BMC Bioinformatics, 2010, 11, 193–208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively.In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach  on five drug targets . In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases.KGS2 can be applied as a convenient “add-on” to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors.The online version of this article (doi:10.1186/s12859-017-1750-5) contains supplementary material, which is available to authorized users. Molecular docking has been an extremely powerful technique in structure-based drug design since the 1980s . The priA number of evaluations of current docking/scoring methods –20 have Most scoring functions are developed as all-purpose models, which are presumably applicable to all types of target protein. However, it is well-known that their performance varies significantly on different target proteins. Development of target-biased scoring function (or customized scoring function) has been proposed as a possible approach for improving the performance of current scoring functions . A numbeIn spite of the appealing prospects provided by customized scoring functions, they are associated with certain technical inconvenience in practice. An obvious limitation is that a new customized scoring function is needed whenever a new target protein is under consideration. It has been estimated that the human genome contains several thousands of druggable targets, which can be classified into at least several dozens of categories. It will need great efforts to develop customized scoring functions to tackle each of them. Moreover, re-calibration of an existing scoring function or formulation of a new model needs some special expertise, which is beyond the capability of most common end users. That is perhaps why customized scoring functions are not widely available yet.We have been seeking an alternative solution for common end users to enhance the performance of current scoring functions in binding affinity prediction without getting into the trouble of formulating customized scoring functions. Our solution is what we call the Knowledge-Guided Scoring (KGS) method. A prototype of this method was published previously in this journal . BrieflyAs a notable new trend in the field of structure-based drug design, structural interaction fingerprints have emerged as a new approach for evaluating protein-ligand interactions . An pionInspired by the concept of protein-ligand interaction fingerprints, we have re-composed the algorithm used by KGS in reference selection. The new implementation will be referred to as KGS2 in this article. In the original KGS method, the reference complex is selected by comparing target-based pharmacophore features deduced inside the binding pocket; whereas in KGS2, the reference complex is selected by comparing 3D protein-ligand interaction fingerprints. We have tested KGS2 in combination with four popular scoring functions. In situ scoring tests were conducted on experimental complex structures formed by five target proteins. Application of KGS2 indeed produced more accurate binding scores than scoring functions alone in most cases. Besides, KGS2 always outperformed the original KGS method. Molecular docking tests were conducted on four additional data sets, each of which consisted of some congeneric ligand molecules for one target protein. Application of KGS2 also led to somewhat improved results. We demonstrate in this study that the performance of current scoring functions in binding affinity prediction can be enhanced by KGS2 with the aid of 3D protein-ligand interaction fingerprints.Q) is computed by a scoring function (SF) as:Our KGS2 method follows the same approach as the original KGS . The binQscore , SF is the binding score of Q computed by SF. Introduction of parameter k and b is necessary for correlating the binding scores computed by SF to experimental binding data because binding scores are often in an arbitrary unit or their values may not be in a range comparable to experimental binding data. Similarly, the binding affinity of an appropriate reference complex (R) computed by SF is:Here, By subtracting Eq. Rexp), one gets:Replacing the predicted binding affinity of the reference complex  was employed as the training set to derive the required k parameter for each scoring function. This data set consists of 3446 protein-ligand complexes with known 3D structures and binding constants, which are selected by a set of quality control filters from the entire PDBbind database [Equation database .By KGS2, the reference complex for a given query complex is determined by searching among a reference library, i.e. an external data set of protein-ligand complexes with known 3D structures and binding data. The complex in this library sharing the highest 3D similarity to the query complex will be selected as the reference and used in Eq. α”) and the SYBYL Mol2 atom type of the ligand atom (e.g. “O.2”). For the sake of convenience, the three atoms on the protein side in each interaction unit will be referred to as the “protein fragment” in this article. An interaction unit is characterized by its components as well as geometry. Geometry of an interaction unit is represented by the relative coordinates of the ligand atom in a local Cartesian coordinate system defined by the protein fragment. In this coordinate system, the origin locates at the protein atom in the middle, the xy plane is defined by the three protein atoms, and the direction of the z axis points toward the same side as the ligand atom  , which px) was used to describe the event when a ligand atom at position x in the local coordinate system interacts with a protein fragment:In our method, “interaction patterns” refer to the interaction units with a higher level of statistical preference, which are presumed to be the key factors in protein-ligand interaction. In order to detect such interaction patterns, the interaction units recorded at the previous step were analyzed. First, if a certain type of interaction unit had an occurrence below 100, it was ignored due to lack of significance. Then, the geometry of each remaining type of interaction unit was examined by using an algorithm based on the Gaussian Mixture Model (GMM) . The samx) is computed as the sum of a number of Gaussian components. μk and a covariance matrix of πk is a weight factor for this Gaussian component. The parameters πk were all derived by maximizing the likelihood of the data point x in the distribution given by GMM through a variational Bayesian analysis. The maximal number of Gaussian components in each GMM, i.e. K, was set to 15 by default. Then, K was reduced during a learning process where parameter πk was adjusted to zero for unnecessary Gaussian components. Then, each remaining Gaussian component, if it had an occurrence over 100 and its weight factor πk ≥ 0.01, was recorded as a significant interaction pattern.Here, p and a Gaussian component of an interaction pattern of the same type (g) was computed as [Then, the key protein-ligand interactions in a given complex structure can be represented by a set of interaction patterns Fig. . For thiputed as :6\documeD was smaller than 2.5, it was considered as a match between x and g. Here, an interaction unit x could be assigned to more than one Gaussian component. This process was repeated until all extracted interaction units had been examined. The outcome was a complete set of protein-ligand interaction patterns observed in the given complex structure . In this way, the complete set of interaction patterns is now simplified into a set of nodes in space , which is too sophisticated for designing an efficient mapping algorithm based on atomic coordinates. Some simplifications are thus necessary here. In our method, an interaction pattern is degraded into a pair of nodes: One node locates on the ligand atom, which records the type of the ligand atom (e.g. “O.2”); while the other locates on the backbone Cace Fig. . The pocP and Q) are detected. Here, two matched nodes must have the same residue type or ligand atom type. A hypothetical graph G is generated using each matched pair of nodes as a new node. Two nodes, e.g. A-D and B-C, are connected with an edge if the A-C distance in set P is close enough to the B-D distance in set Q  or d2 < k·d1 (when d1 < d2), where k is an adjustable parameter with a default value of 1.1. Then, the Born-Kerbosch algorithm for clique detection [G. At the second step, sets P and Q are superimposed by considering only the nodes in the maximal clique. Then, a matched node pair is considered to be geometrically “overlapped” if the distance between them is shorter than 1 Å. Among all possible solutions of superimposition, only the one with the maximal number of overlapped node pairs is retained  between P and Q is calculated by the classical Tanimoto coefficient [In KGS2, a minimum of five pairs of overlapped nodes are required to define complex fficient :7\documeNp and Nq are the numbers of nodes in the interaction fingerprints of P and Q, respectively; Npq is the maximal number of overlapped nodes between P and Q. In order to search for the reference complex for a query complex, each complex in the chosen reference library is analyzed with the algorithms described through section “Extraction of protein-ligand interaction units” to “Detection of protein-ligand interaction patterns”, and its similarity to the query complex is assessed using Eq. Here, Rexp) required in Eq. Kd, Ki, or IC50) curated from literature. This data set is the largest one of this type in public domain and thus is a good choice for our purpose. Each complex structure was processed using methods described in our previous work [The reference library used by KGS2 is an assembly of known protein-ligand complex structures. Importantly, the experimental binding data of each complex should be available, which will provide the reference binding data  by a single-CPU job. Note that this process can be easily accelerated through parallel jobs. Moreover, in reality one probably will not use a comprehensive, non-discriminatory reference library as the PDBbind general set. A more practical approach is to use a smaller, focused reference library, which is composed of, for example, complexes formed by the same protein molecule as the query complex. Application of KGS2 in that way will not require a significant amount of computation time. Thus, KGS2 can work with fast scoring functions nicely.The computation time of KGS2 reported above was obtained by conducting