Many biological processes are carried out by proteins interacting with each other in the form of protein complexes. However, large-scale detection of protein complexes has remained constrained by experimental limitations. As such, computational detection of protein complexes by applying clustering algorithms on the abundantly available protein-protein interaction (PPI) networks is an important alternative. However, many current algorithms have overlooked the importance of selecting seeds for expansion into clusters without excluding important proteins and including many noisy ones, while ensuring a high degree of functional homogeneity amongst the proteins detected for the complexes.|V| log |V| + |E|) time. A seed selection strategy, which prioritises seeds with dense neighbourhoods, was devised. We defined a topological measure, called common neighbour similarity, to estimate the functional similarity of two proteins given the number of their common neighbours.We designed a novel method called Probabilistic Local Walks (PLW) which clusters regions in a PPI network with high functional similarity to find protein complex cores with high precision and efficiency in Our proposed PLW algorithm achieved the highest F-measure  when compared to 11 state-of-the-art methods on yeast protein interaction data, with an improvement of 16.7% over the next highest score. Our experiments also demonstrated that our seed selection strategy is able to increase algorithm precision when applied to three previous protein complex mining techniques.http://wonglkd.github.io/PLWThe software, datasets and predicted complexes are available at  Protein complexes are physical aggregations of proteins that interact with each other at the same location and time. They are a cornerstone of many critical cellular processes, providing the molecular machinery to perform a vast spectrum of complex biological functions. Some important examples include the nuclear pore complexes for regulating the passage of proteins and RNA between the nucleus and cytoplasm  and the Unfortunately, biologists have yet to overcome the many experimental limitations for the large-scale detection of protein complexes, such as the shortcomings of Tandem Affinity Purification (a common wet lab complex detection method) listed in a recent protein complex survey paper . As a reIn contrast, high-throughput methods for detecting pairwise protein interactions  have enabled the interactomes of many organisms to be mapped efficiently, yielding large scale protein-protein interaction datasets that are readily available in public databases for data mining and knowledge discovery. Given the experimental limitations of large scale detection of protein complexes, computational methods for detecting protein complexes from the rich protein-protein interaction datasets present a useful alternative.By modelling a protein-protein interaction (PPI) network as an undirected graph, where a vertex denotes a unique protein and an edge represents an interaction between two proteins, we can expect protein complexes to manifest graphically in the PPI networks as cliques. In practice, given that data derived from high-throughput screening techniques are often incomplete (i.e. have missing interactions) and noisy  , the proet al. [The MCODE algorithm proposed by Bader et al.  was one et al. . Simulatet al. , and haset al. ,12 populet al. . Algoritet al. , MCL-CA et al. ,15 and Ret al.  were proet al. [We can exploit the graph theoretic properties of the biological structures of protein complexes for better complex detection in PPI networks. A protein complex generally contains a core in which proteins are highly co-expressed and share high functional similarity. The protein complex is often surrounded by attachments, which are proteins that assist the core to perform subordinate functions . The coret al. . A few aet al. , CORE [1et al. , MCL-CA et al.  and CACHet al. , have emet al.  has alsoet al. . While tet al.  and misiet al. .In all these approaches, finding high quality seeds to expand without excluding important proteins or including too many noisy ones in the seeds is pivotal to increasing the algorithms' precision. In addition, given that proteins within a protein complex interact with each other to perform a common biological function, the algorithms should also focus on ensuring that the protein members detected as protein complexes have high functional homogeneity. In this paper, we propose a Probabilistic Local Walks (PLW) algorithm to detect protein complexes. We devise a seed selection strategy and formulate a topological measure called common neighbour similarity to estimate the functional similarity in two proteins. Using these, we illustrate how PLW performs probabilistic local walks efficiently to mine protein complex cores by identifying areas of high common neighbour similarity. The effectiveness of common neighbour similarity is established through its high correspondence to functional similarity. Finally, we validate PLW using yeast PPI data and show that it significantly outperforms 11 existing methods for complex prediction in terms of various evaluation metrics .ppi Gfor protein complexes. This PPI graph is formally defined as the undirected graph ppi G=  where ppi E= {|u, v ∈ ppiV}. Our proposed PLW algorithm consists of three main steps:In this section, we present a novel Probabilistic Local Walks (PLW) algorithm to mine a PPI network/graph 1. selecting proteins that are located in a dense region and have high degree centrality as seeds,2. expanding these seeds to find protein complex cores through iterative probabilistic local walks, and3. adding attachment proteins that are closely linked to the cores.Since a complex core is the "heart" of a protein complex, it should be a subgraph that satisfies the two following structural graph-theoretic properties.dense. Let us define a subgraph G' = , where V' ⊆ ppi Vand E' = {| ∈ ppiE, u, v ∈ V'}. We quantify the density of this subgraph using the local clustering coefficient, which is the number of edges |E'| divided by the theoretical maximum number of edges possible for the graph, |V'| * (|V'| - 1)/2.First, given that protein members of a complex core highly interact with each other, it should be Definition 1. The density of the graph G' =  is defined as:common neighbours or interact with a similar set of proteins. We postulate that protein A and B are likely to possess similar functions if protein A shares a number of interaction partners  with protein B-since A and B can bind to the same proteins, they are likely to share common biochemical and physical properties.Secondly, it has been observed that there is a high degree of functional homogeneity in experimentally-verified protein complex cores where proteins work together and share common biological functions ,17. As scommon neighbour similarity in Equation (5) to quantify the degree of similarity between two proteins by considering the number of common neighbours.We will define a topological protein similarity measure called big false complex that can not match with any real protein complex.Choosing high quality protein seeds for expansion is also critical. Most protein complex prediction algorithms have employed a form of local search to expand seeds by including proteins located in the seeds' local neighbourhood graph. However, if a complex does not exist in the neighbourhood of these seeds, the algorithm will never be able to find the complex regardless of the quality of the local search method. Furthermore, low quality seeds may also result in a false positive complex being detected. For example, if a protein on the periphery of multiple complexes is chosen as a seed, the resulting predicted complex may subsume the multiple complexes under an unrealistic Let us first provide a number of definitions for seed selection. Given a vertex, its neighbour set and degree are defined as follows.Definition 2. For each vertex v ∈ Vppi, the set of its neighbours (or adjacent vertices) is denoted as Nv = {u|u ⊆ Vppi,  ⊆ Eppi}. v's degree in Vppi is denoted by deg(v) = |Nv|.i v∈ ppiV, its local neighbourhood graph v and its adjacent vertices (direct neighbours) and the interactions between these proteins, as defined below.Given a vertex Definition 3. For each vertex vi ∈ Vppi, its local neighbourhood graph We devise the following score function that would identify protein seeds likely to be inside protein complexes, and which have high centrality in those complexes.Definition 4. The score of a seed vi is defined as the product of the seed's degree and its neighbourhood graph density.The seed score function takes both degree centrality and neighbourhood graph density into consideration for prioritising the proteins for seeds. We demonstrate its calculation for an example network in Figure Let us discuss two specific scenarios to illustrate the usefulness of the score function:1. Given two proteins with the same neighbourhood graph density but different degrees, the protein with the higher degree is more likely to be in a protein complex core as it interacts with more proteins and therefore more likely to serve as key players or coordinators within complex cores, whereas the protein with the lower degree is more likely to be an attachment or on the periphery of a core.2. Given two proteins with the same degree but different neighbourhood graph density, the protein with the lower neighbourhood graph density might be interacting with proteins from multiple complexes since the connectivity between its neighbours is lower, e.g., vertex 7 in Figure seedsV. This selection is formally defined in Equation (3) using x, the number of proteins selected; the seed set is defined in Equation (4).Proteins with higher seed scores are therefore more likely to be in complex cores and should be subsequently expanded to form cores and corresponding complexes. In this paper, we rank proteins by their seed scores and select a fraction, denoted as λ, to be expanded into