Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions. Study of the entire set of complexes from the data describing the physical interactions among proteins, is thus essential to understand the complex formations, and the higher level cellular organization2.Many cellular activities are carried out by proteins which physically interact with each other to form stoichiometrically stable complexes. These complexes interact with individual proteins or other complexes to form functional modules. The functional modules are observed to work in a coherent fashion even in the relatively simple model organisms like Saccharomyces cerevisiae3. From a biological perspective, this modularity is a division resulted of evolution to provide robustness against mutation and chemical attacks4. From a topological perspective, this modularity is a result of proteins within complexes being densely connected to each other than to the rest of the network6. Thus the computational approaches to protein complex identification from PPI data can be formulated as dense subgraph detection from PPI networks.New technological advancement in biotechnology have resulted in large-scale physical protein-protein interaction (PPI) data for different organisms which is used to reconstruct the PPI network. The PPI network can be described as an undirected graph where a node represents a protein and an edge represents the interaction between two proteins. Protein complexes constitute modular units within the PPI networks10 suffers from issues like resolution limit. Local search based approaches, employing merging or growing of clusters have proved to be more efficient. A popular local search based method, Molecular Complex Detection (MCODE) algorithm, proposed in ref. Since last decade several computational methods have been developed to infer the protein complexes where a protein complex is defined as a dense subgraph. A large class of existing literature for protein complex identification is based on network motif identification like clique finding approaches in refs 13, multi-objective based framework15, PPSampler216 and PEWCC17. Affinity Propagation13 groups data points based on their similarity. All data points are initially considered as potential “exemplars”. The algorithm first finds sub-paths that allow easy message exchanges between nodes. In subsequent steps, message exchange between the nodes are continued until a high quality cluster is formed. The multi-objective based framework14 optimizes two objective functions, one is based on density of a module and another on the Gene Ontology based semantic similarity among proteins, resulting in dense and functionally homogeneous protein complexes. PPSampler216 is a modification of its previous version PPSampler18 which partitions proteins into clusters based on a scoring function generated from the topological properties of the PPI network. A novel algorithm called PEWCC is proposed in Zaki et al., where reliability of the protein interaction data is assessed before partitioning the interaction network. PEWCC performs weighted clustering to partition the refined interaction networks into maximal cliques which serve as protein complexes.Other popular and recent developed methods for protein complex identification includes Affinity Propagationet al.19, reviews game theoretical concepts in cell biology and molecular biology focusing on the subcellular level by considering viruses, genes, and molecules as players. The existing works mostly uses two player simultaneous game for protein complex identification.A common limitation of the above mentioned approaches is that they ignore the self-organizing nature of the protein complexes, primarily triggered by the biochemical factors including hydrophilic energy, electrostatic forces between the proteins. Game theory, a natural choice of framework to model the self-organizing nature, could focus on the underlying factors that drive the protein complex formation. Bohl In this paper, we model the problem of protein complex detection as a multi-player sequential move non-cooperative game that models the proteins as rational players. The underlying hypothesis for the model are: (i) proteins always act in selfish manner: even when the proteins forms complexes it only optimizes its own objective(s) (ii) protein complexes are dense substructures of low diameters: most of the real life networks tend to demonstrate small world property, and (iii) there might exist some proteins which do not belong to any complex: this could be due to the physio-chemical properties. We formulate a Partial Dense vertex Cover (PDC) game for protein complex identification. The strategy set of a player is determined by a preference relation which is a transitive, reflexive function over the set of all possible vertex covers. The Nash equilibrium of the game is the minimum partial dense vertex cover. We propose an algorithm, NashPDC, to find the Nash equilibrium of the PDC game. The resulting partition generated by NashPDC represents the protein complexes.2, MCODE11, RNSC12, MCL20, PPSampler216 and PEWCC17. The biological relevance of the identified complexes are assessed by gene ontology and pathway based analysis. The experiment uses synthetic benchmark network and real life yeast PPI network for evaluation. The experimental results show a significant improvement achieved by the proposed approach over the other methods.A thorough experiment is carried out by comparing the performance of the proposed method with that of the other well-known methods such as clusterONEG =  where a node v ∈ V represents a protein and an edge e ∈ E represents the interaction between two proteins. In this section, we discuss the proposed method which is based on the following basic concepts of graph theory and non-cooperative game theory.We represent a PPI network using an undirected graph G = , G(S) denote the subgraph induced by S on G where S ⊆ V. The set of edges of G(S) is denoted by E(G(S)). For any vertex v ∈ V, the pth order open neighborhood, Np(v), is the set of vertices connected to v by a path of length less than or equal to p. The closed pth order neighborhood of vertex v is Np[v] = Np(v) ∪ {v}. The degree of vertex v ∈ V, dv = |N1(v)|. The degree density, α(G), is defined as the ratio of the minimum degree of G to the maximum possible degree, i.e. δ(G), is defined as the ratio of the number of edges in G to the total number of possible edges, i.e. R(G), also termed as the minimum eccentricity of G is defined as the maximum shortest path between any two pairs of nodes, i.e. R(G) = maxu,v∈V|SP| where SP denotes the shortest path between u, v. The local transitivity of a vertex v, denoted by t(v) is defined as the ratio of the triangles connected to the vertex and the triples centered on the vertex, i.e. C1, C2, …, CK} (K is a positive integer and K ≤ |V|) such that – (i) ∀ k ∈ {1, …, K}, Ck ≠ ∅, (ii) k, l ∈ {1, …, K} with k ≠ l, Ck ∩ Cl = ∅.Given an unweighted graph N rational players. In strategic form game a player’s decision problem is to choose a strategy that will counter best the strategies adopted by the other players. Each player is faced with this problem and the players can be thought of as simultaneously choosing their strategies from the respective strategy sets. A strategic form game is modeled by a three tuple N is a finite set of rational playersSi denote the strategies or actions of player i while S = xi∈NSi is called the set of action profiles (or strategy profiles)a, b either b is preferable to a. The preference relation may also be defined based on the outcomes of the strategies.In non-cooperative game theory, strategic form game models the interaction between a finite set of i ∈ N let si− ∈ Si− where Si− denote the action profile of all the players in N\i. The best responses of player i given the actions of other players, si−, is defined asIt is assumed that in the strategic form game the set of strategies and the players’ preference relations are known to all the players. The only uncertainty concerns the actions chosen by the players. For i has a profitable deviation from s*.An action profile The proposed framework is based on the concept of partial dense vertex cover, defined as below.Definition 1. Given an unweighted graph G = , a partial dense vertex cover C(G) is a collection of subsets of V, {C1, … Ck} with the properties:i ∈ {1, … k}, ∅ ≠ Ci ⊆ V∀i, j ∈ {1, …, k} with i ≠ j, Ci ∩ Cj = ∅∀C1 ∪ … ∪ Ck ⊆ Vi ∈ {1, … k}, R(G(Ci)) ≤ p∀i ∈ {1, … k}, α(G(Ci)) ≥ λ and δ(G(Ci)) ≥ γ∀i ∈ {1, … k}, Ciis a locally maximal cover.