For graph inference, we apply input-output kernel regression (IOKR) in supervised and semi-supervised modes as well as output kernel trees (OK3). In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker’s yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities.In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and  Saccharomyces cerevisiae [S. cerevisiae of the subphylum Saccharomycotina does not naturally secrete large amounts of proteins unlike the filamentous fungi of the subphylum Pezizomycotina. For example the Pezizomycotina Trichoderma reesei (Hypocrea jecorina) is able to secrete its native cellulase proteins with yields of over 100 g/l in industrial cultivations [S. cerevisiae their protein secretion machinery has not been studied in detail.Protein secretion is a fundamental cellular process that is required for transporting proteins into cellular compartments, the cell surface and the external space of the cell as well as for covalent modification i.e. disulphide bond formation and glycosylation of proteins. As can be expected from its central role, the protein secretion machinery is conserved in eukaryotes. Fundamental research to unravel its functioning has been carried out in the fungus revisiae . Howeverivations . With thE. coli, S. cerevisiae and Homo sapiens.Protein-protein interaction data is very useful in defining cellular functions of proteins. Protein-protein interaction (PPI) is a term that covers various possible interactions between pairs of proteins from stable physical interactions to functional associations. While in many cases the molecular function of a new protein can be determined by sequence similarity searches, the molecular function tells little about the cellular function the protein might be carrying out in given conditions. However, the actual high throughput experimental measurement of protein-protein interactions has been limited to the most studied organism such as https://gold.jgi-psf.org/) contained entries for almost 60 000 organisms. The number of sequenced species is growing exponentially and most importantly improvements in sequencing techniques allow the assembly of genome sequences of uncultivable micro-organisms from metagenomics samples [The availability of verified annotations on protein function and interactions especially outside model organisms is currently a major bottleneck. In May 2015 the Genomes OnLine Database  in the secretory pathway. As there are no verified protein interaction data available for these organisms, we assume the cross-species transfer learning setting, where the training data comes from S. cerevisiae, and prediction targets is T.reesei.We focus in predicting the secretion machinery in industrially relevant fungi, in particular, http://www.yeastgenome.org) for S. cerevisiae and from JGI Mycocosm database (http://genome.jgi.doe.gov/Trire2/Trire2.home.html) for T. reesei.In this paper the models are based on features that can be computational derived from protein sequence data. The sequence data for the two studied organisms were downloaded from SGD database . Each different Blosum matrix has been made by combining proteins that are no more similar than a given percentage  to one single sequence and then comparing only those sequences [S. cerevisiae and T. reesei based on the Smith-Waterman alignment is shown. Based on visual comparison, the generated Blosum30 data set has a similar level of sequence similarity to S. cerevisiae as T. reesei. In the experiments, the artificially perturbed sequences were coupled with the labels of the corresponding labels of the original sequences.We used artificial data to test if the different biological network inference algorithm that have been developed for intra-species prediction also work for inter-species prediction with low sequence similarity. They are well below commonly used amino acid sequence identity cut-off values. For obtaining artificial sequences with varying levels of sequence similarity we altered the sequences of the 162 secretion proteins of matrices . These mequences . In Fig T. reesei PPI network was composed by eight publicly available data sets taken from Gene expression omnibus [The transcriptomic data for the validation of  omnibus  plus eig omnibus  and norm omnibus .V = v1, .. ,vm a biological network can be defined as an undirected graph G =  where E ⊂ V × V are the edges between the m vertices. The graph can be represented by a symmetric adjacency matrix Y = (yij) of size m × m where yij = yji = 1 if the nodes vi and vj are connected and yij = yji = 0 otherwise. We will also use the shorthand vi in the network. In addition, we assume that each node has assigned features x(vi) ∈ χ, for some input space χ.Supervised graph inference has been introduced a decade ago in  and has G =  and the feature representation of the nodes, determine a function f: V × V → {0, 1} that best approximates the unknown edges of the graph.The learning task is then defined as follows: given partial knowledge of the graph Note that the main difficulty for solving this problem is that the features are assigned to individual nodes and the labels to pairs of nodes . To tranFinally, for performing inter-species biological network inference we use the protein sequences and their interactions from one species as training set and the protein sequences from the second species as testing set. Note that in this setting the training-testing interactions are not of interest and that the feature representation needs to be the same for training and testing proteins.In this section we present three different approaches for supervised network inference that we have applied to inter-species PPI network prediction. Additionally, we present different approaches for learning kernels that account for the relevance of a data source for the learning task.kY: V × V → ℜ with kY = 〈ψ(v), ψ(v′)〉. The kernel kY is defined such that adjacent vertices have higher values of kY than non-adjacent ones. To achieve this, the diffusion kernel is commonly used KY = exp(−βL) where L is the Laplacian matrix of the graph L = D − Y with D being the degree matrix and Y the adjacency matrix. Additionally, β > 0 is a user defined parameter that controls the diffusion degree.have been proposed by  and are S until the data is homogeneous enough (in our case: the proteins in S have similar connectivity patterns). The data arriving to leaf L of the decision tree is split into two parts Sl and Sr, using a binary test Tt(x) ∈ {0, 1} based on a value of a single input feature of x (e.g. does protein have a given motif or not). The two sets Sl = {x ∈ S|T(x) = 0} and Sr = {x ∈ S|T(x) = 1} will be recursively used to grow subtrees which then will be attached as the children of L.The OK3 algorithm relies on the top-down induction algorithm widely used to learning decision trees (e.g. CART ). The mexi = x(vi), i = 1..m the following score is maximized to select a test T to be inserted in the decision tree leaf given the set of inputs S routed to the current decision tree leaf:ψ(v) is the output feature vector, N, Nl and Nr are the sizes of the training sample S and its left and right split, Sl and Sr, respectively. The variance of the output feature vectors in the set S can be easily computed using the kernel trick:For learning the decision trees on the input vectors One main advantage of the OK3 approach is that the decision tree on the input features results in a ranking of relevant features for the learning task.L is labeled with a prediction NL are the number of samples that reach the leaf. Finally, the kernel value between two vertices v and v′ where x(v) reaches leaf L1 and x(v′) leaf L2 respectively can be approximated by thresholdingLk. For improving the accuracy of the method an ensemble of decision trees also known as a random forest is used. In our experiments we used the C code provided by the authors [Then for prediction each leaf  authors .τ = {,, .. ,} of patterns ui ∈ ℜq with a binary label ti ∈ {−1, 1} infer a function f: ℜq → {−1, 1} for any new pattern u. The main hindrance in doing so is that in network reconstruction the labels are defined on pairs of vertices and the input features or patterns on individual vertices. Thus in a first step a so called linear kernel on pairs of vertices induced by their input features is defined by their inner product kX = x(v)Tx(v′). These kernels kX represent the similarity of any pair of protein sequences that are then used to compute kernels on pairs of protein pairs as followsThe main idea of the biological network reconstruction methods presented in  is to rekDRCT, ) = kX * kXDirect product kernel: kTPPK, ) = kX * kX + kX * kXTensor product pairwise kernel: kMLPK, ) =  − kX − kX + kX)2Metric learning pairwise kernel: kTPPK and the metric learning pairwise kernel kMLPK are best suited for modelling the similarity between protein pairs.Now a standard support vector machine (SVM) can be used to solve the binary classification task. Since PPI networks are undirected the tensor product kernel O(m4) where m is the number of proteins in the biological network which leads to serious scalability problems and usage of computational resources [Despite the method’s good predictive performance it has a major drawback: the kernels between pairs of proteins can become quickly very large even for a reasonable amount of protein sequences. The space complexity for storing the kernel matrix turns out to be esources .This method combines elements of the two previous algorithms that circumvent their respective disadvantages—on the input side it uses the simple kernels on protein pairs and on the output side it uses the diffusion kernel built from the adjacency matrix of the output graph. But the classification problem is addressed by solving a kernel learning problem using regularized regression , 41. Theθ:As the OK3 method, IOKR proposes to solve the link prediction problem by learning an output kernel kY is a kernel, its values can be written as: kY = 〈ψ(v), ψ(v′)〉, where ψ is called the output feature map. The IOKR method approximates the output feature map ψ with a function h and then build an approximation of the output kernel kY by taking the inner product between the values of this function:As fθ reduces to learn the single variable function h.Thus learning hM(v) = Mϕ(v) and assuming a regularized square loss function the parameters of the supervised IOKR model can be estimated based on l training samples as follows:λ1 > 0 is a regularization parameter that is tuned with cross validation for the experiments.Then given models of the general form LXn = exp(−β(Dn − KXn)) denotes the diffusion kernel associated to input kernel matrix on labeled and unlabeled data. The last term constrains proteins that are similar to each other in input to be similar in the predicted interaction network. λ1 > 0 and λ2 > 0 are two regularization parameters that are tuned with cross validation for the experiments. Both minimisation problems lead to a closed form solution that can be found in Propositions 4 and 6 of [The method has also been extended to the semi-supervised setting where the input of unlabeled data is taken into account. The new cost function that has to be minimized is:and 6 of .