cores. For example, if λ = 0.3, the top-ranked 30% of proteins are selected as the seed set Protein complexes have a high degree of functional similarity between their member proteins. Unfortunately, it is infeasible to directly use functional information (say from Gene Ontology) for protein complex core detection, as experimentally verified functional information may not be available for many proteins.common neighbour similarity measure to estimate the functional similarity of two proteins using a topological characteristic, the number of common neighbours. A high number of common neighbours means that the two proteins interact with a similar group of proteins. As the biological function of proteins is determined by the nature of their interactions with other proteins and which proteins they interact with, the number of common neighbours is a good proxy in the absence of functional data. If two protein share a number of interaction partners, they are likely to share biological functions as they could have common biochemical or physical properties to allow them to bind to their common neighbours. In fact, proteins with high vertex common neighbour similarity might even be substitutes for each other since they are able to interact with the same set of proteins to carry out similar or identical biological functions.We define a vertex Definition 5. Vertex common neighbour similarity is defined as the cosine similarity of the vector representations of the proteins' neighbourhoods.i vis represented as a vector i vand 0 otherwise.Each protein common neighbour similarity can also be calculated using the number of common neighbours normalised by the geometric mean of the neighbourhood size of vertex u and v as shown in Figure et al. and Mete et al. [Vertex e et al. ,24.s ∈ seeds Vproteins that are similar in terms of common neighbour similarity, in the vicinity of the seed and which may not be directly connected to the seed by an edge.We propose a novel Probabilistic Local Walks (PLW) algorithm, which will identify for each seed Favouring similar proteins using a weighted random choice. The PLW algorithm takes into account the network structure by favouring edges connecting proteins with higher common neighbour similarity for inclusion in the same complex core. This weighted random choice is achieved by choosing the next protein in the walk with probability proportional to the common neighbour similarity between the current protein and each candidate neighbour. Given a protein v and its neighbour u, we define the probability of walking from v to u in Equation (6) and provide an illustrated example in Figure According to Equation (6), the random walker will pick edges that connect proteins with high common neighbour similarity with a higher probability, and will tend to walk within groups of proteins with high similarity. Performing these probabilistic walks allows us to detect regions of high functional similarity. Making a probabilistic choice instead of greedily choosing the most similar neighbour lessens the chance of getting stuck in local maxima. While a probabilistic local walk can be seen as a finite Markov chain, they are different from the random walks simulated in existing algorithms ,13,14,16ppi Ginto a weighted graph simG:In order to perform our proposed PLW algorithm, we transform our Definition 6. Gsim is defined as the graph where each edge  ∈ Eppi has the weight 1 - common_neighbour_similarity.Identifying proteins in the vicinity of a seed. In our PLW algorithm, we ensure that proteins chosen are close to the seed in the PPI network by limiting the length of the walk using a starting energy α and penalty γ. Each probabilistic walk starts with an energy of α. For each step taken, γ = 1 - common_neighbour_similarity is deducted from the walk's energy, where v is the current vertex and u is the next vertex to be visited. The walk terminates when taking the next step would cause the energy to fall below 0. The penalty term penalises walking to dissimilar proteins by reducing the length of the walk. This limits the reachable vertices to the α-vicinity of the seed, which is defined as follows:Definition 7. The α-vicinity of a seed s is defined as the set of vertices for which the distance to s on Gsim is less than or equal to α. The distance is the length of the shortest path between the two vertices.α was chosen by estimating the diameter (length of longest shortest path) of protein complex cores. We set α to 2.00 to cover direct neighbours as well as neighbours of neighbours, as there may be missing interactions  between a seed and fellow proteins in the same complex core. Indeed, 88.2% of complexes in the CYC2008 manually-curated yeast complex catalogue [atalogue  have a dCompared to existing work RRW , which uOur PLW algorithm can be implemented in two parts:s (demonstrated in Algorithm 1), and1. performing probabilistic local walks and counting how frequently each vertex is visited in walks starting from a seed 2. identifying the core vertices for each seed by evaluating the statistical significance of their visit frequency counts (demonstrated in Algorithm 2).Collate visit frequency counts. Algorithm 1 illustrates the calculation of visitCount, which is the frequency count that a vertex j vis visited from the seed s. For each seed s, we expand the seed w times for w probabilistic local walks, with w set to 100 for this paper. Lines 3-14 represent one walk (one iteration).s, we initialise the current vertex to be the seed s with an initial energy of α in lines 3 and 4. In lines 5-14, the algorithm walks from vertex to vertex until the energy falls below 0. At each non-seed vertex that it visits, it increments visitCount. It then picks the next vertex to visit using the weighted random choice described in the previous section. The algorithm applies the penalty term γ (in lines 10-12) to limit its graph traversal to the seed's α-vicinity. We bound γ to be a minimum of 0.01 in line 11 to ensure termination of the walk even when similarity is high (>0.99).For each probabilistic local walk starting at a seed - 0.89 = 0.11. Should the random walker choose to traverse the vertices 1, 2, 3, 4, 2, 3, 4, 2, 3, 4, 7 in that order, its energy will progress from α (2.00 in this paper) to a final value of -0.26. Note that visitCount is cumulative over the w walks.Table Algorithm 1 Compute visitCount using probabilistic walksfunction ComputeVisitCount(s)1: for i ← 1, w do ▶ Perform w walks2:  v ← s ▶ Initialise random walk at s3:   energy ← α ▶ Initialise energy at α (2.00 in this paper)4:   repeat5:   if v≠s then6:     visitCount ← visitCount + 1 ▶ Record visit to vertex v7:       end if8:     u randomly from GN(v) with P(u) ∝ common_neighbour_similarity9:     select       ▶ Make a weighted random choice in line 9← 1 - common_neighbour_similarity ▶ Compute penalty for traversing edge 10:   γ ← max ▶ Ensure termination when similarity = 111:   γ 12:   energy ← energy - γ13:       v ← uuntil energy <014:     end for15:   end function16: Identification of protein complex cores. Algorithm 2 demonstrates how the protein complex cores are formed using visitCount. We calculate the standard scores for all ln)∀visitCount ≠ 0, and select statistically significant ln values in line 3 using a significance level of 0.5%. We apply a logarithmic transformation in lines 2, 3 and 6 to lessen the impact of outliers. This is a common method of improving the normality of variables [ariables .s ∈ seedsV, we find the significant vertices for walks starting from s and select them to form the complex core (in line 6). We discard duplicate cores as well as cores with two or less proteins, since detecting two-protein cores is more dependent on the interaction data quality than the clustering method [For each seed  in line . We discAlgorithm 2 Identify cores using recorded visitCountfunction MineCores(seedsV)1:   visitCount)∀visitCount ≠ 02:   Calculate Z-scores of all ln) ▶ p = 0.5% is used for this paper3:   Calculate statistical significance of all ln) is significant}6:     if |candidateCore| > 2 then7:     cores ← cores ∪ candidateCore8:       end if9:     end for10:   end function11: C =  is defined as N(C) = {u| ∈ ppiE, v ∈ CV, u ∈ ppiV, u ∈ VC}. N(C) consists of the direct neighbours of the vertices in C connected with v. v ∩ VC| |Nis the number of proteins in the core that are also neighbours of v. By selecting only attachments with We select proteins that interact with more than half of the proteins in the core as attachments. The neighbourhood of a complex core The overall PLW algorithm, which combines all the major steps, is shown as follows in Figure n = ppi| |Vand m = ppi||E. This allows PLW to compete on large-scale PPI networks that can not be handled by the majority of existing methods [x * w * q operations, where x is the number of seeds selected for expansion into cores, w is the number of probabilistic local walks taken and q is the average number of steps taken. Given that w and q are constants (100 and 2.22 respectively in our paper) and x is at most n, the expansion of the cores takes The time complexity of our PLW algorithm is  methods . SortingAlgorithm 3 Overall PLW Algorithm for Mining Protein Complexesfunction MineComplexes)1: 2:   Vseeds ← vertices in Vppi with the x highest scores3:   for all s ∈ seeds Vdo ▶ Core mining in lines 4-74:   s) ▶ See Algorithm 1 for details5:     ComputeVisitCount(end for6:   cores ← MineCores(Vseeds) ▶ See Algorithm 2 for details7:   clusters ← ∅8:   for all sg ∈ cores do ▶ Add attachments in lines 9-159:   for all v ∈ Vppi\sg do10:     sg,v ← {| ∈ Eppi, u ∈ sg}11:       Esg,v Eare the edges connecting v and the core sg          ▶ end for12:     sg ← sg ∪ {v|v ∈ ppiV, |sg,v E|/|sg| > 0.5}13:     clusters ← clusters ∪ sg14:     end for15:   return clusters16:     end function17: We performed extensive experiments to illustrate the effectiveness of our proposed PLW algorithm. We first present our experimental datasets and evaluation metrics, followed by our