∀the residual graphdoes not contain any induced subgraph satisfying the other conditions.Here 0 < λ ≤ γ ≤ 1 and p is a positive integer.k. In this paper, the terms cover and coalition have been used interchangeably.A minimum partial dense vertex cover is a partial dense vertex cover with the minimum value of i from the set of rational players N represents a node i ∈ V in G.Each player Si of a player i ∈ N is to – (i) propose a coalition, by sending out joining requests (ii) accept a joining request (iii) reject a joining request and (iv) leave a coalition. Once a player accepts and joins a coalition he is not allowed to propose a new coalition. A player if rejects a coalition joining request or leaves a coalition can subsequently propose a new coalition. A proposer, if do not receive satisfactory response may choose to leave the coalition.The strategy set i over the set of possible partitions. The preference relation determines the quality of the partial dense vertex cover. A player prefers to belong to a minimum partial dense vertex cover or to stay alone than to belong to any random partition.The proposed method models the protein-complex detection method as a partial dense vertex cover (PDC) strategic form game, p* is in Nash equilibrium, if for all other partitions p and for every player i, N\i plays their best response strategy. In other words, a minimum partial dense vertex cover is a Nash equilibrium of PDC game and vice versa. There may exist multiple Nash equilibriums. We eliminate the two trivial partitions i) where a single cover or a grand coalition forms and ii) where all the players form singleton coalitions.Given the preference relation The following example illustrates the above framework.Example 1. Figure shows a graph G with 12 nodes. We induce the PDC game on the graph G so that every node of G is mapped to a player in PDC. For λ = 0.6, γ = 0.65 and p = 2 the preference relation of the players are given as below.The partition {{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}} is in Nash equilibrium, as no player can gain from unilateral deviation.The players are ranked based on their geometric mean of degree and local transitivity (in descending order). This forms the rule of order. The first player in the rule of order is the one with the maximum links and maximum local transitivity. This is one of the ways to prioritize proteins. However, other approaches may be used.i of rule of order proposes a coalition C which is the pth order bounded neighborhood of i.The first player j in C accepts the coalition joining request only if that is his best response strategy, otherwise rejects the invitation. Once all the players either accept or reject the coalition joining request, a stable coalition C forms. Subsequently, all the players in C quit the game.Each player The game play is continued by repeating the above steps with the players who do not belong to any coalition until no more stable coalition can be formed.We propose an algorithm, Nash equilibrium based Partial Dense vertex Cover Detection (NashPDC) to find the Nash equilibrium of the game PDC. The basic working principal of the Algorithm 1 is described below.O(logN) time. For each proposed coalition at most dmax is the maximum degree in G. Thus the worst case complexity of the algorithm 1 is p = 2 the complexity reduces to At the worst case, every node sends a coalition request. Selecting a player from the rule of order takes In this section, we evaluate the performance of our proposed method with that of some state-of-the art techniques. The evaluation is conducted on both synthetic benchmark and real life yeast PPI network data sets.21. GN benchmark is a special case of the planted 4-partition model where a graph with 128 vertices are partitioned into 4 groups (modules) with 32 vertices each. The inter-module edges of GN model is controlled by the mixing parameter μ. In other words, the mixing parameter μ controls how well the modules are defined by controlling the number of edges with which a module is connected to the rest of the graph. When the value of μ is strictly less than 0.5, the expected number of links joining a node to others in different groups is less than those in the same group. This yields well defined groups (modules). As the value of μ drops to 0.5 or below, it becomes difficult to identify a module boundary from the rest of the network. Irrespective of the size and sparsity of the networks, GN benchmark dataset is a popular choice to test the correctness of different module detection algorithms and compare their performances. By varying the value of μ we generate networks with well defined modules as well as the networks with modules that are not so clearly distinguishable from the rest of the network. The later is a case similar to real life scenarios where all modules are not necessarily well defined. The code to generate the GN networks are freely available at the site https://sites.google.com/site/santofortunato/inthepress2.To test the correctness of the proposed method we first use the synthetic benchmark networks generated using the popular Girvan Newman (GN) modelet al.22, Krogan et al.23), yeast PPI interaction derived from DIP24 and a manually curated high-quality yeast dataset from MIPS25. The Krogan dataset has two variants, namely, the core data set which is referred to as Krogan core and the extended data set which is referred to as Krogan extended. The key topological properties of the PPI networks built from these dataset are given in Table http://yeast-complexes.russelllab.org/. It consists of 491 experimentally verified yeast protein complexes. We consider this as benchmark and compute the extent of overlap with the resulting clusters.Further we use real life yeast PPI networks - two experimental yeast PPI datasets . NMI, defined as below, measures the similarity between two partitions based on entropy28. The nodes being randomly connectedly, the E-R network do not exhibit any modular structure. A good dense cluster detection algorithm should not identify any significant structure from E-R network. We run the proposed method on the E-R graphs. The proposed method do not identify any significant module. The experiment shows that the proposed method works correctly on null model.To verify the performance of the algorithm on the null model, we test our performance on random Erdös-Réyni (E-R) graphsρ). The initial graphs are generated using GN model. We measure the performance by computing the NMI between the known partition of the initial graph and the partition obtained from the perturbed graph for various values of ρ.Presence of noise is a very common phenomenon when dealing with complex real life data sets. Noisy data occur due to many reasons including but not limited to erroneous measurements, sampling bias. The study of stability of a method against noise is thus essential. We study the stability of the proposed method when the endpoints of the edges of a graph is rewired to a random vertex with given rewiring probability  represents degree of node ni within the module M, N represents total number of connections. It simply denotes the ratio of total degrees of nodes within the module to the total number of connections. The experiment studies the relative change in the connectivity density when an identified protein complex is shifted a little. A shifted (replaced) module is obtained by randomly replacing a small portion of the proteins in a module with the same number of proteins outside of that module in such a way that the replacement proteins are connected with the proteins in the module but do not belong to it. For an identified protein module, a little shift is expected to decrease the connectivity density, which should not be expected from a random module. In addition, shift in random modules may results either increase or decrease in connectivity density. The experiment is carried out on the real life yeast PPI networks.To validate the identified modules from topological perspective, we use the connectivity density measureFigure To know whether the obtained results is trivial and can be easily expected for any random modules we have performed an analysis. For this, we have repeated the same experiment for randomly selected modules from the PPI network. Figure D), (ii) sensitivity(Sn), positive predictive value(PPV), and accuracy(Acc)30.In this experiment the performance of the proposed method is compared with the base line methods by using metrics that measure the correspondence between the predicted complexes and the reference complexes. The metrics used for evaluation are: (i) Jaccard index based performance metric, matching score of a clustering result is defined as:Nj is the number of proteins belonging to complex j and Snj is the reference complex-wise sensitivity for reference complex j defined as The metrics PPV) of a clustering result is defined as:i in the contingency table T and PPVi is the predicted complex-wise positive predictive value for predicted complex i defined as The General PPV represents a trade-off between sensitivity and the positive predictive value and is defined as:Since Sn or the PPV metric is low. High accuracy value thus indicates a high performance in terms of both Sn and PPV.The advantage of taking the geometric mean is that it yields a low score when either the D. As shown in the table, unlike other methods, the proposed method performs consistently well on all the data sets. The proposed method outperforms MCODE and MCL in most of the data sets. ClusterONE method attains higher D value for DIP, GAVIN and Krogan_Cr data sets while the same attains lower D value for other data sets. On the contrary, RNSC method attains higher D value for MIPS and Krogan_Ex data sets while the same attains lower D value for other data sets.The results of evaluation are shown in Tables Sn, PPV, Acc. It can be noticed from the table that the proposed method performs consistently well in each of the datasets. In terms