite's P-loop motif recognizes 24.87% but in contrast produces 6792 false positives (precision: 57.4%). Figure The precision-recall curve Figure  for our Some descriptors exhibited great similarity to the P-loop motif, e.g. the descriptor derived from PDB entry 2BEK, a chromosome partitioning ATPase Figure : the cenFigures Note that small motifs like e.g. the poly-proline (PxxP) motif occur frequently in sequence where only 5% are functional. Nevertheless, hits of small motifs are helpful to identify candidate sets that should undergo manual postprocessing.The initial data set comprised 740 interface descriptors of protein-protein interactions, each having at least three non-redundant instances (not more than 98% sequence identity). In order to check the ability of face descriptors to recognize faces from structures in the PDB, we performed 2-, 4- and 8-fold cross validations on interface types with at least 8 non-redundant instances. This yields a set of 45 interface types, i.e. 90 face types. This set was run against domains from PDB that are classified by SCOP. For 61 face types (67%), a reasonable recall of 70%–100% was achieved .We obtained a corpus of 32,000 uncharacterized proteins from the NCBI's non-redundant protein sequence database. Face descriptors were run against these sequences and a shuffled version. The result is shown in Figure E. coli Complex II 2Fe-2S ferredoxin domain  matches this protein with an E-value of 4.4e-7. In contrast, the same descriptor achieves only 0.057 as best E-value when run against the shuffled version of the uncharacterized sequence database.The face descriptor constructed from gi|77956751|ref|ZP_00820831.1|: score 14.5, E = 4.4e-07ferredoxin_binding_site *->yRrSCReavCGSdviR.vD<-*SCR ++CGS+ i vDZP_00820831 --YSCRAGICGSCRITlVDYersinia bercovieri interacting with succinate dehydrogenase flavoprotein subunit. The latter was found to be present in the available Yersinia pestis genome via sequence similarity search.This suggests that uncharacterized Fe-S protein could be part of the Complex II of In order to estimate the quality of descriptors, E-value ratios for all descriptors in shuffled and original databases were analyzed in dependence of descriptor length Figure . Not surThe match of a descriptor pair allows identification of the putative residues responsible for the interaction. This information can be used to guide site-directed mutagenesis experiments that aim at disrupting the interaction.The proposed method for binding site prediction can be applied by itself or in combination with other methods. A common technique for protein interaction prediction is by identifying interacting homologues. This concept of so-called interologues was first noted by Walhout et al.  and was For some interface types only few or no structures are available. This implies that protein-protein interaction descriptors are inaccurately or not at all represented by the descriptor library. On the other side, a pair of homologous sequences from HSSP does not necessarily preserve the interaction of the structural template and thus does not belong to a certain interface type. These sequences "contaminate" the alignments of interface descriptors. It is therefore essential to assure that recruited sequence pairs not only maintain an interaction but also agree in interface type, i.e. have similar sets of interface residues.N and M hits for each face, respectively, induces N × M candidate interaction pairs. Aytuna et al. [A related problem is that of interaction specificity. An interface descriptor with a et al.  argue ana et al.  verify iOur technique to construct interface descriptors is inadequate for short, strongly dispersed or highly variable interfaces (e.g. loops in immunoglobulins). However, it is possible to create a descriptor with our method that spans over the surrounding secondary structures, which are often well conserved.We provide a library of Hidden Markov Model based descriptors that capture important structural features such as protein interfaces, ligand binding sites and active sites of enzymes. The implications for predicting binding sites and binding partners of proteins are many-fold. It provides insights into the biological processes the matched protein might be involved in. Furthermore the method can pinpoint interacting residues. It thus bears the potential for functional annotation and for assisting in discovering new drug targets.Cross validation with the available structural data for a number of interface types reveals that two thirds of the face descriptors have a recall between 70% and 100%. Interaction prediction by recognizing both faces is intrinsically harder than just one-sided binding site detection. The cross validation results reflect this fact, as the recall for predicted interactions drops to 39%.To demonstrate the biological significance of our descriptors, we compare the descriptors to NetPro, a PPI database with literature evidence. This way we could validate the predicted interactions and moreover provide insights about the critical interacting residues.We created a benchmark for ATP-binding site detection. From a database of Swiss-Prot annotated sequences, our descriptors successfully recognized 30% while producing much less false positives than Prosite's regular expression for the P-loop motif.Yersinia bercovieri is presented, which suggests a possible function as Complex II (succinate dehydrogenase) subunit for this