We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model.The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods.The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature. The rapid pace of genome sequencing adds urgency to efforts for determining the functions of newly sequenced proteins. Analysis of protein sequences and structures can lead to new predictions and discoveries of significant patterns, motifs and functionally important sites. In the context of three-dimensional protein structures, the appearance of certain amino acid residues at key structural positions has a central role in protein function, for instance enabling ligand or substrate binding. For proteins of therapeutic importance, identifying these sites as potential targets is a key early step in drug design.The biomedical literature is a rich resource for identifying functionally important sites. There is a growing gap between the knowledge embedded in the literature and what has been formalized in genomic databases  and we hWe previously addressed this problem by developing automated methods for identifying protein residue mentions in biomedical text, and associating these mentions with functional sites ,3. In thOur previous methods ,3 used cA number of related works have been published with a focus on the extraction of point mutations -11. MEMAThe aims of our study are most closely related to those of Nagel et al., 2009 . That woWe focus here solely on developing methods to establish reliable protein-residue associations in situations where validating information may not be available, leaving functional annotation for future work. The Nagel et al. method does not consider any contextual syntactic dependencies between the protein and the residue, an important feature we investigated in this work. We have utilized the gold standard corpus developed by Nagel et al. as an independent test set for the evaluation of our method.rule graph. New relations are then recognized by searching for a subgraph isomorphic to a rule graph within the dependency graphs of complete sentences in the input texts. This approach has also been successfully adopted to extract protein-protein interactions in the biomedical literature  ..." , we extracted a relation between the protein "Vps4p" and "K179A" which seemed to be correct. On manual inspection we found that errors were due to the mismatch in the residue slot. The Nagel annotation for the protein-residue relation specifies "Vps4p" to be the protein and the full phrase "Vps4p-(K179A)" in the residue slot. On the other hand, our extraction fills the residue slot with the mutation information alone ("K179A"). This annotation is in fact not even consistent with the annotation of most other protein-residue relations in the Nagel corpus. If we correct for these mismatches, precision, recall and F-measure increase to 90.38%, 71.57% and 79.88%, respectively. Our overall performance indicates that the subgraph pattern learning approach with very high precision and good recall is more reliable than the co-occurrence baseline approaches, and is suitable for this information extraction task.SOCS3 to the highly conserved phosphorylated Tyr-985 and Tyr-1077 motifs within the mouse leptin receptor" are between leptin and each tyrosine. Our system instead proposes a relation between SOCS3 and each tyrosine. This sentence proved difficult for the parser; the prepositional phrase "within the mouse leptin receptor" was attached too high, to the verb "using" and as such the correct relation would have been difficult to identify via the syntactic analysis. Hence the example also represents two false negatives for our method.Our approach generates some false positives in which the relationship extracted involves the incorrect protein when there is more than one protein mentioned in the sentence. For instance, the correct relations for this sentence from PMID 11108838, "Using a functional assay based on inhibition of leptin mediated reporter induction, and using phosphopeptide affinity chromatography we show binding of We have identified some omissions in the Nagel et al. annotation. For instance, in the sentence, "Our results indicated that human LAB was primarily phosphorylated on three membrane-distal tyrosines, Tyr(136), Tyr(193), and Tyr(233)" from PMID 14722116, the relations between LAB and the three residues in that sentence appear to be valid protein-residue pairs, but are not annotated by Nagel et al. Rather the gold annotation includes only a relation from LAB to a more generic phrase "Tyr to Phe" from another sentence in the abstract. As we have not exhaustively reviewed the Nagel et al. annotation we used as our gold, we cannot determine the full impact of such errors on the performance of the various systems we tested.A notable contribution of our approach is to take advantage of the information in the existing repositories through distant supervision to create positive training instances for the pattern learning system. Although we are able to create high confidence protein relationship statements without manual effort, our approach has some limitations. There is a potential loss of true positive relationship statements, since (a) relationships may not be expressed exclusively within a single sentence, but rather using co-reference or simply by establishing a focus protein for the paper as a whole, and (b) the physical validation we utilize may miss some valid relationships due to variations in sequences, e.g., numbering differences in the PDB. This will result in loss of potential training patterns, which may in turn cause a decrease in recall.Through this work we have demonstrated that the application of a subgraph matching-based relation extraction approach generalizes well to the problem of extracting protein-residue associations. It achieves much better performance than baseline co-occurrence methods. The task itself has broader significance for protein function prediction and subsequent drug discovery, given the context of our ongoing research of into integrating evidence extracted from the biomedical literature into a protein function prediction system ,3.Furthermore, we have shown that the creation of an annotated data set through distant supervision is highly effective for quickly building high quality training instances. The patterns induced from such training data not only achieve high performance on the automatically created test data but also perform well on an independent, manually annotated gold corpus. The results encourage us to explore the use of distant supervision for other information extraction tasks in biology of higher complexity. Automatic creation of training data as shown in this study will significantly reduce the manual effort in creating gold corpora without much compromise on the overall performance of information extraction.http://bionlp-corpora.sourceforge.net/proteinresidue/. We used the version 0.2 of our corpora for the current study.We used multiple manually annotated data sets  for evaluating the performance of our entity detection and protein-residue relations in addition to the silver corpus we constructed. While we introduce the statistics of the silver corpus in Table Nagel et al. 2009  built a Caporaso et al. 2007  created While the silver corpus and the Nagel corpus utilize PubMed abstracts, we manually annotated 50 full text articles for amino acid residues and mutation mentions in them which we call LEAP-FS corpus. These full text articles were selected from among 18,045 PubMed IDs derived from the primary references of the PDB entries used in the LEAP-FS experiments  and descThe LEAP-FS corpus contains 3120 annotations in total, out of which 2831 were amino acid residues and 289 were mutations. A notable aspect of this corpus is that out of the 2831 amino acid annotations, the three letter amino acid residue mentions  Asp280) and single letter amino acid residue mentions  constitute 80% and 16% of the residue mentions, respectively, while the full amino acid residue names  and linguistic expressions (proline at position 127) constitute only the remaining 4%. The corpus has been annotated by a single annotator but is currently being annotated by a second annotator to support eventual calculation of inter-anntator agreement.Figure The premise of our work is that there is a set of frequently occurring rules, or linguistic patterns, that match a majority of protein-residue relations. We suggest that a rule characterizes the typical contextual structure of the relation. As described, we explored a graph-based approach ,17 basedWe created a "silver standard" corpus that contains high-confidence protein-residue relationships, substantiated by a physical match of that specific residue to that specific protein. We use this silver corpus for both inferring our rules to extract protein-residue association and testing our extraction accuracy. This strategy for creating annotated data uses the distant learning paradigm where external knowledge drives relation extraction learning ,28. BiolTo create the silver corpus, we acquired PubMed citations for each PDB entry. A dictionary of protein names was compiled from the BioThesaurus database  for all To enable the acquisition of the linguistic constructs corresponding to expressions of a protein-residue relationship, we first compiled a corpus of relevant abstracts from MEDLINE. We started with a set of 37,980 PDB entries linked to the 106,411 SCOP domains in the dataset we used in our prior work ,33,34. TAll abstract text in the corpus was split into sentences using the LingPipe sentence detector , tokenizOur approach to identify residue mentions and mutations in abstracts and full text articles is similar to earlier work ,11,21,37We used dictionary lookup with fuzzy matching  to recogFigure • The text occurrences for each abstract are grouped by site number to determine if amino acid mentions for that site are consistent - i.e., there is a single primary and, where present, a single mutation amino acid for the site.• A list of text residues was generated by mapping each consistent site in Step 1 to PDB entries using the PubMed ID of the primary reference.