of Sn the proposed method outperforms MCODE and clusterONE in all the data sets. However, RNSC and MCL exhibit higher accuracy (Acc) than the proposed method on MIPS and KROGAN-extended data sets. It is also important to note that only the proposed method consistently gives good Sn value on all the data sets. As shown in the result no other method attain consistently high Sn on all the data sets.Table We further analyze the the complexes identified by the proposed method on Gavin dataset, and compare the same with that identified by clusterONE method as clusterONE attains second highest accuracy score after the proposed method. Among the experimentally verified benchmark complexes in Gavin data set, 10.62% complexes (52 complexes out of 490 complexes) are captured with more than 80% coverage by the proposed method. On the contrary clusterONE covers 8.16% benchmark complexes (40 out of 490) with more than 80% coverage. Figure 31, which consists 6,000 proteins and 50,000 PPIs. We run the proposed method on this data and compare the predicted clusters with the experimentally verified complexes. The results consists of 17 predicted clusters with minimum and maximum size eight and 39, respectively. The resulting sensitivity (Sn = 0.2384), positive predictive value (PPV = 0.4619) and accuracy (Acc = 0.3318) reveals that proposed method performed well in large PPI data.To further know the performance of the proposed method in large PPI data, we have utilized WI-PHI PPI database of yeastα (degree density) and δ(edge density) to lower values  and run the algorithm. We collected the resulting low dense clusters and compare these with benchmark protein complexes having lower density. Figure To investigate whether the proposed method can detect sparse, low dense protein complexes we set the input parameter 33. For understanding the biological roles and functions of proteins, functional similarity is a more informative measure compared to the structural and sequence similarity. The semantic similarity between Gene ontology (GO) terms is used to measure the functional similarity between proteins34. Here we use Relevance measure proposed in ref. It has been observed that proteins within a complex are functionally similarx, while the right pane shows the distribution of similarity score. As shown in the figure for Gavin data set more than 75% modules identified by the proposed method have semantic similarity score higher than 0.5 while for the MCODE, MCL, clusterONE and RNSC it is 53.66%, 39.02%, 61.34% and 48.78%. Similarly, as shown in Fig. Figure https://david.ncifcrf.gov/) to perform the gene enrichment analysis. Here, the obtained p-values are calculated by using Fisher’s exact test. The obtained p-values are also subsequently adjusted for multiple comparisons using a Bonferroni correction based on the number of genes in the modules. The p-value of a gene module signifies the probability of observing at least x number of genes out of total n genes in the module annotated to a particular GO terms, given that the proportion of genes in the whole genome are annotated with that GO terms. So a p-value of a module closer to zero signifies that it is less likely to observe the annotation of a particular GO term to a group of genes occurs by chance. For comparison purpose we have computed the p-values of all the protein complexes identified by MCODE, MCL, clusterONE, RNSC and PPSampler2 algorithm. The distribution of p-values is given in Fig. x while the right pane shows the distribution of p-values. As shown in the figure for any given p-value, the fraction of predicted complexes identified by the proposed technique is larger than the other methods.We have performed a GO and pathway based study to biologically validate the identified protein complexes. GO represents an important resource to describe the functional characteristics of genes in a module. In Table 36 is used to aggregate the individual ranks. A comparison between the predicted complexes identified by the proposed method and that by the clusterONE is reported in Table The above experiments measure the performances based on various metrics and the results show that the different protein complex identification methods rank differently for different metrics. Thus it is difficult to comment on the best performing algorithm. In this section, we evaluate the performances of the methods using an aggregated ranking procedure. We use three topological measures namely density, betweenness centrality and clustering coefficient and a biological relevance measure namely GO based semantic similarity for ranking each complex. Subsequently, a rank aggregation schemeα and β to a low value, the proposed method results low dense clusters which may be treated as possible candidates for sparse, low dense protein complexes.Identifying protein complexes from protein-protein interaction (PPI) data is an important problem in computational biology. The existing literature resulted from a cross-disciplinary research, proposes several methods based on global objective maximization, network motif identification, local search mechanisms etc. In this paper we propose a sequential non-cooperative game based model for protein complex detection from PPI network. We model protein complex identification as a non-cooperative sequential partial dense vertex cover game. The Nash equilibrium of the game corresponds to a minimum partial dense vertex cover of the given network.We carry out a thorough experiment for validation of topological and biological relevance of the identified complexes. The data sets used in the experiment are synthetic benchmarks and real life yeast PPI data. The experiments on synthetic benchmark network and null-models proves the accuracy of the proposed method. We further test robustness of the proposed model on perturbed synthetic benchmarks where edges are rewired with certain probability. The results prove that the developed model accurately identifies the network module even when the rewiring probability is moderately high. The tests on real PPI networks also shows promising results. The validation through connectivity density proves that the identified modules on the real data are dense indeed and good candidates for protein complexes. The partitions are further validated using matching score, sensitivity, positive predictive value and accuracy metrics against experimentally annotated data. Although the proposed method does not outperformed all other state-of-the-art with respect to the performance metrics, but it achieves a consistently good score for all of the datasets. It is noticeable that, although RNSC and MCL achieve higher accuracy than the proposed method in MIPS and KROGAN data, they are not performing well while comparing with respect to the functional similarity score. The experiment shows that the proposed method attains high functional similarity score for most of the identified modules. Additionally, from the experiment we find a common drawback with the existing approaches is that they find a few well-matched clusters and a high number of poorly matched clusters. The proposed approach overcome this issue and finds the clusters whose matching score is far less skewed compared to the existing approaches. Finally we measure the performance using an aggregated ranking approach which proves the superiority of the proposed method over the base-line methods. It is noticeable that the proposed approach is also capable to identify sparse, low dense protein complexes. Setting the input parameters Applying game theory for sub-cellular events like protein-protein interactions is a rather new approach of research. For simplicity, the proposed method is designed to detect the disjoint complexes. However, with the use of appropriate data structure and by removing the restriction that if a protein joins a coalition, then the protein is removed from the game, the proposed method can be extended to detect the overlapping complexes. In future we would like to study the weighted PPI networks for the analysis of protein complexes and functional modules.