protein.Finally, an example for a significant hit for a binding site in an uncharacterized protein from The workflow of our method is illustrated in Figure l/(l+1), where l is the average length of the linker region between two interacting segments. Finally, the merged HMMs are calibrated using HMMer's calibration with 5000 random sequences. The resulting HMMs are directly usable with the HMMer package [For PPI descriptors, data is taken from the SCOPPI database . SCOPPI  package  which prDespite the fact that homodimers account for a large set of interface types, we omit them because it is often unclear whether a homodimer interface is genuine or an artifact from crystallization.Due to the current lack of a comprehensive classification for geometrical associations of protein-ligand interactions, we generate PLI descriptors from single structural templates:0. For each structure in the PDB containing a protein and a ligand:1. Select a ligand2. Iteratively expand the selection to include surrounding cofactors3. Identify the residues surrounding the ligand selection within 4.5 Angstrom4. If this yields at least three well conserved residues:5. Include direct well conserved sequence neighbours6. Include residues that are between selected sequence neighbors7. Add sequences identified as structural homologous by HSSP8. Generate HMMs for each segment9. Combine HMMs into one descriptor connected by inserting states that reflect the linker regions between segments10. Add descriptors (one for each ligand) to library11. The library can now be used to predict ligand binding sites.Conservation of residues is calculated by using the von-Neumann-Entropy in combination with the substitution matrix BLOSUM62 (details are given in ).We evaluate the descriptors' accuracy in terms of standard precision and recall, where precision is defined as TP/(TP+FP) and recall is defined as TP/(TP+FN). TP, FP, and FN denote the numbers of true positives, false positives and false negatives, respectively.ATP – Adenosine triphosphate, PLI – Protein-Ligand Interaction, PPI – Protein-Protein Interaction, HMM – Hidden Markov Model, SCOP – Structural Classification Of Proteins, SCOPPI – Structural Classification Of Protein-Protein Interactions, PDB – Protein Databank, HSSP – Homology-derived secondary structure of proteins, MSA – Multiple Sequence Alignment, TP/FP/TN/FN – True/False Positives/Negatives, resp.The authors declare that they have no competing interests.AH: Concept, Implementation, Validation, Manuscript; CW: Validation, Manuscript; WK: Concept; MS: Supervision, ConceptIndividual recall results per face descriptor. Cross validation results for each interface type.Click here for file


The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification. Protein-protein interactions provide useful insights into functional associations between proteins . The curTo resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, our group  and otheet al. [In this work, we integrate protein-protein interactions with the information content in the GO annotation database to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction . The weiet al.  defined A functional module is defined as a maximal set of proteins that are involved in the same biological process or function. Based on the assumption that the members in the same module strongly bind each other, a functional module is described as a sub-network in a protein interaction network. Thus, identifying functional modules can be a graph clustering problem. We present an efficient algorithm to identify functional modules in a protein interaction network. Our algorithm is capable of detecting overlapping modules, whereas most of the previous graph clustering approaches generate disjoint modules with mutually exclusive sets of proteins. Because a molecule generally performs different biological processes or functions in different environments, real functional modules are overlapping. Our modularization algorithm first selects a small number of informative proteins, which work as representatives of modules. Next, it simulates information flow starting from each informative protein through the whole weighted interaction network. The flow then reveals a set of proteins under the influence of the informative protein as a potential functional module. The modules may overlap with each other if two or more informative proteins influence the same proteins. Our experimental results demonstrate that the modules identified by our algorithm are statistically significant in terms of cellular functions.Graph clustering approaches can be categorized into three groups based on the underlying methodology: density-based clustering, partition-based clustering and hierarchical clustering. Density-based clustering approaches search for densely connected sub-graphs. A typical example is the maximum clique algorithm  for detePartition-based clustering approaches explore the partition of a network including all sparsely connected nodes. The Restricted Neighborhood Search Clustering (RNSC) algorithm  discoverHierarchical clustering approaches can be justified for bioinformatics applications because of the hierarchical organization of biological systems ,27, and Our group recently developed the STM algorithm , which dG with proteins as a set of nodes V and interactions as a set of edges E. N(vi) denotes the neighbors of vi, the set of nodes connected to the node vi. The degree of vi is then