• The text residues are matched against physical residues in a corresponding PDB entry. This is done by exactly matching the text residue site number with a PDB residue number and then either the wild type or mutated text residue amino acid name with the PDB amino acid. If a text residue matches residues in more than one mmCIF entity in a PDB entry, it is labeled as an ambiguous match. Text residues with an unambiguous match are retained, while residues that do not match or have an ambiguous match are eliminated from further analysis.The requirement of a physical match between a residue mentioned in an abstract and a residue of an abstract-associated protein has the effect of filtering out extraneous residue mentions; we effectively filter out false positives of the text extraction method if the text occurrences cannot be linked to any physical residue.To facilitate both training and testing of our method, we require annotated examples of high confidence protein-residue relationships. In the current work our focus is only on extracting the protein-residue pairs that occur within a single sentence.Arg80 conformation in the UPRTase-UMP-CTP complex leaves no room for binding of the substrate PRPP." The protein name dictionary look up detects "UPRTase" as protein and the regular expression detects Arg80 as the residue. This protein-residue pair relation is validated via the PDB entry "1xtv", with PMID 15654744 given as the primary citation. The data in Table To enable the creation of a set of sentences that contains high-confidence relationship statements, we selected all sentences containing both a protein name in our PDB-specific protein name dictionary, as well as an amino acid mention as our initial pool of sentences. We further filtered this initial pool to only those sentences containing a physically validated relationship: i.e., sentences that contain a protein-residue co-occurrence substantiated by a physical match of that specific residue to the mentioned protein, and where the sentence comes from an abstract explicitly associated with that protein in its PDB record. Consider the following sentence from the PMID: 15654744 "CTP binding affects the conformation of Arg80, and the The 1,728 abstracts (last row in Table We learn the protein-residue relation rules from the silver corpus labeled training sentences (972 abstracts) using a graph-based rule induction method ,17. We bThe shortest path between the two entities (protein and amino acid/mutation) is often assumed to contain the most valuable information about their mutual relationship -44. The Figure Subsequently we attempt to match the event rules to each test sentence to extract relevant events. Since the event rules and the sentences all possess a dependency graph, the matching process is casted as a subgraph matching problem , which cAccording to the GENIA corpus, on average there are about 24 words in one biomedical sentence, which correspond to the nodes in the dependency graph. Consequently, the input graphs of sentences and event rules are not large graphs. Therefore, we applied a simpler subgraph matching algorithm using a backtracking approach, developed by Liu et al. ,17, to oWhen matching between graphs, different combinations of matching features can be applied, resulting in different matching criteria. The features include edge features (E) which are edge label and edge direction, and node features which are POS tags (P), and all tokens (A), ranging from the least specific matching criteria, E and P, to the much stricter criterion including A  It relies only on the positive instances and does not require negative examples for training. 2) Pattern induction through distant supervision where the relation between the protein and the residue mentions is known to exist in publicly available databases further reduces the risk of data over-fitting. 3) Unlike statistical methods, the approach produces a human-interpretable model, which allows more straightforward error analysis.The authors declare that they have no competing interests.KER and KV designed all the experiments described in this paper. KV designed the distant learning strategy. KER prepared the data sets, conducted the experiments and evaluated the performance of the system. KER also manually annotated the LEAP-FS corpus described in the paper. HL adapted his graph-based relation extraction system to the protein-residue association problem, provided his approximate subgraph matching algorithm, and assisted with performance analysis of those methods. JDC and MEW designed the physical validation algorithms and provided the data used to generate the silver corpus. KER, HL, and KV wrote the manuscript.


Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes. Protein complexes play crucial roles in a variety of biological processes, such as ribosomes for protein biosynthesis, molecular transmission and evolution of interactions between proteins. In fact, many proteins come to be functional only after they interact with their specific partners and are assembled into protein complexes. Hence, much effort has been made for predicting protein complexes from protein-protein interaction (PPI) networks However, the majority of known protein complexes are heterodimeric protein complexes. CYC2008 C-Support Vector Classification (C-SVC) with several features based on protein-protein interaction weights that are considered as reliability of interactions between proteins. The idea behind the design of feature space mappings is, for example, that the neighboring weights of a heterodimeric complex tend to be smaller than the weight inside of the complex. In addition to features based on weights, we propose feature space mappings based on the numbers of protein domains because those are considered to be functional and structural units in proteins. Furthermore, we propose a domain composition kernel based on the idea that two proteins having the same composition of domains as a heterodimeric protein complex would also form a heterodimer. We perform ten-fold cross validation, and calculate the average F-measures. The results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method.To further improve the prediction accuracy for heterodimeric protein complexes, we propose a method using G be an undirected graph with a set V of vertices and a set E of edges, where the weight of each edge ijw and represents reliability and strength of the interaction related with the edge. Actually, we use the WI-PHI database The problem we address in this study is stated as follows: Given a network of protein-protein interactions, where interactions are weighted, determine whether or not two interacting distinct proteins form a protein complex with size exactly two. A network of protein-protein interactions can be considered as a graph, where vertices represent proteins and edges represent protein interactions. Let C-SVC through linear kernels but are transformed to other kernel matrices using extended diffusion and label sequence kernels.We propose simple feature space mappings based on weights of interactions, which are regarded to be reliabilities and strengths for protein-protein interactions as shown in iP and jP corresponding to an input. iP, jP, and their neighboring proteins kP such that Consider two interacting proteins However, even if In contrast, if the neighboring weights are larger than the weight Even if the maximum of the neighboring weights (F2) is large enough, the proteins In the discussion so far, we dealt only with the value of weights. However, differences between weights are also important for discriminating heterodimeric complexes. Hence, we introduce the maximum of differences between the neighboring weights denoted by (F5).For prediction of complexes, biological knowledge for proteins is helpful. We use protein domains that are parts of proteins known as structural and functional units. Ozawa et al. introduced the domain structural constraint that one domain interacts with at most one other domain for verifying protein complexes In the previous section, we introduced several feature space mappings from an example, that is, a pair of proteins. Kernel functions can incorporate prior knowledge. If a set of proteins has the same composition of domains as a known complex, it is highly expected that the set forms a complex. On the basis of this idea, we propose domain composition kernel for candidate complexes Then, we propose domain composition kernel We can prove that Theorem 1Proof) We show that the Gram matrix K for a set of candidate complexes K is positive semidefinite, and In addition, for the purpose of predicting whether or not two interacting proteins form a heterodimeric complex, we combine some feature space mapping http://www.wiley-vch.de/contents/jc_2120/2007/pro200600448_s.html. The weights of interactions were calculated as follows. They constructed the literature-curated physical interaction (LCPH) dataset using several databases such as BioGRID To perform computational experiments, we needed protein-protein interaction data with weights and protein complex data. We used the WI-PHI database http://wodaklab.org/cyc2008/resources/CYC2008_complex.tab. In the dataset, a positive example was restricted to a pair of proteins that is included as a PPI in WI-PHI and is not a proper subset of any other complex in CYC2008. Thus, we used 152 heterodimeric protein complexes contained in CYC2008 as positive examples, and selected 5345 negative examples from interacting protein pairs in the CYC2008 complexes with size more than two, where positive examples were excluded. To compare our method with the naive Bayes-based method proposed by Maruyama Since the numbers of positive and negative examples of the dataset used in this paper were very unbalanced, we used the extension of We used ‘libsvm’ (version 3.11) To evaluate the performance of our method, we used precision, recall and F-measure, which are defined byTo evaluate our method, we used several sets of our proposed features, (F1–5), (F1–6), , and (F1–7). For example, (F1–5) means that we use a feature vector consisting of five values calculated by (F1), (F2), w are the coefficients of the corresponding features (F1),(F7), respectively. If each element of x is normalized, it can be considered that features with the largest absolute value of In addition, we performed another experiment to validate our method for the rest PPIs, that is, we used 152 positive and 5345 negative examples as training data, and used the rest, 44110 examples as test data. Then, we obtained the prediction accuracy of 98.7% (43554/44110) using the combination kernel with (F1–7) and We proposed several feature space mappings using weights of protein-protein interactions for predicting heterodimeric protein complexes. In addition, we proposed the domain composition kernel based on the idea that two proteins having the same composition of domains as a heterodimeric protein complex would also form a heterodimer, and proved that the domain composition kernel is actually a kernel function. To validate our proposed method, we performed ten-fold cross-validation computational experiments for the combination kernel of the domain composition kernel with the linear kernel using several sets of features. The results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method, even in the case using only feature space mappings (F1–5) from weights of protein-protein interactions, that is,  was not used and the mixing parameter An important contribution in this paper is that we have shown that heterodimeric protein complexes are able to be successfully predicted using only information on weights of protein-protein interactions. Furthermore, we indicated that the use of protein domain information enhances the prediction accuracy.There is some possibility to further improve the prediction accuracy. For instance, we can develop some kernels on protein domains using protein amino acid sequences and multiple sequence alignments. In addition, we can add new features based on other biological knowledge.C-SVC classifier, which is a variant of support vector machines, because the numbers of positive and negative examples were not balanced. It is interesting future work to develop more robust methods against unbalanced data for classifying heterodimeric protein complexes.We used the Figure S1Result on the average F-measures using four sets of features and the domain composition kernel with C-SVC was employed with regularization parameters,  As sets of features, (F1–5), (F1–6), , and (F1–7) shown in (EPS)Click here for additional data file.Figure S2Result on the average F-measures using four sets of features and the domain composition kernel represented by Eq. (S1) with C-SVC was employed with regularization parameters,  As sets of features, (F1–5), (F1–6), , and (F1–7) were used.(EPS)Click here for additional data file.Table S1Result on the average precision, recall, and F-measure using our combination kernel represented by Eq. (S1) in the best average F-measure case for each set of features. As sets of features, (F1–5), (F1–6), , and (F1–7) were used.(PDF)Click here for additional data file.Text S1Results on our kernel by another combination.(PDF)Click here for additional data file.


