The detection of protein complexes is of great significance for researching mechanisms underlying complex diseases and developing new drugs. Thus, various computational algorithms have been proposed for protein complex detection. However, most of these methods are based on only topological information and are sensitive to the reliability of interactions. As a result, their performance is affected by false-positive interactions in PPINs. Moreover, these methods consider only density and modularity and ignore protein complexes with various densities and modularities.To address these challenges, we propose an algorithm to exploit protein complexes in PPINs by a Seed-Extended algorithm based on Density and Modularity with Topological structure and GO annotations, named SE-DMTG to improve the accuracy of protein complex detection. First, we use common neighbors and GO annotations to construct a weighted PPIN. Second, we define a new seed selection strategy to select seed nodes. Third, we design a new fitness function to detect protein complexes with various densities and modularities. We compare the performance of SE-DMTG with that of thirteen state-of-the-art algorithms on several real datasets.The experimental results show that SE-DMTG not only outperforms some classical algorithms in yeast PPINs in terms of the F-measure and Jaccard but also achieves an ideal performance in terms of functional enrichment. Furthermore, we apply SE-DMTG to PPINs of several other species and demonstrate the outstanding accuracy and matching ratio in detecting protein complexes compared with other algorithms. A protein complex is a group of proteins that interact with each other to perform different cellular functions . The detDuring the past decade, because of the development of high-throughput techniques such as yeast-two-hybrid , mass spclusters or communities) such that the nodes in the same cluster are more similar to each other than the nodes in the other clusters . We calculate the function enrichment test to demonstrate the biological significance of detected protein complexes by different algorithms. In this paper, we use LAGO , MCL , CFinder , DPClus , IPCA , CMC , COACH , HC-PIN , SPICi , ClusterONE , WPNCA , CALM , and ClusterEPs , COACH , HC-PIN , HC-PIN , SPICi , SPICi , ClusterusterONE , WPNCA , WPNCA , CALM  is first developed to describe how strongly neighbors are connected. However, Radicchi et al.  note that the edge clustering coefficient may not be suitable for using in PPINs because PPINs are disassortative networks. To overcome this limitation, Zhao et al.  is currently one of the most comprehensive ontology databases in the bioinformatics community . From a topological perspective, the central part of a protein complex often corresponds to a dense subgraph with high clustering coefficient and more reliable weight in the PPINs . In fact, a dense graph could have low modularity, and a graph with high modularity may have low density. Therefore, the density-based algorithms ignore protein complexes with low density and the modularity-based algorithms miss protein complexes with low modularity. Overall, these methods have limitations when identifying protein complexes with various densities and modularities [As mentioned in the Background section, several protein complexes identification algorithms have been presented. Most existing algorithms make many assumptions to define a subgraph of possible protein complexes in the PPINs. However, in terms of the actual performance of these algorithms, the graphs with high density or high modularity in PPINs generally correspond to protein complexes ,35. In flarities . To overSG, its density is donated as DSG: According to the preliminaries section, for an undirected weighted subgraph SG, and |SG| represents the size of the subgraph SG, respectively. The density of a graph measures how close the graph is to a clique, and the density takes value between 0.0 and 1.0.where SG⊆G, its weighted in-degree, denoted as SG, and its weighted out-degree, denoted as SG to the nodes in the rest of graph G. For the subgraph  follows : 14\docdw(SG) is equal to the sum of Clearly, the weighted degree of MSG of a subgraph SG⊆G is defined as follows: The modularity MSG takes values from 0.0 to 1.0. If a subgraph has higher modularity, it has more connections within itself and fewer connections to the rest of the PPIN. A subgraph with a modularity of 1.0 has no connections with the rest of the PPIN.Obviously, SG by considering its density (DSG) and modularity (MSG). DSG describes the density of subgraph SG, MSG describes the modularity of subgraph SG and DSG∗MSG is normalized by the geometric mean of DSG and MSG. The fitness of a subgraph SG in an undirected weighted graph G, denoted as F(SG), is defined as: In this model, in the process of identifying protein complexes, we measure the quality of SG expands, its modularity increases and its density decreases. Thus, by expanding from a node, we can obtain a subgraph with the local maximum fitness score and output the result as a protein complex. Thus, this new model can be used for identifying protein complexes with different topology, including high density but low modularity, high modularity but low density, and high density and high modularity. Therefore, our model can identify the protein complexes with various densities and modularities.Generally, as the subgraph SG) starts as single protein, and then grows and shrinks gradually as proteins are added and removed one by one. The process of adding proteins from the neighbor of SG, and is denoted as Neighbor(SG), and the process of removing proteins from the inner nodes is denoted as inner_nodes(SG). In this process, we first define two concepts: if p∈Neighbor(SG), the neighbor node connects to at least one edge with any protein of cluster SG but does not belong to SG; If p∈inner_nodes(SG), the inner node belongs to SG, but connects to at least one node which is a neighbor of SG. A key problem is to decide the priority to add and remove proteins in terms of SG. In general, if a protein v belongs to SG, it may have a strong connection with its cluster SG=. Therefore, if the protein v is added to SG, it could increase the average of the weighted interactions within SG. By contrast, if the protein v is removed from SG, it could increase the average of the weighted interactions within SG. Here, we introduce a measure to assess the priority, denoted as weightavg(SG), which is defined as: An initial cluster (weightavg(SG) is the average of the weighted interactions of all proteins within SG, |VSG| is the number of proteins in SG and SG. The priority of adding the node p into the cluster SG, where p∈Neighbor(SG), or deleting the node p from the cluster SG, where p∈inner_nodes(SG), SG is determined by the value of weightavg(SG). We choose the highest weightavg(SG) of the boundary node to add it to SG or remove it from SG to maximize the value of F(SG) (see Eq.(where  (see Eq.).SG, in extending step, we first obtain all the neighbors, namely, Neighbors(SG). The priority of all neighbors is determined by the value of weightavg(SG) see Eq. (v is added to SG is determined by whether the fitness (F(SG)) of SG is increased after the highest priority protein v is added and whether the actual edge between the highest priority protein v and the SG, denoted as |SG∩N(v)|, which is the number of proteins in SG connected with v is greater than the expectation edge, denoted as F(SG)∗|SG|, where F(SG) is the fitness of SG and |SG| is the number of proteins in SG. Once the highest priority protein v is added to SG, SG is updated, i.e., the highest priority protein v is removed from Neighbors(SG). Then, the next highest priority protein is tested, and the priorities of list Neighbors(SG) and the fitness (F(SG)) of SG are recalculated, and so on. If the highest priority protein v fails any of two tests, then SG cannot be further extended.For a cluster  see Eq. . WhetherSG, in the correcting step, we first obtain all inner nodes, namely Inner_nodes(SG). The priority of all proteins in Inner_nodes(SG) is determined by the value of weightavg(SG) (see Eq. (v is deleted from SG is determined by whether the fitness (F(SG)) of the cluster SG−{v} is increased after the highest priority protein v is removed from SG and whether the actually edge between the highest priority protein v and SG−{v}, denoted as |SG−{v}∩N(v)|, which represents the number of proteins in SG−{v} connected with v, is greater than the expectation edge, denoted as F(SG)∗|SG|, where F(SG) is the fitness (F(SG)) of SG and |SG| is the number of proteins in SG. Once the highest priority protein v is removed from SG, the cluster SG is updated, i.e., the highest priority protein v is removed from Inner_nodes(SG). Then, the next highest priority protein is tested, and the priorities of Inner_nodes(SG) and the fitness of the cluster SG−{v} are recalculated, and so on. If the highest priority protein v fails any of two tests, then