results.We applied our proposed PLW algorithm on two experimental yeast PPI datasets. One was retrieved from the Database of Interacting Proteins (DIP)  and was After we removed duplicated edges and self-loops, the DIP dataset contains 17,201 interactions among 4,930 yeast proteins, the COMBINED6 dataset contains 17,327 interactions among 3,861 yeast proteins and the BioGRID dataset contains 59,748 interactions among 5,640 yeast proteins,et al. [Two sets of protein complexes were utilised as gold standards to validate the predicted protein complexes. The first set is the CYC2008 catalogue of manually curated protein complexes from Wodak's lab . The secet al.  and the et al. . ComplexP and B be the set of predicted complexes and the set of benchmark complexes. We apply the neighbourhood affinity score to quantify the degree of overlap between a predicted cluster p ∈ P and a benchmark complex b ∈ B, denoted as N A in Equation (10). A predicted cluster p is considered to match a complex b if N A ≥ ω. ω is set as 0.2 in our experiments and the same setting was used in [Let  used in ,10,17,41cp Nin Equation (11) is defined as the number of predicted complexes that match at least one benchmark complex and cb Nin Equation (12) to be the number of benchmark complexes that match at least one predicted complex.cp Nand cbN, we use Recall, Precision and F-measure  in Equation (13) and Equation (14) to evaluate overall algorithm performance.Based on the above definitions of Sn), positive predictive value (PPV) and geometric accuracy (Accuracy) have recently been proposed to evaluate the quality of protein complex predictions [n benchmark complexes (B) and m predicted clusters (P), let ij Tdenote the number of common proteins between the th ibenchmark complex (ib) and th jpredicted cluster (j p), i.e. ij T= i ∩ pj||b. Sn, PPV and Accuracy are then defined in Equation (15). Generally, a high Sn indicates that the predicted complexes have a good coverage of the proteins in the benchmark complexes. High PPV values indicate that the predicted complexes are likely to be true positives.In addition, sensitivity , the average size of complexes (3rd column) and the number of proteins covered (4th column).Table In addition, the comparison results on the other 3 combinations  are shown in Additional file In this experiment, we validate our hypothesis that selecting proteins in dense regions that have high degree centrality as seeds for expansion increases the precision of our algorithm. In addition, we apply our seed selection strategy to three other algorithms, namely, COACH , RRW 1616 and Cl0.463, 0.507 and 0.432 when λ is set as 0.3, as shown in Figure 0.468 for COACH, 0.515 for RRW and 0.439 for ClusterONE. Without the seed selection strategy, the F-measure for COACH, RRW and ClusterONE is 0.453, 0.455 and 0.380, respectively. It is evident that our seed selection strategy enhanced the performance of existing algorithms for predicting protein complexes.For COACH, RRW and ClusterONE, their F-measure is For the DIP dataset, PLW generates 118, 320, 576 and 787 clusters under λ = 0.1, 0.2, 0.3 and 0.4 respectively. With more seeds available as starting points for expansion into cores, the number of possible clusters increases thus explaining this trend.We recommend the use of λ = 0.3 for PLW. This value yields high precision while allowing a reasonable rate of recall, as quantified by the peak in F-measure in Figure Common neighbour similarity is important for PLW's prediction of protein-complex cores, since it enables PLW to select protein pairs with high functional similarity.Our experiment in Figure Figure As the gold standard sets are incomplete , unmatchIn this section, we conduct a qualitative analysis of the protein complexes predicted by our PLW algorithm. PLW was able to detect 16 benchmark complexes in the CYC2008 gold standard with better accuracy than existing methods.11 proteins, covering 11 proteins in the benchmark complex. The next best match was by ClusterONE with 9 proteins, which did not recover the proteins YDL185W and YLR447C. . The next best match was generated by RRW, whose predicted complex has 5 proteins and recovers 4 proteins in the real complexes (with neighbourhood affinity score 0.56). Additionally, the two protein complexes detected only by PLW were the box C/D snoRNP complex (4 proteins) and ISW1b complex (3 proteins), which were matched with neighbourhood affinity scores of 0.25 and 0.33, respectively.In Figure PLW is able to recover the complexes with high accuracy, as shown in Figure As experimental protein complex detection remains a challenging problem, it is important to develop accurate computational approaches for predicting protein complexes from PPI data. The continued explosion in the volume of available PPI data demands more efficient and more precise algorithms. We used our PLW algorithm to demonstrate three techniques, which can also be applied to improve the performance of other protein complex prediction algorithms and even general graph clustering algorithms. These techniques are:A precise and efficient Probabilistic Local Walks (PLW) algorithm for mining protein complex cores. PLW attained the best F-measure , with an improvement of 16.7% over the next best method amongst the 11 methods evaluated. It carries out probabilistic local walks to mine cores efficiently in 1. Seed selection strategy. We developed a scoring strategy that finds important seeds to expand without excluding important proteins or including too many harmful seeds. This strategy yielded increased precision for PLW, COACH, RRW and ClusterONE.2. Common neighbour similarity. We formulated a measure to estimate the functional similarity of two proteins using their common neighbours. We found that common neighbour similarity is highly correlated with functional similarity, rendering it useful in detecting complexes with functional homogeneity. In addition, common neighbour similarity can be applied in situations where functional information is not readily available.3. For future work, we are exploring how to automatically determine a suitable value for the threshold λ in the seed selection strategy to increase its applicability to the large range of agglomerative clustering algorithms. We are also studying the mathematical properties of PLW's novel walking method.The techniques we conceived will be useful for researchers in graph clustering. In particular, PLW could be applied to cluster other biological networks, such as metabolic networks and gene regulatory networks. In addition, PLW could be parallelised to tackle massive networks. We will explore such applications as our future work.The authors declare that they have no competing interests.DLKW, XLL and MW conceptualised and designed the method, and drafted the manuscript together. DLKW was responsible for the implementation and carried out the experiments. JZ and SKN participated in discussion as well as revision of the draft. All authors read and approved the manuscript.Performance of algorithms on various datasets.pdf. This file contains four figures comparing the algorithms' performance on the following datasets and gold standards: 1. DIPS PPI dataset against CYC2008 gold standard, 2. DIPS PPI dataset against NEWMIPS gold standard, 3. COMBINED6 PPI dataset against CYC2008 gold standard and 4. COMBINED6 PPI dataset against NEWMIPS gold standard.Click here for file


We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site:  Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design. Given their importance, it is vital to determine which protein interactions have functional relevance and to characterize the protein competition inherent to crowded environments, as the cytoplasm or the cellular organelles. We show that combining coarse-grain molecular cross-docking simulations and binding site predictions based on evolutionary sequence analysis is a viable route to identify true interacting partners for hundreds of proteins with a variate set of protein structures and interfaces. Also, we realize a large-scale analysis of protein binding promiscuity and provide a numerical characterization of partner competition and level of interaction strength for about 28000 false-partner interactions. Finally, we demonstrate that binding site prediction is useful to discriminate native partners, but also to scale up the approach to thousands of protein interactions. This study is based on the large computational effort made by thousands of internautes helping World Community Grid over a period of 7 months. The complete dataset issued by the computation and the analysis is released to the scientific community. Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design In this work, we tackle two distinct but related questions: (i) can a combination of coarse-grain docking and evolutionary information identify true interacting partners among a set of potential ones? (ii) what is the effect of binding promiscuity on a large and variate dataset of protein structures Previously, we have shown that knowing the experimental binding site of a protein can help to retrieve its native interacting partner within a set of decoys We evaluate our method with a complete cross-docking (CC-D) calculation on a set of 168 proteins belonging to the 84 known complexes described in the Mintseris Benchmark 2.0 The large computational effort necessary to accomplish this work was realized with the help of World Community Grid (WCG), that coordinated thousands of internautes providing their computer time to dock about 300000 conformations per protein pair for the set of 28224 possible pairs in the Mintseris Benchmark 2.0. For each pair, we selected about 2000 decoys. For non-partners, we find weak as well as strong interactions. The decoy set is released and it provides an important reference set of structures that can serve as a proxy for the non-specific protein-protein complexes that occur transiently in the cell or that are avoided by spatial-temporal constraints. These latter are hard to characterize experimentally but they are of biochemical relevance, as highlighted by other studies To simulate the variability of crowded environments for a protein in the cell, we study how easily a protein finds its true partner with respect to many random subsets of proteins supposedly competing with it. We realize a thorough analysis of these interactions and we address the question of whether a successful prediction of a protein partner depends on the environment composition or not. We quantify the effect of competing partners in predictions, and we characterize in a quantitative manner three distinguished populations of proteins interacting with a protein To our knowledge, this is the first study performing a large-scale CC-D calculation, proposing an analysis of the binding promiscuity of the protein set, and providing to the scientific community the associated dataset of decoys Finally, we checked whether evolutionary information can be used to considerably restrict the number of docking interfaces to be examined and to render molecular computation feasible for a larger scale investigation of PPIs, based on thousands of proteins instead of hundreds. This result makes the protocol proposed here feasible for scaling up the analysis.The 168 proteins of the Mintseris Benchmark 2.0 CC-D was realized on the full dataset from unbound structures, leading to 28224 docking simulations. Each calculation explored about 300000 ligand-receptor orientations, corresponding to ligand and receptor complete surfaces, and asked for more than 7 months computational time on WCG. This CC-D scaled up the one introduced in The docking algorithm simulates the actual docking process in which ligand-receptor pairwise interaction energies are calculated. The energy function we used takes into account van der Waals  and electrostatic  terms see .For each protein in the dataset, the problem of partner identification is tackled with two main experiments. The first experiment assumes to know the residues belonging to the experimental interface of the proteins. This means that the residues lying at the interface of two proteins in a native complex are supposed to be known while no knowledge of the complex conformation is assumed. The second experiment replaces experimental interfaces by predictions of binding sites based on docking and evolutionary information. The evaluation of the quality of the interaction signal in this PPI large-scale study is of major importance. In particular, the contribution of docking information compared to evolutionary information in partner identification needs to be quantified. To do so, the analysis based on experimental interfaces allows us to evaluate in a precise manner how much a good prediction of the interaction sites improves partners identification, experimental interfaces playing the role of perfect predictions. In the sequel, we also use it to decipher whether a property of protein interactions that has been observed from computational predictions has a biological origin or whether it is a consequence of the noise of the prediction.As pointed out in To compare interaction indexes computed over different pairs, a normalized interaction index, called The results of the analysis are resumed in the squared matrix reporting the We systematically analyzed complexes in terms of their biochemical classes and difficulty categories see  to verifHere, we drop the information on the location of the experimental interface of true partners and use predicted binding sites instead in order to evaluate our ability to retrieve the true partner in a totally blind experiment. Predicted interfaces are obtained by combining evolutionary information, computed with the program JET JET predictions are used to select a set of residues that are likely to belong to the real interface according to conservation and expected physico-chemical properties for interface residues see  [38];Early studies suggested that docking arbitrary partners together can nevertheless point to the correct interaction surfaces For a given pair of proteins trix see .We analyzed partner prediction performances obtained by using NIP, JET or the combination JET+NIP see . When usTo take advantage of the different behavior of NIP and JET on different biochemical classes, we combined the two approaches. We obtained a global improvement for three quarters of the database (Enzyme-Inhibitors and Others) compared to the results of NIP alone , while increasing the performance for Antibody-Antigens (0.58) and Antigen-Bound Antibody (0.56) compared to JET alone.The poor performance obtained on Antibody-Antigens and Antigen-Bound Antibody possibly results from (i) a faster sequence evolution that blurs conservation signals leading to bad binding site predictions and (ii) from a large number of JET patches , compared to Enzyme-Inhibitors and Others, generating a large number of potential interactions that render more difficult partners discrimination. By excluding Antibody-Antigen and Antigen-Bound Antibody from the dataset  of a complex within an environment to be the position of the NII value of the complex in the ordered list of NII values associated to the complexes involving at least one of the two partners. For each of the 84 native complexes, we generated 100 protein sets containing the desired complex and 19 other randomly chosen complexes (40 proteins). In For a given complex, we also plot the detailed values of AUC and IR of the associated 100 random sets  can be used to measure the strength of the interaction between two arbitrary proteins. Based on it, we ask whether complexes involving wrong partners might display the same average IR value and IR standard deviation of native complexes. Given a protein As seen on the 1BUH complex, there is a strong variability of the average IR values associated to the interactions of a protein with different partners. One distinguishes three populations of proteins in the environment that interact with those that rank always on the top positions: they strongly compete with the native complex by creating structures of very low energy score and displaying the interaction on the expected binding sites, these latter being either predicted or experimentally validated. They are represented by black dots and lie on the bottom of the plots in those that rank always as the last: they never enter in competition with the native complex, possibly due to their physico-chemical characteristics. They are represented by black dots and lie on the top of the plots in those that rank on the middle: they interact with different levels of strength with For each protein, partners belonging to these three populations and level of strength of their interactions measured by the average IR, are precisely computed and they form a signature for the protein interaction with its environment.In many cases (41 complexes over 84), true partners display an average IR For Enzyme-Inhibitors, We can distinguish ligands from receptors with respect to the distribution of their average IR values. In fact, ligands display a higher density of good IR values  chosen from the Docking Benchmark 2.0 and a background of 922 potential interactors  has been analyzed in A qualitative comparison with our predictions based on JET+NIP scores could have been made on the set of 10 complexes discussed in the SI of This analysis shows that shape complementarity docking is not yet ready for PPI identification, due to a currently insufficient performance of binding sites prediction methods. In fact, perfect predictions of interaction sites would strongly support the usage of docking algorithms such as HEX , becauseThe analysis points out two more general observations. First, large environments of the order of a thousand proteins, as the one of 922 proteins considered in The second observation concerns the composition of the set of proteins analyzed in We compared our predictions with those obtained in The docking technique we used is computationally expensive . To reduce the conformation space to be explored, we predicted the location where the interaction takes place and confined the docking to this region. This is done by predicting binding sites for the receptor protein by using JET To evaluate the impact of our restriction on MAXDo execution time, we computed how many docked conformations between protein pairs were dropped. When the 168 proteins are considered together, the average portion of the conformational space that is explored after reduction is cone see  that takin silico large-scale screening of protein structures to find a small set of potential protein partners that could be tested experimentally. The approach reminds the one of drug design aiming to screen large sets of small molecules in order to identify a small set of potential drugs that becomes experimentally testable. These approaches do not pretend to exactly identify a unique solution but rather a set of reasonable candidates, and reduce, in this manner, the amount of experimental time and costs. This means that we are not focused on the correct docking of experimentally known partners, which can be achieved via other more effective but much more computationally demanding methods We have addressed the problem of predicting protein interactions using high-throughput CC-D calculations on a dataset of 168 proteins. We have shown that a simple docking algorithm combined with evolutionary information, can be used to discriminate interacting from non-interacting proteins. The purpose of the method is the We have realized a large-scale PPI analysis by assuming to know the residues forming the experimental interface of the native complexes  and by using predictions of binding sites. Experimental binding sites can be seen as perfect predictions, and the analysis based on them is realized for two reasons: 1. to understand how much evolutionary information can contribute to PPI reconstruction when coupled with a coarse-grain docking algorithm using an