μq are typically restricted to be non-negative to ensure the PSD property of the resulting mixture. Note that setting μq = 1 for all kernels yields the uniform kernel combination. A major step forward in the MKL field was learning kernels based on centered kernel-target alignment [F is the Frobenius product, ∥.∥F the Frobenius norm, KY is a target kernel and Kc denotes a centered version of the input kernel K, achieved by the centering operation1 denotes the vector of ones and I is the identity matrix.The heterogeneous set of features that we extracted from the protein sequences is expected not to uniformly contribute information to the learned model which makes the uniform combination of the kernels over the different data sources suboptimal. Therefore we apply Multiple Kernel Learning (MKL) to take the feature’s relevance into account. We focus on linear mixtures of kernels,lignment ρ^This gives a simple improvement over the uniform combination of kernels be directly using the kernel-target alignment scores LIGN. In  it is clμ‖2 = 1 and μ ≥ 0 the alignment maximization problem can be rewritten as:a = T records the kernel-target alignments of the input kernels and M = (Mql)ql with Mql = 〈Kqc,Klc〉F contains the pairwise kernel alignments between the input kernels. The problem can be solved by quadratic programming [The previously presented independent kernel alignment neglects the correlation between the base kernels which can be overcome by jointly maximization the alignment between the convex combination kernel with the target kernel and is also referred to as ALIGNF:gramming .p-norm, where 1 ≥ p is simultaneously optimized. The proposed generalized ℓp-norm kernel target alignment formulation is as follows:Another approach for optimizing the kernel target alignment has been proposed in . The metF(μ) = 〈μ, μ〉, and μ0 is a fixed point in the domain of F (Following [μ0 = 0 in our experiments.). Additionally, λ1 ≤ 0 and λ2 ≤ 0 are the regularization parameters. For implementing the sparsity inducing lp regularizer p is systematically reduced towards unity till a sufficient level of sparsity is obtained. The solution of the path following is computed with a Predictor-Corrector algorithm [The squared Euclidean distance in the first term is an instantiation of Bregman divergence B¯F(μ)=Following  we used lgorithm .Binary classification problems are typically evaluated with the accuracy measure which is computed as the number of correctly predicted pairs divided by the total number of pairs. For highly imbalanced problems like network inference accuracy is not an appropriate measure because it favours the majority class and thus the non-interactions. In the following Receiver-Operator-Characteristic (ROC) and Precision-Recall (PR) curves are presented which are better suited for evaluating network inference predictions .Both measures are based on a so called confusion matrix which is 2 x 2 for binary classification with the columns and rows representing the predicted and the actual classes respectively. Denoting interactions as positive and non-interactions as negative the confusion matrix is given in From this matrix several measures for model evaluation can be derived:TP/PTrue positive rate (TPR): also known as sensitivity or recall, is the number of true positives divided the number of the actual positives TN/NTrue negative rate (TNR): also known as specificity, is the number of true negatives divided by the number of actual negatives FP/NFalse positive rate (FPR): is the number of false positives divided by the number of actual negatives FN/PFalse negative rate (FNR): is the number of false negatives divided by the number of actual positives TP/(TP + FP)Precision: is the number of true positives divided by the number of predicted positives All of these measures need to be combined in order to give a reliable performance measure of an algorithm e.g. specificity and sensitivity or precision and recall. Note as well that a threshold needs to be defined if predictions are confidence scores. For evaluating algorithms with varying confidence thresholds ROC and PR curves can be used.plot the TPR over the FPR for varying confidence thresholds. More specifically, each threshold corresponds to a different confusion matrix and thus a different pair of values for TPR and FPR and a point on the ROC curve. The end points are always  and  and a perfect classifier would pass through the point , while a random classifier would be a diagonal connecting  and . A common summary statistic of the ROC curve is the area under the ROC curve (AUROC). AUROC is one for a perfect classifier and 0.5 for a random one. For the highly imbalanced network prediction tasks even moderate FPR can lead to more FP predictions than TP predictions and hence a very low precision.P/(P + N)) which corresponds to to predicting all pairs as positive. An optimal classifier would pass as well through . The area under the PR curve (AUPR) is also a common summary statistic. As for AUROC one assumes that the higher the AUPR the better the performance of the method. One advantage of PR curves over ROC curves is that they allow to measure early precision where recall is low and thus gives a tool to evaluate the quality of the top ranks of the result list.plot the precision over the recall for varying confidence thresholds. The curve starts at a pseudo point  and ends at , Tensor kernel SVM on protein pairs (PP), and supervised and semi-supervised Input-Output Kernel Regression (IOKR) for evolutionary distant species. As training data, we use the FPR < 0.1) reveals that the IOKR methods in fact have the best early precision, thus would get the top-ranked interactions more accurately predicted than the competing methods.In terms of AUC, OK3 obtains the best results, tensor kernel (PP) the second best and the IOKR methods being somewhat less accurate. However, closer examination of the method’s prediction performance for top-ranked interactions .Next, we check how well transfer learning of the secretory pathway works in the basic case of the source and target species being the same. In this experiment, the inference models were trained on the Analyzing the ROC and PR results together, semi-supervised IOKR emerges as the best compromise, due to its good ROC behaviour and good precision in the low-recall regime. It appears that the semi-supervised aspect gives some protection for the method against false positives in the low to mid-recall levels.S. cerevisiae when using semi-supervised IOKR as predictor.Next, we compare the different MKL methods ALIGN, ALINGF and p-norm path following on the reconstruction of the set of secretion proteins from the full genome of The results are shown in S. cerevisiae and the target species is T. reesei. However, no experimental protein interaction data exists for T. reesei that could be used as the ground truth. Thus we focus on qualitative analysis of the predicted T. reesei secretion network by expert knowledge. For predicting the PPI in T. reesei we used semi-supervised IOKR and p-norm path following for learning the input kernel, since this method combination achieved the best performances in the previous experiments.Finally, we evaluate the PPI prediction quality in an inter-species setup, where the training data comes from T. reesei secretion network, its genes  were ansion 2.0  match toiss-Prot , Interpriss-Prot , PANNZERiss-Prot  and a maed genes .T. reesei secretion network contains in total 320 genes. According to the annotation described above 27 genes belong to the heterokaryon incompatibly family and are sequence wise very similar. This family contains a GTPase domain that could contain similar features as GTPases involved in secretion. 51 genes belong to other than secretion related categories of cellular function. 18 genes were annotated to be related to cell growth, cell wall synthesis and cell motility and six were found to be related to chromatin modification. In general these 24 proteins contain domains related to small molecule modifications of macromolecules such as glycosylation, phosphorylation, ubiquitinylation and methylation. Similar molecular functions are abundant in the known secretion pathway enzymes. 14 of the 51 were annotated as molecular and cellular function unknown Click here for additional data file.S2 TablePredicted protein-protein interactions of Trichoderma reesei secretion pathway.(XLSX)Click here for additional data file.S1 FigIn this layout interactions of individual genes are easier to inspect with the cost of less clear overall structure.(TIFF)Click here for additional data file.