the cluster SG cannot be further corrected.For a cluster (see Eq. ). WhetheOn the basis of the quantitative description of protein complexes, we develop a novel clustering algorithm based on density and modularity with network topology and GO annotations, named SE-DMTG, to identify protein complexes in a weighted PPIN whose edge weights reflect the reliability of the edge in a protein complex according to topological and biological information.G with GO annotations. The SE-DMTG algorithm broadly consists of four phases. First, SE-DMTG constructs a weighted PPIN-based topological and biological information at lines 2-11. Second, SE-DMTG calculates the scores of all nodes and selects the node with the maximum score as the seed in lines 12-18. Third, starting from the seed node, a greedy procedure is used for adding nodes to or removing nodes from the cluster SG to obtain a subgraph with high graph fitness. The growth process is repeated from different seeds to form multiple, possibly overlapping subgraphs in lines 19-49. Once a new cluster is completed, all nodes in this cluster SG are recorded to prevent them from being used as seed nodes. Then, we select the next seed node from those remaining in the queue SQ to generate the next cluster SG in lines 41-45. Moreover, we discard candidate complexes whose size is less than 3 [The input of the SE-DMTG algorithm is a PPIN, which is described as a simple undirected graph s than 3  and remoIn the first step, we assign a weight to each edge based on common neighbor and gene ontology data (lines 2 ∼11).G are queued into SQ in non-increasing order of Score(v) (line 18).In the second step, SE-DMTG calculates the score of each node (lines 12 ∼17). Furthermore, all the nodes in network Score(v) that has not yet been visited before to bring it up (lines 19 ∼29). The key idea of this step is that any neighbors of the current subgraph SG that make a positive contribution to F(SG) will be added to SG or removed from SG (line 37). The description of iterative generation of a complex is shown in Algorithm 2. Algorithm 2 has two subphases, and we can gradually add neighbors to cluster SG or remove inner nodes from cluster SG. As for the priority of candidate nodes is based on  and twoNext the step-by-step procedure of step 3 is given in Algorithm 2.SG called Neighbor(SG), in lines 4 ∼5. Then, we calculate the graph fitness of SG at line 8. Furthermore, we find the neighbor protein with the highest priority according to weightavg(SG+{p}) in Neighbor(SG), which is added to SG to maximize the value of weightavg(SG+{p}) in lines 7 ∼14. Furthermore, we calculate the fitness of graph SG+{p} in line 15, and Expectation_edges is calculated according to the graph fitness of SG × the size of SG in line 16. Meanwhile, we also calculate the value of Actually_edges which is the size of the interaction set between Neighbor(node_max) and SG, denoted as Neighbor(node_max)∩SG, in line 18. If the node_max with the highest priority is added to increase the value of F(SG) and the Actually_edges is larger than Expectation_edges, then we add node_max to SG and remove it from Neighbor(SG) in lines 19 ∼24. We continually check the next highest priority node in Neighbor(SG) and judge whether the node can be added to the SG in lines 6-25. Otherwise, the iterative addition of the neighbors of SG phase is terminated when one of two conditions is not satisfied in line 19 or when no more remaining neighbor nodes can be added to SG in line 6.In the first phase in lines 3 ∼25, after obtaining a seed protein, we first get an external boundary protein set that consists of the neighbors of SG to maximize the value of F(SG) in lines 26 ∼57. We first find the inner nodes that have edges with nodes that are not in SG, denote as Inner_node(SG) in lines 27 ∼34, and then we test whether each node in Inner_node(SG) can be removed from SG in lines 35-57. We first find the highest priority node according to Eq. (F(SG−{p}) of SG−{p} in line 44. Similarly, we calculate the values of Expectation_edges and Actually_edges in lines 45 ∼47. If the two conditions in line 48 are satisfied, we remove the node from SG and Inner_node(SG) in lines 49 ∼50; otherwise, the second phase is terminated in lines 51 ∼57.In the second phase, SE-DMTG allows the removal of any inner nodes in cluster g to Eq.  in linesNeighbor(SG) to the cluster SG or remove the highest priority node in Inner_node(SG) from the cluster SG to maximize the value of graph fitness F(SG) in lines 2 ∼59. This growth process is repeated until the current cluster SG no longer changes and is a locally optimal subgraph in line 59; then, the detected protein complex is output by Algorithm 1 in line 37.In Algorithm 2, the key idea is to iteratively add the highest priority node in SG by using Algorithm 2 in line 37, and we discard fake protein complexes and complexes whose size is less than 3 [SG in line 40. Meanwhile, SE-DMTG records the nodes in SG in lines 41 ∼45 and selects the next seed node by considering the rest of nodes in seed queue SQ that have not been included in any of the detected complexes found thus far. The next node with the highest score is selected as the seed (lines 31 ∼35). We recursively perform the above key operations in PPIN to identify the remaining candidate protein complexes until no seed nodes remain in seed queue SQ (lines 31-49). Note that when this process is repeated, the nodes in the previously generated protein complex remain in the PPIN; therefore, SE-DMTG is able to generate overlapping complexes.After we obtain a detected complex s than 3  in line Finally, SE-DMTG outputs all identified protein complexes in line 56.


In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes.In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species.We conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species. A protein complex is a bimolecular that contains a number of proteins interacting with each other to perform different cellular functions which is described in many prior works such as the work proposed by Hu at al. in . The ideNetwork alignment provides a possible way to identify protein complexes from multiple PPI networks . Conservx and Net y. When aligning complex  in Net x to Net y, protein x1 and x2 are aligned with y1 and y2. But only maximizing pairwise similarity of proteins might lead x3 to be aligned with y6, which results in disconnected subnetwork in the alignment and doesn’t meet well with the requirement of protein complex.Traditional pairwise network alignment detects functional orthologs of proteins in PPI networks by maximizing the similarity between proteins, while ignoring the subnetwork structure of protein complex. Therefore, the disconnected subnetwork problem might be caused when applying those methods to identify conserved protein complexes. For example, in Fig. x1,x2,x3) in Net x is aligned with  in Net y. When they are expected to be further aligned with the  in Net z from consistent perspective,  might be the best alignment instead if it was a pairwise alignment between Net y and Net z. However, since the goal of multiple network alignment is to find conserved protein complexes across all PPI networks,  should be a better result.Aligning multiple networks promises additional insights into the protein complexes as well as the knowledge-transfer across multiple species. However the alignment of multiple PPI networks has additional challenges. For example, if directly applying the methods of pairwise network alignment to the multiple network alignment, inconsistency problem might be caused. For example, as shown in Fig. We identify the problem of finding conserved protein complex via aligning multiple PPI networks. In this way, the knowledge of protein complexes in well-studied species can be extended to that of many poor-studied species.We propose an efficient method to find conserved protein complexes from multiple PPI networks. In this method, we take the feature of subnetwork connections into consideration, which improves the coverage significantly without compromising the consistency of aligned results.Extensive experiments are conducted on the PPI networks of four species including human, yeast, fruit fly and worm. These results in terms of coverage and consistency illustrate the dominant benefit of the proposed method in finding protein complexes across species.In this paper, we propose a new approach to find conserved protein complexes by network alignment. The main contributions are as follows: Gt= is called target network if the given protein complexes to be aligned belong to Gt, where Vt is the set of proteins and Et is the set of interactions between them.Target network: A PPI network aligned networks.The knowledge such as protein complexes of a target network can be extended to other PPI networks via network alignment. We define the other PPI networks as G={Gi}(1≤i≤ξ) be the set of aligned networks, where ξ is the number of PPI networks to be aligned with target