It differs from a number of servers currently available for such predictions in two aspects. First, rather than relying on knowledge of similar protein structures, identification of surface cavities or estimation of binding energy, LISE computes a score by counting geometric motifs extracted from sub-structures of interaction networks connecting protein and ligand atoms. These network motifs take into account spatial and physicochemical properties of ligand-interacting protein surface atoms. Second, LISE has now been more thoroughly tested, as, in addition to the evaluation we previously reported using two commonly used small benchmark test sets and targets of two community-based experiments on ligand-binding site predictions, we now report an evaluation using a large non-redundant data set containing >2000 protein–ligand complexes. This unprecedented test, the largest ever reported to our knowledge, demonstrates LISE’s overall accuracy and robustness. Furthermore, we have identified some hard to predict protein classes and provided an estimate of the performance that can be expected from a state-of-the-art binding site prediction server, such as LISE, on a proteome scale. The server is freely available at  Once a protein’s 3D structure becomes available, our knowledge about the protein and our ability to use the knowledge gained can be greatly enhanced. One approach for obtaining knowledge from structures is the prediction of binding sites for small molecule ligands that is needed in a variety of structure-based investigations, notably virtual docking in computer-aided drug design. Mainly because of this need, an increasing number of web servers and databases have been established for predicting/archiving small molecule-binding sites. They include those identifying the largest cavities on protein surfaces, those computing energetically favoured binding regions, and those using other computational methodologies . HoweverWe recently reported a new ligand-binding site prediction method, LISE , in whicWe now present the LISE server, which allows users to retrieve LISE’s pre-computed results for 63 783 protein structures or request a new prediction on structures provided in PDB  format. We downloaded the biological units of protein structures from the ftp site of the PDB  on 14 MaTo further extend the evaluation of LISE beyond that described in our previous study , we alsoThe LISE server was written in Java language. A Java script was used to take the input, extract protein sequences and atom coordinates and execute PSI-BLAST  to gene. The resAll computations and file handlings have been automated and users need only provide an input, which can be either a PDB ID or the uploading of a protein structure in PDB format. When the input is a PDB ID matching one of the 63 783 pre-computed structures, prediction results will be immediately displayed (see 2 (n refers to the number of protein surface atoms) because it computes distances between any two surface atoms of the target protein. When the size of the protein or its biological unit becomes very large, such as that of a virus capsid, it could take a week for LISE to finish the prediction, and longer if there are other jobs running on the server at the same time. Users are, therefore, advised to input PDB ID for pre-computed results or structure files of a single protein chain for large proteins. A stand-alone version of LISE is also available for download from the server.On a PC with four 2 GHz CPUs, the LISE algorithm took ∼7 min to complete the computation for PDB 1a6w (229 residues) and ∼47 min for PDB 3dao (539 residues). The algorithm has a time complexity of ncsc (∼50%) on a subset (901 complexes) of the large non-redundant data set (LISE has been shown to achieve significantly better Top1 (>80%) and Top3 (>90%) success rates than many other methods when tested on two commonly used small benchmark data sets . For theTo examine possible factors contributing to the lower success rates of LISE using the large data set, proteins were grouped into functional categories based on the molecular description included in the HEADER section of each PDB entry. As shown in Except for kinases, it is relatively easier for LISE to predict ligand-binding sites of enzymes than those of other types of proteins, as clearly shown in The binding site scoring of LISE was developed from a statistical analysis of >6000 protein–ligand complex structures from the PDB ,12. AbouIn this article, we introduced the LISE web server for a novel ligand-binding site prediction method for any given protein 3D structure. The server takes a PDB ID or a PDB format structure file and, by an automated process, displays/outputs Top3 and Top10 predicted sites. Tests on commonly used benchmark data sets and on a very large non-redundant data set indicated that the new server provides accurate and reliable predictions of protein ligand-binding sites. Therefore, the new server can help in virtual drug design and other structure-based studies, although considerably lower success rates can be expected for some particular types of proteins, such as hormone carriers or hormone-binding proteins.National Science Council of Taiwan [NSC 96-2627-B-001-004 and NSC 100-2811-B-001-067 to Z.R.X.]. Funding for open access charge: Institute of Biomedical Sciences Academia of Sinica, Taiwan.Conflict of interest statement. None declared.


In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. Protein-protein interactions give a fundament knowledge of the biological process within a cell. Such interactions are helpful for deciphering the molecular mechanisms underlying given biological functions. Usually, the connections between proteins can be represented on a graph in which the nodes corresponding to proteins and the edges corresponding to the interactions. There are many ways to identify protein-protein interactions, for instance, according to the proteins similarity calculated based on gene expression profile, biomedical literature, and etc, see ,2. In orMost previous methods -16 for aQi et al.  summarizSohler et al. proposed a joint analysis concept for mining biological networks and expression data in . By inteMost existing functional modules mining methods are unsupervised and they are based on the assumption that complexes for a clique in the interaction graph. However, many complexes with other topological structures, e.g., ’star’ or ’spoke’ model exit in real applications. Yeger-Lotem et al.  have raiHartwell et al.  defined The rest of paper is organized as follows. In Section 2, we describe the methods calculating protein similarity based on GO, and analyze the relationship between functional similarity values and existing PPI networks. Then we propose two prior knowledge based learning methods for identifying functional modules from PPI networks with the aid of GO. Experimental results on Yeast PPI networks and Gene Ontology were described and discussed in Section 3. In Section 4, we make a conclusion and showed our future work in brief.Quantitative measure of functional similarity between gene products has been used in many applications, eg., to validate high-throughput protein interaction, help the development of new pathway modeling tools and clustering methods and enable the identification of functionally related gene products independent of homology ,34. GO  providesIn GO structure, terms and their relationships are represented in the form of directed acyclic graphs. GO-based semantic similarity measures can be classified into two categories. The first category defines semantic similarity based on GO structure. The similarity between two gene products is estimated by the number of nodes two gene products share divided by the total number of nodes in two graphs. The other category is based on information content that is defined as the frequency of each GO term occurring in an annotated data set . This kiRelSim is the maximal possible number of triangles.where ccS. On the other hand, many triangles exit within clusters. Hence the coefficient ccS is a measure of how intercommunication a link is. Note that ccS will be zero when ik ≤ 1 or jk ≤ 1, also, when i.jz = 0.The idea behind the use of this metric is that edges connecting nodes in different communities are included in few or no triangles and tend to have small values of The second metric we use is a Neighborhood-based similarity metric. We use the well-known Czekanowski-Dice distance metric  for thisInt(i) and Int(j) denote the adjacency list (including themselves) of proteins i and j, respectively, and Δ represents the symmetric difference between the sets. The value of this metric ranges from 0 to 1. Note that using this metric, nodes that do not interact with each other may have a non-zero similarity if they have common neighbors.Here, Newman and Girvan  first iniSP,j is the number of shortest paths passing through edge ie,j and mαxSP is the maximum number of shortest paths passing through an edge in the graph.where EB denotes the shortest-path edge betweenness value of the edge between nodes i and j. The edge-betweenness of an edge is the proportion of the shortest paths that edge belongs to. NG method can be taken as a divisive clustering method. It starts with one cluster of all vertices and recursively splits the most appropriate cluster at the edges with a large edge-betweenness value. The process continues until a stopping criterion  is achieved.r) strongly influences the number of clusters, i.e., a larger number of smaller clusters will be obtained with increasing of the inflation value (r). The core concept behind this method is that clusters of related nodes are densely interconnected and hence there should be more long paths between pairs of nodes belonging to the same cluster than between pairs of nodes belonging to distinct clusters. Subsequently, in [http://www.micans.org/mcl/#source) to mine the functional modules from PPI networks.The Markov Cluster algorithm (MCL) ,43 simulntly, in ,9,44 MCLt)). During the clustering process, new members are added only if their node score deviates from the cluster’s seed node’s score by less than the set cutoff threshold. Therefore, small cutoff values create much more smaller-size clusters and vice versa. The third stage is post-processing the above clustering results by increasing the size of the complex according to a given parameter (f), so that there can be overlap among the modules which have already been defined. In this paper, we used the MCODE plugin in Cytospace (http://baderlab.org/Software/MCODE) to mine the functional modules from PPI networks.Molecular complex detection (MCODE)  is a metGiven the prior information , semi-supervised learning approaches  can be i(a) Prior information is combined into the original data set to form a new data set, and then all existing module identification algorithms can be applied on the new data set.