a single-CPU job in a “clean” environment on a Dell Precision T5610 desktop workstation  running the 64-bit RedHat 6.4 Linux operation system.The standard model of KGS2 is described through section “The overall strategy”–“Selection of the reference complex” above. In order to make a comparison, three variations were also considered in our study. As the standard model, these variations all relied on Eq. k in Eq. k for each scoring function under consideration was derived through a regression analysis on the entire PDBbind refined set . Note that there were overlaps between the refined set and the five data sets used in our in situ scoring test. In order to investigate if such overlapping complexes could introduce bias into the final results produced by KGS2, all k parameters used in this variation model were derived on the remaining 2859 complexes in the refined set after excluding the complexes overlapping with the five test sets. All other aspects of this variation model were the same as the standard model.Variation Model 1: This variation differed from the standard model in how the adjustable parameter Variation Model 2: This variation differed from the standard model in the algorithm used for reference selection. It was designed to investigate if the 3D interaction fingerprints used in KGS2 was indeed superior to an algorithm that did not rely on 3D structural information. To compute a given query complex with this variation, the first step was to detect among the entire reference library the complexes formed by the same protein as the query complex. For this purpose, the query complex was compared to each complex in the reference library in terms of protein sequence similarity. If the similarity was above 95%, the two complexes were considered to be formed by the same protein. Here, The similarity between two protein sequences was computed with the CD-hit software released by PDB . At the Variation Model 3: This variation also differed from the standard model in the algorithm used for reference selection. It was designed to investigate if the 3D interaction fingerprints used in KGS2 was superior to an algorithm that was based only on the 3D protein structural information. With this variation, comparison of two complex structures also utilized the interactions patterns identified between the protein and the ligand Fig. . HoweverKGS2 was first validated in so-called “in situ scoring” test, where each scoring function was applied in combination with KGS2 to protein-ligand complexes with known 3D structure to compute their binding affinities. In addition, the three variation models as well as the original KGS method were also tested in order to make a comparison. Performance of each combined scoring scheme was assessed by the correlation between the computed binding scores and the experimental binding data of those complexes. Five well-established drug targets, including HIV-1 protease, carbonic anhydrase 2 (CA-2), beta-secretase 1 (BACE-1), beta-trypsin, and checkpoint kinase 1 (CHK-1), were selected as the test cases. All five proteins are established drug targets. A significant number of complexes formed by each of them are available, which is essential for achieving statistical significance in subsequent analysis. The complexes formed by these target proteins in the PDBbind general set (version 2014) were retrieved, including 304 HIV-1 protease complexes, 230 CA-2 complexes, 223 BACE-1 complexes, 196 trypsin complexes, and 61 CHK-1 complexes, respectively , i.e. ChemPLP , ASP 6060, and GKa). Finally, the Pearson correlation coefficient (Rp) between the experimental binding data and the computed binding scores for the entire test set was calculated for each scoring function. The standard deviation (SD) in fitting the computed binding scores to the experimental binding data was used as a quantitative indicator of accuracy in subsequent analysis. SD was chosen instead of Rp for this purpose because SD is a quantity independent of sample size.Then, all four scoring functions were applied to the five test sets. For each test set, the binding scores of all member complexes were computed first by applying those scoring functions alone. Next, the default reference library used by KGS2  was searched to select the reference complex for each complex in the test set. Because all five test sets under our consideration were also selected from the PDBbind general set, the reference complex selected in each case was examined to ensure that it was not identical to the query complex (otherwise one would obtain 100% accurate “predictions”). If a qualified reference complex was found, adjusted binding scores for all four scoring functions were computed with Eq. http://www.bindingdb.org/validation_sets/) [Our second type of test attempted to reflect the reality in structure-based drug design more closely. The aim was to model the structure-activity relationship of a congeneric set of ligand molecules through molecular docking and scoring. To select the appropriate test sets, we focused on the target proteins already considered in the in situ scoring test. One data set for HIV-1 protease, CA-2, BACE-I, and CHK-1, respectively, were selected among the “validation sets” from BindingDB (n_sets/) . TrypsinAs a useful feature of the validation sets from BindingDB, the crystal complex structure of at least one ligand molecule in each data set is available from PDB. In our study, this particular complex structure was used as the template for deriving the binding modes of all ligand molecules in the same data set. For each ligand molecule, the GOLD software  was employed to generate up to 100 ligand binding poses. The protein structure was kept fixed during this process. The binding pocket was defined by using the native ligand molecule in the crystal complex structure with an envelop of 10 Å. The “200% searching efficiency” parameter set was applied during the sampling process, where the ChemPLP scoring function in GOLD was chosen for ranking the generated ligand binding poses. In order to obtain results in consistence with the other ligands in the same data set, binding poses of the ligand in the template complex structure were also generated through the same procedure.The same four scoring functions  were tested in combination with KGS2 in this test. To predict the binding affinity of a ligand molecule, each scoring function was applied alone first to rank all binding poses of this ligand by binding scores computed with Eq. Our results obtained in the in situ scoring test indicated that the performance of the three variation models and the original KGS method was generally inferior to the standard model of KGS2 see . Thus, tSI ≥ 0.10), the errors produced by X-Score + KGS2 are smaller by 0.3 logKa units (corresponding to one-fold difference in binding constant) than those produced by X-Score alone. Moreover, X-Score + KGS2 achieves this level of improvement  for nearly 1800 complexes in this data set. In contrast, X-Score + KGS achieves the same level of improvement for about 400 complexes. In this sense, KGS2 is about four times more effective than KGS on this data set.KGS2 is developed as an upgrade of the original KGS method. Therefore, we compare the performance of KGS2 and KGS first. The results produced by the X-Score scoring function in combination with KGS2 and KGS on the entire PDBbind refined set are illustrated in Fig. SI ≥ 0.90, the errors produced by X-Score + KGS2 are smaller than X-Score alone by almost one logKa unit (i.e. ten-fold in binding constant). The same trend is also observed for X-Score + KGS at higher levels of required similarity  of this inhibitor was reported to be 32.2 pM (−logKi = 10.49) [Ka units, which deviated from the true value significantly. The reference complex selected by KGS2 was PDB entry 2ZX8  [Ki = 0.47 mM; −logKi = 3.33) [Ka units. In contrast, the binding score was adjusted to the wrong direction by KGS, where the absolute error was increased from 4.15 to 5.51 logKa units.Here, we provide one example to illustrate the advantage of KGS2 over KGS in selecting a more appropriate reference complex. PDB entry 2ZX7, a complex formed by α-L-fucosidase and a small-molecule inhibitor, was chosen as the query complex Fig. . The inh= 10.49) . The bin = 3.33) . This cok needed in Eq. R ≈ 0) on HIV-1 protease complexes to the more acceptable performance (R = 0.50 ~ 0.70) on BACE-1, trypsin, and CHK-1 complexes.Besides X-Score, the other three selected scoring functions  were also applied to compute the entire PDBbind refined set. The statistical results between the experimental binding data and the binding scores computed by all four scoring functions are summarized in Table Ka units even at the lowest similarity required in reference selection (i.e. SI ≥ 0.10). The improvement achieved by KGS2 is even more obvious at higher levels of required similarity, reaching up to 0.5 ~ 0.6 logKa units. It should be noted that in Fig. Ka units (corresponding to one-fold difference in binding constant) more accurate than those produced by standard scoring functions, KGS made it for only 29 complexes; while KGS2 made it for 232 complexes, accounting for 77% of the entire test set.The statistical results produced by KGS2 in combination with all four scoring functions on the HIV-1 protease test set are shown in Fig. Ka units for a large fraction of the test set (60% ~ 100% depending on the partner scoring function).The statistical results produced by the four scoring functions in combination with KGS2 on the other four test sets  are shown in Figs. P and Q is equal and half of their elements are in common, the similarity index between them is 0.33. This gives one a rough idea of how closely the reference complex should resemble the query complex  when KGS2 became effective in this case.On the contrary, KGS2 was not successful on the CA-2 test set. Application of KGS2 in this case produced essentially comparable results as standard scoring functions, i.e. no obvious improvements were observed Fig. . On the The statistical results produced by the original KGS method in combination with the four scoring functions on the CA-2, BACE-1, beta-trypsin, and CHK-1 test sets are given in Additional file Besides the standard model of KGS2, three variation models see  were alsk in Eq. Rp, SD, and k) obtained on the tailored refined set were very close to those obtained on the full refined set. This is understandable since only a minor fraction of the refined set (17%) was excluded here. Besides, because the refined