equivalent to the number of neighbors of vi, |N (vi)|. A functional module in a protein interaction network G is described as a sub-network G' structured by a set of nodes V' where V' ⊆ V and all the edges among the nodes in V' . Because a protein can be included in several different protein complexes to perform different functions, functional modules overlap with each other. However, even though the functional modules share the members, they still provide topological significance with dense intra-connections and sparse interconnections among modules in a protein interaction network.The protein interaction network is represented by an undirected, un-weighted graph A, B, ⋯, L} and {M, N, ⋯, X}, are clearly detected by disconnecting two edges ⟨L, N⟩ and ⟨K, M⟩. The modules can be characterized by the intra-connection rate, which is the number of connections among the nodes in a module over the number of all connections starting from the nodes. The two modules in Figure Figure L, P⟩ and ⟨J, M⟩ from the network in Figure A, B, ⋯, L} and {M, N, ⋯, X} are both 0.81. In this network, each set can grow through new connections to generate the modules with higher intra-connection rates. For example, the set {A, B, ⋯, L} may add the nodes {M, N, O, P} to form a module {A, B, ⋯, L, M, N, O, P}. The intra-connection rate of the module is then increased to 0.87. The other set {M, N, ⋯, X} can also add the nodes {I, J, K, L} for a higher intra-connection rate. The overlap between the two modules thus includes the nodes {I, J, K, L, M, N, O, P}.The network in Figure semantic similarity. Semantic similarity has been used in Information Science to evaluate the similarity between two concepts in a taxonomy [x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term, then they are functionally more similar. See Methods for more details of the semantic similarity.We measure the reliability of protein-protein interactions by two novel definitions that quantify the functional correlation of two proteins using Gene Ontology (GO) annotations. The first metric is taxonomy , and we semantic interactivity. The semantic interactivity is derived by combining the GO annotation data with the connectivity in a protein interaction network. Suppose a protein x is annotated on a GO term gi and a protein y is annotated on a GO term gj. If a large proportion of interacting partners of x appears in the annotation of gj and a large proportion of interacting partners of y appears in the annotation of gi, then x and y are likely to interact with each other. If x and y are annotated on the same GO term gi, then the semantic interactivity increases when more interacting partners of x and y are included in the annotation of gi. See Methods for more details of the semantic interactivity.The other metric is called We assign the reliability of each protein-protein interaction, measured by semantic similarity and semantic interactivity, to the corresponding edge as a weight, and build a weighted interaction network integrated with the functional information from the GO database.In our earlier study , we propdw(x) of the proteins, which is defined as the sum of the weights of the edges between the node x and its neighbors:In Phase 1, informative proteins are selected based on the weighted degree w is the weight of the edge between x and y. Because the weights are obtained from biological knowledge, the weighted degree of a node includes the factors related to the topological significance in the network and biological importance of the corresponding protein. The number of informative proteins selected is a user-dependent parameter in this algorithm.where s flows through every possible path in a weighted network. We can thus quantify how much a protein s functionally influences other proteins in a network.Phase 2 simulates the flow starting from each informative protein. The flow simulation is based on the concept that the functional information of a protein fs(x → y) represents the amount of influence of s that travels from x to y, and infs(y) is the amount of influence of s on y. The algorithm begins with assigning the weighted degree dw(s) to each informative protein s as an initial amount of influence infs(s), whereas 0 to all non-informative proteins. For each informative protein s, the initial amount of influence of s, infs(s), is delivered into each neighbor y of s as being reduced by the weight of the corresponding edge. Thus, the initial flow fs of the influence of s is defined as:As notations, the flow y ∈ N(s), where 0 ≤ w ≤ 1. The amount of influence of s on a protein y, infs(y), is updated by adding the sum of all incoming flow fs to y from its neighbors.for each fs(s → y) is equivalent to infs(y) because all the nodes except s have 0 as an initial amount of influence of s. The influence of s on y then traverses all connected edges in the network by the flow defined as:In the case of initial flow, y, z〉 ∈ E and 0 ≤ w ≤ 1. The algorithm repeatedly sums up the amount of all incoming influence of s on each node using Formula 3 and passes the influence through all connected edges with Formula 4. The influence passing through an edge is reduced according to the weight. If the weight is close to 0, then the influence is quickly reduced. In contrast, if an edge ⟨x, y⟩ is fully reliable, i.e.,w = 1, then infs(x) can be transferred to y intact.where the edge ⟨s on a node x is a major factor to determine how likely s and x are