The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors .The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.The online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users. Proteins carry out a broad range of functions in living organisms such as structural support, signal transmission, immune defence, transport, storage, biochemical reaction catalysis and motility processes. The majority of proteins does not act in isolation: in fact they express their biological roles by interacting with other molecules . ProteinThere are several experimental techniques available which can be employed for the characterisation of protein–protein interfaces at residual and even atomic level. For instance, both X-ray crystallography , 10 and Although impressive progress has been made, there are several limitations to the existing experimental methods in the determination of protein–protein interfaces. X-ray crystallography requires crystallizing the specimen and placing them in non-physiological environments, which can be inherently difficult and occasionally lead to functionally-irrelevant conformational changes. NMR spectroscopy is suitable for macromolecules in solution  and can yield information on the dynamics of various parts of a given the protein or complex, but its applicability is limited to small polypeptides (less than 50 kDa). Cryo-electron microscopy has no sample size constraints and can guarantee a reduced radiation damage to the sample compared to X-ray crystallography, but is generally more difficult, time consuming, and requires operating constantly at temperatures lower than –135°C. These technical challenges make such experiments both labour-intensive and time-consuming, while on the other hand, the ongoing proteomics and structural genomics research continues producing large amounts of data, which need to be interpreted in a timely manner. Efficient computational methods are therefore needed to correctly predict the potential binding sites for a deeper understanding of PPIs.Several computational methods for the prediction of PPI sites are available to date  which caIn , a two-sStructural features associated with the atomic coordinates of proteins are important discriminative attributes for PPI interface prediction, and the absence of such information is therefore expected to reduce the performance of sequence-based predictors compared to structure-based ones. For instance, most interface residues are also located on the protein surface, so structure-based methods can simply identify surface residues and ignore all internal residues. PPI interfaces are comprised of residues that can be located close to each-other in 3D space, while having distant positions in the primary sequence of the proteins. Finally, geometrical complementarity can be evaluated from 3D structures. Structure-based computational approaches offer several advantages over sequence-based ones, but are limited by the availability of protein 3D structures. However, the number and quality of available protein 3D structures has been steadily increasing over the past years and several structural repositories are available to date (i.e. Protein Data Bank (PDB) , The PepPorollo and Meller use “fingerprints” derived from the difference between the predicted and actual relative accessible surface area (rASA) of residues as features for interface prediction . The prehot spots, i.e. energetically critical residues that contribute significantly to the free energy of binding [Experimental evidence supports the hypothesis that the location of binding sites is imprinted in the structures of proteins, and that this information can be extracted even without the knowledge of the binding partner , 35. Int binding . Cluster binding  and arom binding , 39 are  binding  and demo binding , 41 thanThere are also differences among the interfaces of the various types of PPIs . Dependiα − H ⋯ O = C). Moreover, Cα − H ⋯ O = C interactions were found to be predominant in protease–inhibitor interfaces while ion–ion interactions were found to be specific to signal transduction complexes. In [Proteins belonging to the same functional category recognize their interacting partners by certain types of molecular interactions that are specific to their protein family and local environments. As a result, proteins can show specific binding interactions according to their functional classes of PPI interfaces. In , basic dexes. In , six typexes. In .Although significant research has been done in the area of protein–protein interactions, the problem of PPI interface prediction is still not fully understood . The selIn view of the above, we decided to perform binding interface prediction on different classes of proteins in order to gain a better understanding of the various PPI interfaces. In this work we introduce a methodology for the binding interface prediction of proteins given their experimentally-solved 3D structures (PDB files), without any knowledge on their possible binding partners. In order to effectively discriminate between interacting sites and non-interacting sites, we used a set of eight high quality amino acid indices (HQIs) of physico-chemical and biochemical properties extracted from AAindex1 dataset and first introduced in . This sevolumetric pixel) represents a single, discrete data point on a regular grid in the 3D space, and can contain multiple values in order to represent various properties of a certain portion of space in a simple and effective way.In this work we employed the voxelised representation of the Solvent Excluded surface (SES) , which c3, using a 1.4Å radius for the solvent probe. Patch centres are extracted from each protein surface uniformly and at a minimum separation of 1.8Å, while local surface patches are extracted using a sphere with a 6.0Å radius centred at each patch centre. This ensures that there is plenty overlap among patches with neighbouring centres. The 6.0Å patch radius is a recurring value in many algorithms which employ spherical patches [The voxelised SES of proteins were computed with the region-growing Euclidean distance transform methodology described in our previous works , 70 at a patches , 71–73,  patches .interface surface patches, and non-interface surface patches. Consequently, the problem may be solved using statistical and machine learning techniques such as Support Vector Machines. A clear definition of interacting local surface patches is required in order to predict whether a given patch is involved in protein–protein interactions. However, many alternative definitions are being used to define an interaction site based on 3D structural data [The recognition of PPI interface regions can be seen as a classification problem, i.e., each local surface patch is assigned to one of the two classes: ral data  which caP1 and P2 be two proteins in a given complex whose 3D structure is known, and let SES(P1) and SES(P2) be the corresponding voxelised SES representations. The interface P1 is defined as the set of voxels from SES(P1) which are within a 4.5Å distance from some heavy atom in P2, i.e.: In this work, we used the following definition of interface and non-interface local surface patches. Let P2 is defined as: Equivalently, the interface A patch is an interface patch if at least 80% of its surface voxels are located in the current protein’s interface, otherwise the patch is categorised as a non-interface patch.In order to reliably predict PPI interface residues, the physico-chemical characteristics (features) that can best discriminate between interacting and non-interacting sites must be identified. The choice of such features is critical for the success of a predictor . The AAiThe 3D Zernike descriptors (3DZD) were first used as a representation of the protein surface shape in , and havn and repetition m are defined as Each patch of the enriched protein surface is represented by the 3D Zernike descriptors. The 3DZD are a series expansion of a 3D function which exhibit several desirable properties such as compactness of the representation, roto-translational invariance and minimum information redundancy . In what follows we will provide a brief description of the 3DZD. Refer to  for the lth degree, where l ≤ n, m∈ {−l,−l+1,−l+2,…,l−1,l}, with n−l an even number. Rnl(r) are the radial polynomials of radius r which guarantee the orthonormality of the The 3D Zernike moments Using Eq. Mrst is the geometric moment of the object scaled to fit in the unit ball where where l+1)-dimensional vectors Fnl are defined as norms of vectors Ωnl: The 3D Zernike moments N, the number of 3D Zernike descriptors can be easily determined by using the following formula: Given the maximum moment order The physico-chemical and biochemical properties described in the HQI8 amino acid index set are mapped on the voxelised representation of the protein’s SES. Depending on the amino acid it belongs to, each atom in the protein is assigned the corresponding numeric values of the properties scaled by the atom’s radius. For a given amino acid index, each voxel in the protein’s SES is assigned the corresponding value of the atom occupying that voxel. If a voxel belongs to two or more atoms (i.e. if two or more atoms overlap), then the sum of the corresponding values of the overlapping atoms is assigned to that voxel. If a voxel does not belong to the SES of the current protein, its value is set to zero.P, these functions are formally defined as follows. Let AP be the set of atoms in the current protein P, and let i∈HQI8. Then, for a given amino acid index i∈HQI8, the corresponding property is mapped on the SES(P) according to the following 3D function: Eight 3D functions are thus defined, each describing one of the properties of the HQI8 set. For a given protein ra is the radius of atom a, and  is the indicator function for atom a defined as: where f(x) as the difference of its positive part f+(x)= max(f(x),0) with its negative part f−(x)=− min(f(x),0), i.e. f(x)=f+(x)−f−(x), and by computing the 3DZDs of these two functions separately.Zernike descriptors cannot be used to distinguish positive valued functions from negative valued ones  is a binary classification technique introduced by Vapnik et al. –86. Whilxi,yi), where xi is a vector representing the features or attributes of the given sample and yi∈{−1,+1} is the corresponding class label. The goal of SVM is to produce a model based on the training data which predicts the class labels of the