energy function, and 2. to decouple true PPI signal from noise and identify PPI properties that are not consequences of accumulated errors due to predictive algorithms. This second reason allowed us to be confident, for instance, on the promiscuity observed in A few large-scale analyses, that are similar in spirit, have been performed We could only partially compare to in silico predictive approach to the reconstruction of PPI networks. In fact, proposing to a biologist a set of less than 20 interactions to test is very reasonable.The predictive performance of the method is encouraging for the whole Mintseris Benchmark 2.0 and very satisfactory for the enzyme-inhibitor subset . For thiThe analysis on the average IR for the enzyme-inhibitor subset highlights that an average IR threshold  over 46 , the truThe analysis highlights an important point on the behavior of all proteins with respect to their partners. For each protein, there is a small set of partners that displays a systematic  very low average IR that lead to ask whether these partners might physically interact and not be false positives. Three reasonable explanations for this set of highly potential partners can be given: (i) partners can interact on a merely physical base but never meet in the cell due to different cellular compartments localization, (ii) partners can interact for functional purposes, possibly not described until now , (iii) partners can interact in the cell not for functional purposes but generating a competition with the functional partner, possibly participating to the regulation of the protein interactions in the cell. Taking into account these possibilities, this set of highly potential partners becomes interesting for further studies. For instance, these interactions would deserve to be experimentally tested to see how strongly they interact, and whether they form a structurally well-defined complex. Also, for a given protein and a set of highly potential partners, one could ask whether general structural  features of the interface exist and in the positive case, classify these interfaces. These further studies could contribute to give important insights into protein partnership discrimination.For each protein We have shown that evolutionary information can also be used to restrict the conformational space of the docking exploration without an important loss in sensitivity. This result is very important in view of reducing the computational cost of highly time demanding docking calculations  and the perspective of enlarging the dataset size for future CC-D calculations.To conclude, we are the first to perform a CC-D of a pool of proteins covering a large spectrum of functions and interaction modes, performing it on unbound structures and providing energy values (even though simplified) taking into account electrostatic forces. Our approach is the first combining evolutionary information with CC-D simulations. The evaluation of the performance of these two contributions to the problem of partner identification, suggests that there is still room for improvement in the solution. In particular, we have shown that a precise identification of protein binding sites allows for very satisfactory predictions. Data coming from the CC-D calculations and the evolutionary analysis are provided and they will help the community to evaluate further CC-D studies and methodological developments. In particular, the decoy set constitutes a unique dataset of “negative” partners. For them, we provide about 2000 conformations and an associated coarse-grain energy score. It might be extremely useful to suitably parametrize docking scoring functions, more refined than our coarse-grain scoring function, to discriminate partners. In the context of this study, a subset of these decoy structures filtered by our coarse-grain scoring function could be re-scored for a better partnership evaluation by using a more refined score function better discriminating the interaction signals.http://zlab.bu.edu/zdock/benchmark.shtml.The Docking Benchmark 2.0 Molecular docking is performed with the MAXDo (Molecular Association via Cross Docking) algorithm, developed for complete cross-docking (CC-D) studies We used a coarse-grain protein model developed in 2, and for each starting position, 210 different ligand orientations were generated, resulting in a total number of start configurations ranging from roughly 100,000 to 450,000 depending on the size of the receptor. During each energy minimization, the ligand protein was kept at a given location over the surface of the receptor protein, using a harmonic restraint to maintain its center of mass on a vector passing through the center of mass of the receptor protein. The direction of this vector was defined by two Euler angles Our docking algorithm , with the help of thousands of internautes donating their computer time to the project. It took approximately seven months to perform CC-D calculations on the complete dataset of 168 proteins. More technical details regarding the execution of the program on WCG can be found in https://www.grid5000.fr).Each energy minimization for a pair of interacting proteins typically takes 15 s on a single 2 GHz processor. As noted above, approximately 100,000 to 450,000 minimizations are needed to probe all possible interaction conformations, as a function of the size of the interacting proteins. Therefore, a CC-D search on the benchmark, namely Surface residues are residues with at least In order to improve the quality of the predictions of protein interaction partners, in our earlier study we developed a normalized interaction index (NII) that takes into account whether a protein-protein interface involves amino acids belonging to a known interaction site For every protein pair To allow comparison among different partners we defined a normalized index predicted partner of For each protein We consider as true positives  method For each residue Based on the Like previously, in all protein pairs The interaction rank of a protein pair r = 2.9 Å. For each ligand orientation, we check whether the interface of the resulting ligand-receptor complex involves residues predicted by JET or not  of the Mintseris Benchmark 2.0, we considered about 2000 best ligand orientations Click here for additional data file.


Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective.In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples.Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs). In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer.A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer. Equation (1) states that, biologically, the expression level of the target protein i is associated with the expression levels of interacting proteins. A protein association model was constructed for each protein in the protein pool.where, αik,C and αik,N, respectively. In this case, for each protein in each phenotype, i.e., with cancer or without cancer, a mathematical description was constructed to characterize the respective expression association. Once the association parameters for all proteins in the rough PPI network were identified, the significant protein associations were determined based on the estimated association abilities (αik's). Akaike Information Criterion (AIC) [After the protein association model of the rough PPI network was constructed, the association parameters in equation 1) were identified using maximum likelihood estimation method ,28 using were ideon (AIC) ,29 and Son (AIC)  were empBased on the identified protein association abilities, two matrices were established to represent the cancer protein association network (CPAN) and the non-cancer protein association network (NPAN).αij,C and αij,N indicate the quantitative protein association ability between protein i and protein j for CPAN and NPAN, respectively; and K is the number of proteins in the protein association network. For a given protein i and protein j in the protein association network, the association ability αij quantifies the expression relation between the interactive proteins. If αij = 0, there is no association between protein i and protein j. Further, we said that protein i is associated with protein j means that the expression level changes of protein i account for the expression level changes of protein j and vice versa. As a consequence, when the estimated protein association ability αij ≠ αji, the one which has larger absolute value would be selected as the association ability between protein i and protein j, i.e., αij = αji. The resulting CPAN and NPAN constituted the network-based biomarker used for identifying the significant proteins in lung carcinogenesis and diagnostic evaluation.where, According to equations (1) and (2), the protein association models for CPAN and NPAN can be represented as the following equations.EC and EN indicate the noise vectors in cancer and non-cancer cases, respectively. A matrix indicating the difference between two protein association networks is defined as C-N.where ij ddenotes the difference in protein association ability between CPAN and NPAN among protein i and protein j. Using the matrix D to show the difference in network structure between CPAN and NPAN, a carcinogenesis relevance value (CRV) was presented to quantify the correlation of each protein with significance of lung carcinogenesis. To identify the significant proteins in lung carcinogenesis, two important issues were taken into consideration. First, the magnitude of the association abilities αij's denotes the significance of one protein to the other one. A higher absolute value of αij implies that the two proteins are more tightly associated. Second, if a protein plays a more crucial role in lung carcinogenesis, the difference in association numbers linked to the protein for CPAN and NPAN would be larger. For example, if one protein shares a strong association with many proteins in CPAN, but a weaker association (no protein) in NPAN, the protein in question is more likely