set is a large, non-discriminatory mixture of diverse protein-ligand complexes, removing the complexes formed by any particular target protein will not cause a significant change to the genotypes/chemotypes included in this data set. The statistical results produced by Variation Model 1 on the five test sets are given in Table k parameters are really close in both models, this observation is not surprising. Therefore, we conclude that if KGS2 is to be evaluated on other target proteins, it is not necessary either to remove the relevant complexes from its default reference library when deriving the parameter k in Eq. As for Variation Model 1, the parameter Ka units among applicable samples. Interestingly, Variation Model 2 was effective in the case of CA-2; whereas KGS2 was not. Nevertheless, in the case of BACE-1, the performance of Variation Model 2 varied upon the similarity cutoff as well as the partner scoring function, where no obvious trend was observed. In the case of HIV-1 protease and CHK-1, Variation Model 2 failed badly, where it actually produced worse binding scores than standard scoring functions. It is fair to conclude that the performance of the standard model of KGS2 is more robust than Variation Model 2 on those test sets. Thus, it proves that including 3D structural information in the interaction fingerprints employed by KGS2 is indeed helpful.Variation Model 2 was designed to investigate if the 3D interaction fingerprints used in KGS2 was really helpful. It is generally assumed that small-molecule compounds sharing similar 2D chemical structures tend to have similar properties or activities. Within the framework of KGS2, it is also possible to select the reference complex by simply comparing the 2D chemical structures of ligand molecules. The statistical results produced by Variation Model 2 at the similarity cutoff of 0.35 are given in Table Ka units depending on the applied similarity cutoff as well as the partner scoring function. However, it was not effective on the other three test sets. Unlike Variation Model 2, it did not produce worse binding scores in those cases. Compared to the standard model, the overall performance of Variation Model 3 is consistently inferior on each test set. Thus, we conclude that the 3D interaction fingerprints employed by KGS2 is more effective than the algorithm that considers protein structures only.Variation Model 3 was designed to investigate if including the structural information from the ligand side in the interaction fingerprints employed by KGS2 was really helpful. This is a valid question because some forms of 3D interaction fingerprints are designed based on protein structures only . The resIn our in situ scoring test, the binding affinity of a protein-ligand complex was computed based on its experimentally resolved 3D structure. This is of course not practical in reality because obtaining a complex structure is far more difficult than conducting a decent binding assay. Thus, the in situ scoring test in our study actually serves as a proof-of-concept. Our second type of test was designed to mimic the reality in structure-based drug design more closely. Assuming that the binding affinity data of a set of ligand molecules have been measured, now a molecular modeler chooses to employ molecular docking to derive the binding modes of these compounds and also attempt to compute their binding affinities. Here, the purpose is to interpret the structure-activity relationship of these compounds to provide a guidance for further structural optimization. The quality of such a workflow is typically judged by how well the computed binding data explain experimental observations.R > 0.45 (i.e. R2 > 0.20) indicates a statistically significant correlation for a data set of 12 ~ 15 samples at the 90% confidence level. By this standard, only one out of the total 16 trials  achieved a significant correlation, i.e. ChemPLP on the HIV-1 protease test set (R2 = 0.244). This result demonstrates again that binding affinity prediction is still a very challenging task for today’s standard docking/scoring methods.The statistical results produced by four scoring functions on the four selected test sets are summarized in Table R > 0.45 as the threshold, the total number of trials where a significant correlation was achieved increased from one to five after application of KGS2, including ChemPLP and GoldScore on HIV-1 protease, ChemPLP, ASP, and GoldScore on CHK-1.As shown in Table R2 = 0.244 . It is demonstrated in our study that KGS2 indeed worked with different types of scoring functions in a non-discriminatory manner to produce more accurate binding scores. Besides, one does not need to re-engineer a scoring function before applying KGS2. The only important input required from the user side is a “reference library”, i.e. a data set of protein-ligand complexes with known 3D structures and experimental binding data. Ideally, this reference library should consist of a sufficient number of protein-ligand complexes formed by the same target protein as the query. Such a library may be compiled from public data resources, such as the PDBbind database. Users in pharmaceutical companies have the privilege to utilize their internal data for this purpose. Use of an external reference library is more flexible when one needs to deal with different target proteins: Once a different target protein is under consideration, one just needs to replace the reference library. If one does not bother to switch between different reference libraries, a comprehensive reference library containing diverse protein-ligand complexes may be employed as default.Another advantage of KGS2 lies in the fact that KGS2 is essentially an interpolation method. More accurate predictions can be made through interpolation if more data points are known in the problem space. Knowledge of protein-ligand complex structures and binding data is being accumulated rapidly. For example, the total number of protein-ligand complexes recorded in the PDBbind database increases by 10% each year. Consequently, larger and finer references libraries may be complied for KGS2, which in turn is expected to produce more accurate results based on them. Customized scoring functions of course can utilize the increasing knowledge of protein-ligand complex structures and binding data as well, for example, by conducting parameterization on larger data sets. However, the accuracy of customized scoring functions will not improve in a proportional manner with the expansion of data sets. In contrast, an interpolation method benefits from data expansion more directly.The limitations of KGS2 also need to be mentioned clearly. First, KGS2 will not be useful if an appropriate reference complex cannot be found for the query complex. It is usually not a problem for a well-established target protein where abundant complex structures and binding data have been accumulated. However, if one deals with a rare or new target protein, it is possible that KGS2 is not applicable at all. The second limitation is that our definition of interaction units does not account for some special components, such as structural water molecules and metal ions located in the binding pocket. Those components are important in some cases because they bridge the interactions between protein and ligand. Ignoring such components certainly has a negative impact. For example, KGS2 was not very successful in the case of CA-2 in our in situ scoring test, where CA-2 is the only one among the five selected target proteins that has a metal ion inside its binding pocket. In addition, by our current algorithm each interaction pattern makes an equal contribution when two complex structures are compared Equation . But thiFinally, the potential applications of KGS2 in structure-based drug design should be discussed. As indicated by its name, i.e. “Knowledge-Guided Scoring”, KGS2 is designed primarily as a “scoring” method for binding affinity prediction. As demonstrated in the second set of tests conducted in this study (i.e. the molecular docking test), KGS2 can be used in combination with standard docking/scoring protocols to interpret the structure-activity relationship of a congeneric set of lead compounds. The outcome of such effort provides a guidance for further structural optimization of those compounds, which is particularly useful when experimental techniques cannot resolve the desired protein-ligand complex structures in a timely manner. More rigorous methods, such as the free energy perturbation (FEP) method , 68 and Our KGS2 method can serve as a convenient “add-on” to enhance the performance of current scoring functions in binding affinity prediction without the need to re-engineer them. As compared to the original KGS method, the major improvement in KGS2 is the introduction of 3D protein-ligand interaction fingerprints as the basis of comparing complex structures. In this study, KGS2 was first validated on a set of in situ scoring tests, where a non-discriminatory set of protein-ligand complexes and five sets of complexes formed by HIV-1 protease, CA-2, BACE-1, beta-trypsin, and CHK-1, respectively. The results obtained in this test indicated that KGS2 consistently outperformed KGS in all cases. The performance of KGS2 was also more robust than two variations (i.e. Variation Model 2 and Variation Model 3) which employed different algorithms in reference selection. Moreover, KGS2 produced more accurate binding scores in combination with all four different scoring functions  on four out of five test sets. These results verify our assumption that KGS2 in principle can work with any scoring function and is applicable to various target proteins.In this study, a more challenging set of molecular docking tests were also conducted on four sets of ligands of HIV-1 protease, CA-2, BACE-1, and CHK-1, respectively. The four scoring functions, if applied alone, achieved rather limited success on those data sets. Here, application of KGS2 did not lead to an improvement as obviously as in the in situ scoring test. However, it did increase the number of significant correlation from one to five out of a total of 16 trials. As the in situ scoring test serves as a proof-of-concept, this test demonstrates the possible application of KGS2 in practice, i.e. interpreting the structure-activity relationship of congeneric lead compounds to guide further structural optimization. The potential of KGS2 in other types of tasks, such as virtual screening and de novo design, has yet to be explored.