to be included in the same functional module. Since the flow visits all the nodes through every possible path, densely connected nodes close to an informative protein s generally have larger amount of influence of s than sparsely connected nodes.The algorithm also accumulates all the previous amounts of influence on each node during the flow simulation. The accumulated amount of influence of s terminates when there is no more flow in the network. A preliminary module is then created with a set of proteins under the accumulated influence of s. Simulating the flow from all informative proteins generates a set of preliminary modules that can potentially overlap.The flow in a path stops if it reaches a minimum threshold. The flow simulation starting from an informative protein S between two modules Ms and Mt is measured by the weighted interconnectivity defined as:Phase 3 is a post-processing step that merges similar preliminary modules to produce final modules. The similar preliminary modules result from the functional closeness of two or more informative proteins because an informative protein works as the representative of a preliminary module in terms of functionality. The similarity whereThe modules with the highest similarity in Formula 5 are iteratively merged until the highest similarity is less than a merging threshold.We extracted the core protein interaction data of Saccharomyces cerevisiae from DIP, the Database of Interacting Proteins , and meaFor the functional co-occurrence in Figure The localization is conceptually more general than biological functions. Thus, as shown in Figure et al. [As representatives of modules, the informative proteins should be functionally essential. Importantly, Jeong et al.  have obsTo evaluate the functional essentiality of the informative proteins, we employed their lethality information. The lethality is determined from biological experiments in which the protein is knocked out. Protein lethality data were obtained from the MIPS database. We selected the proteins with the highest values of (unweighted) degree and weighted degree using semantic similarity and semantic interactivity, and calculated the proportion of lethal proteins. Figure We implemented the flow-based modularization algorithm with the core protein interaction data from DIP. As inputs, we used two interaction networks weighted by semantic similarity and semantic interactivity. The algorithm requires two user-dependent parameter values: the number of informative proteins and the minimum amount of flow on a node. The number of modules in an output set depends on the number of selected informative proteins. On the other hand, the minimum amount of flow determines the average size of output modules. By changing the two parameter values, we achieved ten different output sets of modules for each weighted interaction network [see Additional file The output modules share a large number of common proteins. To evaluate their overlapping patterns, we counted the number of appearance across different modules for each protein. The average overlapping rates on the sets of identified modules are shown in Figure We compared the overlapping rates to those of annotated proteins in functional categories from the MIPS database. The functional categories in MIPS are hierarchically distributed: there are 17 different categories on the top level as the most general functions, and 77, 170 and 239 categories on the second, third and fourth level from the top, respectively. We calculated the average appearance of proteins on the second, third and forth level categories. Figure p-value from the hypergeometric distribution [p-value, and calculated the negative of log. A low p-value ) between an identified module and a reference function indicates that the module closely corresponds to the function. The functional categories and their annotations from the MIPS database were used as the reference functions.To statistically assess the identified modules, we employed the ribution . We mapplog(p) of the output modules. Overall, weighting with semantic interactivity resulted in the best accuracy of modularization. Besides, both semantic similarity and semantic interactivity outperformed the weighting scheme based on the microarray-derived co-expression.We show the performance improvement achieved from semantic integration. The modularization results using semantic similarity and semantic interactivity were compared to the gene co-expression approach we have previously reported . For thilog(p) of the output modules before and after post-processing. The post-process is the step to merge similar modules after flow simulation. As shown in Table We also monitored the average -Next, we compared the performance of the flow-based modularization algorithm to three competing state-of-the-art methods: the CFinder algorithm  as a denp-value is highly dependent on the module size. Figure log(p) across different sets of output modules produced by varying parameter values for the number of informative proteins and the minimum flow threshold. Although the average value of -log(p) increased as the average size of modules increased, it converged to approximately 34 and 39 with the semantic similarity and semantic interactivity weighting scheme, respectively. In a similar analysis, we found that the average -log(p) of the output modules generated by the betweenness-cut algorithm converged to 20 as shown in Figure The -log(p) converged to approximately 23, which is higher than the result from the betweenness-cut algorithm without pre-processing.False positive interactions in a protein interaction network possibly cause miscalculation of betweenness because the faulty information yields incorrect shortest paths in a network. To resolve this problem, we also implemented the betweenness-cut algorithm with the pre-processing step to filter out potential false positives. We eliminated the edges, whose semantic similarity is less than 0.25, and applied the refined network to the betweenness-cut algorithm. Figure -log(p) was further increased. These results indicate that when large-sized modules are generated by the flow-based algorithm, the modules are enriched for biological function. Furthermore, overlapping modules obtained by the flow-based algorithm have statistically higher associations with functions than the disjoint modules from partitioning methods.Figure log(p) are listed with their informative proteins and functions in Table The subset of the modules identified by our algorithm with high values of -f-measure is defined as the harmonic mean of recall and precision. The average f-measure value of all modules was calculated by mapping each module to the function with the highest f-measure value.To directly compare the identified modules with reference functions, we used a supervised method with recall and precision. Recall measures the tendency of the reference function to match the identified module, whereas precision represents the accuracy of the identified module for matching to the reference function. The f-measure value of the modules that were generated before and after post-processing are shown in Table p-value, the post-process slightly improved the accuracy of modules from our two metrics for interaction reliability measurements, and the semantic interactivity measurement had the best accuracy of modularization.The average f-measure value. In contrast, we had more mismatches when we compared the modules to large-sized functional categories. In Figure We examined whether the flow-based algorithm identifies the various sets of modules on different levels in a functional hierarchy. In the same way to the experiment above, we generated ten different output sets with different parameter values and compared the modules in each set to the annotations on the second, third and fourth level categories in the MIPS functional hierarchy. As shown in Figure Experimentally generated protein-protein interaction data includes an enormous amount of false positives. In this paper, we introduced two novel metrics to measure the reliability of interactions. For this measurement, we use the annotations in Gene Ontology (GO), which provides the comprehensive functional information. When we implemented the Betweenness cut algorithm after filtering out the interactions with low semantic similarity values, the overall accuracy of modules is substantially improved. This result strongly appeals the necessity of integrating of functional information for the analysis of protein-protein interaction data.Most of the previous graph clustering approaches encounter limitations when detecting modules in protein interaction networks. Since they generally generate small-sized dense sub-graphs, they are not adept at identifying hierarchically distributed functional modules. We have developed the flow-based modularization algorithm to identify overlapping modules in a hierarchy. Although the density-based methods, such as CFinder, and the STM algorithm allow overlap among modules, the overlapping rate is very low. However, our algorithm identifies the overlapping modules with high overlapping rate, comparable to the real functional associations of proteins. Since the common proteins in the overlapping modules bridge functional sub-networks, they are topologically and biologically significant.O(n × m) where n is the number of nodes and m is the number of edges. The flow from each informative protein can be simulated in parallel. The total time complexity of the flow-based algorithm depends on the post-process. Suppose we have k preliminary modules. We first insert the interconnectivity values for all possible pairs of modules into a heap-based priority queue in O(k2 log k) time. Next, we iteratively merge the most interconnected pair. Each iteration requires O(k log k) time. Hence, the overall time complexity of merging preliminary modules can be solved in O(k2 log k) time where k is the number of informative proteins. If we simulate the flow from every node in a network, then the algorithm generates n preliminary modules and merges them in O(n2 log n) time where n is the number of nodes.Another strength of our approach is efficiency. Since we use an index for the adjacency-list representing the input network, the time complexity of the flow simulation is dominated by the index construction, which runs in Structuring a modular hierarchy by the flow-based algorithm can be a primary task for deeper analysis of functional relationships among proteins. There are still a large number of functionally uncharacterized proteins in yeast even though it is one of the most well-studied organisms. This study can provide underlying bases for the prediction of functions of the uncharacterized proteins. Moreover, our approach is adaptable to other higher-level organisms because of the efficiency and scalability.gi has a relation "is-a" to