involved in lung carcinogenesis. As a result, the CRV was determined based on the difference in protein association abilities via the following equation.where i-th protein in the network-based biomarker, the implication of equation (5) is that the CRV quantifies the extent of protein associations that differentiate CPAN from NPAN.For the p-value was also obtained for each protein to determine the statistical significance of the CRV. To determine the p-value for an observed CRV, a null distribution of CRVs  were further employed to refine the rough PPI network. The CPAN and NPAN, which consisted of 399 and 393 protein associations respectively, would constitute the network-based biomarker of lung cancer  of GSE4115 downloaded from the GEO database r Figure .The 40 identified proteins contributing to lung carcinogenesis can be divided into three categories according to the functional annotations Table ; the thrCancer is a complex disease of uncontrolled cell growth. Therefore, the proteins responsible for cell growth are likely to play critical roles in lung carcinogenesis. Among the 40 significant proteins identified based on the network-based biomarker, 30 proteins are annotated with cell growth Table . The mitEGFR, FGFR1, and INSR are receptor tyrosine kinases (RTKs) bound by an epidermal growth factor, a fibroblast growth factor, and insulin, respectively. Receptor tyrosine kinases have been shown to not only perform a key regulatory role in normal cellular processes, but also become critically involved in the development and progression of human cancers . EGFR isMany proteins acting as transcriptional regulators were also identified. CREBBP is a transcriptional co-activator downstream of the TGFβ pathway and the mutations and deletions of the CREBBP gene are associated with lung cancer . CTNNB1 The ability of tumor cell populations to expand in number is determined not only by the rate of cell proliferation but also by the rate of cell death . In addiTRAF6 functions as a signal transducer in the NFκB pathway that activates IKK, in response to proinflammatory cytokines. The identification of TRAF6 by the proposed network-based biomarker approach reinforces the linkage between inflammation and cancer ,57. BRCAWith the progression of cancer, the malignant tumor cells acquire the ability to migrate and metastasize to distant sites. As a result, the proteins that are relevant to the cell migration capability are crucial for the carcinogenic process. Twenty-three out of 40 significant proteins were annotated with cell migration. PTK2 (FAK), a protein tyrosine kinase in the RTK pathway, is an important mediator within the cell migration process, as well as in cell proliferation and cell survival. Substantial evidence has shown that activated PTK2 leads to tumor growth and metastasis , and the2+. It is an essential regulator of cell cycle progression and cell survival. Further research is needed to examine its relation with carcinogenesis. PRKACA is a cAMP-dependent protein kinase. The identification of PRKACA as a significant protein implies that cAMP signaling might also be involved in lung carcinogenesis. MAPK8IP3 functions as a scaffold protein in the RTK pathway; NCOA6, HGS, and XRCC6 are annotated with cell growth and/or cell survival. However, until now, no empirical evidence has linked their relevance to carcinogenesis, which makes them potential targets for further investigation into lung carcinogenesis.For nine out of the 40 significant proteins identified, little is known about their roles in lung cancer  was then used to evaluate the diagnostic performance of the proposed network-based biomarker. Among the 35 samples, 26 were accurately classified, resulting in an accuracy of 74.29%. The sensitivity and specificity of the proposed approach were also evaluated. The network-based molecular diagnosis can identify smokers with or without cancer, using a high level of sensitivity of 83.33%, and a moderate specificity of 64.71%: this enables the proposed network-based biomarker to effectively diagnose the smokers with lung cancer. Further, this approach enables the network-based biomarker to act as a screening test, which, with the aid of other clinical diagnostic tools, both accelerates the process and improves the sensitivity of diagnosis.The cause of the moderate specificity was further investigated. The difference between the specificity value and the sensitivity value can be attributed to a misclassification of a number of smokers without cancer in the cancer category. The misclassification may be due to similarities in molecular pattern, i.e., the gene expression profiles of smokers with cancer are similar to those of smokers without cancer. In order to validate the hypothesis, Pearson correlation coefficients  of gene In order to validate the predictive performance of the proposed network-based biomarker, several comparisons were made. First, we tested the predictive performance without the information of protein-protein interactions. The 199 differentially expressed proteins selected by ANOVA using the primary data set were used for classification of the prospective data set. A simple hierarchical clustering was performed (see Additional file Cancer is a complex disease and carcinogenesis in humans is a multistep process that transforms normal cells into malignant derivatives. Many researchers are investigating the underlying mechanisms that prompt the uncontrolled cell proliferation and metastasis. They have successfully identified some key components of the various steps in the carcinogenesis and some therapeutic interventions have been developed to at least slow down the carcinogenic process. However, because of the complexity, the therapy that targets some specific molecules is only partially effective and tumor-specific. Therefore, investigation of the carcinogenesis from the systems perspective is inevitable. On the other hand, biomarker identification for cancer diagnosis has been a primary research focus in the biomedical field since the use of biomarkers could provide early detection of cancer. As a result, in this study, a network-based biomarker approach has been proposed to simultaneously account for molecular investigation and diagnosis. The proposed approach was applied on the sample data obtained from smokers with and without lung cancer and 40 significant proteins were identified in lung carcinogenesis. The network-based biomarker considers not only differentially expressed proteins but also the protein association network structure. This allows an accurate identification of proteins with low discriminative potentials if such proteins were associated with many other significant proteins . This pret al. proposed a hypergraph-based iterative learning algorithm for subnetwork identification, which minimizes a cost function under a unified regularization framework [Although our proposed method is shown to be useful, some limitations exist and the need for further improvements remains. In the proposed network-based biomarker approach, gene expression profiles were overlaid to the corresponding proteins for further analysis. However, levels of mRNA do not always correlate with protein levels and do not provide information on post-translational modification such as phosphorylation that may be critical in regulating protein activity . Consequramework . These gOur network-based biomarker provides both a systematic insight into the lung carcinogenic process and a good method for identifying significant proteins, categorized as lung cancer-related proteins and many others that have not been previously reported. These proteins not only provide new targets for further research into understanding the mechanisms of lung carcinogenesis, but are also potential targets for therapeutic interventions. The main challenge of cancer research is to find an effective therapeutic approach that specifically kills malignant cells. Conventional chemotherapy acts by killing all rapidly dividing cells, resulting in toxic effects and damage to normal tissues . With thLung cancer is the leading cause of cancer deaths worldwide. Understanding the causes and the underlying mechanisms can help fight the disease. In this study, a network-based biomarker approach, which integrated gene expression profiles and protein interaction information, was developed for molecular investigation and diagnosis for lung cancer. From a systems perspective, the constructed network-based biomarker further evaluated the lung carcinogenic process by use of significant protein identification and diagnostic evaluation. The diagnostic results indicate that the network-based biomarker is sensitive to the diagnosis of smokers with lung cancer and can be used as one kind of screening test. More importantly, the significant proteins identified by the network-based biomarker give mechanistic insights into the carcinogenic process and provide potential therapeutic targets to combat cancer.The authors declare that they have no competing interests.YCW developed the method, performed the analysis, evaluated the results and wrote the manuscript. BSC provided essential guidance and revised the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/4/2/prepubSupplementary Methods.Click here for fileThe full names of the significant proteins identified.Click here for fileThe hierarchical clustering for 199 differentially expressed proteins of the prospective data set.Click here for file