Traditional Protein-Protein Interaction (PPI) networks, which use a node and edge representation, lack some valuable information about the mechanistic details of biological processes. Mapping protein structures to these PPI networks not only provides structural details of each interaction but also helps us to find the mutual exclusive interactions. Yet it is not a comprehensive representation as it neglects the conformational changes of proteins which may lead to different interactions, functions, and downstream signalling. In this study, we proposed a new representation for structural PPI networks inspecting the alternative conformations of proteins. We performed a large-scale study by creating breast cancer metastasis network and equipped it with different conformers of proteins. Our results showed that although 88% of proteins in our network has at least two structures in Protein Data Bank (PDB), only 22% of them have alternative conformations and the remaining proteins have different regions saved in PDB. However, using even this small set of alternative conformations we observed a considerable increase in our protein docking predictions. Our protein-protein interaction predictions increased from 54% to 76% using the alternative conformations. We also showed the benefits of investigating structural data and alternative conformations of proteins through three case studies. However, to deeply understand functional roles and binding mechanisms of proteins, we need to include an extra piece of information in these PPI networks which comes from structural data.The most common representation of PPI networks is a graph demonstration. In these PPI graphs, nodes represent the proteins and edges represent their interactions. This abstract representation provides a global picture of biological processes and protein function and helps us to simplify complex cellular systemset al.2 show that the problem arises from the traditional representation of PPI networks. In these networks, all the protein products of a single gene are mapped into a single node. So, in protein interaction networks, each node represents a collection of proteins, each with a distinct conformation or spliced form. Even though the conformations are not totally different, the small differences suffice to bring new interactions. Nevertheless, the number of interactions can vary from one protein to another. Structural analysis suggests that the essential proteins in the PPI networks have more binding sites than other proteins3. On the other hand, it has been shown that the larger the number of partners a hub protein has, the higher the probability of the hub essentiality is4. (B) The next question would be, which interactions can occur simultaneously and which are mutually excluded? To answer this question, we need to investigate a new dimension in PPI networks which is time5. One of the first pioneering works in this field was done by Kim et al.6. They mapped the protein interactions to known interface structures by using their sequence similarity. They identified mutually exclusive interactions by inspecting the usage of the same binding interface on their structural yeast interaction network. Similar to this study, Tuncbag et al.5 used p53 pathway as a case study and predicted the simultaneous and mutually exclusive interactions in their network. Kuzu et al.7 used structural interaction networks to find simultaneous and mutually exclusive interactions on ERK and MAPK pathways. C) Another question would be, how can mutations, located on interfaces, surfaces, or buried in the core regions of proteins, affect the PPI networks? It has been shown that mutations located on interaction interfaces of a protein are more likely causing distinct interruptions in the overall interactome8. Therefore, they can result in different biological phenotypes. In a recent study, Mosca et al.9 created a resource named dSysMap which systematically maps disease-related missense mutations on the structurally annotated human interactome. Their study shows that if pairs of mutations are located in different interfaces of the same protein, they usually cause different phenotypes. However, if they are located in the same interface of interacting proteins, they are most likely producing the same phenotype. To predict the effects of mutations on PPI networks, Moretti et al.10 performed a community-wide assessment of available methods. They computationally designed influenza binders HB36.4 and HB80.3, and then created single point mutant variants corresponding to all 20 amino acids at each position of the binders. They showed that mutations can influence binding if they disrupt the folded state.Addition of the structural information to the traditional PPI networks enables us to answer some essential questions in systems biology: (A) The first question would be, how is it possible for some proteins to have tens and even hundreds of interactions in PPI networks? Since proteins have a limited surface area, a single protein cannot interact with such a large number of partners at the same time. Tsai 12 and their conformations change based on the factors like post-translational modifications, pH level, and environment. Thus, we need to consider an ensemble of protein conformers for each node in PPI networks. The conformational changes dictate the protein function. Therefore, characterization of this association between protein conformation and its function, helps us to understand how to alter and regulate protein activity. The importance of investigating multiple conformers of the proteins in PPI networks lies in the fact that protein conformational changes cause activation of specific pathways15. A proper example could be RAS-family proteins. RAS proteins switch between GDP-bound inactive form to the GTP-bound active form, as two conformational states, in response to receptor-mediated extracellular signals16. They regulate proliferation, differentiation, motility and cytoskeletal re-organization18. The conversion of GTP-bound to GDP-bound form is regulated by guanine nucleotide exchange factors (GEFs), and the conversion of GDP-bound to GTP-bound form is mediated by GTPase activating proteins (GAPs)19. Active RAS interacts with its effector proteins and activates downstream effectors like Raf kinase, phosphatidylinositol 3-kinase and Ral guanine nucleotide-dissociation stimulator20. Schematic representation of RAS conformational states and their corresponding interactions are shown in Fig. The common practice in structural PPI networks is to inspect just one protein structure for each node. However, proteins are flexible21 provides protein structures which include alternative conformations of the same protein coming from different experiments. Kuzu et al.22 used an ensemble of protein conformations to dock the difficult cases of a docking benchmark data set. Applying the alternative conformations of proteins significantly increased the probability of successful protein interaction prediction in their study. They created the network of ERK interactions as a case study. Also, ensemble of protein conformations are used in docking to model small-molecule-protein or peptide-protein interactions24. However, ensemble docking studies are neither at network level, nor for protein interactions, they usually make use of computationally generated conformations rather than multiple conformations obtained from PDB.RAS example intensifies the investigation of different conformers of proteins in structural PPI networks. Protein Data Bank (PDB)25, PDBFlex26, and CoDNaS 2.027. PDBFlex database provides the analysis of structural variations between the different depositions for the same protein in the PDB database. It clusters different structures of proteins based on their sequence similarity and animates its dynamics. CCProf database also offers conformational changes of protein structures plus some other beneficial biological features like potential binding target site, secondary structure, conservation, phosphorylation site and catalytic site. CoDNaS database covers a large proportion of available protein structures derived from X-ray crystallography and NMR experiments in the PDB database. It clusters the protein structures using their sequence identity too. There are ~15 conformers on average for each protein chain in this database.There are some recent databases which explore the conformational changes and flexibility of protein structures deposited in PDB database such as CCProf28. After creating the traditional PPI network of breast cancer lung and brain metastasis, we enriched them by alternative conformations of the proteins. This new enriched network helps us to understand how proteins interact with each other using specific conformations and how these alternative conformations lead to diverse downstream signalling. It also shows us the effects of mutations on protein conformation, its interaction and function.In this study, we concentrate on mapping structural data into the PPI networks and investigating alternative conformations of these protein structures. We have chosen the breast cancer lung and brain metastasis PPI networks of our previous studyc1, pc2, … pcn} which can inter-convert to each other. If p1 and p2 have N and M alternative conformations respectively, the edge {p1, p2} shows that at least one {p1ci, p2cj} i ≤ N, j ≤ M is physicochemically possible. This new representation reveals two important facts about PPI networks. First, each specific protein conformer can contribute to a number of potential interactions the protein can have. In Fig. c2), it can only bind to p4 if p4 is in its c2 or c3 conformations. However, if p3 is in conformation c3, it may bind to p1, p2, or p3. Second, each edge in the PPI network has a strength based on the present conformations of the proteins in the network at any time point. Namely, some