Protein-protein interactions (PPIs) play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP) complexes, and 161 protein-ligand (PL) complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar. Protein-protein interactions (PPIs) play major roles in many biological processes such as bioenergetics, immune response, signal transduction, structural organization, and apoptosis ,2. Recenhttp://mordred.bioc.cam.ac.uk/timbal) which stores data of small molecules modulating protein—protein complexes [http://bindingmoad.org) [http://2p2idb.cnrs-mrs.fr) [http://service.bioinformatik.uni-saarland.de/ABCSquareWeb/) [http://pibase.janelia.org/pibase2010/queries.html) [The Protein Data Bank (PDB)  is the pomplexes , the Motoad.org) –9, the 2-mrs.fr) , the AnaareWeb/) , and thees.html) . One impAlthough protein-protein interfaces are rather large, planar and well packed depending ,13, someIn this study, we analyzed the residue-residue and atomic contact frequencies and propensities of five non-redundant datasets i) 174 protein-protein and ii) 161 protein-ligand complexes from Walter , iii) 43Non-redundant datasets from three different databases were used to investigate the composition of protein interfaces. The first pair of datasets consists of 174 PP complexes and 161 PL complexes compiled by Walter et al.  from theThe same criteria of :  pairs of PP and PL complexes from Walter et al., were then applied to the datasets of PP and PL complexes from the PIBASE database . To avoiInteractions where both interacting chains have > 90% sequence identity are defined as homodimer complexes and the remainder as heterodimer complexes. As a result, the PP complexes from the ABC dataset comprised 94 homodimer complexes and 80 heterodimer complexes .Data from the ABC, PIBASE, and Timbal databases was retrieved by using MySQL queries, Java, Biojava  and analThe solvent accessible surface area (SASA) was calculated using the NACCESS program . As surfThe standard classification according to the Eisenberg hydrophobicity scale  was usedResidue interface propensities were calculated for the homodimeric and heterodimeric protein-protein complexes of the ABC and PIBASE datasets and for the protein-ligand complexes of the ABC, PIBASE and Timbal datasets. These propensities give a measure of the relative importance of different amino acid residues in the interface, compared with the surface as a whole. The propensities were calculated with the following formula:An interface residue propensity of >1.0 indicates that a residue type occurs more frequently in interfaces than on the protein surface in general.For every PP complex, we counted the observed number of contacts between amino acids of the first protein and amino acids of the second protein. A contact exists between two residues of these proteins if any residue of the first protein is within a distance threshold of 5.0 Å from the other protein. This was represented in a 20 x 20 table. From the 400 observed counts of amino acid pairs in the two datasets of protein-protein complexes, we derived normalized pair frequencies with the following formula:In protein-protein and protein-ligand complexes, we considered two surface atoms belonging to separate molecules to be in contact and labeled them as interface atoms if the distance between them is less than 5.0 Å. We counted contacts between all pairs of carbon (C), fluorine (F), nitrogen (N), oxygen (O), phosphorus (P), and sulfur (S) atoms resulting in 36 contact pairs. Then, the absolute counts were normalized as follows:According to Higueruelo et al. , atom tyThe polarity ratio (PR) is a simple measure of the polarity of the interface . It was The interface atom ratio (IR) is a measure for the fraction of surface atoms that are located at the interface. It was calculated for the interfaces of protein-protein and protein-ligand complexes. Only the six heavy atom types C, N, O, S, P and F were considered in the calculation. IR is the ratio of the sum of all atoms at the interface to the sum of all atoms at the surface.PPI interfaces are known to possess particular geometric and physicochemical characteristics, see e.g. ,24–26. CFigs 2 [As expected, hydrophobic and polar residues make up the largest portion of protein interfaces. In fact, this is one of the challenges for targeting PPIs with small molecules as the contact surfaces between proteins typically involve many hydrophobic and polar interactions distributed over a large interface with buried area of ~1500–3000 Å2 . AccordiAt the interfaces of both homodimeric and heterodimeric PP complexes from the ABC and PIBASE datasets, the frequencies of methionine and tryptophan at protein interfaces are at most 3.07%. However, both amino acids have normalized interface propensities clearly larger than one, suggesting that these residues play important roles and thus occur more frequently at protein interfaces rather than elsewhere on the protein surface. Overall, tryptophan, tyrosine and arginine each have propensities above 1.0 at both protein interfaces of homodimeric and heterodimeric PP complexes from the ABC and PIBASE datasets. This reflects that aromatic amino acids and arginine play important roles in protein interfaces, which is a well-known fact. For example, Bogan and Thorn  reportedP-value = 0.99 and propensities, P-value = 0.97).The distributions of the percentage frequencies and propensities of amino acids at the protein interfaces of the PP datasets derived from ABC and PIBASE were compared with the non-parametrical Friedman test as the datasets do not have a normal distribution. As suggested by the graphical representation of Figs The fractions of hydrophobic, polar, charged and glycine residues at protein binding interfaces of PL complexes are 34.08%, 36.97%, 20.31%, 8.64% (ABC dataset), 38.25%, 35.39%, 18.12%, 8.24% (PIBASE dataset) and 42.32%, 32.61%, 18.60%, 6.47% , see P-value = 0.86 and propensities, P-value = 0.96, Friedman rank sum test).In the PL complexes of the ABC dataset, the five amino acids with the highest propensities found at protein interfaces are cysteine (2.20), tryptophan (2.18), histidine (1.75), tyrosine (1.74), and phenylalanine (1.47). In the PL complexes of the PIBASE dataset, the most enriched ones are tryptophan (2.25), tyrosine (1.93), phenylalanine (1.92), histidine (1.89), and methionine (1.66). In the PL complexes of the Timbal dataset, methione has the highest propensity of 1.85, followed by phenylalanine (1.78), tryptophan (1.78), histidine (1.54) and tyrosine (1.53), respectively. In all datasets of PL complexes, tryptophan, phenylalanine, histidine, and tyrosine are found most often at the protein interfaces  complemeThe propensities of amino acid contacts in PP complexes between amino acids of the first protein (Pi1) and amino acids of the second protein (Pi2) were obtained by counting the absolute number of contacts and normalizing this number against the appearance probability of the two involved residues at the surface. In Figs P-value = 0.76, Wilcoxon signed rank test).Tables A and B in Based on the counts of amino acids, we computed the average number of amino acid residues at the interfaces of the two proteins Pi1 and Pi2 of PP complexes and the Pi3 protein of PL complexes using three different atom distances . At the distance threshold of 3 Å, both interfaces at Pi1 and Pi2 contain less than 10 amino acids on average. For thresholds of 4 Å and 5 Å, the average size of the protein interfaces is 26.22 (ABC dataset) and 38.69 amino acids (PIBASE dataset) . Table 2In this section, we analyzed the atomic contacts in the datasets of PP and PL complexes. For atom pairs between the first and second proteins (Pi1–Pi2) in PP complexes and between protein and ligand (Pi3 –Lj) in PL complexes, we counted contacts of less or equal to 5 Å between six types of heavy atoms, namely carbon (C), flourine (F), nitrogen (N), oxygen (O), phosphorus (P) and sulfur (S). This resulted in 36 atomic pair contacts. P-value = 0.76, Wilcoxon signed rank test). Also the frequencies of the atomic contacts between the PL complexes of the ABC, PIBASE and Timbal datasets did not differ significantly .Chen and Kurgan  previousTables Then, we analyzed the polarity ratio (PR), namely the fraction of polar N, O, S atoms at the interface areas of both PP and PL complexes. The interface atom ratio (IR) indicates the fraction of surface atoms that are involved in protein contacts at the interface. As mention before, the interface areas were defined as those residues that are closer than 3 Å (or 4 Å and 5 Å) to at least one residue from the binding partner. Both IR and PR were computed for the datasets of PP and PL complexes from the ABC, PIBASE, and Timbal datasets.At 3 Å distance threshold, the differences in IR and PR ratios are not representative because only the shortest-distance contacts are considered. For example, when a 3 Å cut-off is used, most carbon atoms are not considered as part of the interfaces as this short distance is shorter than twice the van der Waals radius of carbon (1.7 Å) . Table 6In this study, we characterized the residue and atom composition of overlapping protein-protein and protein-ligand interfaces from the ABC and PIBASE databases and compared these to a dataset derived from the Timbal database. According to the statistics, both interface types have, in general, a very similar composition. Among the three datasets of PL complexes, the protein interfaces of the Timbal dataset contain more hydrophobic residues and fewer polar residues than the two other datasets. The ligands in the PIBASE and Timbal datasets bind more flat on the protein surfaces or bind deeper into pockets on the protein surface than ABC ligands. Depending on the respective application in a ligand design project, researchers may consider to bias their principal dataset in one or the other direction. Selecting the appropriate set of reference data may slightly affect the physiochemical characteristics of designed ligands.S1 File(Table A). Dataset of PL complexes from the ABC database (Table B). Dataset of PP complexes from the PIBASE database (Table C). Dataset of PL complexes from the PIBASE database (Table D). Dataset of PL complexes from the Timbal database (Table E).Dataset of PP complexes from the ABC database (XLSX)Click here for additional data file.S2 File(Table A). Dataset of PP heterodimer complexes from the ABC database (Table B). Dataset of PP homodimer complexes from the PIBASE database (Table C). Dataset of PP heterodimer complexes from the PIBASE database (Table D).Dataset of PP homodimer complexes from the ABC database (XLSX)Click here for additional data file.S3 File(Table A). The frequencies of 20 amino acids according to each PDB ID entry in the second protein (Pi2) of the dataset of PP homodimer complexes from the ABC database (Table B). The frequencies of 20 amino acids according to each PDB ID entry in the first protein (Pi1) of the dataset of PP heterodimer complexes from the ABC database (Table C). The frequencies of 20 amino acids according to each PDB ID entry in the second protein (Pi2) of the dataset of PP heterodimer complexes from the ABC database (Table D). The frequencies of 20 amino acids according to each PDB ID entry in the first protein (Pi1) of the dataset of PP homodimer complexes from the PIBASE database (Table E). The frequencies of 20 amino acids according to each PDB ID entry in the second protein (Pi2) of the dataset of PP homodimer complexes from the PIBASE database (Table F). The frequencies of 20 amino acids according to each PDB ID entry in the first protein (Pi1) of the dataset of PP heterodimer complexes from the PIBASE database (Table G). The frequencies of 20 amino acids according to each PDB ID entry in the second protein (Pi2) of the dataset of PP heterodimer complexes from the PIBASE database (Table H). The frequencies of 20 amino acids according to each PDB ID entry of the dataset of protein of PL complexes from the ABC database (Table I). The frequencies of 20 amino acids according to each PDB ID entry of the dataset of protein of PL complexes from the PIBASE database (Table J). The frequencies of 20 amino acids according to each PDB ID entry of the dataset of protein of PL complexes from the Timbal database (Table K).The frequencies of 20 amino acids according to each PDB ID entry in the first protein (Pi1) of the dataset of PP homodimer complexes from the ABC database (XLSX)Click here for additional data file.S4 File(Table A). The propensity values of 20 amino acids of the second protein (Pi2) of the dataset of PP homodimer complexes from the ABC database (ABC-P2-homo) (Table B). The propensity values of 20 amino acids of the first protein (Pi1) of the dataset of PP heterodimer complexes from the ABC database (ABC-P1-hetero) (Table C). The propensity values of 20 amino acids of the second protein (Pi2) of the dataset of PP heterodimer complexes from the ABC database (ABC-P2-hetero) (Table D). The propensity values of 20 amino acids of the first protein (Pi1) of the dataset of PP homodimer complexes from the PIBASE database (PIB-P1-homo) (Table E). The propensity values of 20 amino acids of the second protein (Pi2) of the dataset of PP homodimer complexes from the PIBASE database (PIB-P2-homo) (Table F). The propensity values of 20 amino acids of the first protein (Pi1) of the dataset of PP heterodimer complexes from the PIBASE database (PIB-P1-hetero) (Table G). The propensity values of 20 amino acids of the second protein (Pi2) of the dataset of PP heterodimer complexes from the PIBASE database (PIB-P2-hetero) (Table H). The propensity values of 20 amino acids of the dataset of PL complexes from the ABC database (PL-ABC) (Table I). The propensity values of 20 amino acids of the dataset of PL complexes from the PIBASE database (PL-PIBASE) (Table J). The propensity values of 20 amino acids of the dataset of PL complexes from the ABC database  (Table K).The propensity values of 20 amino acids of the first protein (Pi1) of the dataset of PP homodimer complexes from the ABC database (ABC-P1-homo) (XLSX)Click here for additional data file.S5 File(Table A). The frequencies and propensities of 400 amino acid contacts of the dataset of PP complexes from the PIBASE database (Table B).The frequencies and propensities of 400 amino acid contacts of the dataset of PP complexes from the ABC database (XLSX)Click here for additional data file.