network. Gi=(1≤i≤ξ) is the ith PPI network to be aligned, where Vi, Ei are the sets of proteins and their interactions.Aligned networks: Let Given target network, aligned networks and protein complexes of target network, we define the input of the problem as follows.Input: (1) The set of aligned networks G={Gi,1≤i≤ξ}, where ξ is the number of aligned networks. (2) The set of well studied protein complexes in target network Gt: S={S1,S2,...Sζ}, where ζ is the number of protein complexes to be aligned.Then the alignment result as the output is defined as follows.Output: Without loss of generality, for any protein complex M0, M0∈S, the alignment result is a matchset M={M1,M2,…,Mξ} consists of a set of ξ subnetworks, where Mk⊆Gk, 1≤k≤ξ, Gk∈G, which satisfies: (1) any Mk⊆Gk is a connected subnetwork of Gk; (2) maximizing the similarity score of {M0,M1,M2,…,Mξ}.With the definitions and notations above, our algorithm of finding protein complexes across multiple PPI networks via network alignment mainly follows two procedures: assigning scores to proteins according to both biological and structural features, and then heuristically selecting proteins that form connected subnetwork in each PPI network which finally achieves optimized total score for multiple PPI networks.M0⊆Gt, its match result {M1,M2,…,Mξ} in aligned networks, where Mk⊆Gk, is assigned with a real-valued score Φ: Overall, we utilize both the biological similarity between proteins and the topological structure to assign scores on subnetworks for subsequent heuristic selections of proteins. Formally, given a protein complex of target network ξ is the number of PPI networks, Mk, α is a coefficient to trade off biological and topological scores, δbio and δtopo are the biological and topological scores respectively. In the following, we will describe the details of determining the δbio and δtopo.where Mk⊆Gk is the current subnetwork to be assigned a score, where Gk, 1≤k≤ξ, is the current aligned network. At each time, choose another PPI network denoted as Gh, (h≠k)∧(1≤h≤ξ), then Gt,Gk,Gh construct a group of triple networks. Denote Mh⊆Gh as subnetwork of Gh to align with M0. For every h, we calculate score for the proteins in Gk in the triple networks.Assume M0 is the target subnetwork in target network Net x consisting of , Mk is the subnetwork in aligned network Net y to be assigned scores consisting of . And the subnetwork of  in aligned network Net z is to be aligned with M0.We use Fig. u,v) comes from different PPI networks, and u,v are sequence similar, then  is called a link.Link: If a pair of proteins  comes from three different PPI networks, and there exist links between ,  and  at the same time. Then they form a thread.Thread: If triple proteins  the number of links with the subnetwork Mh, and (3) the number of threads among these three subnetworks which contain the current protein. We denote these three scores as y1 in Fig. y1,x1),  and thread . Therefore, u is: The biological score of a protein consists of: (1) the number of links with the subnetwork  Mh, and  the numbM0, Mh and the number of threads respectively. λ(λ>1) is the parameter of transform.where Gc= is a component of subnetwork Mk if Gc⊆Mk.Component: a connected graph u is: The topological score of a vertex consists of (1) the degree of current vertex; (2) the size of the maximal component that includes the current vertex. As the same with biological score, we adopt a transform techniques by multiplying a coefficient. The topological score of a vertex u’s degree in its subnetwork, and u. ω is a parameter of transform. In our method, ω>1.where Given the multiple PPI networks and target protein complex from the target PPI network, the alignment process is shown in Algorithm 1, which It mainly includes:(1) Generate initial candidate pools.Gi1≤i≤ξ, we construct a pool for a given protein complex M0, where M0∈Gt. All vertices in Gi are put into the pool of Gi if they have links with any vertex in M0, as shown in Line 5 of Algorithm 1. Then, the initial subnetworks M are selected randomly from the pools.Only those proteins that have links with given protein complex can be selected as candidate proteins since links represent the biological similarity between proteins across PPI networks according to Definition (2) Simulated annealing process.M={M1,M2,…,Mξ}.Simulated annealing process adopts iteration method for global optimal solution. In each loop, a protein from the candidate pool is chosen randomly to be determined as aligned protein in the corresponding PPI network (Line 14 of Algorithm 1). On the other hand, there are two kinds of proteins that could be moved out from the current alignment solution (Line 13 of Algorithm 1). The first kind is the protein whose score is the lowest in the current solution: In this section, we evaluate the performance of our method through extensive experiments. We compare our method to LocalAli  since LoReal-world PPI networks of four species are used in our experiments, including Homo sapiens (human), Dorsophila melanogaster (fruit fly), Caenorhabditis elegans (worm) and Saccharomyces cerevisiae (yeast) . The dete−7 as the e-value cutoff, to select the potential homologous proteins across different species. The corresponding Gene Ontology (GO) annotations of the proteins are collected from the Uniprot-GOA database for the alignment evaluations.We also obtained the corresponding sequences of all proteins from manually annotated and reviewed database UniProtKB/Swiss-Prot  for calcAs human and yeast are the two best studied species , we builα=0.5,θ=1.1,K=20,N=100,Tmax=100,λ=4.5,ω=3. The results are compared with LocalAli in terms of coverage and consistence.With most local alignment algorithms that are pairwise, LocalAli  is one oA larger and denser connected component can give more insight of common topology of the network and it could be more biologically significant. The coverage analyzes the numbers of proteins in the aligned subnetworks from each aligned PPI networks with the given motifs in the target network.As shown in Table The calculation of the consistency utilizes the Gene Ontology (GO) annotations associated to each of the proteins, with three basic types of ontologies describing biological properties: biological process (BP), molecular function (MF) and cellular component (CC) . It is aE(M) of a matchset M, i.e. the protein complexes aligned to one protein complex in the target species among all participated PPI networks, with following formulation: The consistency, specifically measured by the mean entropy (ME) and mean normalized entropy (MNE), serves as a specificity metric to measure the quality of alignment. To calculate ME, we first obtain the entropy pi is the fraction of all proteins in the matchset M with the annotation GOi, and d represents the total number of different GO terms in M. Thus the aligned matchset with more consistency will have lower entropy. The ME of the matchset is then calculated by averaging the entropies of all matchsets generated from the alignment to all the protein complexes in the target species, and the lower the ME of the alignment results, the higher consistency a method performs, indicating a better biological quality.where Similar to ME, for the MNE, we first calculate the normalized entropy NE(M) for a matchset as: pi and d have the same interpretation of those in the E(M). The MNE of the alignment results is then computed by calculating the average of the normalized entropy of all matchsets with their size. The lower MNE, the better functional consistency an alignment method achieves.where ME or MNE of our proposed method by that of LocalAli then subtracting one. We can observe that in D1, D4, D5 and D6, our method generates aligned protein complexes with slightly higher ME and MNE than that of the LocalAli, where the ratio of the consistency less to LocalAli range from 0.76 to 6.48%. Meanwhile, we achieve higher ME and MNE than LocalAli in D2 and D3, with 8.12% better consistency at most.The comparison of consistency between the results from LocalAli and our algorithm is shown in Table For PPI network alignment, it is more important to achieve the alignment of functional modules than the alignment of proteins alone. The proposed ConnectedAlign achieves this goal without losing the consistence and coverage. In the future, the genome information could be used for biological network alignment .In this paper, we proposed a novel approach to identify conserved protein complexes across different species. Given target protein complexes in the target network, the proposed method can find conserved protein complexes in multiple aligned PPI networks. Since we take the biological feature and topological feature into consideration, including subnetwork connectivity, our method achieves higher coverage significantly, and keeps stable consistence compared with previous network alignment method. The experimental results demonstrate the significant benefits of our proposed alignment method.