edges can be formed more probably as the corresponding interacting proteins can bind to each other using different conformations. For example, edge {p3, p5} can be speculated to be less probable than {p3, p4}, as the latter can happen regardless of which conformation p3 has.To have a more realistic view of PPI networks, we propose a new PPI representation as in Fig. 21. As it is a redundant database, mapping all of the structures without any curation can cause unnecessary repetition in the 3D protein-protein interaction predictions. Thus, similar PDB structures which correspond to the same protein should be eliminated. For each protein, we cluster its alternative conformations according to their sequence and structural similarities. Then, the possible protein-protein interactions using each specific protein conformation are investigated using a docking method. We use PRISM in this study for interaction prediction because unlike many docking methods designed just for a binary interaction, PRISM is able to get a PPI network as an input which makes it suitable for large scale studies. The graphical illustration of the multi-conformational PPI network creation procedure is illustrated in Fig. To create the multi-conformational PPI network, we followed the steps shown in Fig. After clustering the protein structures according to their similarities, one or more structures remain as their clusters’ representatives for each protein. To predict if two connected proteins in the network can bind to each other or not, we explored all permutations of their representative structures. As we can see in the results section, some interactions in the network are feasible only by using specific conformations of the interacting proteins. This fact may change the topology of the network at each specific time point as each protein can form different interactions based on its conformation in the network. This is one of the major points that traditional PPI networks are not able to show.29 to map proteins to their known 3D structures which provides a list of corresponding available structures in PDB. In this step, protein structures which have less than 30 residues are eliminated. Secondly, all available structures for each UniProt ID are structurally aligned using TMAlign tool30 to each other to quantify their structural differences. In order to find the unique protein structures, we use agglomerative complete linkage clustering method based on sequence similarity and RMSD value of the protein structures. If two protein structures, corresponding to the same UniProt ID, have more than 95% sequence identity and smaller than 2 Å RMSD value, we put them together in a cluster. We applied this procedure until there is not any protein structures left without an assigned cluster information. Representative structures for each cluster are selected based on the best intra-cluster similarity and RMSD value. These representative structures are the alternative structures of a node corresponding to a protein in our merged structural network  interactions.The examination of the interacting protein pairs and their affinities from experimental methods includes both direct and indirect interactions. As we aimed to use only physical protein interactions, we filtered our data by using experimental protein interactions from STRING database45, we downloaded Human Interaction Network (HIN) consisting of 165,184 protein interactions. From this network, we only considered protein interactions coming from experimental data with the confidence score ≥0.4  is a member of Karyopherin family which transports proteins into and out of the nucleus. It has been known that unlike importin α, members of importin β family can bind to proteins directly and import them independentlyKPNB1 is a protein of length 876 and there is almost complete structure in the PDB database. There are 19 PDB structures for KPNB1 which are clustered to 7 different clusters based on our algorithm i.e. 3w5kA, 1qgrA, 3lwwC, 1qgkA, 1ibrB, 2q5dB, and 2qnaA. Figure We submitted all possible interactions of KPNB1 with its neighbours using their alternative conformations to PRISM. For representation purposes we focus only on 6 different interactions of KPNB1 in our network. Based on the PRISM predictions, we reach the structural network shown in Fig. 3G-capped U snRNPs57. SNUPN has an IBB (importin-β-binding) domain in its N-terminal which is used to bind to KPNB1 and then transport to the nucleus. It has been shown that after each round of import, SNUPN uses CRM1 to return back to the cytoplasm58. KPNB1 close conformation 3lwwC bound to SNUPN is represented in Fig. 54. To detect the binding residues in this complex structure we used HotRegion webserver59. The binding residues of this complex structure are listed in Supplementary Table 60 for molecular graphics and analyses.The complex structures of interactions KPNB1-SNUPN and KPNB1-KPNA2 are already available in the PDB database namely PDB ids 3lww and 1qgk, respectively. The close conformation of KPNB1 i.e. 3lwwC is bound to SNUPN (Snurportin-1). KPNB1 uses SNUPN adapter to bind cargo molecules to import m61 provided structural evidence that open and close conformations of KPNB1 bind to SNUPN IBB domain. We had the same results in PRISM predictions as shown in Fig. Notwithstanding the high RMSD values between open and close conformations of KPNB1, Bhardwaj and Cingolani63. The available PDB complex structure for KPNB1-KPNA2 uses a KPNB1 close conformer as shown in Fig. 59 shown with boldface in Supplementary Tables Another existent interaction of KPNB1 is with KPNA2 (PDB id 1qgk). KPNA2  is an adapter protein for KPNB1 which binds to substrates containing a nuclear localization signal (NLS) motif. Importin β binds to the IBB domain of importin α and forms a heterodimer. Cytosolic proteins having NLS-containing motifs form a complex with this heterodimer and transported into the nucleus by using nuclear pore complexes (NPCs) which are embedded on the nuclear membraneet al.64 showed that KPNA2/KPNB1 canonical pathway is responsible for p65  import to the nucleus. They tested 6 different members of the importin α family and found that KPNA2 plays the major role in p65 import. p65 uses different members of importin β and α families for its import but it has a high affinity towards KPNB1 and KPNA2. Based on their results, p65 hetero trimer is imported to the nucleus through Nuclear Pore Complexes (NPCs). The proteins involved in this pathway are connected to each other in our network, in the order RELA-KPNA2-KPNB1-NUP153. Figure Liang 65. Its expression in breast carcinoma is associated with tumour growth and metastasis68. Besides, in vivo experiments showed that silencing of SNAI1 significantly diminishes tumour occurrence and growth69.By looking at the PDB database, we found that there are other complexes for KPNB1 which complex with RAN, SNAI1, and PTHLH. Among all available structures of KPNB1, only 3w5kA is bound to the zinc finger protein SNAI1. SNAI1 is a transcription factor which has been found in a number of carcinomas and melanomas59 shown with italic typeface in Supplementary Tables We would like to know if other conformers of KPNB1 can bind to SNAI1 too, so we submit all other alternative conformations of KPNB1 with SNAI1 to the PRISM webserver. Interestingly, PRISM can only find the complex structure using 2q5dB conformer of KPNB1 for these submissions. In this complex, SNAI1 binds to 2q5dB from the same binding site it uses to interact with 3w5kA. The binding energy score for this complex is −139.17, and the binding residues are listed in Supplementary Data These evidences lead us to infer that some protein conformers are more limited in terms of their binding partners’ quantities e.g. KPNB1 can bind to SNUPN or SNAI1 in open conformation though it can’t bind to SNAI1 in close conformation. Therefore, each specific protein conformer put a limitation on the diversity of possible binding partners.in-vivo inhibition of these two genes reduce breast cancer metastasis progress of lung70. Chemokines are small proinflammatory chemoattractant cytokines which bind to specific G-protein-coupled receptors. CXCL12 is expressed in several organs including lung, liver, brain, skeletal muscle, kidney, heart, skin, and bone marrow. The binding of CXCL12 to CXCR4 is known to induce intracellular signalling through several different pathways initiating signals related to chemotaxis, cell survival and/or proliferation71.CXCL12 (C-X-C motif chemokine 12) is the ligand of CXCR4 (C-X-C chemokine receptor type 4) which is a seed gene in LMSN. This interaction has GUILDify score of ~0.5 which places it in the top 5% interactions in LMSN. It is known that 72 to inhibit cancer malignancy74. It has been shown that binding to CXCR4 N terminus (CXCR4 1–38) promotes CXCL12 dimerization75. Drury et al.76 found that oligomeric changes of CXCL12 induces cellular migration with monomer but not dimer77. Interestingly, they also observed that dimeric CXCL12 exhibited receptor interactions and downstream signalling different from the monomeric chemokine. Their results show that monomeric CXCL12 activates β-arrestin-2 recruitment and filamentous-actin accumulation. On the other hand, dimeric CXCL12 weakly recruits β-arrestin-2 and diminishes actin polymerization compared to monomeric CXCL1278. This indicates that monomeric and dimeric CXCL12 can have different outcomes in metastasis phenotypes upon CXCR4 binding.The CXCL12-CXCR4 interaction is involved in tumour progression, angiogenesis, metastasis, and survival. There are efforts to block metastatic dissemination by inhibiting CXCR4 activation79. However, some studies have also reported that binding of the CXCR4 N-terminus (CXCR4NT) induces H25R mutated CXCL12 (CXCL12H25R) dimerization80. Here we investigate the homodimerization of CXCL12WT and CXCL12H25R, and