Protein-protein interactions (PPIs) are key to understanding diverse cellular processes and disease mechanisms. However, current PPI databases only provide low-resolution knowledge of PPIs, in the sense that "proteins" of currently known PPIs generally refer to "genes." It is known that alternative splicing often impacts PPI by either directly affecting protein interacting domains, or by indirectly impacting other domains, which, in turn, impacts the PPI binding. Thus, proteins translated from different isoforms of the same gene can have different interaction partners.Due to the limitations of current experimental capacities, little data is available for PPIs at the resolution of isoforms, although such high-resolution data is crucial to map pathways and to understand protein functions. In fact, alternative splicing can often change the internal structure of a pathway by rearranging specific PPIs. To fill the gap, we systematically predicted genome-wide isoform-isoform interactions (IIIs) using RNA-seq datasets, domain-domain interaction and PPIs. Furthermore, we constructed an III database (IIIDB) that is a resource for studying PPIs at isoform resolution. To discover functional modules in the III network, we performed III network clustering, and then obtained 1025 isoform modules. To evaluate the module functionality, we performed the GO/pathway enrichment analysis for each isoform module.http://syslab.nchu.edu.tw/IIIDB.The IIIDB provides predictions of human protein-protein interactions at the high resolution of transcript isoforms that can facilitate detailed understanding of protein functions and biological pathways. The web interface allows users to search for IIIs or III network modules. The IIIDB is freely available at  Protein-protein interactions (PPIs) perform and regulate fundamental cellular processes. As a consequence, identifying interacting partners for a protein is essential to understand its functions. In recent years, remarkable progress has been made in the annotation of all functional interactions among proteins in the cell. However, in both experimentally derived and computationally predicted protein-protein interactions, a "protein" generally refers to "all isoforms of the respective gene." Yet, it is known that alternative splicing often impacts PPI by either directly affecting protein interacting domains, or by indirectly impacting other domains, which, in turn, impact the PPI binding . That isThus far, a series of studies have systematically predicted PPIs -10 and eIn this report, we described a database, IIIDB, for accessing and managing predicted human IIIs. In the IIIDB, users can differentiate access high-confidence and low-confidence predictions of human IIIs (see detailed description in Result section), and then see the full evidence values for each predicted III. Figure The IIIDB allows users to easily search IIIs and isoform modules, and then provides the evidence that led to each III prediction. To visualize the interactions with the isoforms of the input gene, we integrated CytoscapeWeb  to gener1 and I2, we only keep this prediction if the gene symbols of I1 and I2 have PPI in IntAct database.To obtain the isoform annotations in the human genome, we used the NCBI Reference Sequences (RefSeq) mRNAs as transcriptome annotation . Figure In the IIIDB, we provided two III prediction sets using the logistic regression model: (a) high-confidence prediction: logit score > 2.575 , it resulted in 4,476 IIIs; and (b) low-confidence prediction: logit score > 1.692 . In addition, given a known PPI, the isoform pair with the best logit score among this PPI will be selected as low-confidence prediction. Thus, a PPI has at least one isoform-isoform interaction. It resulted in 54,605 IIIs Figure .To discover functional modules in the III network, we applied MODES network clustering method  on low-cTo evaluate the significance, we randomly generated the same number and the same size of MODES isoform modules (i.e. 1025 randomized isoform modules). Table http://www.ebi.ac.uk/intact/[Currently, most PPI databases do not provide information at the level of isoforms, which thus presents challenges for constructing a gold standard positive set (GSP) for the III prediction. Fortunately, in the June 2013 version of the IntAct database GSP covered 5,503 RefSeq IDs, and we used these RefSeq IDs to construct gold standard negative set (GSN). The GSN was defined as isoform pairs in which one isoform was assigned to the plasma membrane cellular component, and the other was assigned to the nuclear cellular component by the isoform-specific sub-cellular localization, in which we performed sequence-based predictions using the CELLO  . To obtaTo calculate precision and recall, we used timestamp to divided GSP into training and test GSP sets, in which if an interaction with timestamp after 1st Jan 2012, it will be assigned to test GSP set ; otherwise, it will be assign to training GSP set (948 IIIs). When the GSP is decided, we used the RefSeq IDs covered in GSP to build GSN. Figure To demonstrate the biological importance of the IIIDB, we searched for isoform-associated reports in the literature. Although isoform-specific protein function studies are very rare, we found isoform-specific biological evidences with BCL2L1, which validated our III prediction of BCL2L1. In addition, we also found diverse biological functions with Ras association domain family in our isoform modules.et al. reported that an interaction between Bcl-xL and BAX not only inhibits BAX activity but also maintains BAX in the cytosol [BCL2L1 has two isoforms Table , in whic cytosol . On the  cytosol , suggest cytosol . Thus, tRASSF1 is RAS association domain family member 1. RASSF1 has four isoforms, of which two isoforms belong to isoform modules (Mod-162 and Mod-384). The Mod-162 consists of RASSF1 isoform A, RASSF5 and STK4, whereas the Mod-384 consists of RASSF1 isoform C, RASSF3, RASSF4, SAV1, STK3 and STK4  database  score: (a) For all combinations of human isoform pairs, if a isoform pair has domain-domain interaction in the DOMINE database [19 RNA-seq datasets : (b) the absolute values of Pearson correlations for all isoform pairs derived from RNA-seq datasets. Since each RNA-seq dataset may have different quality and data type, we used the logistic regression model to integrate 19 RNA-seq datasets, and then the coefficients of datasets reflected quality of the RNA-seq datasets.where database ,33, thenThe authors declare that they have no competing interests.The isoform modules. There are 1025 isoform modules with gene symbols and RefSeq IDs.Click here for file


Accurate determination of protein complexes is crucial for understanding cellularorganization and function. High-throughput experimental techniques have generateda large amount of protein-protein interaction (PPI) data, allowing prediction ofprotein complexes from PPI networks. However, the high-throughput data oftenincludes false positives and false negatives, making accurate prediction ofprotein complexes difficult.The biomedical literature contains large quantities of PPI data that, along withhigh-throughput experimental PPI data, are valuable for protein complexprediction. In this study, we employ a natural language processing technique toextract PPI data from the biomedical literature. This data is subsequentlyintegrated with high-throughput PPI and gene ontology data by constructingattributed PPI networks, and a novel method for predicting protein complexes fromthe attributed PPI networks is proposed. This method allows calculation of therelative contribution of high-throughput and biomedical literature PPI data.Many well-characterized protein complexes are accurately predicted by this methodwhen apply to two different yeast PPI datasets. The results show that (i)biomedical literature PPI data can effectively improve the performance of proteincomplex prediction; (ii) our method makes good use of high-throughput andbiomedical literature PPI data along with gene ontology data to achievestate-of-the-art protein complex prediction capabilities. Protein complexes are formed from two or more associated polypeptide chains, andaccurate determination of protein complexes is of great importance for understandingcellular organization and function. Many proteins are only functional after assemblyinto protein complexes. Even in the relatively simple model organism Saccharomycescerevisiae, protein complexes include many subunits that assemble and function in acoherent fashion. A key task of system biology is to understand proteins and theirinteractions in terms of protein complexes .Recent advances in high-throughput experimental techniques such as yeast two-hybrid andmass spectrometry have generated a large amount of protein-protein interaction (PPI)data for numerous organisms ,3. TheseOne major problem with high-throughput experimental PPI data is the high incidence ofboth false positives and false negatives . ComputaThe biomedical literature contain a large amount of potentially valuable PPI data thatcan be used to further improve