Ex situ assays depend on bindings that occur outside of the normal cellular environment and include yeast two hybrids, pull-downs, and nucleic acid-programmable protein arrays (NAPPA). In situ assays depend on bindings that occur inside of host cells and include affinity purification (AP) and proximity dependent labeling (PDL). Either ex or in situ methods can be reliably used for generating protein–protein interactions networks but it is important to understand and recognize the limitations of the chosen methods when developing an interactomic network. In summary, proteomic methods can be extremely useful for interactomics but it is important to recognize the nature of the method when designing and analyzing an experiment.Viral and pathogen protein complexity is often limited by their relatively small genomes, thus critical functions are often accomplished by complexes of host and pathogen proteins. This requirement makes the study of host–pathogen interactions critical for the understanding of pathogenicity and virology. This review article discusses proteomic methods that offer an opportunity to experimentally identify and analyze the binding partners of a target protein and presents the representative studies performed with these methods. These methods divide into two classes:  The interactions between viral and host proteins are responsible for all aspects of the viral life cycle; from infection of the host cell, to replication of the viral genome, and assembly of new viral particles. The analysis of these protein–protein interactions (PPI) is an emerging field in biology and can elucidate the critical pathways involved in immunity, cellular signaling, replication, and cellular division. In addition, viral–host interactions are a potentially potent target for antiviral treatments, such as the HIV-entry inhibitor maraviroc that inhibits viral entry by binding to the cellular receptor (CCR5) and preventing its binding to the viral glycoprotein (gp120) .Recent advances in molecular biology, mass spectrometry, and bioinformatics have increased the throughput of analysis while simultaneously decreased the false-positive rate of interactomic assay results, with an increased focus on separating the true-positives from the false-positives identified during mass spectrometry. The overall goal of this review is to analyze the most common methods of interactome identification with their benefits and drawbacks. In the end, the readers should be able to design an assay to acquire an interactomic dataset and to screen the dataset for the high confidence results along with the system implications of those results.ex situ binding assays  and in situ binding assays [affinity purification-mass spectrometry (AP-MS) and Proximity-Dependent Labeling (PDL)]. In general, these assays use a protein of interest (POI) as a bait for a pool of possible prey proteins. None of these assays are perfect for every protein nor are they exclusive. In fact, a common method of confirming high confidence results is to repeat the experiment using one of the other methods  ] and genex situ and in situ. Ex situ experiments are those where the interactions occur not in a host cell but instead occur either in a non-host cell or solution, while in situ experiments are those where the interactions occur inside a host cell. Generally, ex situ experiments can be adapted to screen large numbers of bait and prey combinations simultaneously, but the identified interactions can include artifacts due to the forced colocalizations or modified folding of the proteins being analyzed  required for analysis. In contrast, interactions identified in these procedures may be completely artificial and unrelated to the protein’s functional role  while a second protein is constructed as a chimera fused to the activation domain of a transcription factor (prey). Both proteins are then expressed in yeast cells and, if binding occurs, an active transcription factor forms and a reporter gene is expressed  and the second protein is bound to the C-terminal portion (Cub) linked to a reporter protein. When the two proteins bind, the Nub and Cub regions join and form a full ubiquitin moiety that binds to ubiquitin-specific proteases that cleaves the reporter protein from the complex producing a positive result  , Hepatitex situ binding assay. Pull-downs involve the use of purified proteins as the bait and can use either purified proteins or cellular lysate as the prey. One of the most common types of pull-down experiment are GST pull-downs. In a GST pull-down, the bait protein is expressed as a chimera fused to a GST domain in a suitable culture, usually Escherichia coli but can also be Saccharomyces cerevisiae or insect cells. The GST-fusion protein is then purified by the binding of the GST to a glutathione-labeled matrix, often agarose beads. The purified proteins can then be eluted from the matrix by exposure to free glutathione then mixed with the prey proteins prior to binding to a fresh matrix or the matrix-bound bait can be mixed with the prey proteins. The matrix is washed to remove any non-specific interactors and the prey proteins eluted for analysis . A chip-based assay, NAPPA uses in vitro expression of proteins encoded by nucleic acids fused to the chip to generate a protein chip studded with a library of proteins. These bait proteins, which are linked to the chips by covalently bound antibodies, are then exposed to purified prey protein labeled with a fluorescent marker. After washing, a spot of fluorescence will be detectable where the prey protein has bound to the immobilized bait . A Gram-negative pathogenic bacterium, L. pneumophila injects nearly 300 effector proteins into host cells and infections can cause legionnaire’s disease. Interestingly, both proteins showed strong affinity to multiple Rab GTPases, a class of proteins which are known to affect membrane trafficking . A common benefit of AP-MS experiments is their relative ease of use. Often the most difficult portion of the assay is deciding which type of affinity to use for precipitation. In addition, depending on the type of analysis used following the precipitation, the sizes of complexes can be analyzed following the initial precipitation. A shared drawback is that AP-MS experiments often precipitate not only the proteins bound to the POI, but also those bound to the tubes, matrix, pipets, etc. To address this, a control sample is used where the assay is repeated without adding the antibody required to precipitate the POI and compared to the samples containing the antibody. Additionally, the lysis of the samples prior to AP-MS requires the addition of detergents to open the cell membranes and solubilize the protein complexes. Depending on the binding strengths of the proteins, even mild detergents may dissociate binding partners from the POI. In this review we will focus on three of the most common versions of AP-MS: native, GFP-tagged, and tandem.Native AP-MS is dependent upon binding the POI with an antibody. The antibody can be precipitated by exposure to a Protein A-, Protein G-, or Protein A/G-bound to a matrix. Proteins A and G are antibody-binding proteins isolated from bacteria while Protein A/G is a chimeric version of both proteins. All three can be readily purchased covalently linked to agarose or magnetic beads. Prior to purchase, the researchers should confirm which type of bead is best for their antibody; some antibodies bind best to one over the others. After the binding of the antibody-bound proteins to the beads, the non-specific and unbound proteins are removed by washing, then the precipitated proteins are eluted and analyzed by MS .The benefits of this assay include the POI can be precipitated from viral-infected host cells including patient samples. The co-precipitated proteins will include proteins that bind either directly or indirectly to the POI and when combined with LC-MS/MS analysis nearly any host protein can be identified. In addition, the MS analysis can also indicate the presence of many post-translation modifications, including phosphorylation.The drawbacks of this assay include the requirement of strong binding for a protein to co-precipitate with the POI – weak binding or interacting proteins will be lost – and the potentially extensive optimization required to precipitate the POI along with its binding partners. Native AP-MS requires an antibody toward the POI. These may be available from vendors but often require inhouse generation, which takes time and increases cost . AnotherWhile native AP-MS can be difficult and requires screening of antibodies for their efficiency, it has been used to map the interactomes of several viral proteins. Using a mouse monoclonal antibody, researchers identified over 50 host and viral proteins that bind to the herpes simplex 1 virus (HSV-1) protein ICP8, a DNA-binding protein required for viral genome replication . In KapoBecause antibodies toward a POI are often unavailable and would require time and money to generate, a common solution is to generate a protein chimera fused with a tag that contains and well characterized epitope. These tags can be range from short epitopes like FLAG or hemagglutinin (HA) to large molecules like GST. One of the most useful tags is green fluorescent protein (GFP). As a well-studied protein, GFP has several commercially available antibodies that can precipitate it during AP-MS experiments. The overall precipitation procedure is identical to that of native AP-MS; the cells are lysed, an antibody added to the mix to create antibody-bound protein complexes, and those complexes being precipitated by the affinity of the antibodies to Protein A-, G-, or A/G-labeled beads. The main difference between native and GFP-tagged AP-MS is the requirement of expressing a GFP-tagged version of the POI in your target cell. This can be accomplished by transient transfection, stable expression , or genome editing  .The benefits of this assay include the large number of commercially available anti-GFP antibodies, including some that come covalently linked to beads so the use of antibody binding beads can be avoided. The common use of this tag also means that the amount of optimization can be reduced since the buffer conditions to compatible with the binding of GFP to its antibodies is readily available. An added bonus of a GFP-fusion is that the GFP signal can be used to confirm expression of the POI prior to lysis and to map the localization of the POI, which can be used to filter out potential false positives. In addition, the GFP molecule can be expressed unfused to the POI to act as a control sample to identify proteins that bind to the beads, tubes, GFP, or antibodies and not the POI.The drawbacks of this assay