gj, then gi is conceptually more specific than gj. A Directed Acyclic Graph (DAG) is then built with the GO terms as a set of nodes and their relationships as a set of directed edges.The Gene Ontology (GO) database provides GO terms and their relationships. The GO terms are well-defined biological terms, comprising three categories as the most general concepts: biological processes, molecular functions and cellular components. The GO terms are structured by the relationships to each other, such as "is-a" and "part-of". "is-a" and "part-of" represent specific-to-general and part-to-whole relations, respectively. For example, if a GO term x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1 ≤ i ≤ m. In the same way, suppose both x and y are annotated on n different GO terms, where n ≤ m. Sj denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1 ≤ j ≤ n. Then, the minimum size of Si(x), mini |Si(x)|, is less than or equal to minj |Sj|.Suppose a protein x and y are annotated, we define semantic similarity Ssem between x and y as follows:Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins wherez is a normalization term using the maximum size of annotation, Sroot, and the minimum size of annotation, Smin, among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term than x and z, then x is semantically more similar to y than z. The semantic similarity Ssem can be assigned to the edge between x and y as a weight.x in a protein interaction network, we compute the probability P that x interacts with the annotated proteins on the GO terms, whose annotation includes y:Based on the connectivity of a protein N'(x) = N(x) ∪ {x}. Since y can be annotated on k different GO terms, we use the maximum size out of k possible sets. If x and all of its neighbors are not included in Si(y) for any i, then P  is 0. If all of them are included in a set Si(y), then P  is 1. Equation 9 thus satisfies the range of 0 ≤ P ≤ 1. We finally measure the semantic interactivity Isem  between x and y using the geometric mean of P and P.where Isem between x and y can be assigned to the corresponding edge as a weight.The semantic interactivity x and y using the Pearson correlation coefficient r:The correlated behaviors in gene expression data, called co-expression, are pertinent to functional associations among molecules. Since the intensities of expressions may include noise due to the microarray experiments, we use the normalized expression data. We then measure the co-expression between two proteins n is the number of time points for the expressional profiles. The absolute value of r can be a weight for the edge between x and y.where p-value from the hypergeometric distribution, which is defined as:As a method for statistical evaluation of modules, we used the V| is the total number of proteins, |X| is the number of proteins in a reference function, n is the number of proteins in an identified module, and k is the number of common proteins between the function and the module. It is understood as the probability that at least k proteins in a module of size n are included in a reference function of size |X|. Low P indicates that the module closely corresponds to the function because the network has a lower probability to produce the module by chance.where |X is mapped to a functional category Fi. Recall, which is also called true positive rate or sensitivity, is the proportion of common proteins between X and Fi to the size of Fi, and precision, which is also called positive predictive value, is the proportion of common proteins between X and Fi to the size of X.Suppose a module andX is likely to include many members out of Fi. As an extreme case, if we generate all the proteins as one module, then we have the maximum value of recall. In contrast, smaller size of modules have higher precision because the members of X are likely to be homogeneous in a particular function. As an extreme case, if we generate a single protein as one module, then we have the maximum value of precision. We can thus assess the accuracy of modules with the f-measure, which represents the harmonic mean of recall and precision.In general, larger size of modules have higher recall because the module The protein-protein interaction data has been accumulated by several high-throughput experiments. We used core interaction data of Saccharomyces cerevisiae from the 2006 version of DIP, the database of interacting proteins . The corThe gene expression data of Saccharomyces cerevisiae from alpha, CDC15, CDC28, ELU, CLN3 and CLB2 experiments was obtained in SMD . It inclThe Gene Ontology (GO) Consortium database  containsYRC designed and implemented the data integration and modularization algorithm, analyzed the results and drafted the manuscript. WH partly designed the algorithm and analyzed the results. MR and AZ coordinated the project and revised the final manuscript.Modularization results of the networks weighted by semantic similarity. Ten different output sets of modules were generated by the flow-based algorithm. The input was the protein interaction network weighted by semantic similarity. To assess the accuracy of modules, the average f-measure and the average -log were measured for each output set.Click here for fileModularization results of the networks weighted by semantic interactivity. Ten different output sets of modules were generated by the flow-based algorithm. The input was the protein interaction network weighted by semantic interactivity. To assess the accuracy of modules, the average f-measure and the average -log were measured for each output set.Click here for file