the interaction of CXCL12 with CXCR4N-terminus by using PRISM to better understand the conformational differences and their downstream effects. Figure WT (PDB ID: 2j7zA) conformation and its H25R mutated conformation (PDB ID: 2kolA). As can be seen from the figure they don’t align perfectly on top of each other, especially in N- and C-terminal parts. Besides, there is a significant difference in their surfaces at residue 25 as shown in Fig. Different signalling outcomes might be related to alternative conformations of CXCL12 upon CXCR4 binding. There are 20 available structures in PDB database for CXCL12 which are divided to 11 clusters based on our clustering method. It is known that conformational changes can be related to point mutations on the protein interfaces which may change their dimerization profile and structural arrangement. There are several mutations known on the CXCL12 protein. One important mutation is H25R substitution at the binding interface which discourages CXCL12 dimerization, thus it is preferentially monomericWT, and CXCL12H25R. However, in case of the CXCL12H25R the binding energy diminished significantly as expected. The binding energy score for CXCL12WT homodimer is −101.41, while it is −17.24 for CXCL12H25R homodimer. The binding residues of these homodimer structures are listed in Supplementary Table WT homodimer and the available structure in PDB database. There is no structure in PDB database for CXCL12H25R homodimer to compare with the PRISM prediction shown in Fig. The PRISM webserver predicted the homodimer formation for both CXCL12NT. For both interactions, CXCL12WT-CXCR4NT and CXCL12H25R-CXCR4NT, the PRISM has predictions with acceptable binding energies of −87.25 and −52.48 respectively. Tyrosine sulfation is known to promote the protein−protein interactions. There are three potential sulfation sites on CXCR4NT at positions 7, 12, and 2180 which PRISM used two of them in its predictions , SMAD3 (SMAD family member 3), and NCK1 (NCK adaptor protein 1) complex is an example of mutually exclusive interactions in LMSN. NEDD9 is a scaffolding protein, and it was previously identified as the lung metastasis causing gene in breast cancer88. It has been found that the residues 301–330 of SMAD3 (PDB ID: 1MK2), which forms the MH2 domain, are more important for binding to NEDD987. NEDD9 has only one structure available in PDB which covers residues 399–563 (PDB ID: 2L81) presenting a serine-rich four helix bundle domain. As shown in Fig. Both N-terminal and C-terminal sequences of NEDD9 are known to bind to SMAD3 causing rapid turnover of NEDD989. Previously, it was identified that NCK1 binds to NEDD9 by using its SH2 domain90. We observed the same result in the PRISM predictions. The predicted interface of NEDD9-NCK1 interaction consists of 26 residues and NEDD9-SMAD3 interaction interface consists of 33 residues. There are 7 residues in common between these interfaces (Supplementary Table On the other hand, PRISM predictions suggest that NEDD9 binds to NCK1 from the same region it binds to SMAD3 Fig. . The binBased on these results, NEDD9-SMAD3 and NEDD9-NCK1 interactions are mutually exclusive so we cannot see these two edges at the same time in the PPI network. Thus, to achieve to the actual topology of a PPI network we need to inspect the structural aspects of proteins. By considering the mutual exclusive interactions, the degree of NEDD9 in LMSN at any specific time point may decrease significantly.The main objective of this study is to emphasize the importance of investigating alternative conformations of proteins in structural PPI networks to be able to analyse the protein interactions more accurately. This information enables us to observe the effects of conformational changes, mutations, and mutual exclusiveness on specific protein interactions and downstream signalling.To this end, we have built extensive protein-protein interaction network for breast cancer lung and brain metastasis by combining various databases. Then, we ranked all available interactions according to their relevance to specific breast cancer metastasis phenotypes. Using the STRING database, we filtered our network to increase the reliability of the protein interactions. To map the protein structures into the network, we proposed a clustering algorithm based on sequence and structural similarity of protein structures to prevent repetition in our analyses.WT-CXCR4NT and CXCL12H25R-CXCR4NT. Based on our results, CXCL12H25R and CXCL12WT can bind to CXCR4NT, and this interaction has been shown to direct the migration of metastatic breast cancer cells to specific tissues. However, binding to CXCR4NT inhibits CXCL12H25R dimerization which is not the case for CXCL12WT. Dimerization of CXCL12 as the ligand of CXCR4 is important as it stimulates intracellular calcium flux but fails to activate F-actin polymerization or β-arrestin 2 recruitment which has been shown to cause cellular idling that blocks the metastatic tumour formation.We used PRISM to model the 3D structures of the PPIs in our networks. However, we could not predict the whole interactions in our metastasis subnetworks since not all of the proteins have structures stored in the PDB database. By using alternative conformations of proteins in our subnetworks, we observed the effects of protein conformational changes on protein interactions and downstream signalling. We inspected the conformational changes of KPNB1 which directs it to bind to SNAI1 in the open conformation and bind to SNUPN in the open/close conformation. It shows the limitations posed by the conformational changes of proteins which fluctuates their ability to stablish various interactions. This is an example of conformational change effects exist in the PDB database. As another case study, we examined the interactions CXCL12Investigating the structural models, we detected the mutual exclusive interactions. As a case study, we looked at NEDD9 mutually exclusive interaction partners. PRISM predicted that NEDD9-SMAD3 and NEDD9-NCK1 interactions are mutually exclusive. This difference can cause activation of different pathways which lead to different metastasis phenotypes. It is known that NEDD9-SMAD3 interaction causes proteolytic cleavage of NEDD9 which induces cell detachment, apoptosis and individual motility of metastasis cells. However, exact role of NCK1-NEDD9 binding was not previously examined in detail.Overall, we created the brain and lung metastasis subnetworks of breast cancer and modelled the 3D structures of the protein-protein interactions to describe the significance of protein structural data and conformational changes through the several case studies. We believe that this study may be beneficial to appreciate the inability of traditional PPI networks, which lack structural and dynamics information of proteins, to provide a detailed view of protein interactions and their functions.Supplementary information


Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction.The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction.In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction. Although essential proteins are only a small fraction of all proteins, they are indispensable to maintain life for an organism , 2. WithTo break through these experimental constraints, some researchers proposed computational methods to predict essential proteins based on features developed in experimental studies. Especially, due to the high-throughput techniques, abundant data of essential proteins has been collected, which served as the basis for several studies that investigate the relationship between characteristics of experimentally identified essential proteins and their topological properties in protein-protein interaction networks (PPI). With the help of computational methods, the burden to test all proteins in experiments can be greatly relieved, so that only tests of top-ranked proteins based on their score of essentiality are prioritized. Jeong et al. used centrality-lethality rule to identify essential proteins in protein-protein interaction networks, which means that proteins most highly connected in the networks tend to be essential proteins . PereiraMoreover, recently many studies found that the subcellular localization of proteins may play an important role in identifying essential proteins. Acencio and Lemke discover that integration of information from multiple sources including subcellular localization of proteins can improve the accuracy of essential proteins prediction . Peng etThis paper is organized into four sections. Our algorithm is presented in “In this section, we will present our method SCP, that can rank the importance of proteins with computed scores. The final importance scores of our SCP method is determined by two components: the results ranked by our modified PageRank algorithm (MPR) from subcellular localization information, and the results ranked by Pearson correlation coefficient (IPCC) from gene expression data: λ is an adjusting parameter for weighting the two components. In this paper the parameter λ is set as 0.5. The MPR is the importance scores computed from modified PageRank algorithm. The IPCC is the importance scores predicted by Pearson Correlation coefficient. In order to predict essential proteins, we propose a novel algorithm combining MPR with IPCC. We expect that protein with a higher SCP score would be more likely to be an essential protein. As the scores of MPR and IPCC may have different range, they should be scaled into  first. We normalize the two importance scores as follows: where We first create a weighted PPI networks derived from subcellular compartments information, and then perform a modified PageRank algorithm on the network to compute importance score of proteins. For most eukaryotes, the subcellular compartments generate a specific environment that regulates