include that cloning the POI into a GFP-tagged vector could lead to changes in the fold and function of the POI, which can affect its binding partners. Also, the GFP tag can affect the localization of the POI in cells, which can lead to its binding to new partners. Additionally, the most common way to express the GFP-tagged POI is transient transfection, which can lead to unnatural expression levels of the POI and limits the availability of host cells because not all host cells are compatible to transfection. Lastly, while the GFP-tag is easy to add to a POI in a plasmid using modern cloning techniques, this tag will not be present on the POI in natural samples such as those gathered from patients. This means that GFP tagged AP-MS cannot be used on wild type virus.With the recent improvements in molecular cloning, GFP tagged AP-MS has become one of the most common AP-MS techniques. GFP AP-MS was used to identify the binding partners of the human respiratory virus (HRSV) RNA polymerase complex and found that replication of the viral genome requires the binding of heat shock protein 90 (HSP90) to the polymerase by stabilizing the viral L-protein . In HSV-In most AP-MS experiments a single affinity mechanism is used to link the POI-containing complexes to the matrix. An alternate procedure is two use two affinity mechanisms sequentially to precipitate the POI-containing complexes. Termed tandem AP-MS (TAP-MS), this process begins by cloning a tag containing two binding regions separated by an endoproteinase site  motif). This tag allows for a stage one precipitation using the distal tag followed by cleavage of the POI-containing complexes from the first matrix by adding TEV protease. The released proteins are then precipitated using the proximal tag and the POI-containing complexes eluted from the second matrix .The benefits of this assay include the increased selection of using a two-step precipitation which can limit the amount of false positive results by limiting the accumulation of matrix binding proteins. This benefit can be enhanced by using tags that involve differing mechanisms, such as the binding of antibody to antibody-binding beads followed by the binding of streptavidin to biotin. Similar to a standard tagged AP-MS, TAP-MS requires less optimization since the bindings of the tags to the matrix is well established prior to use. Additionally, the extension of the tag from the POI reduces the chances of the tag being blocked during the precipitation by a binding protein or fold.The drawbacks of this assay include the need for twice the material for two-step purification process than for one-step process. The purification process could also require advanced equipment such as a high-performance liquid chromatograph (HPLC) or specialized spin columns. The addition of the tag to the POI could also affect the fold of the protein leading to artificial localization and binding partners. In addition, because the tag must be cloned onto the POI and the resulting chimera expressed in host cells, the procedure cannot be done on patient samples or wild type virus and the expression levels of the POI can be vastly different from that found in nature.Epstein-Barr Virus-encoded nuclear antigen 5 (EBNA5) is a protein that acts with the EBV protein EBNA2 to activate the expression of the EBV oncogenic protein latent membrane protein 1 (LMP1) and can affect the processing of mRNA. Using TAP-MS, researchers found that EBNA5 binds to several proteins involved in pre-mRNA processing, protein folding, protein degradation, and transcription . In a brA recent addition to the interactomics toolkit is PDL, a process where an enzyme chemically labels a protein(s) in close proximity to the POI. For a PDL assay to be a viable experiment the labeling must not be restricted by any other factor than distance from the POI. The labeling must also be able to survive the precipitation process until the non-specific proteins have been separated from the coprecipitated fraction. In addition, it would be beneficial if the mechanism of the PDL had little or no background labeling to enhance the difference between the non-specifically labeled proteins and the interacting proteins. The first system to fulfill these requirements is the biotinylation based BioID system.Biotinylation is the process of covalently binding a biotin molecule to a lysine by a biotin ligase. While biotin can be linked to lysines by a ligase, it can also be bound by avidin, streptavidin, and NeutrAvidin. This allows a biotinylated protein to be precipitated from a solution by mixing the protein solution with avidin-bound beads or a similar matrix. In addition, the binding of biotin to avidin is one of the strongest non-covalent bonds ever studied, which allows for the use of extremely stringent wash strategies. To use biotinylation in interactomics, mutants of naturally occurring biotin ligases have been generated to produce a promiscuous biotin ligase (termed BioID) that would biotinylate proteins in close proximity of the BioID-fused protein. The biotinylated proteins are then precipitated by binding to an avidin matrix .There are many benefits of this assay. The biotin ligase will label any proteins in close proximity to the POI inside the cell including both strong binding proteins and weak interacting proteins. In addition, the binding affinity of biotin to avidin is one of the strongest in nature and thus allows for extremely stringent washing of the matrix to remove non-specific binding proteins. While the original BioID had a slow reaction rate and required hours of labeling time to generate enough biotinylated proteins for a proper analysis, newly developed versions of the ligase termed TurboID and MiniID offer enhanced biotinylation .It is worth mentioning that several other proximity labeling methods have been successfully developed and used in other types of studies, so they may prove useful for identifying virus–host interactions in the future. These include the ascorbate peroxidase assays (APEX and APEX2) ; and theThe drawbacks of this assay include the possible effects of the cloning of the chimeric fusion with a biotin ligase on the activity and localization of the POI. When designing the chimera, it is also important to consider the positioning of the biotin ligase. This is due to the nature of the labeling – the biotin ligase has a range of 10–15 nm and cannot label across plasma membranes. If the prey protein binds the bait distant from the fused biotin ligase, then the assay can produce a false-negative result. In the case of transmembrane proteins, fusing the ligase to the portion of the protein on one side of the membrane can provide a completely different interactome from the other. In addition, the 10–15 nm labeling range means that not only those proteins that bind to or interact with the POI may be labeled but also those proteins in close proximity to the POI at the time of labeling, leading to false positive results.Using BioID, researchers were able to generate interactomes for all ten polypeptides encoded by the Zika virus (ZIKV). In total, 1224 human proteins were identified as interacting with Zika viral proteins. An analysis of the proteins identified many pathways previously reported as required for viral infection, including translation, protein processing, vesicle trafficking, and lipid metabolism, along with several novel interactors . In EBV,ex situ or in situ binding assays can generate lists of possible binding proteins, it is important to be able to filter the false-positives from the true-positives. While yeast two-hybrid and NAPPA screens are relatively straight forward to analyze, with a simple comparison to a control bait as a useful comparison to identify preys that specifically interact with your POI  and to compare the quality of the identification by the LC-MS/MS , intensity of signal, etc.). After this sorting, the high confidence results were compiled into a searchable database of known non-specific interactors called the contaminant repository for affinity purification (CRAPome) .H. sapiens, S. cerevisiae, and E. coli results based upon the previous results of labs from across the globe. In addition, the software can use a user provided control sample to generate a fold change score (FC) of the biological replicates for each protein in the data set. The program then compares the average FC (FC-A) to the geometric mean FC (FC-B) to generate a graph of with the FC-A score in the Y-axis and the FC-B score on the X-axis. The end result of this analysis is that the weakest candidates are closest to the origin and the strongest are the furthest away.A free to use tool, CRAPome acts as an initial filter of AP-MS and BioID data that can remove the common non-specific interactors found in Once you have removed the common false-positives, it is important to sort the list based upon a set of criteria designed to target the potential high-confidence true-positives. To rank the possible candidates, the results from the LC-MS/MS runs can provide invaluable information beyond the identification of the proteins in the sample. To make better use of this additional data generated by the LC-MS/MS, researchers have generated several software packages that compare the results from controls to samples and then generate a score for each candidate protein. Three of the most commonly used programs are the CompPASS, SAINT, and MiST.To move beyond a simple presence v absence binary evaluation the first piece of data to consider is the number of PSMs or total spectral counts (TSC) for each protein. By generating a table of the identified proteins with the number of PSMs for each, the candidate proteins can be ranked by comparing the TSC of each protein. To accomplish this, researchers developed the CompPASS, a free to use tool that uses a standard (Z) score and a newly developed Normalized Weight D (NWD) score. The Z score is a traditional statistical measurement of deviation in the number of spectral counts per protein in the experimental samples over the whole dataset. The NWD score is an improvement on the Z score because it includes reproducibility and accounts for differences between amount of each unique protein . A benefWhile CRAPome uses a comparison between a control sample and an experimental set and CompPASS uses a comparison of all the proteins found in the experimental set, a combination of both could be a potentially more effective process. The combination of both factors is the basis of the significance analysis of interactome (SAINT). During a SAINT analysis, the TSC of each protein in the experimental and control datasets, along with the TSC of the bait protein found in each dataset, are analyzed using Bayesian statistics to generate