(b) Prior information is used by the proposed learning algorithms:– Semi-supervised hierarchical clustering (ssHC): prior knowledge is used to construct the transitive closure , and the– Semi-supervised NG, Semi-supervised MCL and semi-supervised MCODE : using NG, MCL or MCODE to group PPIs into a relatively large number of sub-modules, and then establish the connections between sub-modules according to the pairwise constraints.ccHC[nbHC[ccHC , nbHC, NG, MCL, MCODE and etc.. For ccHC and nbHC, the prior information using protein functional pairs was used to construct the transitive closures [iP,jP. A set of constraints C makes up of all the must-link constraints. In this case, an undirected graph G, with one node for each point appearing in the constraints C, and an edge between two nodes if the corresponding points appear together in a must-link constraint. Then, the connected components of G give the sets in the transitive closure. For instance, in our example in last Section, there are 162 transitive closures on 654 Yeast proteins with 1488 protein functional pairs, where different closures may cover different numbers of proteins. The biggest closure has 21 proteins and 207 pairs, while the smallest closure has 2 proteins and 1 pair, as shown in Figure The first approach  can be closures . The traccssHC and ccssHC respectively.Such transitive closures and the other proteins which are not included in these closures will be set as the initial clusters of hierarchical clustering methods. Next, hierarchical clustering methods will merge a pair of clusters if they have a smallest distance or a largest similarity . The merging procedure will end when the given number of clusters are obtained. These two semi-supervised hierarchical clustering methods (based on clustering coefficient and neighborhood) are denoted by s, the number of splitting steps, will be given for NG algorithm, a large inflation r will be set in MCL method and a small cutoff (t) will be set in MCODE method. Then, connections between sub-modules are established according to the protein pairs with higher functional relationship in the second stage. Finally, three semi-supervised methods, ssNG, ssMCL and ssMCODE, are designed to mine the functional modules.For NG, MCL and MCODE, we adopted a two-stage semi-supervised learning approach with the aid of the prior information . In the first stage, the PPI networks are grouped into a relatively large number of sub-modules by relaxing the parameters of the existing algorithms. For instance, a large value for ccHC, nbHC, NG, MCL and MCODE) with our proposed prior knowledge based strategy. Yeast PPI networks in MIPs database [In this section, we conducted a series of experiments to show how the protein functional pairs improve the performance of the existing modules identification methods ) from the enriched annotation terms association with one or more of the module members. The enrichment score essentially shows how the module is involved in the important annotation association with the module members. Probably, the higher the score the more important biology to the gene group.Since our goal is to find functional modules from PPI networks, it is necessary to test if the obtained modules correspond to known functional modules. This can be done by validating the modules with the predefined biological annotations from the MIPs database . MIPs prT1) have functional similarity value equal to 1, 3375 protein pairs (noted as T2) have functional similarity value great than and equal to 0.999, 4146 pairs (noted as T3) have similarity value great than and equal to 0.998, 6951 pairs (noted as T4) have similarity value great than and equal to 0.997 and etc. In our experiments, we tested the performance of prior knowledge based strategy with the first four sets of protein pairs . The first prior knowledge based strategy, combining the protein functional pairs into the PPI networks, was used to show how many protein functional pairs are suitable to be the prior information.Based on the functional similarity analysis for all Yeast protein pairs in Section , there are total 14090 protein pairs with similarity greater than 0.99, as shown in Figure r for MCL, the number of splitting steps s in NG method, the node score cutoff t in MCODE, and the number of clusters k in ccHCand nb.HC In this case, the evaluation measure, modularity, was used to validate which parameter value makes the algorithm perform best. For reference, we listed the experimental results for two data sets MYP and MYPT2. The best modularity value for both data sets was obtained at the point 4200, i.e., the number of splitting steps on the modularity of NG method is 4200, as shown in Figure r is equal to 1.4, as shown in Figure t = 0.2, as shown in Figure k = 350. That is, the final number of clusters identified by hierarchical algorithms is 350, as shown in Figure log of the significant modules identified by the corresponding method on one data set. Larger value shows the better performance. Because the smallest number of modules in all experiments is sixty, we showed the top sixty modules for all methods on all data sets. We can find that all algorithms got the best performance on data set MYPT2, adding 3375 protein pairs with similarity ≥ 0.999 on the original protein networks. Even though the other three data sets, MYPT1, MYPT3 and MYPT4, do not make the identification method obtaining the best result, all of them increase the identification performance by comparing with the original PPI networks (MYP). For MYPT3 and MYPT4 adding 4146 protein pairs with similarity (≥ 0.998), and 6951 protein pairs with similarity (≥ 0.997) respectively, the identification results are better than on the original data set MYP, but less than on MYPT2, the reason is that more added protein pairs may add more noise. For MYPT1 adding 1488 protein pairs with similarity (= 1), the identification results are better than on MYP but less than on MYPT2, the reason is that MYPT1 may not have enough functional information. Figure log of the significant modules identified by the corresponding method on one data set.Five module identification methods were used in our experiments as described in last Section. For each approach, there are some parameters to be predefined, e.g., the inflation factor ty ≥ 0.99, and 695ccHC, nbHC, NG, MCL, and MCODE) on the original PPI networks (MYP), the other for these five algorithms on the combined PPI networks (MYPT2), another one for the five proposed semi-supervised ccssHC, nbssHC, ssNG, ssMCL, and ssMCODE on MYPT2. For the last part of experiments, the protein functional pairs would be taken as the prior information of the identification algorithms.According to the above experimental results, we selected MYP and MYPT2 as the data sets to test the performance of our proposed prior knowledge based strategy. Three parts of experiments were conducted, one for the five existing identification methods   is better than neighborhood-based similarity (nbHC).Table nd Wodak . Newman-T2, the protein pairs with functional similarity (≥ 0.999) were used as the prior information. In ssMCL, ssMCODE and ssNG, T2 was used to merge the sub-modules identified by MCL, MCODE and NG respectively, where the initial sub-modules are identified by MCL at r = 5, by MCODE at t = 0.05 and by NG at s = 6500. For hierarchical clustering algorithm ccssHC and nbssHC, T2 was used to construct the initial clusters, and the parameter k was set to be 350. Meanwhile, we listed the identification results of the original methods  at the given parameter value to compare with the proposed prior knowledge based methods. Obviously, the proposed strategy obtained better performance in terms of both coverage and complex annotation p-value. Even comparing with the best performance of the original methods on MYP in Table Table MCL, MCODE, NG, ccHC and nbHC) and our proposed prior knowledge based strategy with the first method  into the data representation) in Table MCL, NG and ccHC on two data sets MYP and MYPT2 than those detected by MCODE and nb.HC More importantly, we find that there is only one module commonly detected by five different algorithms on MYP, namely, the complexID is 510.190.10. There are around 14 Hits for this module detection. However, there are four modules commonly detected by five different algorithms on MYPT2, namely, the complexIDs are 260.50, 550.1.213, 550.1.147 and 440.30.10. There are totally around 91 Hits for these modules detection. These results show that the enhanced data set can provide a better network for functional module detection. Furthermore, a function module identified by the proposed strategy (ssMCL) is given in Figure MCL on the original PPI networks (MYP), we did not find this module, while the proteins in the module were divided into different modules. Therefore, we can say prior knowledge based strategy has ability to effectively mine function modules.In order to investigate the identified modules, we listed the top ten functional modules identified by the original methods (p-value.In this paper, we presented a prior knowledge based strategy for mining function modules from PPI networks with the aid of GO. The functional protein pairs were extracted according to their functional similarity in GO, and then such pairs were taken as the prior information of the proposed mining methods. Two kinds of prior knowledge based methods were designed: one for encoding the prior information into the data representation, i.e., combining the functional protein pairs and PPI networks to a new PPI networks, and the other for using the prior information as pairwise constraints to supervise the existing ming methods. Experimental results on Yeast PPI networks and GO knowledge resource have shown that our proposed strategy performs well in terms of coverage and complex annotation


Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological features available. High-throughput assay methodologies, such as microarrays and mass spectrometry, have resulted in the rapid growth of protein data sets, the analysis of which can potentially yield insights into the mechanisms of human diseases and the discovery of new therapeutic interventions 2]. Syst. Syst2].PPI data sets provide us the good opportunity to systematically analyze the structure of a large living system and also allow us to use it to understand essential principles like essentiality, genetic interactions, functions, functional modules, protein complexes and cellular pathways . Cellulaet al.21],[21],20],20]• Proteins may participate in multiple protein complexes. Therefore, protein complexes may overlap with each other. These overlaps correspond to proteins’ participation in multiple pathways and the crosstalk between different biological modules. Thus, the traditional paradigm for clustering and putting each protein into one single cluster doesn’t suit our problem well. Instead, we would prefer a method that can detect subgraphs with possible overlaps. Our proposed semi-supervised method overcomes this drawback that many existing graph clustering methods suffered and gives a promising result.• How to represent protein complexes. Most existing clustering methods assume protein complexes as dense subgraphs, which is not always true for the protein complexes in the PPI networks . In addiFor our experiments, we built our weighted protein interaction networks from DIP data set , which cIn order to study the relative performance of different supervised learning algorithms, we need to define an evaluation criterion that determines if a predicted protein complex matches a complex in benchmark set. In , the autA is the predicted complexes, B is the true protein complexes, AV is the set of proteins in the subgraph A, and BV is the set of proteins in the subgraph B. In this paper, we use an overlapping threshold of 0.20 to determine a match for all experiments. Predicted protein clusters that match one or more true protein complexes with overlapping scores higher than this threshold are identified as “matched clusters,” and the corresponding true complexes are noted as “matched complexes.”where f-measure for different algorithms. They are defined as:To measure the accuracies of prediction, we calculate prediction, recall and clusterM is the number of the matched clusters and clusterP is the number of the predicted clusters, andwhere complexesM is the number of matched complexes and complexesT is the number of the true complexes. Because smaller size complexes and clusters have high probabilities of occurring by chance and they are not meaningful, here we only consider clusters and complexes whose size is 4 or larger.where f-measure is defined as the harmonic mean of recall and precision. It reflects a combination of precision and recall.where f-measure, separately. As we can see, the proposed method is superior in all measures. The recall rate of our method is around 49%, which is impressive. Our precision is not as high as recall but it is still better than the other existing methods. In addition, our f-measure is the highest among all 4 methods. Since many protein complexes are not included in the benchmark set, the predicted protein complexes could be correct protein complexes that are not in the available data. The recall value of MCODE is relatively low compared with other methods. This is because the protein complexes that MCODE found are relatively larger than the clusters found by the other approaches and thus affect the result 29].[29].28][.[29].28]Topological features:E| is the number of edges in the graph and |V| is the number of vertices in the graph.1. Graph density: graph density is defined as 2. Degree statistics: these features are calculated from the degree of vertexes in the subgraph. A degree is defined as the number of neighbors of a vertex. Mean degree, variance of degrees, median degree and maximum degree are chosen for degree statistics.3. Edge weight statistics: we only consider edges with nonzero weights here. Like degree statistics, mean and variance of weights are taken as features.iT = (|iE| – |iE+1|)/|iE|, where iE is the number of edges with different cutoffs i.4. Topological change : This gre| gives the number of triangles that go through node v, whereas d(v)(d(v) – 1)/2 is the total number of triangles that could pass through node v.5. Clustering coefficient: the clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. It is defined as 6. Topological coefficient : the topBiological features:1. Protein length: the number of amino acids in a protein sequence.2. Polarity of amino acids: the longer and more complementary the binding sites, the majority of which would be polar, of the protein, the stronger the proteins would be bound.backpropagation algorithm [x is the input pattern and y is the teaching or target vector. During the network training, the signals generated by the output layers are compared with the target vector y, and any difference is used in training the weights throughout the network. Some key parameters in the neural network are set as follows. The number of units in the input layer is the number of features that we use . The number of output layers is one in our case, since we only need to know if a subgraph is complex or not, which is a classic bi-class classification. The number of hidden layers is two, and we set 11 units for the first layer and 7 units for the second layer. Those numbers are obtained by experiments starting from a full connected network with a sufficiently large number of nodes to a smaller number of nodes. The activation function for each unit we use is the logistic function. Given the net input jI to unit j, then jO, the output of unit j, is computed as:A neural network is a set of connected input/output units in which each connection has a weight associated with it. In the last couple of years, many different kinds of neural networks and corresponding algorithms have been developed. Among the existing algorithms, the lgorithm  is the mlgorithm . An examj isThus, the error of a hidden layer unit jkw is the weight of the connection from unit j to a unit k in the next higher layer, and kErr is the error of k. The weights and biases are updated to reflect the propagated errors. Weights are updated by the following equations, where ∆ijw is the change in weight ijw:where l is the learning rate. Backpropagation learns using a method of gradient descent to search for a set of weights that fits the training data so as to minimize the mean squared distance between the network’s class prediction and the known target value of the tuples [jθ is the change in bias j:θwhere e tuples . Biases ijw get so small as to be below some specified threshold.The whole updating process terminates when all ∆Based on the above model, we could use it to evaluate the candidate subgraphs. If the evaluating value exceeds the threshold, the candidate subgraph is predicted to be a complex. So the problem becomes finding subgraphs with high evaluating values in the weighted PPI network. However, as proved in , identifHere we propose a new heuristic method using an evaluation value as the objective function. At the beginning, each cluster starts at a deterministic single node which we call seed node. In our method, we choose the highest weight node as our seed node. The weights of nodes are determined by summing up the weights of the incident edges. Then we add the neighbor nodes of the cluster one by one to the new cluster and the order is based upon their impact on the evaluation score. The adding process stops when there are no more proteins that could be added and our new protein complex is created. The whole subgraph generation routine is illustrated in Figure The completely proposed algorithm for protein complexes identification is described below and the flow chart of the whole algorithm is shown in Figure Input• – Weighted DIP PPI network and a training set of protein complexes and randomly generated non-protein complexes.Learning parameters step• – Extract features from the above two groups of complexes.– Use neural network to train the parameters for the prediction model.Identifying for complexes• – Start from the seed nodes, add neighboring proteins of the cluster one by one based on the priority and the impact on the cluster.– Output the complexes when there is no more proteins to satisfy the criterion given above.– Use the newly generated complexes to recursively update the parameters of the model in the second step and find the new complex.Output• – Predict protein complexes.Our input is the weighted PPI graph and a set of known complexes and non-complexes as training data. The known protein complexes are drawn from MIPS protein complexes and the non-complexes are generated randomly from the DIP protein interaction dataset. First, we use the neural network model to learn model parameters from the training data. Once we get the prediction model, we will start searching for the protein complex. Next, when we have more protein complexes, we recursively train our prediction model and find new protein complexes until there are no more proteins that could be added. The final output complexes are those detected clusters which have a higher evaluation score than the threshold.LS designed and implemented the algorithm and the framework, analyzed the results and drafted the manuscript. XL participated experiments of the algorithm. AZ coordinated the project and revised the manuscript.The authors declare that they have no competing interests.