test data given only the feature vectors of the test data. This is achieved by solving the following optimisation problem: A binary classification problem usually involves separating data into training and test sets. The instances (samples) of the training set are the pairs (ϕ(xi) maps xi into a higher-dimensional  space, and C>0 is the penalty parameter of the error term. In practice the dual formulation of this problem is solved instead, due to high dimensionality of the vector variable w: where where w is given by After solving the dual problem, the optimal and by setting ϕ(x) explicitly. Instead, only the dot products between mapped feature vectors are calculated K is also known as kernel function.Please note that there is no need to compute the mapped feature vectors ϕ(·). This is easily done by using different kernel functions generating ϕ(·). The most used kernels are given in Table SVM can perform non-linear classification in the feature space by finding a separating hyperplane with maximal margin in the higher dimensional space generated by In this work, interface local patch descriptors are labelled as positive samples (+1) and non-interface ones are labelled as negative samples (−1). Therefore, our interface recognition problem is actually a binary classification problem which can be handled by a SVM. In this work we used the SVM implementation provided in the scikit-learn Python module for machine learning version 0.18.1 .1 score (F1) and the Matthews correlation coefficient (MCC) , precision (P), recall (R), Fb in Eq. b.The Receiver Operating Characteristic (ROC) and the Precision–Recall (PR) curve plots and their Area Under the Curve (AUC) can also be used to assess the quality of a binary classifier. The ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is a very good way to summarize its performance in a single number. In this work, the ROC curve of an SVM classifier is created by plotting the True Positive Rate  against the False Positive Rate , at various threshold values of the intercept term The Protein–Protein Docking Benchmark 5.0 (DB5)  was usedIn order to assess the predictive capabilities of the proposed method on different protein complex classes, we considered the 8 different classes in the DB5 separately. For each class, we also separated the receptor proteins from the ligand ones, thus obtaining 16 separate datasets. We maintained the separation between classes A and AB, although not being biologically different, in order to be able to evaluate the performance variations due to conformational changes upon binding, as there are no unbound structures available for the receptor proteins in the AB class. For each of the 16 datasets, we further reduced redundancy to a maximum of 90% sequence identity between pairs of different (unbound) proteins with the CD-HIT tool . Each daThe interaction interface generally corresponds to a small portion of a protein’s surface, thus, a uniform sampling of the protein surface into local surface patches results in a highly-imbalanced classification problem where the interface patches are the minority class. Most machine learning algorithms do not perform well when the number of instances of one class far exceeds the other, especially when classification accuracy is employed as a figure of merit. This can lead to classifiers that tend to label all the samples as belonging to the majority class, thus trivially obtaining a high accuracy measure.1 score (instead of classification accuracy) as a figure of merit during model evaluation on the training samples. The test samples, on the other hand, were obtained by uniformly sampling the surfaces of the proteins in the test set with a minimum separation of 1.8Å between patch centres, thus retaining the original distribution of positive and negative samples. Table In this work we used a combination of undersampling of the majority class and oversampling of the minority class in order to balance the training set. The surface of each protein in the training set was first sampled into local surface patches with a minimum separation of 4.5Å between patch centres. Then, only the interface regions were sampled with a minimum separation of 1.0Å between patch centres. This procedure yields more balanced training sets (see Table C and the kernel parameters) is critical for achieving good classification performance with SVMs. Although grid-search is currently the most widely used method for hyper-parameter optimisation in learning algorithms, it can be prohibitively time-consuming since not all hyper-parameters are equally important to tune. Grid search experiments might end up allocating too many trials to the exploration of dimensions with low impact on the final performance and suffer from poor coverage of the more important ones. On the other hand, randomised search experiments were recently proven more efficient in several learning algorithms and datasets [Choosing an appropriate kernel function with the corresponding best hyper-parameters  in order to reduce the number of features to a subset of relevant ones, since its benefits are manifold  . In thisC was sampled from the continuous exponential distribution with mean 2000 for all kernel functions. The γ parameter was sampled from the continuous exponential distribution with mean 0.01 for the polynomial, RBF and sigmoid kernel functions. The degree d parameter of the polynomial kernel was sampled from the discrete uniform distribution r parameter of the polynomial and sigmoid kernels was sampled from the continuous uniform distribution After the feature selection, we performed a randomized search over the hyper-parameters for each of the kernel functions described in Table k proteins, in turn, each protein is removed from the training set, and a model is trained on the samples of the remaining k−1 proteins. The resulting model is then validated on the samples of the protein that was left out. The performance measure reported by LOOCV is then the average of the values computed in the loop. We used the F1 score as a performance measure throughout all experiments.The hyper-parameter evaluation was carried out through leave-one-out cross-validation (LOOCV) at the protein level. If the training set consists of d), where d is the minimum distance from its atoms to the current patch centre. At the end of the procedure, each residue in the query protein will be assigned a score which indicates its likelihood of belonging to the PPI interface. Each residue can then be classified as interacting or non-interacting by thresholding on this score.In order to predict the set of interface residues in a target protein the predicted interface surface patches must be mapped on the underlying residues. The mapping procedure can be summarized as follows. Each residue in the query protein is assigned an initial score of 0. Then, for each predicted interface surface patch we identify the set of its underlying residues, that is, all the residues with at least one atom within a 6Å distance from the patch centre. The score of each underlying residue is incremented by 1/ to a much less satisfactory prediction for class A l (in both bound and unbound cases). The effect of the conformational changes proteins undergo upon binding can be observed in the differences between the obtained performance values for the bound and unbound versions of the protein classes: for most protein classes, the bound versions obtain better prediction results than the unbound ones.The performance results for the proposed methodology at the surface patch level on the test set are presented in Table l is 41.52%, which is higher than in some other classes. However, we achieve the lowest classification performance in this class. For this reason we conclude that the performance discrepancies are due to the varying capability of the HQI8 index to adequately represent the diverse interaction patterns that characterise PPIs in the different protein classes.To investigate the reasons behind the different performance rates achieved for different protein classes, we measured the average pairwise sequence identity for each protein class . These are the most generic classes in DB5 , Others, Receptor containing (OR) and Others, miscellaneous (OX)), thus the benefits of using a class-specific training set are less evident.In order to further demonstrate the necessity of developing class-specific protein interface predictors, we trained a generic SVM model based on all the structures in the training set, only differentiating between bound and unbound structures, and evaluated its performance on the test set structures for each protein class. The comparison of the average ROC curves of the class-specific and generic models are given in Fig. t=0) used by the SVM classifier  algorithm for outlier detection  was usedHomology-based (or template-based) approaches constitute the best performing PPI interface prediction methods to date (given the availability of close homologous structures) . These mThe assessment was carried out on the structures of the test set described in Table r and AB r: the ROC-AUC and PR-AUC values obtained by our predictor are significantly higher than the others. In the unbound version of protein class A r, our method achieves a ROC-AUC of 94.2% and a PR-AUC