In recent times, there has been an exponential rise in the number of protein structures in databases e.g. PDB. So, design of fast algorithms capable of querying such databases is becoming an increasingly important research issue. This paper reports an algorithm, motivated from spectral graph matching techniques, for retrieving protein structures similar to a query structure from a large protein structure database. Each protein structure is specified by the 3D coordinates of residues of the protein. The algorithm is based on a novel characterization of the residues, called projections, leading to a similarity measure between the residues of the two proteins. This measure is exploited to efficiently compute the optimal equivalences.Experimental results show that, the current algorithm outperforms the state of the art on benchmark datasets in terms of speed without losing accuracy. Search results on SCOP 95% nonredundant database, for fold similarity with 5 proteins from different SCOP classes show that the current method performs competitively with the standard algorithm CE. The algorithm is also capable of detecting non-topological similarities between two proteins which is not possible with most of the state of the art tools like Dali. Retrieval of similar proteins from a database is a fundamental problem in Bioinformatics. Traditionally, similarity is defined in terms of scores of optimal sequence alignment between the amino acid sequences of the two proteins involved. However, it is well known that structures of proteins relate more accurately to their functions and evolutionary history than amino acid sequences. Thus, retrieval of structurally similar proteins becomes an important problem.There has been a very rapid growth in the number of structures in protein databases in the past few years, with PDB  having mMany heuristics have been proposed for comparing protein structures, e.g. Dali ,4, C-alpindels tolerated.• To what extent are extra atoms (those that are not present in the other structure) called non-topological similarities (those not following sequence order of the protein) are detected.• Whether Unfortunately, most of the above mentioned algorithms are very slow for searching through databases of size similar to PDB. CE  was desiThe aim in this paper, is to derive an alternate formulation of the protein structure comparison problem, resulting in development of a fast algorithm for comparing protein structures, called Matchprot. Moreover, this algorithm is capable of detecting non-topological similarities. Our formulation is motivated from the distance matrix overlap formulation . A novelSpeedup in the computation of alignment is achieved by using the similarity function between residues from two structures, as opposed to similarity function on pairs of residues defined by the distance matrix formulation. Such an attempt has been made in SSAP , by usinThe algorithm was tested extensively using 2 comprehensive benchmark datasets. The results obtained on benchmark datasets are comparable to those obtained with Dali, a state of the art protein structure comparison algorithm. Moreover, the algorithm was used to query a database of about 10000 protein structures (about 9 GB of data), for 5 different query proteins from the different protein classes. Results of this search, compared to the standard database search algorithm CE, are encouraging. Since the residues are characterized in the context of the whole structure, indels in the structures affect the actual characterization values. However, experimental results show that, in case of high structural similarity, correct equivalences are retrieved for upto 40% indels. Experimental results also confirm the algorithm's ability to detect non-topological similarities. Timing tests show that the algorithm is 2–3 times faster than CE, and 15–20 times faster than Dali.A comprehensive set of experiments were performed in order to evaluate the fitness of Matchprot for protein structure retrieval and to ascertain the possible applications of such a retrieval tool. For this purpose, we implemented the algorithm on GCC/GNU-Linux. LAPACK  was usedFirstly, in order to assess the quality of alignments generated by Matchprot vis-a-vis the standard protein structure comparison algorithm Dali, we run both the algorithms on two benchmark datasets: Fischer's  and NovoCircular permutations are a specific type of non-topological similarity observed in nature ,10, haviStructural Classification of Proteins (SCOP)  is a manFinally, we compare the running times taken by Matchprot, CE and Dali for proteins of different sizes. It clearly shows that matchprot is much faster than Dali and significantly faster than CE.In order to assess the performance of Matchprot on benchmark datasets, we ran Matchprot on Fischer's  and Novok, parameter for calculation of nearness matrix α, threshold for the similarity function T, and gap penalty g. In absence a clearly best set of values for parameters, Matchprot was run for 72 combinations of parameters, making a total of 4896 pairwise comparisons. Total time (user + system) taken by Matchprot was 27 min 38.58 s, whereas Dali took 52 min 8.21 s. The range of parameter values over which search was made is: k : {1, 3}, α: {5, 10, 15, 20}, T : {0.2, 0.5, 0.8}, and g: {0.1, 0.4, 0.7}.There are 4 parameters to Matchprot, dimension of representation level if one has higher RMSD and higher length than the other.Table Z-score measures the statistical significance of an alignment. However, it was noted that the Z-scores computed by the Matchprot and Dali were different even in case of similar alignments. For this reason, we chose to use RMSD and length of alignment, rather than Z-score for comparing alignments between Dali and Matchprot. The test on the benchmark data set shows that the quality of alignments given by Dali and Matchprot are comparable.In order to demonstrate the performance of Matchprot in detecting non-topological similarities, we show the alignment of the famous circularly permuted pair 2pelA-5cnaA in detail. Figure blue ↔ red and yellow ↔ green. It is very clear that Dali detects only a portion of the total alignment.For 2pelA-5cnaA, Matchprot gives a 223 residue alignment with RMSD 1.48Å whereas Dali gives a 117 residue alignment with RMSD 1.3Å. The superpositions generated by the 2 programs are also shown. The equivalenced portions are shown in color combination Next, to test the extent to which Matchprot can handle indels in the structures, we compared 2 multi-domain proteins (each having 3 separate domains), taken from Novotny et. al.'s article , with vaNext six entries in Table Finally, in order to ascertain whether the local alignment procedure is fooled by proteins having internal symmetry, we compared 6 proteins showing high degree of internal symmetry with their homologues. Table We tested the alignments given by matchprot on more than 100 other protein structure pairs. The experiments suggest that in most cases, Matchprot performs comparably with Dali.In order to judge Matchprot's performance in retrieving structurally similar proteins from databases, we searched the SCOP 95% non-redundant database, for similarities with 5 randomly selected SCOP domains  from the 5 major SCOP classes. The ASTRAL (a derived dataset from SCOP)  95% non-Table Recently, CE  was rateIt can be seen that Matchprot performs favorably with CE. Thus, Matchprot is found to be effective for retrieving proteins having similar folds.Matchprot was designed for fast comparison of protein structures. In this section, we compare the time taken by Matchprot with those taken by CE and Dali. Each of the programs was run 30 times on protein structures for each size. The homologous structure was created from the parent structure by perturbing it randomly. The average time taken the programs for each size is reported in fig. In this article, we describe a new protein structure retrieval algorithm called Matchprot. The algorithm was first validated on two comprehensive benchmark datasets against the standard protein structure comparison algorithm Dali. Performance of Matchprot was shown to be competitive with DALI on the benchmark. Next, it was shown that Matchprot can successfully detect non-topological similarities between two protein which is missed by DALI. Further validation was performed with proteins having multiple domains and internal repeats. Comparison of multi-domain proteins with individual domains showed that Matchprot is able to detect correct similarities in presence of up to 40% indels.As a retrieval tool, Matchprot was successfully used to search SCOP 95% non-redundant dataset, having nearly 10000 protein structures, for 5 diverse types of proteins. The results showed encouraging accuracy when validated against SCOP. Matchprot was found competitive the standard database searching algorithm CE, in terms of search accuracy. Finally, the previous section shows that Matchprot outperforms state of the art programs in terms of speed.O(n3).The key to the speed of Matchprot is the a similarity function, based entirely on the structures, which measure the structural similarity between 2 residues, one from each structure. Such a function is designed using a novel characterization of residues of a structure based on projections that try to preserve neighborhoods. This characterization also connects to the spectral graph theoretic techniques popular in many disciplines. Optimal equivalences are calculated using a greedy fragment pair search heuristic. The algorithm has a running time of The main drawback of the current algorithm is it's inability to perform in the presence of a large number of indels. Thus, it needs to be made more robust toward indels. Though a small amount of indels (about 20%) are tolerated by the algorithm, in the case of multiple domains, only the largest one is detected. A work arround this problem could be to search through a domain database, e.g. ASTRAL, instead of the PDB, and decompose the query into constituent domains. Theoretical bounds on performance is another desirable development. Since, the problem of comparing protein structures is NP-hard, approximations having a comprehensive theory behind them are highly desirable.Empirical results show that Matchprot is capable of detecting non-topological similarities (circular permutations in particular) between two proteins. Circular permutations are connected to many important questions in molecular biology. A study of suitability of Matchprot for automatic detection circularly permuted pairs of proteins is underway and will be reported elsewhere.Cα atoms of the residues to describe each protein structure. Thus, a protein structure having m residues will be given by a set of points A = {x1, x2,...,xm),xi ∈ ℝ 3, 1 ≤ i ≤ m, where each point represents one residue. The sequence ordering of the residues of the protein is given by the indices of the points.A protein is a polymer of amino acids . The protein sequence is a string comprising of the type of residues arranged in the order they are connected in the polymer. The protein structure is described by the 3D coordinates of all non-hydrogen atoms present in the protein. However, following the common practice we will use the coordinates of the structural alignment between two protein structures A and B is given by a set of equivalences (1 – 1 correspondences) between the residues of the 2 proteins. So, a structural alignment Φ between structures A and B, of length L, is denoted as:A Φ = {|1 ≤ l ≤ L, 1 ≤ il ≤ m, 1 ≤ jl ≤ n, and il = ik or jl = jk iff l = k}. In the alignment the  residue of protein A is said to be matched or equivalenced with the jlth residue of protein B.RMSD = , where  is the optimal transformation. However, small RMSDs can be obtained by choosing a very small no. of matched residues, which can be superposed tightly. Thus, the no. of matched residues  is also an important parameter. Detection of non-topological similarities increases the length of an alignment while keeping the RMSD same. For example, in figure Intuitively, an alignment is said to be good if the matched residues could be superposed on each other using a rigid transformation. This is captured by the most commonly used measure of similarity called Root Mean Square Deviation (RMSD), defined as Minimization of RMSD has been the objective of many methods including those described in ,21, etc.distance matrix formulation of the problem T be the vector of projections of a protein having n residues. We require that whenever ij is high, |fi - fj| should be low. This can be directly translated as the objective function:Let .f = ce, e = T ∈ ℝn. Instead, consider the problem:However, this problem has a trivial solution, i.e., fT f.which is same as: f, to some constant value c.However, this objective function has an unbounded solution. So, we fix the norm of c, solving (4) is equivalent to finding the eigenvector corresponding to the maximum eigenvalue of , normalized to c2. This formulation essentially tries to keep |fi + fj| high in addition to keeping |fi - fj| low, for i, j for which ij is high. The optimal solution to (4), is the optimally neighborhood preserving projection of a protein with nearness matrix .For a given A and B, consisting of m and n residues, the second criterion requires that the respective projections fA and fB be comparable. We use this criterion to fix values for cA and cB. To do this, one can impose various criteria. We require the mean squared distance of the projection values of two proteins, from the origin to be equal, i.e.,Given two proteins, cA)2 = m and (cB)2 = n. Our final optimization problem for calculating comparable, optimally neighborhood preserving projections becomes:Thus, we have  time [As noted earlier, this problem is equivalent to finding the eigenvector corresponding to the largest eigenvalue of n2) time . In the In the previous section, we have described a formulation for computing projections of residues that optimally preserve the neighborhoods. It turned out to be same as computing the eigenvector corresponding to maximum eigenvalue of the nearness matrix, and assigning it's components to the corresponding residues.A structural alignment is a 1–1 map between subsets of residues of the 2 proteins, or equivalently, a 1–1 map between subsets of basis directions of the space in which the projection vectors are embedded. From the distance matrix overlap formulation , we can  where ζi is the ith eigenvector of  normalized to 1. For convenience, arrange λis and ζis such that λi ≥ λj if i <j.From the spectral decomposition of square symmetric matrices having full rank, we know that, A and B are nearness matrices of two similar proteins, they will have a common similar sub-matrix under a permutation of their bases. Considering only the two sub-matrices, and only the first term on the right hand side of the above equation, it is clear that the corresponding sub-vectors of  and  will have similar values under the same permutation of bases, as they are the same eigenvectors of similar matrices.If Under the assumption that the eigenvector entries for equivalenced residues are not significantly perturbed by inclusion of other entries in the matrix (we will come back to this later in the article), we can compare entries of the first eigenvector for finding the best permutation .We make amendments for unequal number of residues in the two proteins by normalizing the eigenvectors to the number of residues in each protein. Explanation for the choice of eigenvector corresponding to the maximum eigenvalue can be given using the equation:η) of  is minimized if the values of λjs on the RHS of eqn (6) are low. So we arrive at the same formulation of the characterization as in eqn (5).It can be seen that the error in reconstruction  = , i = {1, · · · , n}, j = {1, · · · , k}. Experimental results on random matrices show that about 20% and 40% error in incurred by choosing k to be 3 and 1 respectively. Empirically, it is seen that not much accuracy is gained by chosing k greater than 3. In the next section, we use this characterization of the residues to define a similarity function between them.In order to maintain the property of being comparable across structures, we normalize all the eigenvectors to the number of residues. Thus, each residue f is a normalized eigenvector of a matrix, then so is -f. Thus the solutions of eqn (7) for two proteins can not be compared directly.The problem we encounter while defining a similarity function using the characterization of the residues obtained in the previous section is that the solution to eqn (7) is not unique. It can be easily seen that if k combinations of ± fj, j = 1, · · · , k and choose the best. The characterization of residues in the k-dimensional projection space, as derived the previous two sections can be compared in various ways. We consider the norm of difference of projections of 2 residues, a measure of distance between them. Thus, we define the similarity between residue i from protein A and residue j from protein B as:There can be many ways of taking care of this ambiguity in representation. For example, we could have compared the absolute values of the projections. However, this has less discriminative power due to the loss of sign. Another way is to consider all 2T (threshold) is a parameter and pi(j) = , i = {1, · · · , n}, j = {1, · · · , k}.where The most important feature of the above similarity measure is that it can score 2 residues one from each structure based on purely structural properties . Secondly, this similarity measure gives a similarity between 2 residues, one from each structure, as opposed to the typical ones which take 4 residues, 2 from each structure . Such aThe above formulation works perfectly when there are no extra residues (indels) in the two proteins. However, if there are a large number of indels between the structures, the projections of the residues participating in the alignment are likely to be disturbed by the extra residues, thereby giving incorrect equivalences. We have also investigated the maximum percentage of indels that are tolerated. In the next section, we describe a method for finding the optimal equivalences from the above derived similarity function.Φ = {|1 ≤ l ≤ L}, and a scoring function of the form s, an obvious measure of goodness of Φ is:Given an alignment The problem of finding alignment that maximizes the objective function (9), can be posed as an assignment problem and solved exactly. However, the solution will be slow and will not have any relation to the protein sequence.s as the score function. However, this will not capture the non-topological similarities between the two proteins.On the other hand, one can compute a structural alignment between two protein structures by globally aligning the sequences of the two proteins using Local Alignment problem [High Scoring Fragment Pairs (HSFPs). The algorithm operates in 2 stages:We propose to greedily pick common subsequences with high structural similarity. The problem can be posed as that of finding two subsequences which have maximum similarity with the pairwise similarity score given by eqn (8). This is same as the  problem  and can (1) Calculation of the local alignment matrix with similarity score given by equation 4.(2) Iterative determination of HSFPs and their elimination from the local alignment matrix.L, is computed as:The local alignment matrix g is the gap penalty provided as a parameter to the program. The highest scoring entry (corresponding to highest scoring fragment pair) is detected and traced back to get the highest scoring fragment. The indices corresponding to the residues participating in the current alignment are eliminated from the matrix and the above step is repeated to get the next highest scoring fragment. This is stopped when there are no more positive scoring fragments. The steps are given in Algorithm 1.where, Algorithm 1 Finding EquivalencesAlignment ← φ.1: highest = maxi,j Li,j.2: Compute u, v) = arg maxi,j Li,j. {u and v are the residues indices}3: Compute  {traceback returns the alignment obtained by tracing back from it's argument}5: L corresponding to the residues returned in the current alignment done.6: Mark the rows and columns of highest = maxi,j Li,j such that i or j is not marked done.7: Compute u, v) = arg maxi,j Li,j such that i or j is not marked done.8: Compute  × (n + 1) entries of the local alignment matrix takes O(mn) time. Detection of alignment fragments is done by searching through the (m + 1) × (n + 1) matrix for at most min times, which takes O time. This computation is repeated for 2k times, thus making the overall time complexity of the algorithm to be O). For the current algorithm, k is taken to be very small thus making it O(n3). At any point of time, the program stores a constant number of m × m, n × n and m × n matrices. Thus, it consumes O) memory space.Computation of the  = 5.0 - ||i X- jY||. The final equivalences are calculated using Algorithm 1. This post-processing helps to keep the total RMSD low and also corrects some minor shifts in equivalences. We compute the Dali Z-score [From the equivalences, we can compute the rigid transformation of one structure into the other that minimizes the RMSD between the matching residues, using the method by Horn . The opt Z-score  to give S.B. and C.B. devised the algorithm; S.B. implemented it. N.C. helped in analyzing the results and interpreting the biological implications.