protein complex prediction algorithms. In this study, weattempt to use this resource by first employing a natural language processing techniqueto extract PPI data from the biomedical literature. This is then integrated withhigh-throughput PPI and GO data by constructing attributed PPI networks that can be usedfor protein complex prediction. This novel approach automatically calculates therelative contributions of high-throughput and biomedical literature PPI data. The methodis compared with current protein complex prediction tools. The advantages of the method,potential applications and improvements are discussed.The biomedical literature contains lots of potentially valuable PPI data, andextraction of this data is an important research topic in the field of biomedicalnatural language processing ,16.Our method of extracting PPI data consists of three phases: (i) named entityrecognition (NER); (ii) normalization; (iii) extracting PPI data Figure . NER aimProtein name normalization is used to determine the unique protein identifiersmentioned in the literature, linking these entities to biological databases. It isdifficult to choose between ambiguous protein names based on context and shorttextual descriptions. We decide to adopt a disambiguation method  based onIn the three phases, we extract PPI data from biomedical literature based on NER andnormalization. Pattern-based methods is an established methodology for PPI extractionthat usually uses defined lexical patterns and retrieves text segments that match thepatterns. Because this approach is too rigid to capture semantic/syntacticparaphrases or distant relationships, such pattern-based methods always suffer fromlow recall rates. Instead, publicly accessible annotated PPI corpora such as GENIA  and AImeInitial filtering of sentences that contain at least two protein entities isperformed, and using the Stanford lexical parser generates syntactic information. Atthis stage, including a dependency graph or syntactic parse tree of candidatesentences maximizes the chances of efficient and accurate data extraction. However,syntactic information such as syntactic parse tree is not easily represented by flatfeatures. Kernel methods can efficiently compute the similarity between structuraldata in a recursive manner without explicitly enumerating with feature vectors,avoiding complex feature construction and selection processes. We use the hashsubgraph pairwise (HSP) kernel method to extract PPI data from biomedical literature,as proposed in our previous work . HSP kerMost computational methods for complex prediction are clearly limited by the poorquality of high-throughput PPI data. Further improvements for complex prediction canbe obtained by integrating biomedical literature PPI data. GO is another usefulresource for protein complex prediction, which is currently one of the mostcomprehensive ontology databases in the bioinformatics community . GO aimsG =  where V is the set of protein vertices, Eis the set of PPIs, v A= {GS1,GS2,...nGS} is the set of GO slimattributes for protein vertices, and v Fis a function thatreturns the set of GO slim attributes of a protein vertex. Each protein vertex piin V has a set of GO slim attributesvF(ip) ={GSi1,GSi2,...,GSim},where m = |Fv(Pi)| andFv(Pi) ⊆ Av. Likewise,Ae = {T1,T2,...sT} is the set of type attributesfor PPIs, and e Fis a function that returns the set of typeattributes of a PPI. Each PPI i ein E has a set of typeattributes eF(ie) ={Ti1,Ti2,...,Tir},where r =|Fe(ei)|,Fe(ei) ≠ Øand Fe(ei) ⊆ Ae. In this study, the type attributesof PPIs included high-throughput type and biomedical literature type.We define an attributed PPI network as a 6-tuple P2 has two GO slim attributes (GS1 and GS2), ande3 has two type attributes . Given the set of GO slim attributesvA, we define an attribute set S as a subset ofAv (S ⊆ Av). Moreover, we denote byV|(S) ⊆ V the vertex set induced by S = {i P∈V|S ⊆ vF(iP)}) andby E(S) ⊆ E as the edge set induced by S = { ∈ iE|P, jP∈ V (S)}). The subgraph G(S),induced by S, is the pair (V (S), E(S)).Figure GS1}and{GS1,GS2},respectively.Figure C andan edge set c Ein the induced subgraph G(S)(C ⊆ V (S), c E={ P∈E(S)|P=, j P∈ C}),an ontology correlated clique is a pair ,S), such that for each protein vertex Pi in C, thedegree of i Pis |C| −1. S is the commonontology attribute set of C.Definition 1 - Ontology correlated clique. Given a protein vertex set In general, we can mine many Ontology correlated cliques with different commonontology attribute sets in an attributed PPI network. Figure η. Given an ontology slimattribute set S, the structural correlation of S,η(S), is given as:Definition 2 - Structural correlated function s Kis the set of vertices in ontology correlated cliques inG(S). In this study, we only considered cliques of three ormore.where S and the density of the associated vertices 8], ClustF-scores for the Krogan dataset are listed in Table The extend_thres parameter, wefirst evaluate our method on Attributed PPI network I. Our method proves sensitive toextend_thres between 0.05 and 0.6  provides a reasonable combination of both annotation attributesimilarity and topology connectivity. In order to maintain a closeness score largerthan 0.6, the candidate proteins must have highly similar ontology attribute set andtopology connectivity to seed cliques during the seed cliques augment phase. However,there are few such candidate proteins in the attributed PPI networks. Therefore,performance precision, recall and F-score are relatively unaffected whenextend_thres varied between 0.6 and 1.0.We construct two attributed PPI networks to integrate high-throughput PPI data,biomedical literature PPI data and GO as described in the Section 2.2. Attributed PPInetwork I is constructed using GO, the Gavin dataset, and extracted PPI data usingthe AImed corpora as the training dataset. Attributed PPI network II is constructedusing GO, the Krogan dataset, and extracted PPI data using the AImed corpora as thetraining dataset. To study the effect of the extend_thres is changedfrom 0.05 to 0.6, PPV increased whereas Sn decreases. When extend_thresranges between 0.6 and 1.0, Sn, PPV and Acc do not change appreciably . This is due to more neighbor proteins being added tothe seed cliques when exend_thres is low, resulting in the predictedcomplexes having better coverage of the benchmark dataset complexes, and improvingthe Sn metric. In contrast, only the closest neighbor proteins are added to the seedclique when exend_thres is high. This increases the likelihood of predictedcomplexes being true positives, and improves the PPV metric. Acc is defined as thegeometric mean of Sn and PPV, which is potentially more comprehensive for evaluatingperformance. Similar to F-score, Acc is maximized (0.531) whenextend_thres = 0.1.The Sn, PPV and Acc metrics are then evaluated. When extend_thres on Attributed PPI network II(Table F-score (0.477) and Acc (0.551) are achieved whenextend_thres = 0.1.We also evaluate the effects of IITable . Again,We evaluate the relative contributions of high-throughput experimental and literaturePPI data to protein complex prediction performance. Another strength of our method isthat it automatically computes contribution weights. The statistics of thecontributions of literature data are listed in Table We compare our method with the following established leading protein complexprediction methods: Cluster ONE , COACH , CMC 8]8], HUNTEGO provides GO terms or slims to describe gene product characteristics in threedifferent aspects, including Biological Process (BP), Molecular Function (MF) andCellular Component (CC). In the GO data, Some of CC attributes are directly pertinentto protein complex. Firstly, we evaluate the effect of CC attributes of GO data toour method. In the Table Secondly, we compare our method using Attributed PPI network I with Cluster ONE,COACH, CMC, HUNTER, MCL and MCODE using the Gavin PPI network. Our method achieves anF-score of 0.447, which is significantly superior to the other methods Table . ClusterFinally, we compare our method using Attributed PPI network II with the other methodsusing the Krogan PPI network. From Table In summary, our method can integrate high-throughput experimental PPI data,biomedical literature PPI data, and GO by constructing attributed PPI networks. Thisapproach outperforms existing protein complex prediction tools.We exploite the natural language processing technique to extract PPI data from thebiomedical literature and integrate this data with high-throughput experimental PPI dataand GO to construct attributed PPI networks. Using these networks, we develope a novelmethod for protein complex prediction that automatically calculate the relativecontributions of experimental and literature data. This approach outperforms establishedleading protein complex prediction tools. In the future, we intend to incorporate apost-processing phase and make even better use of literature data extraction to furtherimprove protein complex prediction performance.The authors declare that they have no competing interests.