of 67.7% while, for the competitors, the maximum ROC-AUC is 78.0% (for NPS-HomPPI) and the maximum PR-AUC is 12.4% (for SPPIDER). Similarly, in the bound version of protein class A r, our method achieves a ROC-AUC of 95.4% and a PR-AUC of 56.4% while, for the competitors, the maximum ROC-AUC is 79.6% (for NPS-HomPPI) and the maximum PR-AUC is 12.2% (for SPPIDER). In the unbound version of AB r, the proposed method achieves a ROC-AUC of 84.0% and a PR-AUC of 39.2% while the maximum ROC-AUC for the competitors is 78.9% (for PrISE) and the maximum PR-AUC is 13.5% (for SPPIDER). For the bound version of AB r our method obtains a ROC-AUC score of 81.3% and a PR-AUC of 33.5%. The best ROC-AUC obtained by the competitors in the same class is 77.6% (for PrISE) and the best PR-AUC is 14.6% (for SPPIDER). Noticeably better prediction performance is also achieved in the unbound and bound versions of class EI r: the achieved ROC-AUC values are 74.4% for the unbound and 75.5% for the bound version, while the achieved PR-AUC values are 33.2% for the unbound and 34.2% for the bound version. Although PrISE obtained the same ROC-AUC in the bound version of EI r, the corresponding PR-AUC is only 27.0%. Slightly better than average prediction results were also obtained in the unbound versions of classes AB l, OG l, OX l and in the bound version of class EI l. Our prediction method underperformed compared to the competitors in the unbound versions of classes ES r, ER l and in the bound versions of classes OR r, OR l, OG l. In all other protein classes the prediction capabilities of the proposed methodology followed the average trend of the competitor methods.By thresholding on the residue score values we computed the average ROC curves and average Precision-Recall curves for each method which are shown in Figs. The obtained results agree with the initial hypothesis that proteins belonging to different classes exhibit diverse interaction mechanisms. To this end, the choice of a correct set of physico-chemical and biochemical properties characterising the interaction site is crucial, although it might not be possible to identify a comprehensive set of features that works well for all protein classes as the recognition patterns can be very different. Our results suggest that, although a given set of features can effectively discriminate between interface and non-interface surface regions for a given protein class, it can perform very poorly when used on other protein classes. Interface prediction could be further improved by better feature representation and selection methods that can effectively capture complex protein recognition patterns in diverse types of interactions, however, different protein classes should be treated separately.r, AB r) while under-performing in others (A l). Other sets of properties could exhibit better discriminative power in the protein classes where we obtained low prediction performances. Ideally, an optimal set of features could be selected for each protein class in order to correctly identify the class-specific PPI patterns. The proposed methodology can be easily extended to other sets of amino acid properties, which can be similarly mapped on the voxelised protein surface and represented by 3DZDs.The HQI8 amino acid index set of physico-chemical and biochemical properties showed very good discriminative capabilities for the interface recognition of some protein classes  and even outperformed the competitors also in terms of precision–recall. These results are very encouraging, thus we are planning to develop a specific antigen-binding interface  prediction method for antibodies with known structure using the 3D Zernike descriptors and the HQI8 amino acid index set. The field of paratope residue prediction appears to be somewhat underdeveloped, with a general paucity of specific predictors, thus any future development in this direction should be quite promising.This method was tested on 16 protein classes extracted from the Protein–Protein Docking Benchmark 5.0, on both the bound and unbound versions, and was compared with three other state-of-the-art PPI interface predictors, namely SPPIDER, PrISE and NPS-HomPPI. With a resulting ROC-AUC of, respectively, over 94% and 81%, we obtained very good classification results on protein classes A Our results show that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that the optimal set of features strongly depends on the specific protein class. For further improvement of prediction performance, it is necessary to identify an optimal set of features for each protein class or interaction type. As a future development, we plan to test several sets of features on different protein classes in order to widen the predictive capabilities of the proposed method. Including informations regarding possible binding partners in the prediction procedure is also expected to increase the overall performance, although tackling the resulting class imbalance will not be trivial.The comparison of the class-specific interface prediction models with a generic one, trained on all training samples regardless of the protein class, also confirmed the hypothesis that interface prediction model development should be carried separately for different protein classes. In a certain way, this is similar to the homology-based interface prediction approach: for a given query protein, its closest homolog proteins with known binding sites are retrieved, and the query’s binding site is determined by comparison with the known structures. However, these methods cannot yield good predictions when adequate homologs are not available. Similarly to homology-based predictors, the proposed method requires the availability of several protein classes in order to reliably predict interface regions. Given the ever increasing number of available high-resolution 3D protein structures in public repositories, we expect that more benchmark sets and databases such as the Protein–Protein Docking Benchmark 5.0 which classify proteins into biologically relevant classes will be available. As a future work, we will expand the proposed methodology to other protein classes in order to increase the coverage of its predicting capabilities to as many proteins as possible. A pre-processing step for the determination of the most adequate protein class will be required for proteins with unknown classes. The predictor will then be made available to users throughout a dedicated web server.The majority of the available protein interface identification methods make predictions at the residue resolution level. Protein–protein docking algorithms, however, require high-resolution atomic level knowledge in order to correctly predict native binding configurations between interacting proteins. The predictions at the local surface patch level can be readily used to guide protein–protein docking methods by limiting the docking search space to the sole surface patches which were predicted as belonging to the interaction interface. Docking algorithms based on local surface descriptor matching can greatly benefit from the proposed approach since this will sensibly limit the number of candidate patch pairs to be evaluated, thus reducing the conformational search space, and consequently reducing both the number of false positives and the required calculation time.Additional file 1Contains additional information on some technical aspects of the research. (PDF 1360 kb)Additional file 2Contains the indices of the selected features for each protein class. (CSV 1.36 kb)Additional file 3Contains the prediction results on the test set for each protein, summarised in Table


Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure‐based bioactivity data mapping, ii) structure‐based identification of scaffold replacement strategies for ligand design, iii) ligand‐based target prediction, iv) protein sequence‐based binding site identification and ligand repurposing, and v) structure‐based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well‐established standards allows the re‐use of these protocols and facilitates the design of customized computer‐aided drug discovery workflows. There is a need for eScience technologies to process the large volumes of rapidly generated, heterogeneousThe current work describes the integration and analysis of several chemical, biological, and structural data types in workflows that can be used for: i) structure‐based bioactivity data mapping, ii) structure‐based identification of scaffold replacement strategies for ligand design, iii) ligand‐based target prediction, iv) protein sequence‐based binding site identification and ligand repurposing within a protein family, and v) structure‐based pharmacophore comparison for ligand repurposing across protein families.The flexible workflows and protocols presented here can be used as templates for the standardization of protocols, the integration of data and analyses, and can readily be reused or extended for the creation of new computer‐aided drug discovery workflows for other protein targets and applications. The cases will focus on two of the pharmaceutically most relevant protein targets, namely G protein‐coupled receptors (GPCRs) and kinases.Moreover, this work presents new KNIME nodes that enable the analysis