One of the most evident achievements of bioinformatics is the development of methods that transfer biological knowledge from characterised proteins to uncharacterised sequences. This mode of protein function assignment is mostly based on the detection of sequence similarity and the premise that functional properties are conserved during evolution. Most automatic approaches developed to date rely on the identification of clusters of homologous proteins and the mapping of new proteins onto these clusters, which are expected to share functional characteristics.Here, we inverse the logic of this process, by considering the mapping of sequences directly to a functional classification instead of mapping functions to a sequence clustering. In this mode, the starting point is a database of labelled proteins according to a functional classification scheme, and the subsequent use of sequence similarity allows defining the membership of new proteins to these functional classes. In this framework, we define the Correspondence Indicators as measures of relationship between sequence and function and further formulate two Bayesian approaches to estimate the probability for a sequence of unknown function to belong to a functional class. This approach allows the parametrisation of different sequence search strategies and provides a direct measure of annotation error rates. We validate this approach with a database of enzymes labelled by their corresponding four-digit EC numbers and analyse specific cases.The performance of this method is significantly higher than the simple strategy consisting in transferring the annotation from the highest scoring BLAST match and is expected to find applications in automated functional annotation pipelines. The gap between the growth rate of biological sequence databases and the capability to characterise experimentally the roles and functions associated with these new sequences is constantly increasing . This reThis type of annotation process relies on the assumption of a strong relationship between protein sequence and function. This hypothesis is generally fair  even thocorrespondence indicators (CIs) between sequences and functional classes. Then, we explore two Bayesian annotation frameworks based on the comparison of the CIs of a sequence of unknown function with the observed CIs for the reference protein sequences. This framework provides probabilities for a sequence to belong to the different functional classes. We advocate the use of these probabilities as a direct measure of the reliability of annotations.In this work, we address this issue by developing a probabilistic framework to the homology-based annotation process. Our approach relies on the usage of a reference dataset where protein sequences are classified into functional classes. Here, an annotation is a membership to a functional class, thus, function sharing is evident. The possibility for a protein to perform a particular function is then assessed based on its similarity relationships with all protein sequences known to perform this function; it enables for instance to take into consideration both the presence and the absence of similarity. This possibility is used during the training step of machine-learning approaches for sequence annotation, which relies on the availability of a classified reference dataset -23. NoteTo validate both probabilistic methods for automatic annotation, we applied them to the well-established classification of enzymes. Our results show that both methods allow distinguishing proteins whose annotation is reliable from the others. At the highest level of reliability, the two methods predict the four EC digits with a very low error rate (~0.002) for 90.6% and 96.0% of enzymes respectively. We compared these results with the simple strategy consisting in transferring the EC number of the BLAST best hit. Our best method has an error rate half that of the best-hit strategy at the same coverage level.Correspondence Indicator (CI) between the new protein and each of the functional classes, and second, formulated a classification scheme based on these indicators. This section is devoted to the first point, whereas the second one will be treated in the following section.Given a functional classification, annotating a new protein consists in establishing to which functional class or classes it belongs. To approach the problem we defined a Using the bit-scores of sequence alignments (See Methods), we can imagine many different scoring strategies to measure this correspondence. For instance, we could use the number of hits (with a bit-score above a given threshold), or the best bit-score between the new protein and the functional class members. Alternatively, we might choose to compromise between the two above options by taking the sum of the bit-scores between the new protein and the class members.1, ..., Ωn symbolise the set of n functional classes with respective sizes N1, ..., Nn. We denote Sc,d the BLAST bit-score between two proteins c and d. Then, we define the CI c) parameterised by α∈ . This mechanism is illustrated in N (d) ± λ) is always equal to 10. This can be viewed as an adaptive smoothing of the data: λ is increased until the interval  contains a predetermined quantity of information (10 proteins).where