Biol. 7:e1002073, 2011) has proposed a metric for the “functional similarity” between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the “ortholog conjecture” . First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an “open world assumption” (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis.A recent paper . As representatives of the international consortium that produces the GO, we show how the apparent evidence against the “ortholog conjecture” can be better explained as an artifact of how molecular biology knowledge is accumulated. In short, a complementarity between knowledge obtained in mouse and human experimental systems was incorrectly interpreted as a disagreement. We discuss the proper interpretation of GO annotations and potential sources of bias, with an eye toward enhancing the informed use of the GO by the scientific community. The primary evidence the authors use to draw their conclusions is a score based on the normalized intersection of the experimentally-supported Gene Ontology annotations for different pairs of genes. The authors interpret the score as representative of functional similarity. We contend that the score is more accurately described as annotation congruence. These two interpretations are very different: functional similarity refers to similarity in the actual biological function of two gene products, while annotation congruence refers to agreement in the representation of the functions that have been experimentally demonstrated so far for two gene products. If our experimental knowledge of biological function were complete, and adequately represented by GO annotations, these would be equivalent. Unfortunately this is not yet the case in general. It is very important to note that GO annotations are subject to an “open world assumption”, i.e. absence of a GO annotation does not mean that a function is absent from a particular gene product. Even the limited knowledge that we do have about biological function is not yet completely represented by GO annotations, due to limitations of time and resources. Perhaps most importantly for this discussion, different model organisms are used to study different aspects of biology using different assays, and so the annotation of orthologs in different species will reflect these systematic differences in experimental systems and outcomes. In fact, complementarity with other established systems is a key factor in the development of different model organism experimental systems. As a result of these and other considerations, we suggest that the authors, rather than testing the “ortholog conjecture,” instead tested an “unbiased annotation conjecture.” Similar suggestions have been made in post-publication review forums (http://f1000.com/12462957?key=5g7rjmt7xzv2y32) and blogs , but not yet in the peer-reviewed literature.Nehrt et al. describe, it would indeed be contrary to expectations if paralogous genes in humans or mice were functionally more similar than orthologous genes between these species. This would not only challenge the so-called “ortholog conjecture”: it would challenge the longstanding research programs in model systems and comparative biology, and even the tenets of current evolutionary theory with its emphasis on inheritance and divergence from a common ancestor. Surprisingly, then, the rejection of the “ortholog conjecture” by Nehrt et al. is based almost entirely on statistical analysis of existing GO annotations, with no in-depth analysis of specific examples. In particular, the section entitled “Case studies” provides no citation of experimental evidence for the authors' claims, thus complicating overall evaluations. Here, we examine these specific cases, and find no evidence for the conclusion that within-species paralogs are more functionally similar than orthologs. Instead, we suggest that the statistical bias observed by Nehrt et al. is better explained by a bias in annotations arising at least in part because research programs in human and mouse experimental systems tend to discover aspects of orthologous gene function that are complementary rather than conflicting.As Nehrt et al. examined two different case studies that showed particularly large increases in annotation similarity between paralogs as compared to orthologs. In order to support the interpretation of annotation similarity as functional similarity, the case studies would need to present evidence of true biological similarity rather than evidence of annotation similarity. We therefore examined these case studies in more detail.Nehrt MAP4K2…While the human hMAP4K2 shares 94% sequence identity with its ortholog in mouse, their functional similarity is only 5% . In contrast, its functional similarity with its own outparalogs was 69% on average, including 82% similarity with hMAP4K3, a within-species outparalog.” The GO biological process annotations for human MAP4K2, mouse Map4k2 and human MAP4K3 are shown in MAP4K2 and human MAP4K3 are annotated with intracellular protein kinase cascade (GO:0007243) and protein phosphorylation (GO:0006468), while mouse Map4k2 is only annotated with vesicle targeting (GO:0006903). So the finding that the annotation congruence for MAP4K2 and MAP4K3 in humans  is greater than for human MAP4K2 and mouse Map4k2 (orthologs) is correct.Mitogen activated protein kinase kinase kinase kinases (MAP4K) are protein kinases that participate in the MAP kinase signal transduction cascade MAP4K2 and MAP4K3 are known in humans to participate specifically in the JNK  cascade, one of four different known MAPK pathway variants MAP4K2 and MAP4K3, and mouse Map4K2 can all participate in an intracellular protein kinase cascade. However, from a GO annotation standpoint, only human MAP4K2 and MAP4K3 have been experimentally characterized as participating in an intracellular protein kinase cascade. Mouse Map4K2, the ortholog of human MAP4K2, has apparently not been characterized at the molecular level, though there are several reported effects of mouse mutants lacking Map4k2, including an effect on vesicle targeting. This lack of experimental characterization cannot, however, be taken as evidence that Map4k2 differs from its human ortholog in that it does not participate in MAPK signaling. On the contrary, a molecular link between the JNK cascade and vesicle targeting (through the conserved JNK-interacting protein JIP-1) has been established in DrosophilaMap4k2 (and likely its human ortholog) may affect vesicle targeting through MAPK signaling. In summary, the different annotations for mouse and human orthologs of MAP4K2 do not constitute evidence that the orthologous genes have different functions; a more likely explanation is that they are instead providing complementary information about a conserved biological system, representing the current, incomplete, state of experimentation results.However, decreased annotation congruence can be explained more easily in terms of annotation incompleteness  and complementarity rather than functional differences between orthologs. MAP4Ks are upstream of MAP3Ks in the mitogen-activated protein kinase (MAPK) cascade, and both THRA in human, Thra in mouse) gene product binds thyroid hormone, a tyrosine-based hormone, and has effects on tissue growth, differentiation and metabolism Esr1 in mouse) gene product binds the steroid hormone estrogen , and has physiological effects ranging from reproduction to cognition Esr1, than to its human ortholog THRA. Indeed, such a convergence in function between paralogs would be a revolutionary finding.Nuclear receptors are transcription factors, influencing transcription of specific target genes, that are activated by binding a specific ligand. The authors find that, in this family, “a paralog was more functionally similar than the ortholog for the majority of the targets, and the specific paralog with the highest functional similarity was most often an outparalog in the same species.” The biological functions of nuclear receptors are known to be highly dependent upon their biological ligands, and the evolution of ligand specificity has been studied for some members of this family Thra is greater with mouse Esr1 than with human THRA  protein binding, and 2) ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity, while THRA is annotated with 1) TBP-class protein binding and 2) thyroid hormone receptor activity. TBP-class protein binding is a subclass of protein binding, while thyroid hormone receptor activity is a subclass of ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity. It is important to consider the semantics of a non-specific GO annotation: an annotation of mouse Thra as possessing ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity means that the gene product functions as a nuclear receptor for some (unspecified) ligand, which of course does not preclude that the ligand is thyroid hormone. Thus differences in annotation specificity, a form of annotation incompleteness, cannot generally be interpreted as differences in actual biological function. Differences in annotation specificity, even for similar experiments, may arise for non-biological reasons such as variability in annotation processes between different curation groups  inparalogs/within-species outparalogs, versus 2) orthologs/between-species outparalogs. In short, within-species comparisons yielded greater annotation similarity scores on average than between-species comparisons. The authors claim that “the sparsity of annotation… is unlikely to affect comparisons between classes of homologs,” but this claim is essential for their interpretations and requires supporting evidence. As shown in the examples above, annotation incompleteness can result in annotation differences even in the absence of functional differences. We reasoned that the bias uncovered by Nehrt et al., in which within-species comparisons showed greater annotation similarity than between-species comparisons, would arise if GO annotations for mouse genes in general—not just for paralogous genes—are more similar to each other than to human GO annotations, and vice versa.Nevertheless, assuming that the annotation similarity scores are calculated correctly, the statistical differences reported by Nehrt all human experimental annotations to the set of all mouse experimental annotations in the GO database. et al.'s results (but contrary to their interpretation), biological process annotations are even more biased, with mouse being enriched for organism-level processes including development and cell differentiation, and human for cellular biochemical-level processes such as protein modification and molecular catabolism. These differences in overrepresented functional classes are very unlikely to reflect actual functional differences between human and mouse orthologs; rather they reflect biases both in the kinds of experiments that are performed in that organism, and in the curation process (e.g. which published papers are prioritized for annotation by a given curation group). Some of the most significant biases can be explained by the fact that mouse is used in genetics experiments to probe organism level processes that cannot be approached