and prediction of protein–ligand interactions using freely accessible structural cheminformatics tools, including: i) web service nodes to extract and combine data from GPCR (GPCRdb)https://www.knime.com/3d-e-chem-nodes-for-knime), the source code for all nodes and all workflows themselves are available via GitHub (https://github.com/3D-e-Chem/workflows), and everything is also embedded within an updated version of our 3D‐e‐Chem virtual machinehttps://3d-e-chem.github.io/3D-e-Chem-VM/). This enables all users to download, apply, customize, and extend the workflows to their own protein targets of interest in order to answer different chemogenomics or drug discovery related questions.All nodes and tools used to perform the analyses described in the current work are available as community contributions in KNIME under “3D‐e‐Chem”  to ligand binding modes, specific protein–ligand interactions, and to extrapolate these insights to other protein targets. In the next workflow Figure  we have Protocol:Collect protein information and the molecular structures of co‐crystallized ligands (here from KLIFS)Retrieve the available bioactivity data for the ligands (here from ChEMBL)Clean, curate, and process the bioactivity dataSelection of the compounds and kinase targets of interestFormatting and visualizing the datath, 2017) in SMILES format. The InChIKeys of the inhibitors were subsequently used to retrieve the ChEMBL IDs for the compounds (1583 matches) including all corresponding bioactivity data (166 976 data points). Using the human kinase list from KLIFS all bioactivity data was reduced to solely the human kinome (86 601 data points for 432 kinases). The top 100 compounds with the largest number of available bioactivity data (excluding single concentration measurements) for kinasesThe molecular structures of all 2552 unique co‐crystallized small molecule kinase inhibitors were collected via KLIFS nodes , and molecular structures of the co‐crystallized ligands (here from KLIFS)Perform full pairwise ligand‐based similarity and IFP similarity analysesFilter the data by selecting ligand pairs with a low molecular similarity and a high interaction similarityObtain the aligned structures and compare the binding modes of the molecule pairs of interestDesign a scaffold hop based on the selected molecule pair and dock them into the desired protein kinaseVisually evaluate the obtained binding modes, compare their interaction fingerprints, or perform another binding mode comparison technique.http://www.rcsb.org/pdb/explore/explore.do?structureId=4DIT)http://www.rcsb.org/pdb/explore/explore.do?structureId=4PTG)http://www.rcsb.org/pdb/explore/explore.do?structureId=4PTG) using the newly developed PLANTSStarting from the KLIFS nodes all structural information on human kinases (7552 unique monomers) was downloaded including the kinase‐inhibitor interaction fingerprints (IFP) and the SMILES of the co‐crystallized kinase inhibitors. Subsequently, a group loop is started that processes all structures per individual kinase. Within the loop, a pairwise interaction‐based IFPThe derivation of similarity measures between different protein receptors may be used to explore cross‐reactivities and to explore the potential for compounds to display (useful) polypharmacology. The PP_GPCR (protein–protein association GPCR) workflow Figure  A followProtocol:Collect available bioactivity data for a protein family or (full) set of proteins of interestClean, curate, process, and filter the bioactivity dataCalculate ligand‐based fingerprint descriptors for each compoundGoal 1: Protein–protein association prediction○ Perform an all‐against‐all comparison of the fingerprints and select relevant hits based on a user‐definable cutoff○ Group the number of hits per protein target pair and calculate an E‐value○ Output of the results for visualization in, for example, CytoscapeGoal 2: Identification of potential protein targets for small molecules○ User input of the small molecules of interest and calculate their ligand‐based fingerprint descriptors○ Perform a fingerprint comparison against the protein dataset and select hits based on a user‐definable cutoff○ Group the number of hits per protein target and calculate an E‐value50, IC50, AC50, Kb, KD, Ki) and threshold activity (pAct ≥5) are applied, a minimum compound set size of 5 is required, and a restriction on the number of calculated rotatable bonds (maximum of 18) is used to limit the number of very large, flexible compounds. The latter is performed as in our experience the presence of large numbers of peptide/peptoid compounds can lead to some targets being routinely overrepresented in later comparisons. Fingerprint descriptors  were calculated for each compound and the similarities between the receptor sets were determined using a user‐definable threshold for similarity, here set to a minimum of 0.7. Use of the raw similarities and set size following KeiserThe protocol is applicable to any combination of data sets with unknown distributions of structures and biological activity values, user intervention to vary thresholds, similarity measures, fingerprints and statistical approaches is made possible. The PP_GPCR workflow reads in data from a public data source, ChEMBL, for all non‐olfactory GPCR receptors as derived from the GPCRdb.Ki<1000 nm). Using the default similarity cut‐off of 0.7, for four of the five compounds  previously predicted activities were reflected in the top‐five nearest neighbors in the PP_GPCR workflow , is then used to create a subfamily  as input for the double entropy analysis by the ss‐TEA node. All residue positions are scored according to the entropy within the subfamily  compared to the entropy outside the subfamily . The 20 residue positions within the seven transmembrane helices with the lowest score  were selected for further processing. These residues have a high conservation of a residue within a subfamily but a low conservation outside a subfamily, which is an indication of the subfamily‐specific relevance of the residue for, for example, ligand recognition or receptor function. For visualization of the results, a scatterplot is created displaying the internal versus the external entropy with all residue positions (each dot) colored according to their ss‐TEA score  and the serotonin 2B receptor (5‐HT2BR), which are also identified by the cross‐reactivity assessment using the ChEMBL bioactivities of the known SST5R inhibitors  and Loperamide (a μ opioid agonist) on SST5R. Vice versa, a series of benzoxazole SST5R inhibitors showed nanomolar affinities for 5‐HT2BR , all class A GPCRs (11 731), and the aligned and numbered protein residues for each GPCR  using the GPCRdbLigand repurposing across protein families can be enabled through the comparison of known protein binding sites based on the available crystal structures.Protocol:Collect available PDB entries for the protein families of interestObtain the KRIPO fragments information based on the PDB entries of the reference protein family and search for similar KRIPO fragments in the PDBExtract similar fragments that match with PDB entries from the query protein familySelect interesting fragment pairs and further explore them by, for example, KRIPO pharmacophore alignment and 3D similarity comparison.http://www.rcsb.org/pdb/explore/explore.do?structureId=3HEG) and the Ergotamine‐bound 5‐HT2B receptorhttp://www.rcsb.org/pdb/explore/explore.do?structureId=4IB4), consistent with studies showing that the FDA‐approved kinase inhibitor Sorafenib has nanomolar affinity for 5‐HT2BR.http://www.rcsb.org/pdb/explore/explore.do?structureId=5LJJ) and the triazolecarboximidamide‐bound A2A receptorhttp://www.rcsb.org/pdb/explore/explore.do?structureId=5UIG). Reversine shows weak binding affinity for the adenosine A2A receptor, and has sub‐micromolar affinity for the homologous adenosine A3 receptor.With the GPCRdb KNIME nodes, an overview of all GPCR crystal structuresαC.24) in the αC‐helix of MAPK14 and the key aspartate D1353.32 of 5‐HT2BR are matched.The KRIPO pharmacophores of each structure were downloaded and aligned using the KripoDB pharmacophore and Align Pharmacophores nodes, respectively. The rotational matrix obtained from the alignment was then used to align both pharmacophores as well as the complete PDB entries in the pharmacophore viewer. To compare the structure‐based pharmacophore alignment of the molecules with a ligand‐based approach both molecules were aligned using a ligand‐based pharmacophore approach (Align‐it) and a shape‐based approach (Shape‐it). The SMILES of both co‐crystallized ligands were obtained from the PDB using the PDB Connector Custom Report node. Then the RDkit Add Conformers node was used to generate 30 conformations for each ligand as input for the Align‐it and Shape‐it nodes. The ligand‐based alignments were again visualized with the Pharmacophores Viewer and the Ligands and Proteins viewer. Interestingly, the urea moiety of Sorafenib binding in the back pocket of MAPK14 is aligned with the basic amine in the fused tetracyclic head of Ergotamine. This ligand alignment originates from the KRIPO pharmacophore alignment as the negatively charged centers of the conserved glutamate (E71The volume‐based Shape‐it overlay shows a good overlap (Tanimoto