the biological processes of proteins within cells. Therefore, knowing the subcellular localization of proteins may shed light on understanding the functions of these proteins. Many studies found that proteins interactions in vivo tend to co-locate in the same cellular compartment or adjacent compartments . For exaBased on this intuition, we develop a metric to weigh the protein-protein interactions based on the information of subcellular localization. We assume that protein-protein interactions co-located in a small subcellular compartment can be more reliable in predicting essential proteins than those within a large subcellular compartment.C1,C2,⋯,CK, and the numbers of them are Ci, denoted by ISC, is defined as: We model the importance of subcellular compartments based on their scales. Suppose there are K subcellular compartments Pi, let SCL(Pi) be the subcellular compartments where protein Pi located. The weight of Pi and Pj interaction is denoted by WPPI, which is defined as: The importance of protein-protein interactions can be impacted by different subcellular compartments they share. For a given protein where Pi and Pj are co-located in. We assume that a pair of proteins in the smaller subcellular compartments is most likely to interact with each other than them in the bigger compartments. Therefore, if a pair of proteins are co-located in at least one subcellular compartment, that is A pair of proteins may be co-located in several subcellular compartments because many proteins are annotated by multiple subcellular compartments. Here Pi as its prior importance (denoted by IPSC(Pi)): By analyzing the weighted protein-protein interaction network, we can achieve prior estimate on the importance of each protein. The proteins which have stronger interactions with others to be more important proteins . Guided by this idea, we sum up all the weights of protein-protein interactions related to a protein PageRank is one of the most famous methods that rank the importance of nodes in networks based on link structures of nodes. The basic idea of PageRank algorithm is that the importance of a node is determined by the importance of their parents nodes and the number of their parents nodes. Therefore, by analyzing the quantity and quality of their parents nodes, PageRank algorithm can give a rough importance estimates for all nodes in networks.In the classic PageRank algorithm, the importance of nodes can be defined as follows: N is the number of the nodes, and L(Pj) is the number of outbound links for node Pj, which belongs to the set of nodes that link to Pi, also denoted by SCL(Pi). α is a dampening factor set to 0.85 in this paper.where Equation where and We propose a modified PageRank algorithm to calculate the importance of nodes MPR, defined as follows: Here the modified iterator matrix where sparse hyperlink matrix and the reset probability matrix M2 comes from the prior importance of proteins: Finally, the importance of nodes is normalized as follows: X= and Y=, the importance of protein-protein interactions between the two proteins can be calculated as follows: Pearson correlation coefficient (PCC) is a popular method to measure linear correlation between two variables. Here we utilize PCC, derived from gene expression data, to calculate the importance of protein-protein interactions. Given gene expression data of two proteins, denoted by Pi: Finally, the importance of each protein Pi, denoted as IPCC(Pi), is computed by summing up all weights of protein-protein interaction importance of protein In this section, experiments are carried out to evaluate the effectiveness of our algorithm. We take advantage of three types of datasets, namely protein-protein interactions data, gene expression data and subcellular localization data, to predict essential proteins for Saccharomyces cerevisiae. We compare the performance of our algorithm SCP against other five methods  on real dataset of essential proteins. The results show that our method SCP outperforms the other five methods.We downloaded protein-protein interaction networks from the Biogrid database (BIOGRID-3.2.111), which is a freely accessible database to provide physical and genetic interactions . The netThe gene expression data of yeast was obtained from the NCBI Gene Expression Omnibus website. This dataset was collected at 36 different times from 9335 probes , since there is evidence that the expression of gene is periodic during metabolic cycle of Saccharomyces cerevisiae . In totaThe COMPARTMENTS database  containsThis set of essential proteins were downloaded from DEG , MIPS 3, SGD 3232 and SGThe proteins of Saccharomyces cerevisiae are classified into essential and nonessential proteins, so the prediction of essential proteins is actually a two-class classification problem. Hence, ROC curve is a proper metric to evaluate the performance of a binary classifier, plotted at different thresholds. In an ROC curve, the horizontal axis represents the values of false positive rate (FPR) and vertical axis represents the values of the true positive rate (TPR). The false positive rate is also known as specificity and the true positive rate is also known as sensitivity or recall. They are defined as follows: where FP is the number of false positive, which means a prediction is positive and the actual value is negative. Conversely, FN is the number of false negative, which means the prediction is negative while the actual value is positive. Then TP is the number of true positive when both the prediction and actual value are positive. TN is the number of true negative when both the prediction and true value are negative.Furthermore, the size of the area under the curve, named AUC, is used to evaluate the performance of a binary classifier. Therefore, the larger the AUC value is, the better classifier is. In Fig. In this section, we attempt to visualize the proportion of essential proteins in top ranked proteins by all methods, including our method SCP and other five methods. First, we rank proteins by their importance scores in descending order computed by all six methods. Second, we select the top 1,5,⋯,25 percent of all 6304 proteins in their ranked order as essential protein candidates. Then we count the number of real essential proteins in these essential protein candidates according to the golden standard dataset of real essential proteins. The comparative results are shown in Fig. In the Fig. In this section, we compare our method with five other methods by the jackknife curves, which is proposed by Holman et al.  to show In this section, we employ precision-recall (PR) curves to compare the performance of our method SCP with the other methods. The recall has been defined as the true positive rate (TPR) in “To analyze a binary classification, precision is a measure of the proportion of results that are relevant to the query, and recall is a measure of the proportion of results relevant to the query that are successfully retrieved. If AUC is high, both precision and recall are high. High score of precision suggests the classifier achieves accurate results, while high recall indicates the classifier obtains a majority of all positive results. Because there are 1204 essential proteins in our dataset, we also plot PR curves to analyze the top 1204 proteins ranked by all six algorithms. It is shown in Fig. In this section, we will do some further analysis of the links between top ranked proteins for all the methods. We construct small PPI networks based on the top 50 ranked proteins and the links depending on the whole yeast PPI networks. The results are shown in Fig. λ. As the prediction of essential proteins is an unsupervised learning procedure, we can’t learn a best parameter λ from the data. Therefore, we only choose λ∈{0,0.5,1} to analyze the performance of our algorithm SCP. In reality, when λ=0, the results of SCP only come from IPCC. Conversely, the results will only be calculated by MPR when λ=1. In this paper, we chose λ as 0.5, which means the results of SCP integrate MPR and IPCC. In order to compare the performance of the method on various λ, we calculate the number of essential proteins at different top percentages of ranked proteins . From Table λ=0.5, SCP obtains the best performance. Therefore, in this paper the parameter λ is set as 0.5. As a result, SCP successfully integrates the results of MPR and IPCC and has achieved a great boost on the performance of essential proteins prediction.In this section, we discuss the selection of parameter In this section, we will analyze the performance of CIC and SCP. Both CIC and SCP utilize the subcellular localization information to predict the essential proteins, while SCP also use the information of the gene expression data. Therefore, we will compare CIC with modified PageRank (MPR), part of our method SCP, which only uses the subcellular localization information as CIC does to predict essential proteins. The results are shown in Table Essential proteins are crucial to the development and survival of life. Many computational methods are proposed to detect essential proteins based on biological and topological features of proteins. In our study, we also found that integration of information from multiple sources can boost the identification of essential proteins. Specifically, the utilization of subcellular localization information can make a remarkable contribution to the prediction of essential proteins. In this paper, a SCP method is proposed, which integrates the ranking function by a modified PageRank algorithm with weighted subcellular localization with Pearson correlation coefficient based on gene expression data. Several experiments are carried out to compare the performance of SCP with five other methods in identification of essential proteins. Experimental results show that our method SCP performs the best among all six methods.