a plot of the spectral counts versus the estimated distribution. The relationship between two points is then analyzed to probability of interaction score ranging from 0 to 1. Because the SAINT score is based upon a comparison of the experimental and control datasets, a SAINT score can also be calculated using peptide ion intensity along with other continuous variables . BenefitMass spectrometry interaction statistics (MiST) is a scoring system for interactome results that was designed to target viral–host interactions. The basis of the MiST score is to compare the abundance of the protein in the elution, the reproducibility of the result from run to run, and the specificity of the recovery of the prey based on the bait. While SAINT uses a two-axis comparison to plot the data as the basis of its analysis, MiST uses a three-axis comparison to produce a single MiST score that ranges from 0 to 1 . As a beOnce an interactome is established the next question is what processes or pathways are affected by the binding of the proteins. With the large amounts of PPI identified in the literature, many proteins have known binding partners in at least one or more species. By comparing the genomes or proteins in the database between species it is also possible to generate a network of possible connections for even unstudied proteins. One of the most expansive and potently useful is the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Another useful tool that builds on the STRING suite, while enhancing the coverage of interactions to include not only host PPI but also host-viral and viral-viral PPI, is Viruses.STRING. While both of these programs are very useful tools for analyzing PPI, it is important to remember that databases are only as good as the data entered into them. If the data entered is incomplete or incorrect then the interactions listed will also be flawed. This requires the researchers to always consider the confirmation of novel interactions or loss of a previously reported interaction.p-value, which indicates the confidence that STRING can link your protein and its identified binding partners to an indicated pathway. When used on a pathway known to be involved with your POI, the p-value can indicate the quality of your precipitation. In addition, STRING reports an enrichment analysis of the dataset based on gene orthologies, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and domains ], the co-expression score [uses microarray data along with known interaction pathways (KEGG)], and the experiments score (uses previously reported interactions imported from up to six PPI databases)  change, mutate, or evolve at a slow rate, viral proteins can rapidly mutate and as such orthologous proteins from related viruses may not retain the same interactomes even when comparing between strains of a single viral species (While STRING has been extremely useful in analyzing PPI in a host species, the database does not account for the interspecies interactions found in viral-infected hosts. While innumerable studies have been conducted to map the interactomes of viral proteins, there have been very few suitable databases that compile this data and allow for its analysis. Recently, a new modified version of STRING named Viruses.STRING attempted to address this deficiency. Building on the methodology used to develop the STRING database, Viruses.STRING was built by text-mining the available scientific literature along with incorporation of experimental data from several common public databases prior to scoring based on the KEGG database. Once the pathways were established in one host-viral set, the interactions were then compared to orthologous in related species of both hosts and viruses. The end result is a database containing over 170 thousand PPI from 239 viruses that infect 319 host species. Similar to STRING, Virus.STRING allows the researches to map their proteins into GO-term pathways and provides a FDR  species .ex situ, or those methods where the binding occurs outside of their natural localization, and in situ, or those methods where the binding occurs in their natural localization. Once the results of the assay are compiled, the filtering of the true-positives from the false-positives followed by the ranking of the true-positive results to generate a high confidence protein target list is critical to efficiently study the role of the viral protein in the cell. These high confidence protein targets can then be further analyzed with pathway analysis tools, to generate a systems map of the viral life cycle with important nodes clearly defined. By targeting the critical nodes for the virus, we can develop new avenues of antiviral therapy and patient care.Viral-host PPI are responsible for every aspect of infection and thus the root cause of all viral diseases. To analyze the binding of proteins researchers have developed several methods of interactomic analysis. These methods can be divided into two distinct classes: JG and AN-L conceived the study and reviewed and edited the manuscript. JG drafted the manuscript. AN-L supervised the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana, Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of  For more on the application side, PPIs are also important targets for drug design6 and artificial design of protein complexes7.Identification of protein-protein interactions (PPIs) is important for understanding how proteins work together in a coordinated fashion in a cell to perform cellular functions. PPIs are essential for individual protein functions, forming various cellular pathways, and are also involved in the development of diseases. PPI data is directly useful for identifying protein multimeric complexes8, fluorescence resonance energy transfer9, and surface plasmon resonance10. Ultimately, biophysical methods such as nuclear magnetic resonance spectroscopy (NMR)12, X-ray crystallography13, and electron microscopy14, can be used to determine the tertiary structure of protein complexes to obtain detailed atomic or molecular level information about how the proteins interact. Moreover, from late 1990’s, PPIs have been determined in a large-scale using yeast-two hybrids18 and affinity chromatography combined with mass spectrometry22. However, experimental methods have several shortcomings for detecting PPIs. First, these experimental methods are time-consuming and labor-intensive. Second, the applicability of experimental methods depends on how well assay protocols are established in target organisms. Also, a method may not work on some classes of proteins24. Third, it is known that experimental methods often have difficulty in identifying weak interactions, which leaves out many transient interactions25. Fourth, it is discussed that the results of large-scale methods often have a substantial disagreement with each other, which may be partly due to false positives and false negatives28.There are experimental methods for determining individual PPIs, such as co-immunoprecipitation29. Surprisingly, except for Arabidopsis thaliana, virtually no other plant species have experimentally determined PPI data available. Even for Arabidopsis, known PPI data cover interactions with only about 34.55% of proteins. Other representative plant species cover even less protein involved in known PPIs. Thus, it is apparent that plants are largely lagged behind from PPI studies. In this omics era when various types of large-scale data are combined and used for formulating hypotheses and to interpret experimental data, PPI networks are fundamental reference data to have for studying an organism.Consequently, PPIs have been identified only for a limited number of organisms; moreover, the coverage of PPI networks is very small for the majority of organisms. This is particularly true for plant species. Table 30. These methods typically use a machine learning framework and consider various features of proteins as input. Protein features used for PPI prediction include occurrence of functional domains33, short sequence patterns 38, interlog (interaction inferred from homology)43, codon usage44, function46, similarity in phylogenetic trees49, phylogenetic profiles50, gene expression51, and protein tertiary structures57. Although many approaches were explored, there are not many works that applied developed methods to provide new proteomics-scale PPI predictions. Existing works are mainly limited to microbial genomes and eukaryotes65, and only applications to the plant domain are for Arabidopsis thaliana64 and Oryza sativa (rice)63.To complement experimental methods for identifying PPIs, several computational methods have been developedArabidopsis thaliana, Zea mays (corn), and Glycine max (soybean). To capture different aspects of proteins that are relevant to PPIs, we used a combination of four features for predicting PPIs, i.e. protein sequence properties, protein functional similarity, co-expression patterns, and phylogenetic profile similarity. To provide a confidence level of predictions, two machine learning methods, support vector machine (SVM) and random forest (RF), were separately trained on different features, and commonly predicted PPIs by the two methods were considered to have high confidence. The machine learning methods were trained on known PPIs from Arabidopsis. The accuracy on the testing dataset of Arabidopsis achieved a high accuracy of over 90%. PPIP predicted 50,220, 13,175,414, and 13,527,834 confident PPIs in Arabidopsis, corn, and soybean, respectively.In this work, we developed a computational method for PPI prediction, named PPIP (PPI prediction for Plant genomes) and applied to three major plant proteomes, http://kiharalab.org/PPIP_results/) so that they can be referenced by plant biologists.Examples of predicted novel PPIs with high confidence are discussed. All confident predictions are provided on our lab website  and random forest (RF) on the dataset of known 67. One is to pair proteins from different cellular localizations and thus highly unlikely to interact with each other. The cellular localization information was downloaded from the TAIR database. The dataset with the positive set and the negative set constructed in this way is named as PPIloc. Another one is to randomly pair proteins in the positive set and then exclude pairs that are already in the list of positive interacting pairs. The dataset with the positive set and the negative set constructed in this way is named as PPIrand. Both PPIloc and PPIrand included the equal number of interacting and non-interacting pairs; thus there are 9,518 pairs in total.To train and test a machine learning method, we also need a negative dataset, i.e. a dataset of protein pairs that do not interact. We construct negative