The recognition of protein folds is an important step for the prediction of protein structure and function. After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered. Therefore, the recognition of 27-class protein folds with the interaction of segments and motif information is very important. Based on the 27-class folds dataset built by Liu et al., amino acid composition, the interactions of secondary structure segments, motif frequency, and predicted secondary structure information were extracted. Using the Random Forest algorithm and the ensemble classification strategy, 27-class protein folds and corresponding structural classification were identified by independent test. The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively. When the training set and testing set were combined, the overall accuracy by 5-fold cross validation was 81.16%. In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained. With the accomplishment of the Human Genome Project, the “post genome era” has presented a large number of protein sequences that have challenged to develop a high-throughput computational method to structurally annotate the sequences coming from genomic data. One of these critical structures, the protein fold, reflects a key topological structure in proteins as it contains three major aspects of protein structure: the units of secondary structure, the relative arrangement of structures, and the overall relationship of protein peptide chains .A protein can only perform its physiological functions if it folds into its proper structure. Abnormal protein folding causes molecular aggregation, precipitation, or abnormal transport, resulting in different diseases. For example, the prion protein (PRNP) accumulates in the brain and causes neurodegenerative diseases, such as scrapie, Creutzfeldt-Jakob disease, Parkinson's disease, Huntington's disease, and mad cow disease. And the PRNP is a pathogenic protein caused by the abnormal folding of proteins. Thus, the correct identification of protein folds can be valuable for the studies on pathogenic mechanisms and drug design –8. Thus,Proteins with similar sequences tend to fold into similar spatial structures. When proteins have close evolutionary relationships, the similarity-based methods can achieve very reliable predicted results , 10. HowAfterwards, based on the dataset built by Ding and Dubchak and the feature parameters, some researchers suggested algorithm improvements for the identification of folds. For example, Chinnasamy et al.  introducBased on the dataset built by Ding and Dubchak, other previous researchers suggested the selection of feature parameters for the identification of protein folds. For example, Shamim et al.  used theBased on the dataset built by Ding and Dubchak , many piOther previous researchers have constructed new 27-class fold datasets and performed corresponding researches. For example, based on the Astral SCOP 1.71, with sequence identity below 40%, Shamim et al.  construcIn this paper, based on the dataset built by Liu et al.  in our ghttp://202.207.29.245:8080/Ha/HomePage/fzxHomePage.jsp.The dataset used in this paper was built by Liu et al.  in our gThe dataset built in our group was according to Ding and Dubchak's description about the construction of protein folds dataset in literature . The numThe distributions of the 20 amino acid residues in protein sequences for different protein folds are obviously different, and previous researches have shown that amino acid composition is associated with protein folding information , 17, 22.http://meme.nbcr.net/meme/cgi-bin/meme.cgi). Motif information (M) includes functional motifs and statistical motif.A motif is the conserved local region in a protein during evolution , which o1) Functional Motif. The PROSITE dataset was used to gather protein sequence patterns with notable biological functions. PS_SCAN packets provided by the PROSITE dataset and compiled by a Perl program were used as a motif-scan tool to search the sequences of 27-class folds training set; 45 functional motifs were obtained and selected. For an arbitrary sequence in dataset, the frequencies of different motifs in the sequence were recorded. If a motif occurred once, the corresponding frequency value was recorded as “1”; if the motif occurred twice, the value would be 2, and so on; otherwise if the motif did not occur, the corresponding frequency value was recorded as “0.” Thus, the frequencies of different functional motifs in a protein sequence were converted into a 45-dimensional vector.(2) Statistical Motif. For statistical motifs, MEME was applied as the motif-scan tool. MEME has been widely used to search protein motifs and DNA sequences [(equences . In thishttp://bioinf.cs.ucl.ac.uk/psipred/) was used as the tool to obtain the predicted secondary structure of each protein sequence in the 27-class protein fold dataset. As a protein fold is a description based on the secondary structure, the interaction between secondary structures plays an important part on the folding of protein. In this paper, we extracted the average interactions between predicted secondary structure segments as a feature parameter for the recognition of protein folds.A previous study showed that predicted secondary structure information is a main feature parameter for the identification of multiclass protein folds , 21, 22.1) The Calculation of Autocross Covariance (ACC). The autocross covariance (ACC) [β-hairpins [i is one kind of secondary structure segment , L is the number of the secondary structure segments in a protein sequence, and Si,j is a property value of secondary structure segment i at position j. i along the whole sequence.(ce (ACC)  has beence (ACC) , G-protece (ACC) , 35, proce (ACC) , and β-hhairpins . Howeveri contains m residues, and Si,j represents the summation of hydrophobic values for the m residues. The dimension of AC variables is 3 ∗max (lg).For example, given the hydrophobicity values  for 20 ai1 and i2 are two different types of secondary structure segments  and Si1,j is a property value of secondary structure segment i1 at position j. i1(i2) along the whole sequence. The dimension of CC variables is 3∗2∗lg.CC variables describe the average interactions between different types of secondary structure segments, which can be calculated according to the following equation:2) The Selection of the Maximum Value of lg. The statistical analysis of the number of the secondary structure segments in the 27-class folds dataset is shown in (3) The Values of Physicochemical Properties. In this paper, four physicochemical properties used by many researchers were selected as ACC feature values to calculate the interactions of segments: hydrophobicity (H1), hydrophilicity (H2), polarity (PL), and solvent accessible surface area (SASA). The values of physicochemical properties for the 20 amino acid residues were taken from the literature of Guo et al. [ was used to get the predicted secondary structure of each protein sequence.As the protein fold is a description based on the secondary structure, the formation of secondary structure in sequence influences the folding of protein. From the researches of published literatures , 22, 25,m , and (2) the number of decision trees represented by k . The Random Forest algorithm has been successfully used for the prediction of antifreeze proteins [β-hairpins [http://www.r-project.org/) was used to perform the Random Forest algorithm by calling the randomForest program package.Random Forest is an algorithm for classification developed by Breiman . The basproteins , DNA-binproteins , the metproteins , β-hairphairpins , and so For the 27-class fold dataset, amino acid composition, motif frequency, predicted secondary structure information, and the interaction of secondary structure segments were extracted as feature parameters, with the combined feature vector as input parameters for the Random Forest algorithm. The overall accuracy of the testing set in the dataset measured up to 78.38% by independent test.For further comparison, identification results from the gradual addition of relevant feature parameters are listed . Furtherhttp://202.207.29.245:8080/Ha/HomePage/fzxHomePage.jsp).When only one feature parameter, amino acid composition, was used, the overall accuracy was 43.66% . After aTo test the efficiency of our method, with the same feature parameters, classification strategy, and algorithm used above, the 27-class folds dataset built by Ding and Dubchak  was alsoAccording to Shen and Chou's  descriptIn early 2001, Ding and Dubchak built the 27-class folds dataset and started research on the identification of 27-class protein folds with multiple feature groups. Researchers have since been devoted to the improvement of feature parameters, algorithms, classification strategies, and the datasets for the identification of protein folds and have achieved good identification results. Based on previous researches, we combined sequence information, structural information, and functional information as input feature parameters of the Random Forest algorithm for protein folds identification and obtained better results. Therefore, the addition of segment interactions and motif information for recognizing 27-class protein folds is a valid and novel approach.Given the same dataset, when different feature parameters are used, the same sequence can be correctly or falsely classified. Here, our approach achieved the improved results with the following possible reasons. First, in considering the correlation at the level of secondary structure segments, we calculate the interaction information of secondary structure segments, which reflects the segments-order and long-range correlation information of the sequence and has a major influence on protein folding. Second, in considering the local conservation of kernel structure in protein folds, motif information was extracted, including functional motifs and statistical motifs. Finally, the Random Forest algorithm is a combination classifier of convenience and high efficiency whose final classification results are decided by votes from decision trees.In our future work, with the same parameters and classification strategy, testing methods  would be used. Different physiochemical properties also would be analyzed for the recognition of protein folds. Moreover, Precision-Recall curves or ROC curves for individual folds could be presented.


Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users. With the technological advances in high-throughput screening techniques, large-scale protein-protein interaction (PPI) data have been generated and catalogued for many species–3. Protestatic network that can not be used to detect temporal protein complexes. In reality, however, cellular systems are highly dynamic and responsive to environmental cueset al. idstable interactions that perform fundamental roles for the cell are conserved across different time points, the corresponding complexes will also occur in multiple consecutive dynamics PPI networks and they should thus change smoothly across time and β ∈  result in competitive results. On the other hand, we can find from Figureτ. Overall, TS-OCD achieved best performance when τ = 0.3. In order to avoid evaluation bias and over-estimation of the performance, we do not tune the parameters for a particular dataset and fix τ = 0.3, λ = 2(-4) and β = 24 in the following experiments. Nevertheless, it is worthy to mention that better performance may be achieved if the parameters are tuned for a particular PPI dataset or for a particular complex reference set.From Figure β ∈ 24,2 result idynamic PPI networks, we compare our proposed TS-OCD method with five state-of-the-art algorithms, namely ClusterONEdynamic PusterONE, COACH[usterONE and SPICusterONE, which arecall and precision for each algorithm.We also apply TS-OCD on static PPI networks, i.e., discard the smooth regularization term in the objective function (5) and take the static PPI networks as input (we denote it as OCD). For fair comparison, optimal parameters are also set for OCD to generate its best results. In addition, we discard their predicted complexes with less than three proteins, for all the 7 methods. Figuref-measure 0.472, which is 8.5% higher than the second best f-measure 0.435, achieved by SPICi. On BioGrid data, TS-OCD also achieves the highest f-measure 0.487, which is 15.4% higher than the second best f-measure 0.422 achieved by ClusterONE. In Tablerecall and precision. Additional fileAs shown in Figureet al.[Recently, Park et al. proposedf-measure on the static DIP data, but it increases to 0.427 on the dynamic DIP data. Similarly, SPICi achieves 0.355 f-measure on the static BioGrid data, but it increases to 0.402 on the dynamic BioGrid data. Therefore, the information contained in dynamic PPI networks are indeed useful and they complement to the static PPI data for better complex detection.Figureet al.[et al., please refer to[et al. are shown in Additional fileBesides NMF, there are also some other algorithms that could be used to merge those similar complexes. Another widely used method is based on the overlap between different complexes. To study the effectiveness of NMF in merging those similar complexes, we also apply the reduction strategy proposed by Wang et al. to mergerefer to. The resProtein complexes predicted by various methods can be used for protein function prediction – a unknYOR210W is a multi-functional protein which is shared by three complexes, namely, the DNA-directed RNA polymerase I, DNA-directed RNA polymerase II, and DNA-directed RNA polymerase III, 42. EmpMoreover, when running SPICi on dynamic BioGrid PPI networks, it predicts two different complexes with YOR210W, i.e., {YOR210W, YOR224C, YGR005C, YOR341W, YOR340C, YDR156W, YJL148W} and {YJL164C, YER125W, YHL024W, YOR151C, YOL005C, YGR005C, YOR210W, YOR224C}. These two complexes match with both the RNA polymerase I and II complexes. Recall that SPICi can only generate one cluster based on the static BioGrid data involving YOR210W. Hence, dynamic networks indeed provide us with more insights into the proteins’ temporal activities for dynamic complex formation.In addition, as shown in Figurestatic PPI network model, which can not capture the inherent dynamic nature of protein interactions as well as modular temporal protein complexes.In real biological environments, protein interaction networks are not static – they dynamically change across different time points. Many exTemporal protein complexes are typically constructed by the dynamic assembly or disassembly of proteins to perform various biological functions. As theyTo address these problems, in this study, we introduce a novel Time Smooth Overlapping Complex Detection model (TS-OCD) to detect overlapping temporal protein complexes from the dynamic PPI networks. Particularly, we construct a series of dynamic PPI networks by detecting stable interactions and transient interactions via integrating protein interaction data and gene expression data. Our proposed TS-OCD allows individual complex to be assembled and disassembled across different time points. Furthermore, with the smoothness regularization term, our model can detect conserved protein complexes that play fundamental roles in cellular systems. The analysis on real biological data shows that our proposed TS-OCD significantly outperforms existing state-of-the-art temporal complex detection methods. Furthermore, with the constructed dynamic PPI networks, our method could detect multi-functional proteins more correctly. All the experimental results, including the predicted stable complexes and temporal complexes, are shown in Additional filesIn summary, compared with existing methods, our model has the following advantages: We have distinguished two different types of protein interactions for constructing dynamic PPI networks. In particular, the stable interactions are reserved across different time points to serve as the backbone of the protein interaction networks, while transient interactions are only presented under certain conditions and thus occurred in dynamic part of PPI networks. It allows the dynamic assembly process, i.e. individual complex to be assembled and disassembled across different time points. In addition, with smoothness regularization, it prevents the value of the assigned co-complex similarity for proteins with stable interactions from changing too dramatically. It generates the overlapping temporal protein complexes, which clearly reflect the biological reality on proteins’ multi-functional roles. Finally, our proposed method is unsupervised and thus is generic enough to apply for the dynamic complex detection of other species.Ht)( is O (N2rt), where N is the number of proteins, and rt is the number of complexes at time t. Thus the overall time cost of TS-OCD is O (N2(r1 + … + rT)I), where T is the number of time points and I is the number of iterations. In practice the time cost will be much smaller since Ht)Additional file 1:Table S1. Complete lists of the predicted protein complexes. (XLSX 116 KB)Additional file 2:Table S2. Complete lists of the predicted stable complexes and temporal complexes. (XLSX 46 KB)Additional file 3:


The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration. The Gene Ontology (GO) Several functional similarity measures that quantify similarity between proteins based on their GO annotations have been introduced and successfully applied in many biomedical and biological applications The annotation family uses GO term statistics in the corpus under consideration. Despite the issue of protein annotation dependence , which leads to shallow annotation problem The topology-based family, which only uses the structure of the GO DAG in the computation of the IC values, has been proposed to correct for the effect of annotation dependence and provide an effective way of measuring functional similarity between proteins based on their GO annotations. The earliest type of topology-based family, namely edge- or path-based semantic similarity measures, suffers from a serious drawback of producing uniform scores for terms at the same level of the hierarchy under consideration as these scores are obtained using path lengths between terms Here, we investigate the behaviour of several different IC-based functional similarity measures suggested in the context of annotation-based and topology-based approaches, using different biological data, including protein-protein interaction networks, protein domain and other functional data. Each measure performs differently for different applications To evaluate the existing IC-based functional similarity measures which have been used in the context of biomedical and bioinformatics applications, we use different functional data, including protein sequence, Pfam domain and enzyme commission (EC) similarity data, human gene expression (microarray) and protein-protein interaction (PPI) datasets. All these data represent some form of ‘grouping’ of proteins that should be functionally related and thus provide useful tests for GO similarity measures. The complete set of GO data and protein-GO term associations were extracted from the GO and GOA databases, respectively, released on the 15th April, 2014. We have considered three topology-based approaches, namely the GO-universal metric proposed by Mazandu and Mulder Several measures have been proposed for estimating functional similarity scores in the context of annotation-based IC approaches to facilitate protein comparisons at the functional level. These functional similarity scores are obtained using statistical measures of closeness, such as average (Avg), maximum (Max), best-match average (BMA) and averaging all the best matches (ABM). The average and maximum measures are computed as follows: The ABM Note that the four functional similarity measures above require GO term semantic similarity scores, and are referred to as IC-based non-direct term or term semantic similarity- or pair-wise term-based measures In the context of the annotation-based family, it has been observed that measuring the semantic similarity of two GO terms based only on the most informative common ancestor terms cannot discern the semantic contributions of the ancestor terms to these two specific terms and thus may negatively impact functional similarity scores. The GraSM and XGraSM approaches have been proposed and shown to perform better than those using only the most informative common ancestors (MICA) strategy Using the observation above, we proposed two other possible functional similarity schemes Note that this study provides the first evaluation of these SimDIC and SimUIC measures and their comparison to other functional similarity measures. Unlike the Avg, Max, ABM and BMA measures, in which semantic similarity between GO terms is required in the computation of functional similarity scores, the SimGIC, SimDIC and SimUIC measures use the IC of terms directly and they are referred to as IC-based direct term measures. Note that there exist other functional similarity models, such as shortest-path graph kernel (spgk) We systematically assess different functional similarity measures on different types of functional data, including sequence similarity, Pfam domain and Enzyme Commission (EC) similarity data on a selected set of proteins, and human protein-protein interaction (PPI) and co-expression networks. These datasets represent different types of biological data used to evaluate GO semantic similarity measures http://xldb.di.fc.ul.pt/tools/cessm/ for BP and MF using a dataset of selected proteins with known relationships downloaded from the CESSM website.Generally, the comparison of different semantic similarity measures is performed using Pearson's correlation measures with sequence, Pfam domain and Enzyme Commission (EC) similarity data. This correlation provides an indication of how effective the functional similarity measure is in capturing sequence, Pfam, and EC similarity. This means that a measure with a higher correlation is better, since it captures these similarities well and it is likely to be an unbiased measure. To compare different measures, we ran the Collaborative Evaluation of Semantic Similarity Measures (CESSM) online tool http://web.cbio.uct.ac.za/ITGOM/funcsimdata.Different measures were assessed in terms of their ability to capture functional coherence in a human PPI network based on how interacting proteins are functionally related to each other. Human PPI datasets were downloaded from several different PPI databases, including the IntAct, DIP, BIND, MIPS, MINT and BioGRID databases, and integrated into a single network in which only interactions predicted by at least two different approaches and found in the STRING dataset are considered, to reduce the impact of false positives. This produced a human PPI network with 6031 interactions from which a total of 5366 and 5580 interactions with both interacting partners were among 29844 and 31683 proteins annotated with respect to the GO BP and CC ontologies, respectively. These interaction datasets are available in the supplementary data . We only considered proteins annotated with BP and CC terms in the network produced since two proteins that interact physically are more likely to be involved in similar biological processes or localized in the same cellular component, but there is no guarantee that they share molecular functions http://web.cbio.uct.ac.za/ITGOM/funcsimdata). We are only considering the BP ontology as co-expressed genes are more likely to share common processes and may at least belong to the same pathway or contribute to a similar biological process We use the human co-expression network retrieved from the Bossi et al. Let Finally, the Rank Index Previous work on semantic similarity measures has suggested that the appropriate use of functional similarity measures depends on the biological applications and different measures perform differently for different applications We used a dataset of proteins with known relationships downloaded from the CESSM online tool. The GO annotations of different proteins in the dataset were retrieved from the GOA-UniProtKB dataset. The CESSM tool has made the comparison of different functional similarity measures using Pearson's correlation measures with sequence, Pfam domain and EC similarity possible. We ran the CESSM online tool and results are shown in Recently, it was shown that the normalization model and correction factors have an impact on the performance of functional similarity measures As displayed in We used human PPI and co-expressed networks to assess the performance of different functional similarity measures. In the case of the PPI network, we are using the AUC values computed using the ROCR package under the R programming language as a measure of classification power. The larger the upper AUC value, the more efficient the functional similarity measure is. For the co-expression network, we computed the NI and RI values as measures of clustering power, the higher these values, the more powerful the functional similarity measure is. Different values found for different measures are shown in It was unexpected to find that the Wang et al. approach performs poorly in terms of AUC values when using the BMA and ABM measures for BP, whereas these measures have shown good performance when used in EC, Pfam domain and sequence similarity data and the authors of this approach initially suggested using the ABM measure. Other approaches show good performance when used with their initial measures even though the Avg measure achieves the best performance. On the other hand, the Max approach performs poorly compared to other approaches, independently of the network (PPI or co-expression) and performance measure. This may be due to the fact that the Max approach tends to over-estimate functional similarity scores between proteins, for example by assigning the similarity score of 1 to two proteins sharing at least one GO terms independently of the number of unrelated terms between these proteins.Finally, note that the good performance of the annotation-based family is related to the corpus under consideration because of its dependence on the frequencies of GO term occurrences in the corpus. These annotations may be unbalanced in their distribution across the DAG. This constitutes a serious drawback to these approaches, specifically for organisms with sparse GO annotations and may negatively affect their performances Several IC-based GO functional similarity measures have been proposed over recent years and have enabled comparison of proteins at the functional level on the basis of their GO annotations. These measures are being used in different biological and biomedical applications and have largely contributed to the efficient exploitation of the biological knowledge embedded in the GO structure. While annotation-based functional similarity measures have been intensively studied and topology-based measures very often deployed to specific applications, none of the previous studies has attempted to quantitatively perform all-against-all semantic similarity measure comparisons. As a result, there were still gaps in our knowledge on the performance of these measures when applied to different biological data or applications, making the choice of the most ‘appropriate’ measure difficult, especially for someone who just needs a quick GO semantic similarity measure for their biological question. Thus, a comparative study was necessary in order to provide a global assessment of these different semantic similarity measures.Here, we have carried out a quantitative performance evaluation of several different semantic similarity measures between GO terms for different term IC families or semantic similarity approaches and different biological data. Results indicate that a measure used for a given biological data type was not always the most appropriate even for the ‘well’ studied family measures, namely annotation-based measures. In fact, though the SimGIC or the BMA or ABM measure was confirmed to be the best measure, in general, when using EC, Pfam domain and sequence similarity data, this measure was not the best for applications related to PPI and co-expression data , where the Avg measure showed overall best performance. This is also the case for the topology-based approaches where, in general, the initial measure suggested for use does not provide the overall best performance. This study bridges the gap between the large variety of GO semantic similarity measures and their performance in different biological and biomedical applications by comparing different protein functional similarity measures using different biological data. This should help researchers choose the most appropriate measure for their biological application.File S1Combined file of supporting tables. Table S1: A human protein-protein interaction dataset used to assess the classification power of different functional similarity measures using Receiver Operator Characteristic (ROC) curve analysis. Table S2: A set of human protein-protein interaction with both interacting partners annotated with respect to the GO BP ontology. Table S3: A set of human protein-protein interaction with both interacting partners annotated with respect to the GO CC ontology. Table S4: A human co-expression network used to assess the clustering power of different functional similarity measures using using Normalized Mutual Information and Rank Index scores. Table S5: A set of human co-expressed protein pairs among human proteins annotated with BP terms.(ZIP)Click here for additional data file.