P(c∈Ωj|{c) ... c)}) of a new protein c to belong to Ωj knowing the set {c) ... c)} of its CIs with all the functional classes i.e. we estimate probabilities based on multiple variables (indicators). Using Bayes theorem, we can show ) are closer to the EC 1.2.1.12 cloud than to the other EC 1.2.1.59 proteins in the CI space and thus are wrongly re-annotated as EC 1.2.1.12 proteins. The erroneously re-annotated EC 1.2.1.59 sequence O34425 is Bacillus subtilis gapB protein. Protein gapA [Swiss-Prot:P09124], also from B. subtilis belongs to class EC 1.2.1.12. It was shown that gapA can acquire the gapB activity with only two amino acids mutations (D32A and L187N)  is annotated as a member of EC 1.4.1.3 (NAD(P)-utilizing glutamate dehydrogenase) but the multivariate Bayesian method assigned it to EC 1.4.1.4 (NADP-specific glutamate dehydrogenase) with an assignment probability of 1. Indeed, P94598 is close to a group of EC 1.4.1.4 proteins in the CI space. Tracing the source of this annotation, we noted that its strong CI value with EC1.4.1.4 originated from a strong sequence similarity with protein P95544 [Swiss-Prot:P95544] annotated as EC 1.4.1.4. By checking the publication associated with the annotation of P95544, we noted that this protein was wrongly annotated and actually belongs to EC 1.4.1.3 [P17692] that we classify as EC 2.4.1.19  in disagreement with its database annotation: EC 3.2.1.1 . Actually, the EC 2.4.1.19 activity of P17692 has been described in the literature [Q11119]  should be annotated as EC 3.1.2.15 (ubiquitin thiolesterase). The experts of ENZYME have validated these two annotation errors and corrected the corresponding database entries.In the present work, we considered the annotations associated to the sequences in the ENZYME database to be exact. Nevertheless, analysis of the origins of annotation errors using visual representations of the CI space as shown Figure  1.4.1.3 . Correct 1.4.1.3 . The corterature . In addiThe maintenance of various aspects of protein function is intricate due to the inhomogeneity of the sequence-function relationship. For example, 60% of EC classes with more than 2 members could not be perfectly discriminated by sequence similarity at any BLAST threshold . MoreoveS0 = 45) with the proteins of the other classes. This illustrates that there exists a high level of clustering of enzymes sharing their four EC digits in the CI and sequence spaces. Thus, for the filtered ENZYME database we considered in this analysis , CI based on sequence similarity is a meaningful clue to predict the full EC code. In contrast, considering EC digit conservation based on pairwise sequence comparison, it was found that a good practical rule was to transfer 2 EC digits above 15% sequence identity [Interestingly, with both Bayesian approaches, a large majority of proteins have been re-annotated with an assignment probability of 1 Tables . In the identity . There iThe importance of standardising the systems by which biological functions are described is now generally recognised . This haAn important aspect of this work is the construction of correspondence indicators between sequences and functional classes (Eq.(1)). Here, we used BLAST bit-scores for this process but the score from any pairwise protein comparison can be used instead e.g. structural comparison ,34 or al+ or H+ transporting two-sector ATPases" that we can reliably assign to. A key feature of the proposed methodologies is the quantification of the reliability of annotations; the assignment probability represents an attractive candidate, both versatile and compact, to qualify non-experimentally based annotations. In principle, it could be taken into account by the Bayesian annotation framework allowing its iterative usage without risking the propagation of annotation errors [The analytical development leading to CIs can be extended to construct a measure of correspondence between two functional classes that describes the degree of their overlap in the CI space Fig. . Since an errors . It is o-5 for the database under consideration).In the present work, we put forward a method of classification of uncharacterised proteins, based on their pattern of homology with a reference set of classified proteins. We validate this approach on a database of enzymes annotated by their four-digit EC number. Annotations and sequences have been retrieved using release 30 of ENZYME ) and relThe tree-like structure of the EC nomenclature [See In order to quantify the performance of the different annotation strategies presented above, they were applied to re-annotate the filtered ENZYME database using a leave-one-out procedure. This method consists in removing in turn each enzyme from the reference dataset and to re-annotate it as if it was a new enzyme of unknown activity. The so-obtained classification of enzymes was then compared to the original classification. For the two Bayesian methods, new enzymes were assigned to the functional class for which the estimated probability is the highest.All authors participated in the design of the study and writing of the manuscript. EDL implemented the methodology and performed the analysis. All authors read and approved the final manuscript.contains a more detailed description of the EC nomenclature to complement section "A database of enzymes" and the full calculation leading to equations(2) and(3) along with a figure illustrating their meaning.Click here for file