sets in two different ways as mentioned by Guoloc or PPIrand, the total number of the dataset for RF has become 6,854. The dataset was were split into a training, a validation, and a testing set, respectively, and a rigorous nested cross-validation evaluation was performed to evaluate the prediction accuracy of PPIP.For training and testing RF, protein pairs that lack co-expression information needed to be removed. This reduced the number of interacting pairs to 3,427. By adding the equal number of non-interacting protein pairs either from PPIloc and SVMrand corresponding to the PPIloc and PPIrand dataset used, respectively. The features used were hydrophobicity, hydrophilicity, side-chain volumes, polarity, polarizability, solvent-accessible surface area, and net charge index (NCI) of side-chains. In parallel, the complementary features of gene co-expression, functional similarity, and the phylogenetic profile68 were used to make another independent prediction by RF. The RF protocol was named as RFloc and RFrand corresponding to refer to the PPIloc and PPIrand dataset used, respectively. Predictions with RF were performed in two settings, one with all the features and the other without gene expression features  because gene expression data is currently not available for corn and soybean. See Methods for more details about the features.PPIP predicts if a pair of proteins is likely to have physical interaction or not in physiological condition from the proteins’ sequence and proteomic features. As illustrated in Fig. loc and PPIrand datasets of known PPIs and non-interacting protein pairs of Arabidopsis, parameters of SVM and RF were trained and tested using six-fold nested cross-validation. In this rigorous validation procedure, the dataset is split into six subsets, and prediction accuracy was measured on each of the subsets using parameters optimized on the rest of the five subsets. See Methods and Supplementary Tables On the PPIloc and PPIrand datasets were 91.9% and 70.8%, respectively  and the mean decrease of Gini importance (MDGI) (see Methods and Supplementary Note)50. Since SVM used in the PPIP pipeline perform binary classifications and do not provide probability values that are comparable to the STRING scores, we used SVM regression for this comparison. Two types of SVM regression models were used, SVM-70. SVM-loc and SVMrand. As for RF, we used the fraction of decision trees in RF that vote for protein-protein interaction as the probability.We compared our prediction with the confidence score of protein-protein interactions provided in the STRING databaseloc dataset, the prediction by SVMloc-ε was consistent with SVMloc on 99.2% (9445 among 9518) of the cases while SVMloc-ν was consistent with SVMloc on 97.8% of the cases (9312 among 9518). On the PPIrand dataset, SVMrand-ε’s results were consistent with SVMrand on 73.9% (7032 among 9518) of the cases while SVMrand-ν’s results agreed with SVMrand on 73.4% (6987 among 9518) of the proteins.First, we checked how consistent the predictions by SVM-ε and SVM-ν were with the SVM models in PPIP if a probability value of 0.5 in the SVM regressions was used to convert a probability value of SVM-ε and SVM-ν to binary prediction. On the PPIloc and PPIrand, were used, and the comparison was made using Area Under the Curve (AUC) of Receiver operating characteristic (ROC)  than STRING. RF showed the same AUC value as STRING (0.88). On PPIrand, RFrand performed the best with AUC of 0.98 and STRING came the second (AUC: 0.86)  Fig. . On the set Fig. , two SVM86) Fig. . Thus, oloc and RFrand in the PPIP pipeline , which remained 1,048,575 pairs in the dataset. RFrand recorded a small false positive rate of 4.36%. Finally, as designed in the PPIP pipeline, we took the overlap between SVMloc and RFrand, which yielded a very small false positive rate of 2.68%. The results confirm that the design of PPIP was effective in reducing false positives and that PPIP is suitable for a genome-scale prediction.We have further tested the performance of SVMArabidopsis dataset, which was obtained from the BioGRID database29. BioGRID contained PPI data that were not included in the TAIR database, partly because it was updated more recently. In total, 3,280 Arabidopsis physical PPIs which have been verified by at least two experiments and not included in the TAIR-based dataset were found in BioGRID. The data were further pruned by the sequence identity cutoffs, 80%, 50%, and 30% (Table loc and PPIrand) were excluded. The sequence identity is a measurement of the protein sequence similarity from BLAST. Prediction by RF was applied for a smaller fraction of PPIs, only to those which have co-expression information. The data sets used by SVM and RF with different sequence identify cutoffs are provided in Supplementary Table We further tested the prediction ability of our prediction method PPIP on a different loc and 0.5606 for SVMrand, which can be computed as the fraction of the sum of “True Positives” from the 1–6 test sets among the total of positives, i.e. the sum of True Positives” and “False Negatives” in Supplementary Table 8loc, 0.7610 for RF4loc, 0.8984 for RF8rand and 0.9050 for RF4rand from Supplementary Table The recall values of SVM were somewhat lower than what was observed on the TAIR-based dataset , and Glycine max (soybean). The genome sequences of the three plants were downloaded from the UniProt database71. Since the number of all possible protein pairs in the whole genome is too large, we applied PPIP only for protein pairs that are likely to co-locate in a cell, having a sufficient similarity in their Cellular Component (CC) Gene Ontology (GO) category terms72, which describe the sub-cellular locations of proteins. Since many protein genes in corn and soybean do not have GO term annotations in UniProt, we used a function prediction method, PFP75 to predict GO terms to supplement annotations to proteins. PFP is one of the top performing function prediction methods, which performs better than conventional methods, e.g. BLAST76, as was also demonstrated in a community-wide function annotation assessment, CAFA78. From PFP, only high confidence GO predictions with a score of over 10,000 were used73. The similarity of CC terms of two proteins was evaluated by the FunSim score, which essentially is the average pairwise similarity of CC GO terms80. Protein pairs with a FunSim score of CC terms over 0.4 were subject to the prediction with PPIP. This cutoff was determined from the distribution of the CC-FunSim score of predicted Arabidopsis PPIs in three previous papers that made predictions based on the assumption that PPIs are conserved across species64  seems relatively small, but this fraction is consistent in Table http://kiharalab.org/PPIP_results/).The numbers of predicted PPIs in the three genomes by PPIP are summarized in Table p(k) ~ k−γ where k is the fraction of proteins with a certain number of interactions, and γ is a parameter called the degree exponent, which determines the slope of the distribution82. Figure γ being 1.362 in Arabidopsis, 0.204 in corn, and 0.401 in soybean. Smaller degree exponents for corn and soybean indicate that these two plants have more hub proteins that interact with many proteins.It is known that the degree distribution of a PPI network of an organism follows a power-law distribution, i.e. the histogram of the number of interactions  for each protein is well approximated with a power-law, Arabidopsis with three existing genome-scale prediction results. These three works used a very different approach for prediction, the interlog concept39, which assumes that interactions of orthologous proteins across different species are conserved. Geisler-Lee et al.61 and De Bodt et al.42 used the same reference organisms, S. cerevisiae, C. elegans, D. melanogaster, and H. sapiens, whereas Dutkowski et al.64 used the same four organisms with two more organisms, M. musculus, and R. norvegicus , here we discuss examples with three different levels of confidence. Supplementary Table Arabidopsis. All the listed PPIs in the tables were predicted consistently by the SVMloc and RFrand and the probability score of RFrand was over 0.95. The difference of the confidence levels is based on the availability of additional supporting data.From the predicted PPIs for the three plant genomes  the two proteins are known to locate in the same pathway in the KEGG database83, (b) the two proteins are co-mentioned in a literature. The predicted PPIs with correlated elution profile in PPI detection using mass spectrometry (MS)85 are also included in Supplementary Table et al.85, proteins in Arabidopsis were fractionated using size exclusion chromatography, and abundance profiles across the column fractions were quantified using label-free precursor ion (MS1) intensity. Proteins with correlated profiles and clustered together among all detected proteins were more likely to form complexes . Supplementary Table rand.The predictions are separated into three classes for each genome according to the availability of other evidence that supports the predictions. Supplementary Table Arabidopsis PPIs with literature that describes evidence of their interaction. This list selected the most confident prediction in the Arabidopsis. Most of the interacting proteins are ribosomal proteins. Besides ribosomal proteins, several pairs of Sm-like proteins are predicted to interact, which are known as subunits of the heteroheptameric complexes and function in mRNA splicing and degradation87. Another predicted PPI is plastid division protein PDV2 (AT2G16070) and protein accumulation and replication of chloroplasts 6 ARC6 (AT5G42480), which has been shown to interact in the intermembrane space to coordinate the division machinery of chloroplast membrane88. Our predicted PPIs also included some subunits of known complexes such as coatomer, RNA polymerase, DNA directed RNA polymerase, augmin, and adaptor complex 1 (AP-1).The first half of Supplementary Table 85. For example, V-type proton ATPase subunit C (AT1G12840) and V-type proton ATPase subunit G2 (AT4G23710) are predicted to interact by RF and SVM. Additionally, they have highly correlated protein elution profile and are involved in the two common KEGG pathway including oxidative phosphorylation (ath00190), and phagosome (ath04145).The latter half of the table provides PPIs with similar MS elution profilesKNOX gene expression in Arabidopsis89. Another interesting example is protection of telomeres protein 1a POT1a (AT2G05210) and CST complex subunit TEN1 (AT1G56260). It is known that CTC1, STN1, and TEN1 consist of telomere complex and POT1a interplay with CST components to regulate the telomerase enzyme activity90.Supplementary Table