These valuable data are scattered, and there have been no systematic attempts to document the various proteins regulated by hypoxia. Compilation, curation and annotation of these data are important in deciphering their role in hypoxia and hypoxia-related disorders. Therefore, we have compiled HypoxiaDB, a database of hypoxia-regulated proteins. It is a comprehensive, manually-curated, non-redundant catalog of proteins whose expressions are shown experimentally to be altered at different levels and durations of hypoxia. The database currently contains 72 000 manually curated entries taken on 3500 proteins extracted from 73 peer-reviewed publications selected from PubMed. HypoxiaDB is distinctive from other generalized databases: (i) it compiles tissue-specific protein expression changes under different levels and duration of hypoxia. Also, it provides manually curated literature references to support the inclusion of the protein in the database and establish its association with hypoxia. (ii) For each protein, HypoxiaDB integrates data on gene ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, protein–protein interactions, protein family (Pfam), OMIM (Online Mendelian Inheritance in Man), PDB (Protein Data Bank) structures and homology to other sequenced genomes. (iii) It also provides pre-compiled information on hypoxia-proteins, which otherwise requires tedious computational analysis. This includes information like chromosomal location, identifiers like Entrez, HGNC, Unigene, Uniprot, Ensembl, Vega, GI numbers and Genbank accession numbers associated with the protein. These are further cross-linked to respective public databases augmenting HypoxiaDB to the external repositories. (iv) In addition, HypoxiaDB provides an online sequence-similarity search tool for users to compare their protein sequences with HypoxiaDB protein database. We hope that HypoxiaDB will enrich our knowledge about hypoxia-related biology and eventually will lead to the development of novel hypothesis and advancements in diagnostic and therapeutic activities. HypoxiaDB is freely accessible for academic and non-profit users via http://www.hypoxiadb.comDatabase URL:  Hypoxia is a pathophysiological condition and refers to an abnormally low partial pressure of oxygen in atmosphere, low content of oxygen dissolved in per liter of blood or less percentage saturation of hemoglobin with oxygen, either found singly or in combination , 2. DiffIn humans, hypoxia is a multivariate disorder and is influenced by factors like varying oxygen concentrations, time duration of hypoxic exposure, altitude, physical stress and environmental and genetic factors . Thus, iHomo sapiens. Only those papers that report changes in expression pattern of gene/proteins were selected. They were scrutinized for relevant information. Further, Gene Expression Omnibus (GEO) database  (R package org.Hs.eg.db (ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/) (R packages org.Hs.eg.db and KEGG.db (R packages org.Hs.eg.db and Pfam.db (ftp://ftp.ncbi.nih.gov/pub/HomoloGene/current/) (Protein GI numbers were extracted either from the reference papers or from BioDBnet . GenbankHs.eg.db  was usedE_INFO/) . The PfaHs.eg.db . GO annoe/DATA/) . KEGG pa KEGG.db . In addi Pfam.db . As HPRD Pfam.db . Entrez  Pfam.db  were use Pfam.db  enlists urrent/) .HypoxiaDB is built on Apache HTTP server 2.2, with CGI-Perl and Perl scripts at the back-end and the HTML, Javascript and CSS at the front-end. Apache, CGI-Perl and Perl are preferred, as these are open-source software and are platform independent. The overall layout of the database is shown in HypoxiaDB provides simple and user-friendly interface to ‘browse’ and ‘search’ data. In addition, ‘other applications’ are provided.The browse option provides an interface to retrieve data without entering keywords. To help the users browse data conveniently, HypoxiaDB provides three different methods for browsing the hypoxia-regulated proteins:Browse by alphabetical order: Each alphabet links to a table that lists protein symbols and protein names starting with the respective alphabet. Each protein symbol is then linked to the corresponding protein page, which contains detailed information about the protein and its expression changes in hypoxia. The details in the protein page are discussed further. The user can also browse through the complete alphabetical list of proteins.Browse by category: This option allows the user to browse through the list of proteins according to the categories listed below:List of upregulated and downregulated proteins: Using this option, the user can retrieve a list of proteins that are up/downregulated during hypoxia.List of up/downregulated proteins in different levels of hypoxia: This option is useful if the user wants to browse the list of proteins that have been reported to change under specific hypoxia conditions.List of up/downregulated protein in a particular tissue: This option enlists the proteins up/downregulated in hypoxia in different tissues. The results can be advantageous for the user interested in tissue-specific studies.The browse result is in the form of a table containing protein symbol, up/downregulation, fold change, percentage hypoxia, tissue, PubMed ID and type of study (genomic/transcriptomic/proteomic). The protein symbol links to the detailed protein page and the PubMed links to the specific research publication.iii Browse by chromosomal location: The user can click on the human chromosome number to display the proteins on each chromosome and then browse them individually. Users can also browse the complete list of proteins arranged according to the chromosomal location.Data can be searched at two levels in HypoxiaDB: (i) Quick search and (ii) search based on biological information.Quick search can be done using any of the following protein identifiers, namely, protein name, protein symbol, Gene/Entrez ID, Uniprot ID, HGNC ID, Vega ID, Ensembl ID, HPRD ID, HomoloGene ID, GI number, Genbank accession and Unigene ID. Among these options, protein name and protein symbol search for similar matches, whereas the others search for exact matches. The result is a list of matching entities, which are further linked to corresponding detailed protein pages.In this search option, user can give the following protein identifiers as the query term: GO ID or term, KEGG pathway ID or term, Pfam ID, PDB ID or OMIM ID.The program searches for matches containing the query term in the GO field. For example, the query ‘GO:000’ will list all the GO IDs containing ‘GO:000’. The result is retrieved in the form of a table having the following fields, viz, Gene/Entrez ID, GO ID, GO term, category and evidence (PubMed). Each Entrez ID is linked to the respective protein page; GO ID is cross-linked to GO database; evidence is linked to the GO evidence code; and PubMed ID is linked to the article from which the evidence of the GO term was taken.This program searches the query term in the KEGG pathway field. The result is retrieved in the form of a table having Entrez ID, KEGG ID and KEGG term. The Entrez ID is linked to the detailed protein page, and the KEGG ID is hyperlinked to KEGG pathway database.The query result is obtained in the form of a table having the fields Entrez ID, IPI ID, Pfam ID and protein family. The Entrez ID is linked to the detailed protein page, IPI ID is linked to the IPI database and Pfam ID is linked to the Pfam database.The program searches the query term for exact match in the PDB field. The result is a list of Entrez IDs of proteins, which take the same fold as the query PDB. The Entrez ID is linked to the detailed protein page.The program searches for exact match in the OMIM field. The result is a list of Entrez IDs of proteins, which have Mendelian inheritance specifications matching with the enquired OMIM ID. The Entrez ID is linked to the detailed protein page.The information presented on the protein page is divided into three sections:The general information present on the protein page includes protein name, protein symbol, aliases, chromosome and organism name . The proHypoxiaDB is made after intensive literature search, and correlation of a particular protein with hypoxia may be demonstrated by more than one paper. Hence, this section contains tissue-specific expression pattern changes of the protein under different levels and durations of hypoxia compiled from various publications. The data are organized as level of regulation (up/down), fold change, percentage of hypoxia, tissue of expression, PubMed link to the research publication and the type of study (genomic/transcriptomic/proteomic) done to state the correlation A. DifferThis section provides information about the GO of the protein. It gives information about the GO ID, which is cross-linked to the AmiGO database, GO term, GO category and evidence (PubMed), which is further cross-linked to the research publication B.This gives information about the KEGG IDs associated with the protein and the respective KEGG names. The KEGG ID is linked to the KEGG pathway database  Figure B.This link provides details on the homologous proteins in other genomes. It lists the HomoloGene group ID for the protein, which is linked to HomoloGene database. Information regarding organism, Entrez ID, protein symbol and protein accession number is also present on the homology page B.OMIM links to the OMIM ID of the protein and OMIM name. The OMIM ID links to the OMIM database B.A click on the protein family information lists the IPI ID, Pfam ID and the protein family name. The IPI ID and Pfam ID are hyperlinked to the IPI database and the Pfam database, respectively B.This link provides the PPI information for each protein. The information is depicted in the form of a table and an interaction network figure. The figure is present only if the interacting partners are <20. It gives information about interactor’s name and its HPRD ID, interacting partners and their HPRD ID’s and evidential experiment and the respective PubMed ID. The HPRD ID links to the HPRD database. Interacting partners that are not present in HypoxiaDB are linked to NCBI. If the interacting partner is present in HypoxiaDB, it is highlighted in red and hyperlinked to the respective detailed protein page. This helps to identify proteins that are interacting in hypoxia conditions and may help in deciphering new hypoxia-linked pathways B.The link provides the sequence of the protein in FASTA format B.The other applications available for the users at HypoixaDB include the following:A customized