experimentally in humans, while many of the experiments in human systems are performed on isolated cells and proteins.To test this alternative explanation, we compared the set of et al.'s metric of GO annotation congruence as functional similarity is problematic, and therefore it cannot be used to draw valid conclusions about the ortholog functional conservation hypothesis. From a theoretical standpoint, the semantics of GO annotations must be interpreted using an “open world assumption” in which absence of an annotation does not mean absence of a function (a true negative). Thus, lack of annotation congruence may simply be due to false negatives: incompleteness either in the state of our experiment-derived knowledge of a particular gene's function, or in representing that knowledge as GO annotations. From an empirical standpoint, we demonstrate that the bias noted by Nehrt et al. between different classes of homologous gene in human and mouse, is likely to be reflecting a global bias over all human and mouse genes. This global bias is consistent with the common use of mouse as a genetic system for probing system-level processes via observed phenotypes, and of the use of human cell lines for probing cellular-level processes. It may also reflect a tendency for researchers not to “repeat” a particular experiment that has already been carried out in a closely related organism.We have shown that the interpretation of Nehrt et al. did attempt to address potential sources of bias in GO annotations, though they apparently missed a major contributor as discussed above. The authors' observation that there are “preferences toward the same annotation when multiple homologs were functionally annotated in the same article: functional similarity went up 0.1–0.3 across orthologs and paralogs for both Biological Process and Molecular Function” supports the “biased annotation conjecture” interpretation we propose here. We would also expect annotation congruence to increase accordingly if homolog annotations were derived from research groups and co-authors addressing the same biological questions, or for annotations made during the same time period, when they would be constrained by the availability of similar GO terms.We note that Nehrt et al. confirmed a greater annotation congruence between paralogs as compared to orthologs, but showed that this difference is due to incomplete and complementary annotations, and not to functional divergence among orthologs or convergence among paralogs. This in-depth analysis suggested possible types of bias that we explored with further interrogation of biological knowledge and statistical analysis.Nevertheless, whenever a novel type of GO-based statistical analysis is presented, a manual review of key examples or case studies should be considered as an important component of validating its biological implications. GO-based analysis can be an excellent way to generate biological hypotheses, but in order to draw defensible conclusions, it is important to verify actual biological examples, particularly if analyses may be affected by global differences between the sets of annotations being compared. Between-species comparisons based on different annotation sources (i.e. organisms), are particularly sensitive to subtle differences in annotation and experimental testing bias. Users of GO should ensure that they test for, and adjust for, potential biases prior to interpretation. Our re-analysis of the case studies presented by Nehrt SUV3 (Saccharomyces cerevisiae)/rpm2 (Schizosaccharomyces pombe) and MGT1 (S. cerevisiae)/atl1 (S. pombe). In these cases, the gene product in S. pombe has been demonstrated to lack a function found in the S. cerevisiae ortholog, and this has been captured with negative annotations for the S. pombe genes If the annotation congruence is not appropriate, are there alternative ways in which GO annotations might be used to test the ortholog functional conservation hypothesis? One way that functional differences between orthologs and paralogs could be addressed using GO would be to consider homologs for which similar experiments had been performed, and where negative results were captured as negative GO annotations  to indicate the absence of functionality. We note that GO curators have already made numerous negative annotations—though these are still very incomplete—often where a particular function was suspected/expected for a gene (one possible reason being that it was found for an ortholog) but shown not to be present. Two examples of orthologs with divergent functions are We applaud the use of the Gene Ontology resources in new and creative ways. At the same time, we strongly encourage careful consideration of the interpretations of such uses. Do they reflect actual biological insights, or are they in fact due to inherent biases in annotation and or the experimental data or systems available? This phenomenon is certainly not limited to GO analyses. The process of knowledge representation of any kind will always introduce issues that must be properly considered in meta-analyses. We strongly and actively encourage researchers to contact us when proposing a novel type of GO-based analysis, to ensure appropriate interpretation and use of the GO.http://www.geneontology.org/GO.evidence.shtml). For the MAP4K2 and nuclear receptor examples (http://www.geneontology.org) on 2011-11-29.Term overrepresentation analysis  was perfexamples  and 2, G


Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases. Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The  Protein-protein interactions (PPIs) handle a wide range of biological processes, including cell-to-cell interactions and metabolic and developmental control . Proteinin vivo [Typically proteins hardly act as isolated species while performing their functions in vivo . It has in vivo . The subin vivo . The stuin vivo . Charactin vivo . The resin vivo .modify the kinetic properties of enzymes;act as a general mechanism to allow for substrate channeling;construct a new binding site for small effector molecules;inactivate or suppress a protein;change the specificity of a protein for its substrate through interaction with different binding partners;serve a regulatory role in either upstream or downstream level.PPIs canUncovering protein-protein interaction information helps in the identification of drug targets . StudiesIn recent years, PPI data have been enhanced by guaranteed high-throughput experimental methods, such as two-hybrid systems, mass spectrometry, phage display, and protein chip technology . Comprehin vitro, in vivo, and in silico methods. In in vitro techniques, a given procedure is performed in a controlled environment outside a living organism. The in vitro methods in PPI detection are tandem affinity purification, affinity chromatography, coimmunoprecipitation, protein arrays, protein fragment complementation, phage display, X-ray crystallography, and NMR spectroscopy. In in vivo techniques, a given procedure is performed on the whole living organism itself. The in vivo methods in PPI detection are yeast two-hybrid  and synthetic lethality. In silico techniques are performed on a computer (or) via computer simulation. The in silico methods in PPI detection are sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, mirror tree, phylogenetic tree, and gene expression-based approaches. The diagrammatic classification was given in Protein-protein interaction detection methods are categorically classified into three types, namely, in vivo [TAP tagging was developed to study PPIs under the intrinsic conditions of the cell . Gavin ein vivo . The TAPThe advantage of the affinity chromatography is that it is highly responsive, can even detect weakest interactions in proteins, and also tests all the sample proteins equally for interaction with the coupled protein in the column. However, false positive results also arise in the column due to high specificity among proteins, even though they do not get involved in the cellular system. Thus protein interaction studies cannot fully rely on affinity chromatography and hence require other methods in order to crosscheck and verify results obtained. The affinity chromatography can also be associated with SDS-PAGE technique and mass spectroscopy in order to generate a high-throughput data.Coimmunoprecipitation confirms interactions using a whole cell extract where proteins are present in their native form in a complex mixture of cellular components that may be required for successful interactions. In addition, use of eukaryotic cells enables posttranslational modification which may be essential for interaction and which would not occur in prokaryotic expression systems.Protein microarrays are rapidly becoming established as a powerful means to detect proteins, monitor their expression levels, and probe protein interactions and functions. A protein microarray is a piece of glass on which various molecules of protein have been affixed at separate locations in an ordered manner . Proteinin vitro [Protein-fragment complementation assay is another method of proteomics for the identification of protein-protein interactions in biological systems. Protein-fragment complementation assays (PCAs) are a family of assays for detecting protein-protein interactions (PPIs) that have been introduced to provide simple and direct ways to study PPIs in any living cell, multicellular organism, or in vitro . PCAs cain vitro . For pepThere are different implementations of the phage display methodology as well as different applications . One of X-ray crystallography  is essenIn the recent past, the researchers have shown interest in the analysis of protein-protein interaction by nuclear magnetic resonance (NMR) spectroscopy . The locin vivo method applied to the detection of PPIs [Y2H method is an  of PPIs . Two pro of PPIs . Y2H ana of PPIs . During  of PPIs . Other w of PPIs .in vivo genetic screening which tries to understand the mechanisms that allow phenotypic stability despite genetic variation, environmental changes, and random events such as mutations. This methodology produces mutations or deletions in two or more genes which are viable alone but cause lethality when combined together under certain conditions [Synthetic lethality is an important type of nditions –33. Compin vitro and in vivo approaches resulted in large-scale development of useful tools for the detection of protein-protein interactions (PPIs) between specified proteins that may occur in different combinations. However, the data generated through these approaches may not be reliable because of nonavailability of possible PPIs. In order to understand the total context of potential interactions, it is better to develop approaches that predict the full range of possible interactions between proteins [The yeast two-hybrid (Y2H) system and other proteins .in silico methods have been developed to support the interactions that have been detected by experimental approach. The computational methods for in silico prediction include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, mirror tree, phylogenetic tree, gene ontology, and gene expression-based