A new parsimony approach for the prediction of domain-domain interactions is presented and demonstrated to provide improvement in prediction coverage and accuracy. We propose a novel approach to predict domain-domain interactions from a protein-protein interaction network. In our method we apply a parsimony-driven explanation of the network, where the domain interactions are inferred using linear programming optimization, and false positives in the protein network are handled by a probabilistic construction. This method outperforms previous approaches by a considerable margin. The results indicate that the parsimony principle provides a correct approach for detecting domain-domain contacts. Knowledge about protein interactions helps provide deeper insights into the functioning of cells. Protein interaction data are collected from various studies on individual biological systems, and, more recently, through high-throughput experiments, such as yeast two-hybrid and tandem affinity purification followed by mass spectrometry -8. This Proteins typically contain two or more domains. About two-thirds of proteins in prokaryotes and four-fifths in eukaryotes are multidomain proteins . InteracMany domain-domain interaction prediction methods tie the goal of predicting domain interactions to the seemingly related goal of predicting protein-protein interactions. For example, the Association method  scores eNye and colleagues  evaluateMore recently, Riley and colleagues  introducIn this work, we explore an alternative model for predicting domain-domain interactions. In our approach, we completely decouple domain-domain interaction prediction from protein-protein interaction prediction. We hypothesize that interactions between proteins evolved in a parsimonious way and that the set of correct domain-domain interactions is well approximated by the minimal set of domain interactions necessary to justify a given protein-protein interaction network. We refer to our approach as the 'Parsimonious Explanation' (PE) method. We formulate PE as a linear programming optimization problem, where each potential domain-domain contact is a variable that can receive a value -score'), ranging between 0 and 1, and each edge of the protein-protein interaction network corresponds to one linear constraint. This formulation allows for a novel way of handling the noise  in the protein interaction data. Namely, we construct a set of linear programming instances in a probabilistic fashion, in which the probability of including an LP constraint equals the probability with which the corresponding protein-protein interaction is assumed to be correct, and average the results to get the LP-score for each pair.To control for possible over-prediction of interactions between frequently occurring domain pairs, we assign a promiscuity versus witnesses (pw)-score to every predicted domain-domain interaction. The pw-score, derived from two observations, measures the confidence in the prediction. First, domain-domain interactions that have many witnesses (interacting pairs of single domain proteins that support it) are more likely to be correct than ones that have a few or no witnesses. Second, there are promiscuous domain-domain interactions that are scored high due to the frequency of their appearance and not to the specific topology of the protein-protein interaction network. In view of these observations, the pw-score formulation rewards domain interactions that have many witnesses and penalizes promiscuous interactions.We assess the performance of our method with two different types of evaluations. Our first evaluation, which is very similar to that done by Riley and colleagues , documenOur second type of evaluation of the PE method involves finding whether or not the predicted domain pairs do, in fact, mediate interactions between specific protein pairs. In other words, given a protein-protein interaction, we are interested in finding whether the highest scoring domain pair between those proteins is, in fact, known to interact. If it does, then we consider our prediction to be correct. In case of multiple highest scoring pairs, each one of them is considered in the evaluation. This type of 'protein interaction specificity' evaluation has been used before . For thiWe applied the PE method on a protein-protein interaction dataset comprising 26,032 interactions underlying 11,403 proteins from 69 organisms. This set was constructed by Riley and colleagues  from theThe PE method assigns a LP-score and a pw-score to each potential domain-domain interaction. Intuitively, the LP-score estimates the potential of a given domain pair in explaining protein interactions, based on the overall goal of parsimony principle, while the pw-score factors in the influence of the number of occurrences of a pair in the data set, and the number of witnesses present. Potential interactions whose LP-scores are above a certain threshold and whose pw-scores are below another threshold are predicted to be putative interactions. We model the experimental error  in the protein-protein interaction network by a probabilistic construction of the linear program, as described in Materials and methods.We performed experiments with assumed reliabilities of 50%, 60%, 70%, 80%, 90%, and 100%. The most tangible general effect of increasing the assumed network reliability is an increase in the LP-scores, resulting in a higher coverage, but with lower prediction accuracy with respect to the set of interactions confirmed by crystal structures. Figure The pw-score is an indicator of the possible over-prediction of interactions between domains that occur frequently, which also takes into account the number of witnesses for that given pair in view of the assumed reliability of the network. More precisely, for a given domain pair, the pw-score is the minimum of a p value (which measures the probability of obtaining the same or higher score in a random network of interactions for the same protein set) and a probability based on witness support and the network reliability rate . A high LP-score can be due to the sheer number of occurrences of the given domain pair in proteins included in the interaction network. However, we verified that many promiscuous domains do interact despite of a high p value. To detect such interactions, we rely on the evidence from the set of witnesses. The confidence in the witness is a function of network reliability as described in Materials and methods. The role of the pw-score is to allow some control over these factors. A pw-score close to one indicates a promiscuous domain pair that can obtain a high LP-score independent of the topology of the underlying protein-protein interaction network, and does not have significant witness support. Choosing a smaller (more stringent) pw-score cutoff naturally leads to higher prediction accuracy, as can be seen in Figure Based on observations that the reliability of high-throughput protein-protein interaction networks is about 50% -11, we hMotivated by Riley and colleagues , we deveWe compared the PE method with previous methods , by plotting curves of their positive predictive value versus their sensitivity. The comparison plot is given as Figure We also performed a comparison of the number of predictions by the PE and the DPEA methods confirmed to interact based on crystal structure evidence; we analyzed easy and difficult predictions separately. The necessity of evaluating predictions based on how difficult they are to predict has been justified before . To sepaFigure In Table In Table The SNARE domain (Pfam PF05739) is thought to act as a protein-protein interaction module in the assembly of a SNARE protein complex. Out of the 223 potential domain pairs in our dataset involving SNARE, almost all of which are difficult, only 5 are in the gold standard set. All but one of the PE method's eight predictions of SNARE interactions are in the difficult category, and four of them are documented by crystal structures.When interpreting the results for such families, one has to keep in mind that the PE method predicts domain interactions based on the evidence found in the underlying protein interaction dataset, that is, a predicted domain interaction is expected to mediate at least one protein-protein interaction in the dataset. Large superfamilies like Ras contain several related but yet different subfamilies, such as Ras, Rab, Rac, Ral, Ran, and so on. Since Pfam has classified all Ras-type families into one big superfamily based on their sequence similarity, a prediction between Ras and Mss4 does not necessarily mean that all subfamilies interact with Mss4; it only means that there is at least one subfamily in the Ras superfamily that is predicted to interact with Mss4. Since Ras and SNARE are large domain families, to recover true interactions, many of which may have high pw-scores, we used a pw-score cutoff of 0.05 to construct Table Given a pair of interacting proteins, predicting the domain pair(s) that mediate the interaction is a problem that has been studied before. In ordeWe estimated the PPV and the sensitivity of the Association, EM, PE, and DPEA methods, and we also estimated the performance measures that could be expected by chance using a Random method . The results for PE with pw-score cutoffs of 0.01 and 0.05 were very close, so we present only one set of numbers. The scores for the Association, EM, and the DPEA methods were taken from those generated by Riley and colleagues .In Figure We observed that the Random method outperforms both the Association and the EM methods. This is