Arabidopsis, Supplementary Table 91.The last table for loc and RFrand with a high RF probability score of 0.9 or higher. As in the tables for Arabidopsis, Supplementary Table Predictions for corn are shown in Supplementary Tables In Supplementary Table Arabidopsis protein pairs), these scores also support the PPI predictions.Supplementary Table 92, 224 of them (99.1%) are clustered into four subnetworks . We hope they are used as a reference and found informative.It is inevitable that a computational method often makes wrong PPI predictions. However, as discussed in the introduction, the situation would be similar in experimental methods, as it has been reported that independent experiments have substantial disagreementsres Fig. . This arBelow we describe details of features and the machine learning methods used to predict PPIs.108: hydrophobicity, hydrophilicity, side-chain volumes, polarity, polarizability, solvent-accessible surface area, and net charge index (NCI) of side-chains. Then each query protein sequence is represented with auto-covariance (AC) using strings of the seven features as follows:lag is the distance between covariant residues to consider, which ranges from 1 to 30, j is the j-th physiochemical feature, i is the position in the sequence, Pi,j is the value of the physicochemical feature j of amino acid position i, and L is the length of the sequence. Thus, AC of a physicochemical feature with a certain lag length will be large if amino acid with a large  property value appears periodically with an interval of lag. AC is computed for each protein sequence, and thus a query protein pair is represented as a 2 (sequences) * 7 (features) * 30  = 420-dimensional vector. The vector representation was used as input of SVM for predicting if protein pairs are interacting or not interacting. We took this approach because it was reported to be successful in a previous paper67.We explain protein sequence features used in PPIP. It is the left branch of the flowchart in Fig. On the other branch of PPIP Fig. , we usedhttp://atted.jp/)109. It is a database of pre-calculated Pearson’s correlation coefficients (PCC) and the mutual rank (MR) of co-expressed genes. MR is defined as the geometric mean of the rank of the correlation gene A to gene B among proteins in the genome and the rank of gene B to gene A. The smaller MR is, the stronger the genes are co-expressed. Since gene expression data are provided in two sources, microarray and RNA-seq, four features  were used to represent the co-expression profile of protein pairs.If two proteins are upregulated or down-regulated simultaneously under various conditions, it is highly likely that the two proteins are involved in the same pathway and have a higher chance that they physically interact with each other. Thus, co-expression patterns can provide indirect evidence for predicting PPIs. We obtained gene coexpression information derived from microarray experiments and RNA-seq experiments from the ATTED-II database (72. GO annotations were obtained from UniProt71 and TAIR (for Arabidopsis). We used three GO similarity/relevance scores, Interaction association score (IAS)45, Co-Occurrence Association Score (CAS), and PubMed Association Score (PAS)110. IAS, CAS, and PAS quantify how significantly a pair of GO terms appear in physically interacting proteins, annotations of individual genes, and PubMed abstracts. Thus, they evaluate co-occurrence of GO terms in biological contexts and shown to be effective in identifying proteins that physically interact)45 or in the same pathways111.The second feature used is protein functional similarity. Proteins with the same or similar biological functions are likely to physically interact because they may form permanent complexes or take part in the same pathway. Functional similarity of proteins was quantified by established similarity scores of Gene Ontology (GO) terms112. We use the phylogenetic profile to exploit the evolutionary co-occurrence patterns of interacting proteins. The basic assumption of the phylogenetic profile method is that interacting proteins either co-present or co-absent across organisms68. The original phylogenetic profile68 is a binary pattern of presence or absence of homologs in a set of reference genomes, but we used a modified version of the profile that used BLAST bit score instead of binary values as follows113:i and j are defined in Eq. k is the k-th reference genome, i in the k-th genome divided by the BLAST search bit score of i in the query genome (Eq. n = 100)  and the mean decrease of Gini importance (MDGI) Supplementary InformationSupplementary Table S1Supplementary Table S8Supplementary Table S12, S13, S14Supplementary Table S15, S16, S17Supplementary Table S19, S20, S21


The importance of understanding interactomes makes preeminent the study of protein interactions and protein complexes. Traditionally, protein interactions have been elucidated by experimental methods or, with lower impact, by simulation with protein docking algorithms. This article describes features and applications of the BiGGER docking algorithm, which stands at the interface of these two approaches. BiGGER is a user-friendly docking algorithm that was specifically designed to incorporate experimental data at different stages of the simulation, to either guide the search for correct structures or help evaluate the results, in order to combine the reliability of hard data with the convenience of simulations. Herein, the applications of BiGGER are described by illustrative applications divided in three Case Studies: (Case Study A) in which no specific contact data is available; (Case Study B) when different experimental data  on one of the partners is available; and (Case Study C) when experimental data are available for both interacting surfaces, which are used during the search and/or evaluation stage of the docking. This algorithm has been extensively used, evidencing its usefulness in a wide range of different biological research fields. Protein-protein recognition processes are crucial for a wide range of biological functions, including gene transcription and translation, cell growth, cell differentiation, immune response, and neurotransmission. Recent developments in array technology and high-throughput yeast two-hybrid screens led to an increase in the list of known protein-protein associations ,2,3,4 anMoreover, the obvious importance of protein-protein interactions motivates the design of drugs that can specifically inhibit interactions between relevant proteins ,7, a tasd: nM–fM), intermediate (Kd: μM–nM) or weak (Kd: mM–μM). Complexes with high and intermediate affinities have long-lifetimes and can be considered static complexes. Examples of these include protease-inhibitor, antigen-antibody, T-cell receptor-peptide-MHC and signal transduction complexes  centers are present within a single polypeptide chain and no flavin component has been detected . Howeverhydrogen  (an examhydrogen .Desulfovibrio (D.) gigas AOR (PDB ID 1HLR)  center in a configuration that resembles the structural arrangement of XO and the homologous [Fe-S] in the single polypeptide chain C,D. MoreWe believe this is a good example for the versatile solutions found by Nature in the construction of complex electron transfer chains and another exploratory method for the use of the BiGGER algorithm in situations where no constraints can be imposed a priori.2OR). This enzyme catalyzes the last step of the denitrification pathway, the reduction of nitrous oxide to molecular dinitrogen, which provides an electron sink for some bacterial species to grow under anaerobic conditions  cluster-containing proteins. BiGGER was used in this case as a validation and prediction tool in order to characterize the most likely complex geometry according to NMR-derived experimental data. Thus, upon acquisition and analysis of 2D NMR [1H-15N HSQC] titration spectra, it was possible to identify the regions involved in the interaction by chemical shift perturbation mapping. Residues near the metal center of Zn-substituted rubredoxin  were found to suffer the largest perturbations . One other interesting case where both algorithms were used can be found in [Some authors have used in their work both BiGGER and HADDOCK. Although their mode of operation is slightly different, they have found that the algorithms calculated very similar results from the same initial data ,103. In found in , whereinThe results available, especially from test cases in real applications, show that BiGGER can provide useful interaction information by integrating a variety of experimental data from different sources, either during the search or the evaluation stages after running the algorithm. The evaluation and validation of the structural model of the protein complex is the most important stage, and it should be performed with care, using all the experimental data available. To this end, BiGGER allows the use of different types of filters to restrain the solutions, such as the properties of the encounter complex , identification of important residues in complex formation and the distance between redox centers (if present).The case studies presented included examples with limited experimental data or with experimental data on one or both interacting surfaces to show how BIGGER can be used to integrate any available information at different stages of the docking simulations, either as constraints that can guide the predictions or as scoring functions to validate the candidate models. Even when no experimental information is available (Case study A), the docking program can help design mutants or develop other strategies to better understand the protein complex by providing well defined hypotheses regarding the domains involved at the interface.BiGGER has also an important role in the validation and corroboration of experimental evidence regarding a protein-protein interaction (Case studies B and C). In this case, the algorithm prediction helps make use of the experimental data by finding the structures that are consistent with the information available. In addition, we would like to point out that the final model complex(es) can be energy minimized, which can be performed using software such as UCSF Chimera , in ordeThese results give an overview of the protein-protein algorithm BIGGER, which has been extensively used by biochemical and biological community since the year 2000, as shown by the different case studies that illustrate the potentiality of the algorithm in both prediction and validation of a protein-protein complex. Moreover, the case studies give detailed examples of the procedure for protein docking and the analysis of the results, providing a helpful guide for non-specialist users.