score=0.67) between the two compounds, however, most pharmacophore features are not aligned due to a 180‐degree flip of the core scaffold to maximize the shape overlay. The ligand‐based pharmacophore overlay using Align‐it results in a poor score (Tanimoto score=0.22) and an alignment in which the whole molecules are flipped 180 degrees, illustrating that the structure‐based KRIPO pharmacophores were key for the elucidation of this off‐target effect.The presented structural cheminformatics tools and integrated workflows combine heterogeneous data analyses that enable the prediction of protein–ligand interactions and the identification of protein–protein relations. The reusable workflows provide general guidelines that can be used for the construction of automated computer‐aided drug discovery protocols, or for the customization and extension to other targets and applications:The use of well documented and amenable workflow management platforms like KNIME facilitate the construction of consistent, reproducible,KNIME contains a rich and continuously growing set of cheminformatics nodes to handle and process chemical and biological data in multiple formats. Custom nodes can be developed, such as the nodes presented in the current study, and scripts and external tools can be embedded to extend the functionalities of this toolkit in order to address a plethora of biochemical research questions, for example, structural protein–ligand interaction analysis and prediction functionalities.Carefully annotated and standardized data resources are required to perform integrated cheminformatics analyses.The infrastructure of a workflow management platform such as KNIME allows for interactive checks during execution of the workflow. Checking the input and output for each step during the development of a workflow makes for easy debugging resulting a more robust and less error‐prone workflow. To enhance this process customized data visualization nodes, such as the proteins and ligands viewer and the pharmacophore viewer nodes presented in the current study, are also required to inspect the validity of, for example, docking studies, pharmacophore‐based structure alignments, and binding mode similarity assessments.Combining complementary techniques within the same workflow allows for the creation of more advanced or more accurate (consensus)Newly developed KNIME nodes: The KNIME workflows described in this article use a series of 3D‐e‐Chem KNIME nodes that have been newly developed in addition to a set of previously published 3D‐e‐Chem nodes. An overview of the new nodes is shown in the list below and the nodes themselves are discussed in more detail in the next few paragraphs.Pharmacophore: Retrieval of the KRIPO pharmacophore based on the KripoDB fragment identifier.Ligands Viewer: visualization of  small molecules.Ligands and Proteins Viewer: the combined visualization of  small molecules and proteinsProteins Viewer: visualization of  proteinsPharmacophores Viewer: visualization of  pharmacophores, small molecules and proteinsAlign pharmacophores: align the query pharmacophores to the reference pharmacophore.Extract pharmacophore points: extract the points of a pharmacophore as rows.Merge pharmacophore points: create pharmacophores from a table with x, y, z coordinates, pharmacophore type, alpha and optional directionality.Pharmacophore from molecule: create a pharmacophore from a molecule by mapping atoms to pharmacophore points.Pharmacophore to molecule: generate a molecule from a pharmacophore by mapping pharmacophore points to atoms.Pharmacophore reader: reads a pharmacophore file (*.phar) in the Silicos‐it phar file format.Pharmacophore writer: writes a pharmacophore to a file (*.phar) in the Silicos‐it phar file format.PLANTS bindingsite: calculates the binding site definition for docking based on a reference ligand or pocket atoms of the protein.PLANTS session builder: takes the protein, binding site and ligands from KNIME and creates the docking session.PLANTS virtual screening: runs the actual docking itself based on the session created by the session builder.PLANTS virtual screening results reader: reads the docking results into a KNIME table.Align‐it: aligns molecules to a reference molecule based on their pharmacophore features and scores the alignment.Align‐it Pharmacophore generator: generates pharmacophores for molecules based on their pharmacophore features.Filter‐it: filters a set of molecules based on molecular property ranges.Filter‐it property calculator: calculates molecular properties for a given set of molecules.Qed Calculator: performs a quantitative estimation of drug‐likeness (QED) of a set of given molecules. Requires qed.py Python package to be installedShape‐it: performs a shape‐based alignment and scoring of a set of ligands to a reference ligand.Strip‐it: strips a given set of molecules to its scaffold based on a user‐selected scaffold definition.Ss‐TEA score: calculates the ss‐TEA score for each residue position of a sequence alignment for a set of family members.https://www.apache.org/licenses/LICENSE-2.0). The PLANTS binaries for docking (embedded within the PLANTS nodes) are freely available for academics, and the Silicos‐it source is available under the GNU Lesser General Public License v3 (https://www.gnu.org/licenses/lgpl‐3.0.en.html). A more detailed overview per node set and tool, including license information, dependencies, and their application, is given in Supporting Information Table S1.Most of the nodes are available under the permissive Apache License 2.0 (GPCRdb nodes: The GPCRdbKLIFS nodes: KLIFS contains kinase‐ligand interaction information derived from over 3900 structures covering more than 270 different kinases in complex with ≈2500 unique ligands (accessed August 2017). All kinase structures within KLIFS are curated, annotated, aligned, and processed in a systematic manner with automated weekly updates. All KLIFS content can be accessed from within KNIME using one or more of the nine KLIFS nodes from four different categories, as published in McGuire et al.KripoDB nodes: The pairwise pharmacophore similarity of more than half a million (sub)pockets extracted from structures in the Protein Data Bank is available in the KripoDB. KRIPO encodes pocket pharmacophores into a fuzzy 3‐point pharmacophore fingerprints that are subsequently used to assess this similarity.Molviewer nodes: The freely available molecule viewers in KNIME are primarily oriented at visualization of small molecules. To enable displaying proteins, protein–ligand complexes, and pharmacophores in KNIME we created a set of visualization nodes. When opening a KNIME view of one of the new viewer nodes a web browser will be opened with an interactive 3D canvas portraying the input molecule(s). There are four molecule viewer KNIME nodes: one to view a set of  small molecules , one to view a set of  small molecules and proteins , one to view a set of  proteins , and one to view a set of pharmacophores and their aligned protein and/or ligands . The molecule viewer KNIME nodes supports HiLiting, which means that a selection of molecules inside the viewer can be sent to other KNIME nodes and vice versa. The web‐based molecule viewers use the NGL protein viewerhttps://github.com/arose/ngl) as its 3D canvas and use React, Redux, and Bootstrap for controls. The KNIME nodes are written in Java. The web application files are hosted by a Jetty‐based webserver and the Jersey‐based web service, which are both embedded inside the nodes.Pharmacophore nodes: The pharmacophores nodes are a set of KNIME nodes that enable the conversion and alignment of pharmacophores. The nodes support (directed) pharmacophore features with the following supported types: aromatic, H‐bond donor, H‐bond acceptor, lipophilic, positively charged, negatively charged and exclusion. The pharmacophores nodes comprise nodes that read and write pharmacophores in the Silicos‐it phar file format, nodes to convert a pharmacophore from or to a molecule by mapping the pharmacophore types from or to elements, nodes that convert 3D points with a type information into a pharmacophore and vice versa, and finally there is a node to align pharmacophore(s) to a reference pharmacophore. The pharmacophore alignment is performed by comparing all the point pair combinations the pharmacophores can have in common and then identifies the maximum point pair combinations using Bron–Kerboschhttp://ejml.org/) for matrix operations. The alignment algorithm is based on the KRIPOPLANTS: PLANTShttps://github.com/3D-e-Chem/knime-plants/blob/master/examples/plants-virtual-screening-example.knwf.Silicos‐it nodes: Silicos‐itss‐TEA: The ss‐TEA scoreWorkflows: All KNIME workflows described in this article, including the source code for all 3D‐e‐Chem nodes, are available from the 3D‐e‐Chem GitHub repository (https://github.com/3D-e-Chem/workflows). The individual steps of each workflow are described in more detail in the main text. All 3D‐e‐Chem nodes used to perform the analyses described in the current work are available under community contributions in KNIME under “3D‐e‐Chem” (https://www.knime.com/3d-e-chem-nodes-for-knime).The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. 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