not surprising considering the fact that it has been shown before that RanBelow we present some additional discussion on the performances observed. A plot similar to Figure There are two main reasons for the PE method's improved performance, both of which relate to interaction specificity. An ideal example of a non-specific interaction between domains A and B is illustrated in Figure X,X'}, {Y,Y'} and {Z,Z'}, respectively. We call this effect 'a shift towards rare domains' phenomenon. Since the PE method seeks an explanation that involves the smallest possible (weighted) number of domain pairs, it is immune to the shift towards the rare domains phenomenon. Figure The second shortcoming with machine learning methods, which are trained best to predict the protein interaction network, is their tendency to use infrequent domains to justify interaction between multi-domain proteins. Consider a hypothetical situation where a set of proteins containing domain A interacts with a set of multi-domain proteins containing domain B Figure . If domaBased on the mathematical formulation of the PE method, one may be concerned about possible over-prediction of interactions between frequently occurring domains. To address this question, we introduced the pw-score as a measure of confidence in our prediction. With the assumed network reliability of 50%, about 10% of the gold standard pairs achieve a pw-score >0.05, and about 25% of the gold standard pairs achieve a pw-score >0.01, hence those pairs cannot be recovered. Since the number of the promiscuous domain pairs is relatively small, false-positives between them are easier to detect, and subsequent knowledge on 'non-interaction' between such domains can be included in the model.We present a new method for identifying interacting domain pairs. The method, abbreviated as PE, is based on the parsimony principle: domain-domain interaction partners are predicted by identifying the minimal weighted set of domain pairs that can justify a given protein-protein interaction network. The corresponding optimization problem is formulated using linear programming. Our results show that the PE method outperforms previous methods considerably. The most dramatic improvement is evident in the recovery of known true domain-domain interactions that are considered to be difficult to recover.We estimate PPV and sensitivity of our method to be 75.3% and 76.9%, respectively. However, one has to keep in mind that such estimations are, in this case, very difficult due to lack of interaction data. Our test set for this experiment makes the assumption that domain-domain interactions that have been proven to mediate a specific protein-protein interaction are also likely to mediate other protein interactions that contain those domain pairs. In this case it is reasonable to presume that domain pairs not in the gold standard set do not interact in the context of the given protein pairs. Nonetheless, there may be cases where that is not true; therefore, the reported numbers should be considered as estimates.Our method provides a unique way to represent uncertainties of the protein-protein interactions in a high throughput protein-protein interaction network. In this work, we assumed that the probability of error for each protein-protein interaction represented in the network is the same. However, our approach can also be applied when probability of correctness of each interaction is assessed individually, based on the type of experiment used for its detection and other supplementary information. For example, the confidence values, based on logistic regression, assigned to links in the network by Bader and colleagues .The PE method is a significant departure from the underlying assumption of the EM method. While EM methods work well for the problem of identifying interacting protein pairs based on their domain composition, it does not provide an effective approach to detecting interaction between domains ,22. We sWe used the protein-protein interactions and the protein domain composition dataset used by Riley and colleagues . This seWe validated our method using two types of experiments. In the first experiment, we evaluated the retrieval of the gold standard positives among the top-scoring domain pairs. We used the Association, EM, and DPEA scores provided by Riley and colleagues to compare the methods by estimating their PPV:PPV = (TP/(TP + FP))and their sensitivity:Sensitivity = (TP/(TP + FN))where the number of true positives (TP), false positives (FP), and false negatives (FN) were estimated with respect to the gold standard set. One should keep in mind that, in this experiment, the set of negatives includes all potential domain pairs occurring in the dataset that are not in the gold standard set and, thus, it is most likely to contain interacting domains that have not yet been documented by crystal structures.In the second experiment, we focused on whether the methods correctly identify domain interaction(s) mediating a given protein-protein interaction. For this part of the experiment, we selected only the set of interacting protein pairs that had at least one of the gold standard domain pairs among their potential domain contacts. To avoid distortions imposed by protein pairs with exactly one potential domain contact, only protein pairs with at least two potential contacts were considered. The set of 1,780 protein pairs used for this experiment is available as Additional data file 6; it contains a total of 2,641 occurrences of gold standard pairs.We considered as gold standard negatives all potential domain-domain interactions that are in some protein-protein interaction of that selected set of protein pairs and do not meet the gold standard positive criteria. It is important to keep in mind that, while the gold standard set that we used is widely accepted, selection of gold standard negatives is difficult as there is no proof of non-interaction of domains.i and j has a variable xij if and only if the interaction data contains an interacting protein pair Pn and Pm containing domains i and j, respectively. Variable xij represents the score of the potential interaction between domains i and j. The goal is to minimize the objective function ∑ij xij subject to the set of constraints, which require that Informally, we consider the problem of predicting interacting domain pairs as an optimization problem, in which the objective is to minimize the number of domain-domain interactions necessary to justify the underlying protein-protein interaction network. We formulate this problem using linear programming, in which a pair of domains I = , where P = P1, P2,...,PN is the set of proteins in the network and E is the set of protein interactions, and a set of unordered pairs denoting all possible domain-domain interactions D = {{i, j}|i ∈ Pn, j ∈ Pm, and Pn and Pm interact}, solve the linear program (LP):Formally, given a protein-protein interaction network Minimize Subject to: Pm, Pn}.for all interacting pairs of proteins {® . Our LP had 177,233 variables and 26,032 constraints.The variables  and the constraints (interacting protein pairs to be explained) were coded into a sparse matrix, and the system was solved using an optimization toolbox in Matlabr, we include the corresponding constraint with probability r. We performed experiments setting the reliability at different rates. For each rate, the experiment was performed 1,000 times, with different numbers of constraints for each run, and the values obtained were averaged to generate the reported LP-score.The noise in the protein-protein interaction data is modeled by randomizing the set of constraints. Namely, if we assume that the interactions are reliable with probability w be the number of witnesses for a given domain pair  and let r be the assumed reliability of the network, that is, the probability that the interaction represented by an edge actually exists. The quantity (1 - r)w is the probability that all edges in the network that correspond to an interaction's witnesses are false positives. We compute the pw-score by taking the minimum between (1 - r)w and p value, an estimation of the influence of the frequency of appearance of the domain pair in its LP-score, computed as:The pw-score for a given domain-domain interaction integrates two factors: the number of witnesses for the interaction and its 'promiscuity'. Let i,j) = min, (1 - r)w)pw-score.Click here for fileThe 3,000 top scoring domain pairs with a pw-score cutoff of 0.05 (network reliability 50%).Click here for fileThe 3,610 domain pairs with a LP-score ≥ 0.4 and a pw-score ≤ 0.1 (network reliability 50%).Click here for fileThe 3,944 domain pairs with a LP-score ≥ 0.4 and a pw-score ≤ 0.1 (network reliability 60%).Click here for fileThe 783 gold standard domain pairs occurring in our dataset.Click here for fileThe 1,780 interacting protein pairs used in the mediating domain pair prediction experiment.Click here for fileThe estimated sensitivity measures for the mediating domain pair prediction experiment.Click here for file


Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-evolutionary information of the interacting partners, e.g., correlations between distance matrices, where each matrix stores the pairwise distances between a protein and its orthologs from a group of reference genomes.We proposed a novel, simple method to account for some of the intra-matrix correlations in improving the prediction accuracy. Specifically, the phylogenetic species tree of the reference genomes is used as a guide tree for hierarchical clustering of the orthologous proteins. The distances between these clusters, derived from the original pairwise distance matrix using the Neighbor Joining algorithm, form intermediate distance matrices, which are then transformed and concatenated into a super phylogenetic vector. A support vector machine is trained and tested on pairs of proteins, represented as super phylogenetic vectors, whose interactions are known. The performance, measured as ROC score in cross validation experiments, shows significant improvement of our method (ROC score 0.8446) over that of using Pearson correlations (0.6587).We have shown that the phylogenetic tree can be used as a guide to extract intra-matrix correlations in the distance matrices of orthologous proteins, where these correlations are represented as intermediate distance matrices of the ancestral orthologous proteins. Both the unsupervised and supervised learning paradigms benefit from the explicit inclusion of these intermediate distance matrices, and particularly so in the latter case, which offers a better balance between sensitivity and specificity in the prediction of protein-protein interactions. Protein-protein interactions play a key role in cellular functions, and thus, to complement the experimental approaches ,2, many Of particular interest is the so-called mirror tree method by Pazos and Valencia . The miret al  [DB - Ave(DB)] over all  in calculating the Pearson correlation coefficient as defined in Eq(1). The very rich intra-matrix correlational information is almost entirely neglected and is condensed to just a single number – the average Ave(DA), to which the deviation [DA - Ave(DA)] is measured for each element  and correlated with its counterpart from the other matrix B and then factored into the inter-matrix correlation ρAB. For example, if element DA is above the average in the matrix DA and DB is above the average in matrix DB, then the product [DA - Ave(DA)] [DB - Ave(DB)] is positively contributing to the correlation ρAB, and thus to the similarity. In another case, if DA is above the average in the matrix DA but DB is below the average in matrix DB, then the product [DA - Ave(DA)] [DB - Ave(DB)] is negatively contributing to the correlation ρAB, and thus to the similarity. And each element in the distance matrix is treated independently and equally. However, to a very large degree, it is the intra-matrix correlations among the elements that determine the protein tree, as manifested in the distance based phylogenetic tree reconstruction algorithms such as UPGMA and Neighbor-Joining [A - Ave(DA)] [DB - Ave(DB)] and [DA - Ave(DA)] [DB - Ave(DB)] differently when contributing to the correlation ρAB in Eq(1). When measuring matrix similarity, not only may elements in a matrix contribute differently, but the very fact that a tree can be reconstructed out a distance matrix imparts a clear indication of some embedded "intra-matrix" correlations among matrix elements. Therefore it is reasonable to hypothesize that the matrix elements need to be regrouped in a certain way such that the hierarchical relationships among matrix elements can be unraveled and flattened to achieve the effect of "weighted" Pearson correlation between two matrices. This is somehow similar to the ideas in /|Cα| |Cβ|     (4)D     (5)d = D - /(|L| - 2)     (6)ret al's modified mirror tree method Eq(2), only now that |vi> and |u16s> are substituted with the super-phylogenetic vectors. It should be noted that since the background subtraction in tol-mirrortree method and Sato et al's method also utilizes the phylogenetic tree, combining the TreeSec procedure and background subtraction may introduce some redundancy.where |L| is the dimension of the matrix D. Another refinement is to use the same projection procedure introduced in Sato The use of the species tree instead of individual protein trees for hierarchical clustering has a twofold effect. One effect is more theoretical; it is to reveal how individual protein trees (embedded in the distance matrices) would differ from the underlying species tree of the host genomes, in the same spirit of subtracting the common background as in ,10. The The classifier used here is a support vector machine. As a powerful statistical learning method, support vector machines (SVMs), originally proposed by Vapnik ,22, haveThere are a couple of reasons to use SVMs. First, the data are already in the vector form, particularly suitable as inputs for SVMs. Second, SVMs have been used to predict protein-protein interaction in previous works ,26, thouThe basic idea of SVMs is simple; it is to find a hyperplane that separates two classes of objects, as represented as points in a vector space, with the maximum margin to the boundary lines. Such a hyperplane ensures good generalization and unseen data are then classified according to their location with respect to the hyperplane. The power of SVMs comes partly from the data representation, where an entity, e.g., a pair of proteins, is represented by a set of attributes. However, how those attributes contribute to distinguishing a true positive from a true negative may be quite complex. In other words, the boundary line between the two classes, if depicted in a vector space, can be highly nonlinear. The SVMs method will find a nonlinear mapping that transform the data from the original space, called input space, into a higher dimensional space, called feature space, where the data can be linearly separable.In general, the mapping can be quite complex and the dimension can be very (even infinitely) high in order for the mapped data to be linearly separable. The trick of SVMs is the use of kernel functions, which define how the dot product between two points in the feature space, which is the only quantity needed to solve the quadratic programming problem for finding the maximum margin hyperplane in the feature space. The use of kernel functions avoids explicit mapping to high dimensional feature space; high dimensionality often poses difficult problems for learning such as over-fitting, thus termed the curse of dimensionality. Polynomial kernel and Gaussian kernel are the two most commonly used generic kernels – linear kernel is not really useful in most cases except when the data are linearly separable. For vectors x and y, Gaussian RBF is defined as2/c)],     (7)K = exp[- K = [1 + s(x · y)]s and d are parameters adjustable in the software package SVMLight [s and d, and the Gaussian kernel yielded the best results reported in this paper. Because the polynomial kernel performs significantly worse with the default setting, we also tested with changing the polynomial degree d from the default value (d = 3). We found that the performance is quite sensitive to the degree. Details are given in the next section. Besides using the separation of training and testing as a mechanism to alert us to overfitting, another mechanism built into SVMLight for avoiding overfitting is the use of "soft" margin, i.e., to allow for misclassification for some outlier training data points, and a cap on the number of iterations to stop the optimization process during the training even if the preset error rate is not reached. And we have used SVMLight's default setting for our experiments.where c, SVMLight . Both keIt is worth noting that, overall our method can be viewed as a hybrid that employs in tandem both an explicit mapping, from phylogenetic vectors to super-phylogenetic vectors, and the use of a generic kernel.We test our TreeSec method in a series of leave-one-out cross-validation experiments on the data set described above. To prepare an experiment, one of the 13 interacting pairs is selected and reserved as the positive testing example, and 48 non interacting pairs that contain one protein from the positive testing example are reserved as negative testing examples. The rest 325 - 49 = 276 pairs are used as training examples, among which there are 12 interacting pairs as positive training examples. By rotating the positive testing example among the 13 interacting pairs, we can design 13 such leave-one-out cross validation experiments, and the average performance is reported.For each experiment, the training examples are taken as input to train a support vector machine. The implementation of the support vector machine is adopted from the SVMLight package . The twoet al's procedure receives a ROC score 0.6587. A slightly higher ROC score (0.6731) is obtained when everything is kept the same except for substituting the phylogenetic vectors with the super-phylogenetic vectors prepared via TreeSec method.The ROC scores of the mirror tree method and our TreeSec method, with a few variations, are reported in Table d = 3, we tested with changing the degree to different values and found that the performance is significantly better for even values than odd values of d. This phenomenon may be an indication of the parity of the hyperplane in the feature space: symmetric with respect to changing the sign of the coordinates. Overall in Table The advantage of TreeSec method becomes more obvious when used in supervised learning. In this case, TreeSec and MirrorTree are compared for their capability of representing proteins in a way which is more conducive for classification. Once proteins are represented as super-phylogenetic vectors via TreeSec or as phylogenetic vectors via MirrorTree, they are fed into the same classifier, in this case, a SVM. As we see in Table et al's approach – the same distance matrices with the same size for proteins and the same distance matrix for species (based on 16S rRNA sequences). The extra