Proteins play an important role in biological processes in living organisms. Many protein functions are based on interaction with other proteins. The structural information is important for adequate description of these interactions. Sets of protein structures determined in both bound and unbound states are essential for benchmarking of the docking procedures. However, the number of such proteins in PDB is relatively small. A radical expansion of such sets is possible if the unbound structures are computationally simulated.Dockground public resource provides data to improve our understanding of protein–protein interactions and to assist in the development of better tools for structural modeling of protein complexes, such as docking algorithms and scoring functions. A large set of simulated unbound protein structures was generated from the bound structures. The modeling protocol was based on 1 ns Langevin dynamics simulation. The simulated structures were validated on the ensemble of experimentally determined unbound and bound structures. The set is intended for large scale benchmarking of docking algorithms and scoring functions.The http://dockground.compbio.ku.edu.A radical expansion of the unbound protein docking benchmark set was achieved by simulating the unbound structures. The simulated unbound structures were selected according to criteria from systematic comparison of experimentally determined bound and unbound structures. The set is publicly available at  Proteins play an important role in biological processes in living organisms. Many protein functions are based on interaction with other proteins. The structural information is essential for adequate description of these interactions. Protein interaction is characterized by structural and physicochemical recognition factors –3, and cThe docking algorithms are generally based on the concept of structure complementarity, observed in experimentally determined complexes. Thus, most docking procedures perform better when the bound  protein structures are used, assuring the perfect match between the structures. Such bound docking allows one to neglect the internal degrees of freedom , providing for an effective search of the six-dimensional rigid-body space of the external coordinates. However, in the real-case scenario, the bound structures of the participating proteins are unknown, and one has to rely on the unbound  proteins. Because of the huge number of potentially relevant internal degrees of freedom, the problem of unbound docking is far from being solved.The rigid-body docking of unbound proteins results in structural mismatches at the putative interfaces. Thus, one approach to the unbound docking lowers the resolution of the structures, alleviating the difference between unbound and bound structures, and decreasing the structural overlap . The dowFor the development of docking techniques applicable to the unbound proteins, it is essential to learn the experimentally determined difference between bound and unbound states. A number of proteins have structures experimentally determined in both unbound and bound states , 12. In Dockground resource [Dockground, was generated by changing the side chain conformations according to the rotamer library [The utility of the unbound docking approaches is tested in the CAPRI blind experiment . To provresource  is flexiresource , based o library . In the Dockground with the following criteria: mean area buried ≥ 500 Å2, include alternative binding modes, homo/hetero n-mers, and oligomers, and the redundancy cutoff 97 %. The resulting set contained 1918 protein-protein complexes. Program Profix from the Jackal package (http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software) was used to build the disordered residues and missing atoms.Protein complexes were selected from the Bound part of It was expected that dynamic simulation of separate bound protein structures, without the interacting partner, would relax interface side-chain conformations constrained by the interacting partner and thus approximate the unbound form of the protein. To speed up the calculations, we chose the Langevin Dynamics (LD) simulation in CHARMM (CHARMM22 force field), with electrostatics by Generalized Born approximation, performed on each bound structure without its interacting partner. Prior to LD simulation, the initial structures from PDB files were minimized (by 50 steps of steepest descent minimization followed by 500 steps of adopted basis Newton–Raphson minimization).In the simulation, the backbone atoms of helices and strands were constrained with a force constant of 5 kcal/mol, the temperature was set to 309.6 K, the bond lengths were fixed using shake with tolerance 1.0E-8, the friction force fbeta was 5.0, and the time of simulation was 1 ns, with 100 snapshots saved.simulated unbound structure. Among the simulated proteins there were 1245 non-obligate and 1960 obligate complexes, according to NOXClass procedure [Protein structures that crashed during the simulation were removed. The simulation yielded 3205 protein structures. The number of resulting complexes with both proteins simulated was 1530. In 145 complexes only one partner was simulated. The structure with the largest all atoms RMSD from the bound structure was designated as the rocedure .α RMSD interval . The exThe number of unbound structures in the ensemble ranged from 27 to 394 per protein. The difference between bound and unbound structures varied in 0.7 - 7.3 Å full structure RMSD, and in 0.3 – 11.7 Å interface RMSD. The mean RMSD between bound and unbound structures was 1.9 Å .The majority of simulated proteins had RMSD < 2 Å from the initial structure  for full structure, and 1.5 Å (0.3 – 5.0 Å range) for the interface. The relatively small RMSD between bound and unbound structures could partially be explained by the fact that some proteins designated as monomers (and thus treated as unbound) are crystallized as homodimers. If proteins with bound/unbound RMSD ≤ 1 Å (likely not true unbound cases) are deleted, the average RMSD is 1.4 Å for the full structure, and 1.8 Å for the interface, similar to the difference between the bound and the simulated unbound structures.For consistency, as an option for users who would like to utilize structures of same origin, we simulated the unbound structures in cases where the X-ray unbound structure is known. The Examples of conformational change between the X-ray bound, unbound and simulated unbound states of proteins are shown in Figs. Eglin C also belongs to the potato chymotrypsin inhibitor family and has a flexible binding loop . Compariin vivo/vitro, the algorithms that distinguish between obligate and non-obligate complexes have limited reliability [Dockground resource. An example of such complex is the nerve growth factor protein , obligate complexes were included as an option. Although they would not have an unbound structure iability . The optein Fig. , which hDockground site (http://dockground.compbio.ku.edu) on the “Unbound - > Build Database” page, and as a “Quick Download” link. Users can download either the entire set or any combination of the available subsets. In addition to the obligate and/or non-obligate complexes, the interface offers to download structures, for which simulated unbound structures were generated for both monomers in the complex or only for one. Users can also include simulated unbound structures, for which corresponding X-ray unbound structure exists in the Dockground unbound docking benchmark 3.0. The names of the files start with the PDB code of the initial bound structure, followed by _u1 or _u2 for the first and second chain in the initial complex, respectively. Chain IDs and residue numbering were kept as in the original PDB files.The resulting set of 3184 PDB-formatted files is available on the Dockground public resource provides data to improve our understanding of protein–protein interactions and to assist in development of docking algorithms and scoring functions. Sets of protein structures determined in both bound and unbound states are essential for benchmarking docking procedures. However, the number of such proteins in PDB is relatively small. A radical expansion of such sets is possible if the unbound structures are computationally simulated. Such simulated unbound protein set was generated for the Dockground resource. The modeling protocol was based on 1 ns Langevin dynamics simulation. Simulated unbound structure was selected according to criteria from systematic comparison of experimentally determined bound and unbound structures. The set is publicly available at http://dockground.compbio.ku.edu.The