approaches. The list of all webservers of in silico methods was given in A variety of The idea behind the structure-based method is to predict protein-protein interaction if two proteins have a similar structure. Therefore, if two proteins A and B can interact with each other, then there may be two other proteins A′ and B′ whose structures are similar to those of proteins A and B; then it is implied that proteins A′ and B′ can also interact with each other. But most proteins may not be having known structures; the first step for this method is to guess the structure of the protein based on its sequence. This can be done in different ways. The PDB database offers useful tools and information resources for researchers to build the structure for a query protein . Using tRecently, Hosur et al.  developeIn Silico Predictor of Protein-Protein Interactions (UNISPPI), based on primary sequence information for classifying protein pairs as interacting or noninteracting proteins [Predictions of PPIs have been carried out by integrating evidence of known interactions with information regarding sequential homology. This approach is based on the concept that an interaction found in one species can be used to infer the interaction in other species. However, recently, Hosur et al.  developeproteins . Kernel proteins . However(1) Ortholog-Based Approach. The approach for sequence-based prediction is to transfer annotation from a functionally defined protein sequence to the target sequence based on the similarity. Annotation by similarity is based on the homologous nature of the query protein in the annotated protein databases using pairwise local sequence algorithm [lgorithm . SeveralThe prediction process starts with the comparison of a probe gene or protein with those annotated proteins in other species. If the probe gene or protein has high similarity to the sequence of a gene or protein with known function in another species, it is assumed that the probe gene or protein has either the same function or similar properties. Most subunits of protein complexes were annotated in that way. When the function is transferred from a characterized protein to an uncharacterized protein, ortholog and paralog concepts should be applied. Orthologs are the genes in different species that have evolved from a common ancestral gene by speciation. In contrast, paralogs usually refer to the genes related by duplication within a genome . In broa(2) Domain-Pairs-Based Approach. A domain is a distinct, compact, and stable protein structural unit that folds independently of other such units. But most of times, domains are defined as distinct regions of protein sequence that are highly conserved in the process of evolution. As individual structural and functional units, protein domains play an important role in the development of protein structural class prediction, protein subcellular location prediction, membrane protein type prediction, and enzyme class and subclass prediction.Conventionally, protein domains are used for basic research and also for structure-based drug designing. In addition, domains are directly involved in the intermolecular interaction and hence must be fundamental to protein-protein interaction. Multiple studies have shown that domain-domain interactions (DDIs) from different experiments are more consistent than their corresponding PPIs . So, it With the ever increasing number of the completely sequenced genomes, the global context of genes and proteins in the completed genomes has provided the researchers with the enriched information needed for the protein-protein interaction detection. It is well known that the functionally related proteins tend to be organized very closely into regions on the genomes in prokaryotes, such as operons, the clusters of functionally related genes transcribed as a single mRNA. If the neighborhood relationship is conserved across multiple genomes, then it will be more relevant for implying the potential possibility of the functional linkage among the proteins encoded by the related genes. And this evidence was applied to study the functional association of the corresponding proteins. This relationship was confirmed by the experimental results and shown to be more independent of relative gene orientation. Recently, it has been found that there is functional link among the adjacent bidirectional genes along the chromosome . InteresGene fusion, which is often called as Rosetta stone method, is based on the concept that some of the single-domain containing proteins in one organism can fuse to form a multidomain protein in other organisms , 49. ThiThe method is based on the assumption that interacting proteins should undergo coevolution in order to keep the protein function reliable. In other words, if some of the key amino acids in one protein changed, the related amino acids in the other protein which interacts with the mutated counter partner should also make the compulsory mutations as well. During the analysis phase, the common genomes containing those two proteins will be identified through multiple sequence alignments and a correlation coefficient will be calculated for every pair of residues in the same protein and between the proteins . AccordiAnother important method for detection of interaction between the proteins is phylogenetic tree. The phylogenetic tree gives the evolution history of the protein. The mirror tree method predicts protein-protein interactions under the belief that the interacting proteins show similarity in molecular phylogenetic tree because of the coevolution through the interaction . The undE-value is used to detect the presence or absence of the homological proteins on the target genome with the source genomes. This method gives promising results in the detection of the functional linkage among the proteins and, at the same time, assigns the functions to query proteins. Even though the phylogenetic profile has shown great potential for building the functional linkage network on the full genome level, the following two pitfalls should be mentioned: one is that this method is based on full genome sequences and the other is that the functional linkage between proteins is detected by their phylogenetic profiling, so it is difficult to use the method for those essential proteins in the cell where no difference can be detected from the phylogenetic profile. Moreover, even though the increasing number in the source genome set can improve the prediction accuracy, there may be an upper limit for this method.The notion for this method is that the functionally linked proteins tend to coexist during the evolution of an organism . In otheMany genomic events contribute to the noise during the coevolution, such as gene duplication or the possible loss of gene functions in the course of evolution, which could corrupt the phylogenetic profile of single genes. Phylogenetic-profile-based methods conceded satisfactory performance only on prokaryotes but not on eukaryotes .The method takes the advantage of high-throughput detection of the whole gene transcription level in an organism. Gene expression means the quantification of the level at which a particular gene is expressed within a cell, tissue or organism under different experimental conditions and time intervals. By applying the clustering algorithms, different expression genes can be grouped together according to their expression levels, and the resultant gene expression under different experimental conditions can help to enunciate the functional relationships of the various genes. A lot of research has also been carried out to investigate the relationship between gene coexpression and protein interaction . Based oEach of the above methods has been applied to detect the protein-protein interaction in both the prokaryotes and eukaryotes. The results show that most of them fit better for the prokaryotes than eukaryotes . The sigRecent technological advances have allowed high-throughput measurements of protein-protein interactions in the cell, producing protein interaction networks for different species at a rapid pace. However, high-throughput methods like yeast two-hybrid, MS, and phage display have experienced high rates of noise and false positives. There are some verification methods to know the reliability of these high throughput interactions. They are Expression Profile Reliability (EPR index), Paralogous Verification Method (PVM)  Protein the protein interactions are not stable;one protein may have different roles to perform;two proteins with distinct functions periodically interact with each other.A PPI network can be described as a heterogeneous network of proteins joined by interactions as edges. The computational analysis of PPI networks begins with the illustration of the PPI network arrangement. The simplest sketch takes the form of a mathematical graph consisting of nodes and edges . ProteinPredicting the protein functionality is one of the main objectives of the PPI network. Despite the recent comprehensive studies on yeast, there are still a number of functionally unclassified proteins in the yeast database which reflects the impending need to classify the proteins. The functional annotation of human proteins can provide a strong foundation for the complete understanding of cell mechanisms, information that is valuable for drug discovery and development . The incProtein functions may be predicted on the basis of modularization algorithms . HoweverProtein-protein complex identification is the crucial step in finding the signal transduction pathways. Protein-protein complexes mostly consist of antibody-antigen and protease-inhibitor complexes . CrystalThe complete analysis of PPIs can enable better understanding of cellular organization, processes, and functions. The other applications of PPI Network include biological indispensability analysis , assessiThe massive quantity of experimental PPI data being generated on steady basis has led to the construction of computer-readable biological databases in order to organize and to process this data. For example, the biomolecular interaction network database (BIND) is created on an extensible specification system that permits an elaborate description of the manner in which the PPI data was derived experimentally, often including links directly to the concluding evidence from the literature . The datThe Molecular Interaction (MINT) database is another database of experimentally derived PPI data extracted from the literature, with the added element of providing the weight of evidence for each interaction . The HumWhile available methods are unable to predict interactions with 100% accuracy, computational methods will scale down the set of potential interactions to a subset of most likely interactions. These interactions will serve as a starting point for further lab experiments. The gene expression data and protein interaction data will improve the confidence of protein-protein interactions and the corresponding PPI network when used collectively. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signal transduction pathways and protein complex identification in specific diseases.