bits of information are not really extra; they are the result of how we unravel the information embedded in the input data. In a sense, our method is a hybrid of combining the explicit mapping (to higher dimension) and the use of kernels, which may explain why our method bodes well with the learning task. Nonetheless, care should be taken to not let the increase of dimension go unchecked, as redundancy may arise and lead to overfitting and bad generalization. That is part of the reason that only "snapshots" of evolutionary history are incorporated.The dimension of the super-phylogenetic vectors from TreeSec is obviously higher than that of the phylogenetic vectors in MirrorTree, since the former is derived by concatenating extra bits of information to the latter. Although this may raise concerns with a judicious reader about the fairness for comparing the two approaches if they have different sizes of data, it should not be a problem in our case, because we use the same amount of input data as the Sato It is not surprising that the neighbor-joining distance adjustment is essential; without it as shown in Table To further examine the performance, in Figure 2 - n)/2 possible pairings of these n proteins. That is, the interacting network, with nodes representing the proteins and edges representing the interactions, is quite sparse, but our method is still applicable when there are more edges. Because every possible pair of nodes is assigned a score in our method, predictions can be made by going down a list of all pairs that are ranked by their scores in decreasing order. So, regardless the number of the actual edges in the network (sparse or not), the method works, and perfectly so as long as the true interacting pairs are ranked higher than non interacting pairs in the prediction. Indeed, this scheme is also widely used in predicting protein interaction networks in general, both in an unsupervised paradigm such as the original mirror tree method, and in a supervised learning paradigm.It is worth noting that, the highly skewed learning problem is likely a reflection of situations in the real world, i.e., there are far more negatives than positives. In our case, given n proteins that uniquely interact with only one other member, there are only n/2 positive pairings among the  declare that they have no competing interests.LL conceived the ideas and prototyped some of the implementation, and RAC collected the data and implemented the methods. Both authors participated in writing the manuscript.


The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest.This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.61 and a recall of 0.97 were obtained, (f = 0.74) and when allowing for two-hop and three-hop relations discovered by graphical analysis, the precision was 0.74 (f = 0.83).This combination of linguistic and statistical approaches appears to provide the highest precision and recall thus far reported in detecting protein-protein relations using text analytic approaches. Scientists in molecular biology find that a significant technique for studying protein function is through the study of protein-protein interactions. While the actual experimental study of such interactions remains the most important manner of obtaining these data, the number of protein-protein interactions reported in the literature is substantial and growing rapidly. There are a number of tabulations of these interactions, such as that provided by the Munich Institute for Protein Sequence (MIPS); these tabulations are of necessity incomplete.To address this problem, we have been developing a group of biology-specific computational annotators that work in conjunction with our group's text analytic software, for the discovery of protein-protein relations in text.In this paper, we undertook a study that utilizes a combination of computational linguistics, statistics and domain-specific rules to detect protein-protein interactions in a set of Medline abstracts.The system we describe here is particularly appealing because it can be used both to find known interactions and to find interactions not yet tabulated. According to the National Library of Medicine, Medline contains over 11 million abstracts, with about 40,000 being added each month. Thus, having a scalable, robust system for protein interaction discovery provides a major information tool for molecular biologists.A number of workers have tackled portions of this problem previously with some partial success. The SUISEKI system  recognizet. al [inhibit relations, with 90% precision and recall of 57%.In a more narrowly focused experiment, Pustejovsky et. al  describeRecently Leroy  describeWe  have preThis paper discusses the text analytic tools used, and then describes our experiments against a gold standard of protein relations. Finally the results of mining relations across a large set of Medline abstracts are described.The system used in these experiments is constructed using the TALENT  text mining system . The curIn this work, the approach is to use tagger and shallow parser annotators primarily for sentence boundary recognition, and use a dictionary annotator derived from public sources to recognize the protein names. The goal of this approach is to be fast and scalable as well as to improve precision and recall over other methods.After each Medline abstract is processed by the annotators, an annotation consumer program converts these annotations into entries in a database load file. This file contains all of the salient terms per document, their part of speech and their relative token positions in the document. An additional database load file contains the Medline document metadata: dates, titles, authors and ID numbers.The flow of this process is show in Figure Then it is possible to use a few simple database queries to construct a database table of all the unique terms in the document collection, and compute their frequencies, and the number of documents in which they appear once and more than once. Using these data the salience or IQ  of each We describe two experiments; one using a small amount of data derived from the MIPS tables on the web, and a larger experiment utilizing 125,000 Medline abstracts.saccharomyces cerevisiae) protein interactions along with a reference to the Medline abstract of the paper in which the interaction is reported. This table contains 2050 protein names and 2604 pairs of protein interactions and provides links to additional information on each protein. The interaction table was parsed and reduced to 959 unique relations, and the protein names and the 564 Medline abstracts downloaded.The Munich Institute for Protein Sequences (MIPS) maintains a database of published yeast . Synonyms for most of these proteins are available on pages linked from the original page on the MIPS web site. The dictionary was expanded using these synonyms and the various allowed capitalizations and the analysis rerun, storing all terms and their document positions in a database table.Even with the expanded protein synonym table, only 388 protein interactions were detected within single sentences that matched those in the MIPS interaction table, and 432 other interactions were detected which did not match those in the MIPS table. This amounted to a precision of 0.47 and a recall of 0.68. Further, there was no particular correlation between the computed strength  of the relation  and the In an effort to improve the accuracy of protein-protein interaction detection, a detailed study of 65 of the abstracts was undertaken to determine what algorithms and approaches would be most effective. In this study, each abstract was examined along with a list of the interactions reported by the MIPS table, including all of the synonyms for each protein. This process led to the following conclusions:1. Some interactions were not reported in the abstracts, but only in the full papers. In fact some review articles contained no protein names at all in the abstracts. This finding is similar to that previously described [2. Some interactions were described that were not tabulated by MIPS. For example, the abstract might mention prior work.3. Protein complexes were frequently mentioned. For example references are made to dimers such as "Ddc2-Mec1" and trimers such as "Hap2p-Hap3p-Hap5p." Such complexes do, in fact, represent protein interactions and should also be detected and reported.4. Proteins were frequently referred to by two synonyms separated by a slash, such as "GIM1/YKE2."5. In all but one case, the interactions were described in the same sentence, and thus resolving co-reference issues would add only marginally to the quality of the interaction detection. Thus, the fact that two proteins occurred in the same abstract, but not in the same sentence was not a good metric for the number of relations we should be able to find.6. No instances of negation were found.7. A database query of verbs that lay between two proteins led to the small list shown in Table Accordingly, two additional annotators were written. One annotator recognized protein complexes: dimers and trimers, and the other recognized protein synonyms in the "slash notation" we illustrated in point 4 above. When the annotator found these synonyms, it only annotated one of the two mentions, to avoid skewing the mention statistics. All protein complexes were treated as reports of interactions and annotated as such.We also annotated the verbs or their noun-equivalents in each sentence, that contained two or more different proteins.Examination of protein interactions detected in a few documents