BLAST tool has been made available that searches user-provided query against the sequences available in the database. It may be useful for characterization of the unknown sequences and identifying homologous sequences from the database. The users can also perform a customized search by choosing the type of matrix and an e-value cut-off.To make the database useful and to further improve it, it is essential to create an efficient and automatic data updating system. In this regard, we have prepared an online submission web page. The web page provides an intuitive interface to add the data. The data will be validated at an interval of 2–3 months and would be added automatically to the main HypoxiaDB database through a Perl script. If the users want to modify an already existing data within the database, they can write to the authors. The information would be validated and updated. The inputs from research community would continue to improve the quality and scope of the database. Our team is also continuously searching and adding new entries from published literature.An online feedback form is also provided to help improve HypoxiaDB and to meet the needs and requirements of the scientific community working in hypoxia and related disorders. Hypoxia research has been continuing to grow, and HypoxiaDB encourages users’ feedback, including error reports and feature requests, with the hope to make HypoxiaDB a comprehensive resource to facilitate hypoxia proteomic research, which may lead to some novel treatments.For the convenience of the user, a detailed and self-explanatory tutorial is provided.It has been reported that ∼1–1.5% of the genome is transcriptionally responsive to hypoxia; nevertheless, the response varies in different cell types in a time-dependent manner , 37. CalGO annotation have been used widely to characterize protein functions and to divulge trends in protein datasets; we have also classified hypoxia-regulated proteins according to their molecular function, cellular component and biological process. Assignment of molecular functions to the set of proteins revealed that the top five molecular function categories represented in the dataset are protein binding followed by nucleotide binding, metal ion binding, ATP binding and DNA binding . This suHypoxia is a complex condition and there may be huge biological networks and pathways contributing to the pathogenesis of hypoxia. In this view, each protein in the database is linked with KEGG pathway and PPI information/networks. The KEGG pathways enriched in HypoxiaDB are metabolic pathways, pathways in cancer, cytokine–cytokine receptor interaction, focal adhesion and MAPK signaling pathways . Thus, tThe PPI link allows the user to graphically view and study the interacting protein networks. Interestingly, the user can also identify the interacting partners within HypoxiaDB and elucidate major hypoxia-linked pathways/networks. Analyzing the interacting partners for each protein in HypoxiaDB, it was found that 215 proteins have their 75% interacting partners within HypoxiaDB itself . NotablyMus musculus (mouse) followed by Canis lupus familiaris (Dog), Bos taurus (Cattle) and Pan troglodytes (Chimpanzee)  and Oryza sativa (2.2%). The presence of homologous proteins in various high complexity organisms implies that these proteins have evolved late along the evolutionary line. These proteins are probably responsible for varying oxygen adaptability and may help in distinguishing natural habitats of complex and simple eukaryotes. They may also help to distinguish between eukaryotic and prokaryotic mode of life. Many other research publications also highlight that prokaryotes and lower eukaryotes have simple mechanisms to respond to low-oxygen concentrations, whereas the mammalian cells have a complicated hypoxia response, which involves multiprotein complexes to regulate several transcription factors  exhibit homology to other eukaryotic genomes; it suggests that hypoxia-regulated proteins have been conserved over the years in eukaryotic domain of life. The proteins exhibit homologous sequences in other genomes; the majority is conserved in mpanzee) , and onldvantage . The hum factors , 43. AllSupplementary Figure S1A). GeneCards database reports 1538 proteins as hypoxia-related proteins. GeneCards search is a phrase-based search engine and looks for association between gene/protein and disease based on co-occurrence in abstracts. Both these databases do not provide tissue-specific expression profile of the protein under different levels and durations of hypoxia. ArrayExpress reports 18 757 proteins to be hypoxia-regulated from only four GEO IDs (corresponding to four publications). Of 18 757 proteins, 18 476 are reported from one GEO ID, i.e. E-GEOD-9649. Of these, only 1109 are significantly up/downregulated (based on P < 0.0000005). Besides, ArrayExpress would take data only from GEO dataset. There is a huge gap between published data and GEO datasets. Many experiments use Serial Analysis of Gene Expression (SAGE), Fluorescence In-Situ Hybridization (FISH) and western blots to identify the differentially expressed proteins; these are not deposited in the GEO database. HypoxiaDB currently contains 72 000 manually curated entries taken on 3500 differentially regulated hypoxia proteins extracted from 73 peer-reviewed publications selected from PubMed.We have compared HypoxiaDB with some of the popularly used generalized databases like AmiGO , GeneCarSupplementary Figure S1A–G). Searching AmiGO database for hypoxia genes/proteins retrieved 17 results . PGK1 was not listed in these results. Exploring the ‘term association’ tab on the PGK1 page in AmiGO database also did not report any association with hypoxia . ArrayExpress reports PGK1 being overexpressed in only two experiments (E-MEXP-445 and E-GEOD-9649) . In contrast, HypoxiaDB reports 15 association studies for PGK1 and hypoxia . The PGK1 page in the GeneCards database shows the expression profile of PGK1 in different tissue types . However, it does not provide the expression profile of the protein with respect to duration and level of hypoxia. HypoxiaDB uniquely provides the tissue-wise expression pattern of the proteins in different levels and durations of hypoxia . It additionally provides manually curated literature references to support the inclusion of the proteins in the database and establish the association with hypoxia. Furthermore, the database assimilates data on GO, KEGG pathway, PPIs, protein family, OMIM, PDB structures and homology to other sequenced genomes . HypoxiaDB allows researchers to search interested proteins that have been reported to be regulated in the hypoxic conditions, to compare their proteomic discovery with the previously published data and to relate the protein expression changes under various hypoxic conditions.Using a specific hypoxia-regulated protein (namely PGK1), the results from the three web servers  were compared with that from HypoxiaDB (With the considerable increase of hypoxia-regulated molecular data over the past few years generated by the use of various research techniques, including high-throughput transcript and proteomic analysis, there was a need to develop a database to facilitate hypoxia research at a molecular level. Yet, to date, to our knowledge, there is no resource available that provides detailed information about the proteins known to be associated with various hypoxic conditions. Therefore, we compiled the first hypoxia database ‘HypoxiaDB’, a comprehensive non-redundant catalog of proteins where manual curation along with the information from other resources has been integrated to provide a knowledgebase that will allow researchers and clinicians an overview of biology of the proteins involved in hypoxia and its related disorders. The data are presented in a systematic way, and apart from the search facility, many browsing options facilitate fast, efficient and user-friendly retrieval of information. HypoxiaDB serves as a ‘one-stop shop’ database, where information regarding a particular protein can be extracted from other databases and enriched with information from several additional analyses not obtainable from other repositories. It is also cross-referenced to external databases as leverage for harnessing new biological insights. To find homologous protein sequences from the database based on sequence similarity and for the characterization of orphan protein sequences, HypoxiaDB has a customized protein database that could be used with BLASTP, and this helps to provide one more layer of utility to this hypoxia database. Such layered curation and annotation of hypoxia-related proteins could be useful for better understanding of both the value of proteomics study in hypoxia and hypoxia-related research and the biological meaning of protein expression change under varying hypoxic conditions and tissue types.We hope that HypoxiaDB would help in improving the existing knowledge about hypoxia and generation of some useful hypothesis and novel therapeutic strategies. This hypoxia-specific database would not only save time and effort of researchers but would also assist in deeper understanding of hypoxia biology.Mus musculus and Rattus norvegicus. We hope that this thorough and comprehensive database would be extensively used and sufficiently updated to enable efficient research in the field of hypoxia.It is strongly proposed to update this database frequently and add new data from literature as well as other data analysis tools that will help improve the existing knowledge about hypoxia. More information like single nucleotide polymorphisms, transcription binding factors, motif analysis, etc will be added in the next version to make the database more useful for clinical as well as research purposes. Also, the present version has only human proteins; in the next version, we aim to include proteins of http://www.hypoxiadb.com.HypoxiaDB is freely available at Supplementary data are available at Database Online.