showed that nearly all of the relations detected by our proximity algorithm actually existed in the document, whether tabulated by MIPS or not, and that of those our algorithm missed, nearly all were not discussed in the abstract at all.With these encouraging preliminary results in hand, a study of a larger dataset was undertaken. It is recognized that the initial experiments were on the same data as the rules were developed on, and this second larger set is used to provide a more independent measurement of the accuracy of this approach. In the following experiments, no new protein interaction algorithms were developed, although we did introduce some further filtering to reduce spurious results. The dictionary of protein names and synonyms was the same one derived from MIPS tables. It is our assumption that this is a complete list, though if it is not, the precision and recall numbers would be slightly different.The query "yeast protein" was submitted against our local indexing of Medline documents through 2002 and a list of the top 12,300 documents was obtained. The MIPS protein interaction table was enhanced by one from Stanley Fields . These dThe initial results of this experiment returned 912 relations, but only 133 (14%) agreed with the combined gold standard MIPS-Fields table. These and the following results in this section are summarized in Table In this larger set of data, protein names may co-occur in more ways that our initial approach allowed for. To reduce the error rate in these experiments, the annotator was further modified to exclude sentences which did not contain one of the verbs in Table To further explicate the reasons for the remaining 75% apparent false positives, each relation reported was studied in each abstract where it was detected and conservatively rated either true or false. Of the 343 unmatched relations, 140 additional relations were discovered. While these relations were not in the combined MIPS-Fields table, they were definitely reported in the abstracts. In all questionable cases, the opinion of a biologist was obtained. This leads to 234 out of 437 (53%) relations being discovered correctly.To further reduce the false positives, sentences containing any negation word . We then analyze the cohesion of GN for each P and Q and collect statistics on the cohesion, as described in [More formally, given a interaction between two proteins, ribed in  .n, in an undirected graph, if the degree (number of incident edges) of n is d we define the neighborhood of n as the set of nodes including the endpoints of these d edges, and all edges whose endpoints are in this node set. Say there are e such edges. The cohesion, C(n) is then defined in Equation (2).The cohesion of a graph or subgraph is defined as the ratio of the number of edges present to the possible number of edges. In the case of a single node, P and Q above. There are also three types of edges in this graph. There are thus many possible definitions of cohesion. For simplicity, we take the conservative approach of only considering 2- and 3-hop paths .In this paper, we are analyzing the cohesion of a subgraph defined over the union of the neighborhoods of two nodes, specifically In the 104 protein interactions found by our method, but not in the combined true relations table, 30 are related by at least one 2-hop path, 34 are related by at least one 3-hop path and 39 are related by at least one 2-hop or 3-hop path.In 18 of the 30 cases where 2 hop paths were present, more than one path was present, with the average being 2.3, and similarly, 25 of the 34 occurrences of 3-hop paths were multiple occurrences, with the average being 4.1. Since the network contains 30,135 possible pairs, the assumption that these paths support new interactions found by our method seems statistically persuasive. These results are summarized in Table Computation of all relations having 2-hop and 3-hop paths which do not have direct reported interactions gave 30 relations deduced from a combination of 2-hop and 3-hop paths and 9 relations deduced only from 3-hop paths. Of the 2-hop path relations, 15 (50%) of them were found to be true by careful re-examination of the text of the abstracts. This gives us a precision of 74%.We were unable to discover any abstracts supporting the interactions predicted by the 3-hop relations. However, of the 39 predicted interactions, 20 had 4 or more total 2-hop or 3-hop bridges, and of these 8 were validated by consulting our abstract collection. We suggest that the remaining 12 merit further investigation.homologous [While protein interactions may not be the only reason for this close co-occurrence, other reasons for these graphical relations may be of interest. For example, FAR1 and STE5 are related by 1 2-hop bridge and 6 3-hop bridges. These proteins are reported to be mologous , that isRecall, of course, can only be approximated in such a large collection. In the 12,300 document collection, 451 documents were returned as containing one or more of the computed interactions. In reading these documents to validate these interactions, we found only one interaction which was missed by the algorithm because it was referred to across 2 sentences and the co-reference was not resolved by this system.It is difficult to devise a method for measuring recall when 12,300 documents constitute the sample. We examined 100 randomly chose abstracts from this collection for description of any protein-protein interactions. Since none of the 100 we selected happened to be members of the set of 451 described above, we were looking for additional un-found interactions. None were discovered. On this basis, the recall was effectively 100%.Since this seemed to be exceedingly optimistic, an experiment was devised which would return the most likely candidate documents where protein relations might have been missed. If we found many missed relations, the recall is reduced.The method for finding protein interactions we have described above amounts to using a dictionary of yeast proteins names and their synonyms to find co-occurrences of two (or more) such names within a single sentence. Since this amounts to sentence parsing using a well-described tokenizer and parser, followed by simple string matching, and since by direct examination we found almost no cases where the statement of interactions spanned more than one sentence, it is extremely likely that we have found all such cases that exist within the entire 12,300 document collection, and the documents that are returned represent all that actually exist. The recall measurement thus amounts to examining the documents after suitable filtering to find how many actually describe (or do not describe) protein interactions.Accordingly, we reduced the stringency of our filtering of sentences so that more candidate relations might be discovered. In this experiment, the verb filters Table  were excany protein interactions anywhere in the abstract, and 12 such interactions were found. Of these, 2 were discovered across sentence boundaries, requiring anaphora resolution and 2 more occurred in sentences containing the word "gene." This means that 118/130 documents were correctly identified as having no relations, or only 12/130 contained relations, resulting in a recall of at least 90.1%. If we use all 581 abstracts, the recall is much higher (97%). This gives us an F-measure of 0.83.These additional 130 abstracts were examined in detail for the description of This section summarizes the rules and techniques used in finding the protein interactions.1. Exclude any sentence containing the terms in Table 2. Recognize proteins from a dictionary of proteins and their synonyms and variant spellings. Exclude all lowercase spellings, which usually represent mutations.3. Recognize protein complexes by hyphenation.4. Recognize protein synonyms when separated by a slash.5. Require any sentence with two or more proteins to contain one of the verbs in Table 6. Allow any sentence containing "form" and "complex" along with two or more proteins.7. Recognize secondary interactions based on those found by 2-hop and 3-hop connections in the primary table of correct interactions.In a large set of 12,300 abstracts, the primary task is filtering out sentences in documents which describe genes and other non-protein interactions. Once this is done, 61% precision is possible, and if the graphical predictions of secondary interactions hold true, the precision is at least 0.74. Based on reading of the abstracts the recall is estimated to be 97%. The f measure is 0.74, based on a precision of 0.61, and is 0.83 based on the precision of 0.74.These experiments result in respectable precision and considerably higher recall than previously reported methods and tend to indicate that a combination of statistical and linguistic methods -4 can giFinally, we note that there is apparently no "silver bullet" to improve detection of protein-protein relations. Instead, the process is one of incremental improvement based on rules and filters of data. However, the set of rules we report here appear to have the highest F-measure yet reported.The MIPS data were downloaded and mined using custom Java programs. Sentence boundaries and tokenization was accomplished using JTalent, a Java wrapper for the TALENT system described above. The noun phrase and verb data were stored in a DB2 database, along with the document number, sentence number and offset, so that the verb co-occurrences listed in Table JC carried out the annotator design, text mining and database design. AK carried out the graphical analysis.


The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved.Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors.We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources. The nuclear receptors (NRs) comprise an ancient family of transcription factors (TFs) that are found in metazoa and are involved in the regulation of development, metabolism, homeostasis, reproduction, and cell death . They arNRs form a complex and highly connected dimerisation network. They bind to DNA as monomers, homodimers and heterodimers ,3,7. Thee.g. the scale-free nature of protein-protein interaction, protein-DNA interaction, and metabolic networks  could refer to any of the 3 paralogues . Since paralogues share very similar dimerising DBD and LBD domains, it is often observed that all members of one phylogenetic group will interact with all members of another group, as is the case between the RXR-PPAR [NR2B-NR1C] and RXR-RAR [NR2B-NR1B] groups. This has also been observed for the bHLH ) and many but poorly connected proteins that comprise the peripheral members of the network. A similar observation can be made for another dimerising network of TFs, the bHLHs  is always present, apparently create a competitive environment for the binding of peripheral members to the hub protein. This extremely competitive environment could be related to the fact that many NR dimers are ligand-activated, thus introducing an additional check-point in the network, in order to reduce noise.Protein-DNA interactions among human NR genes were also retrieved , the bHLH family, has revealed that they form complex and conserved dimerisation networks that are not random, but hub-based . Such toSince the bHLH and NRs are dimerising TFs that also share a hub-based topology, it is interesting to compare the two families and their networks in order to reveal common features, which could be universal, as well as unique features that characterise each network.Fast information flow as a result of high connectivity and hub-based topology  seem to Despite these similarities, the two dimerising networks have a number of distinct features. Although RXR [NR2B] is a potent dimerisation hub, NRs have a great ability to bind DNA as monomers or homodimers. In addition, we observed a large number of interactions between peripheral members that were verified both by text-mining and Y2H approaches. In the bHLH network, on the other hand, heterodimerisation with the hub is the essential control mechanism. Therefore, the role of the hub seems more important in the bHLH network. This results in the network being vulnerable to mutations in the gene encoding the hub protein. Nevertheless, the role of the peripheral members should not be underestimated: when they dimerise with the hub, they regulate a large number of genes, thus becoming hubs as well in the overall genetic network. Furthermore, the efficiency of the bHLH network is increased since it allows suppression of a large number of peripheral members by suppressing only one protein, the hub.In the NR network, the ability of several peripheral members to be functional either as monomers or homodimers makes them less dependent on the hub. This may explain why two major mechanisms of suppression have evolved. One uses the COUPTF NR2F] repressor proteins, that compete with several RXR [NR2B] heterodimers for common DNA-binding sites . The secF repressOne of the advantages of a hub-based topology is economical control at the genome level, since inhibition of the whole system can be easily achieved by evolving a minimal number of repressors that target the hubs.Data integration and static modelling are only the first steps in this new era of "omics" and systems biology. They allow us to capture a snapshot of the global picture, understand the properties of the system as a whole, generate new hypotheses (like prediction of protein-protein interactions) and perform, in the future, targeted experiments. Quantitative measurements of protein-protein, protein-DNA binding affinities, and mathematical modelling should be the next steps that would allow us to comprehend, to an unprecedented extent, the biology of nuclear receptors.We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.The most comprehensive and publicly available protein-protein interaction database, HPRD, ,31 was sFor the extraction of protein-protein interactions from the literature (comprising both abstracts and full text), the following methodology was used:1) We used an established classification scheme for the human NRs  and based on sequence identity  Synonyms for every NR protein were retrieved from GeneCatalogue, an AstraZeneca gene and protein reference database, and from a review paper  see syn.3) The QUOSA software was used to retrieve full-text articles and abstracts from MEDLINE that referred to NRs .4) A keyword term ("nuclear receptor") was identified that would retrieve the highest number of relevant articles with the QUOSA software.5) The following were downloaded:a. The relevant 5,241 full text articles of 2003 in PDF or HTML form, depending on availability.b. The relevant 46,300 abstracts of the 13 years from 1993 to December 2005 in plain text form.6) Full-text documents were converted from PDF and HTML into plain text format, using the freely available MULTIVALENT  and HTMLe.g. "dimer", "interact", etc).7) PERL scripts were written to extract sentences where two different NR protein names or any of their synonyms co-occurred with terms that described an interaction (8) All (~3500) sentences retrieved from full text and (~3000) sentences retrieved from abstracts were read manually and those that described a physical or protein-DNA interaction were marked  While reading the extracted sentences, we earmarked dimers that were shown to bind to DNA. In addition, the Nuclear Receptors Factsbook was scanned to identify dimers that bound to DNA. If a dimer between two phylogenetic groups was shown to bind to DNA, any other dimer among proteins of the same two phylogenetic groups was predicted to bind to DNA as well, due to the very high sequence conservation of the DNA-binding domain. For example, a DNA-binding dimer is formed between RXR_b [NR2B2] and RAR_b [NR1B2]. Based on this fact, we predict that the dimer formed between RXR_g [NR2B3] and RAR_b [NR1B2] also binds to DNA.10) A large number of sentences from the full-text subset did not provide the specific names of the interactors (due to PDF conversion) and therefore were not used subsequently for the dimerisation dataset.11) From the 46,300 abstracts, 1128 abstracts contained 1802 sentences that mentioned a true protein-protein interaction.12) Graphs of protein-protein and protein-DNA interactions were generated using the Adobe Illustrator software. Matrices of protein-protein interactions and co-expression were generated using a customized version of the R gplots package .(human) - B-factor(human) and mouse B-factor(mouse) - C-factor(mouse) interactions were collapsed into the mammalian A-factor - B-factor - C-factor interactions.Protein interactions among mammalian members of the NR family were identified. Protein interactions for mammals were extracted for both human and murine orthologues, due to their high amino-acid sequence identity (>70%). Identification of murine-human orthologues was based on literature reports. Interactions from different species were collapsed on the same graph as in . For exaThe validity of this approach has been verified by cases where interactions are conserved even when one of the heterodimeric partners is the ortholog from another distant species .2 value of 0.654 and 0.7427 respectively, concluding that the networks resemble a scale-free topology. For the whole network (adding the DNA and non-DNA binding dimers together), we obtained an R2 value of 0.3935, concluding that the network does not resemble a scale-free topology.In order to assess whether a network is scale-free or not, the distribution of connectivity is plotted  for the 48 human NR genes (provided by AstraZeneca) and ii) for the 49 mouse NR genes, by mining the published dataset of Bookout et al .The human gene expression dataset was based on the Affymetrix HG_U133 chip. The focus was on expression of probe sets that mapped to an exon or a 3'UTR in tissue samples that were classified as having normal morphology and pathology over 99 different human tissues the Nomenclature and synonyms of NRs, ii) the protein-protein interactions, their source, the level of confidence, in how many tissues they are co-expressed, iii) the same as previous worksheet, but each interaction mentioned only once, not in both directions, iv) the predicted protein-protein interactions and in how many tissues they are expressed, v) the analysis of distribution of connectivity for the NR protein interaction network, vi) the protein-DNA interactions.Click here for fileTissue presence of NRs. It contains in 4 worksheets, i) the presence or absence of NRs in the 61 normal human tissues, ii) the number of human tissues where any potential pair of NRs is co-expressed, iii) the presence or absence of NRs in the 39 normal mouse tissues, iv) the number of mouse tissues where any potential pair of NRs is co-expressed.Click here for fileSentences from text-mining. It contains all the manually curated sentences that describe a protein-protein interaction, and the PUMED identifier of the relevant document.Click here for file