Intuitively, proteins in the same protein complexes should highly interact with each other but rarely interact with the other proteins in protein-protein interaction (PPI) networks. Surprisingly, many existing computational algorithms do not directly detect protein complexes based on both of these topological properties. Most of them, depending on mathematical definitions of either “modularity” or “conductance”, have their own limitations: Modularity has the inherent resolution problem ignoring small protein complexes; and conductance characterizes the separability of complexes but fails to capture the interaction density within complexes.Finding Low-Conductance sets with Dense interactions) to predict overlapping protein complexes with the desired topological structure, which is densely connected inside and well separated from the rest of the networks. First, FLCD detects well-separated subnetworks based on approximating a potential low-conductance set through a personalized PageRank vector from a protein and then solving a mixed integer programming (MIP) problem to find the minimum-conductance set within the identified low-conductance set. At the second step, the densely connected parts in those subnetworks are discovered as the protein complexes by solving another MIP problem that aims to find the dense subnetwork in the minimum-conductance set.In this paper, we propose a two-step algorithm FLCD . Additionally, results of FLCD show higher biological relevance with respect to Gene Ontology (GO) terms by GO enrichment analysis. Recent developments of high-throughput profiling techniques, such as yeast two-hybrid (Y2H) and tandem affinity purification (TAP) with mass spectrometry (MS), allow scientists to generate large-scale protein-protein interaction (PPI) datasets for different species –5. TheseBased on the inherent topological structures of protein complexes , predictThese existing algorithms can be grouped into three categories. The first category includes the algorithms that mimic Markovian random walk on graphs, pioneered by MCL . MCL doeAlgorithms in the second category do not directly predict complexes according to the topological structure of subnetworks but resemble traditional clustering methods based on derived similarity measures between nodes or edges. For example, MCODE , CFinderAlgorithms in the third category detect complexes based on explicit topological definitions of protein complexes. For example, modularity  and condFinding Low-Conductance sets with Dense interactions) to detect protein complexes that have dense interactions inside and sparse interactions outside in a given PPI network. FLCD explicitly takes care of both the internal and external connectivity of protein complexes in two steps. FLCD first identifies a low-conductance set around a protein, which is locally well separated from the rest of the network. Then a densely connected subnetwork within the low-conductance set is detected based on the definition of the edge density of a subnetwork proposed in [In this paper, we propose a two-step algorithm FLCD  to 0.2 that achieves the best performance empirically in our experiments. For SR-MCL, we set the inflation parameter I=3 and other parameters to their default settings since they yield the best results in our experiments. We set the only parameter k of our FLCD, the size of local neighbors based on personalized PageRank computation, to 20.We compare our FLCD algorithm with other three state-of-the-art overlapping complex prediction algorithms, which are ClusterONE , LinkComWe take four yeast PPI networks for performance evaluation: SceDIP, SceBG, SceIntAct, and SceMINT, extracted respectively from the Database of Interacting Proteins (DIP) , the BioDue to the possible incompleteness of the reference protein complexes, we further examine the biological relevance of every predicted complex by GO enrichment analysis. We download the mappings of yeast genes and proteins to GO terms according to  ≥0.25,∃cj∈C} is the set of the complexes that match to one or more reference protein complexes; |Ncs| is the size of the set Ncs. Ncp={ci∈C|NA≥0.25,∃sj∈S} is a set of reference protein complexes that are matched by predicted complexes. We consider a reference protein complex cj is matched by a predicted complex sj if NA≥0.25 [in which j)≥0.25 , 22, whem predicted and n reference complexes, let tij denote the number of proteins that exist in both predicted complex si and reference complex cj, and wj represent the number of proteins in reference complex cj. Then Sn and PPV can be computed as The geometric accuracy (Acc) score is the geometric mean of two other measures — the cluster-wise sensitivity (Sn) and cluster-wise positive predictive value (PPV) . Given mThe Acc score provides a balanced measure of Sn and PPV: The maximum matching ratio (MMR) is the ratio of the weight of maximum weight matching to the size of the reference set.N proteins with M proteins annotated with one GO term and the predicted complex has n proteins with m proteins annotated with the same GO term. The p-value of the complex enriched with that GO term can be calculated as similarly done in [Suppose that a given PPI network has  done in : 4\docup-value of all its enriched GO terms for a predicted complex as its final p-value. A GO term is statistically significantly enriched when the p-value of any complex corresponding to this GO term is lower than 1e−3.We choose the lowest We apply all competing algorithms to search for potential protein complexes in four yeast PPI networks and compare them in terms of the composite score, consisting of F-measure, Acc score and MMR based on both the SGD and MIPS reference protein complex datasets.We note that the different sizes and different numbers of detected complexes would affect the scores for the metrics that we have employed. However, in the context of complex prediction, there is no universal gold-standard metric. Hence, we apply three aforementioned metrics that have been commonly adopted in many other related works , 9. We aAs shown in Figs. When we take SGD reference dataset as our gold standard protein complexes, from Table For MIPS reference dataset, we notice the similar trend for the evaluation scores in Table In summary, considering the composite score based on three metrics, our FLCD outperforms the other algorithms. To further validate all competing algorithms, we perform GO enrichment analysis in the next section to see whether all predicted complexes by different algorithms have significant biological meaning.We perform GO enrichment analysis for all protein complexes predicted by the competing algorithms and report the percentages of the predicted protein complexes that are significantly enriched with at least one GO term and the total number of GO terms that are enriched in the predicted complexes in Table p-values of the complexes under GO terms of biological process, molecular function, and cellular component domains. We use the lowest p-value for each predicted complex and show the comparison of the statistical significance of the complexes detected by all competing algorithms in Fig. p-values while the x-axis is the ordered list of the complexes detected by all competing algorithms in terms of their negative log-p-values. Since complexes with significant biological relevance have lower p-values, higher values in Fig. To further examine the statistical significance of the complexes detected by the competing algorithms, we compare the We further show the differences between the competing algorithms by illustrating the predicted complexes corresponding to two specific reference protein complexes. The first reference protein complex is the Smc5-Smc6 complex. In Fig. Similarly, we show the predicted RNase complexes by all competing algorithms in Fig. We propose an algorithm FLCD to predict protein complexes in protein-protein interaction networks. FLCD can better characterize the topological structure of a protein complex, which is densely connected inside and well separated from the rest of the networks. We compare FLCD with other three state-of-the-art algorithms on protein complex prediction. The comparison results show that FLCD achieves superior performances. Furthermore, GO enrichment analysis of the results of the competing algorithms demonstrates that FLCD finds more biologically meaningful complexes, within which proteins tend to be in the same cellular components and have similar functions and/or participate in the same biological processes.G= represent a PPI network, where V denotes the set of proteins in G and E is the interaction set. A is the adjacency matrix of G with Aij=Aji and Aij=1 denoting node i interacts with node j and Aij=0 otherwise. The degree matrix D of G is a diagonal matrix with Dii=di, where i.Let an undirected graph S of proteins, the conductance of S in G is defined as [For a set fined as  5\documS and its complement set T. Here we make a mild assumption that vol(S)≪vol(V) for a small protein complex S in the large-scale PPI network G, which means where AS is the adjacency matrix of the induced subnetwork with respect to set S and DS is the degree matrix for the nodes in S, where i∈S. For the same set S, the density of S is defined as [where fined as  7\docum1i∈S is the indicator function depending on whether i∈S.where FLCD is motivated by conductance minimization to identify well separated subnetworks in a given network. However, FLCD can overcome the problem of conductance minimization, which pays no attention to the internal connectivity within subnetworks as potential protein complexes. Figure FLCD explicitly considers both the separability and internal edge density of complexes in two steps respectively. At the first step, it takes care of the separability of complexes by ensuring low conductance to hope for the complexes to have unique biological functions. At the second step, FLCD preserves the densely connected parts of the complexes identified in the first step. Because PPI networks are noisy and typically sparse, instead of finding cliques, we use the definition of internal density in  to searcv, our goal is to find a protein set v. We first apply the algorithm proposed in [H with low conductance, then the minimum-conductance set H is identified through solving a mixed integer programming (MIP) problem exactly.Given a starting protein posed in  to find v can be efficiently approximated via the personalized PageRank vector of v. The personalized PageRank vector p of v on G is the stationary distribution of the random walk on G, in which at every step, the random walker has the probability of α to restart the random walk at v and otherwise performs a lazy random walk. Mathematically, p is the unique solution to Following , a low-cα∈k for the


Arabidopsis thalianaProtein Interactome Database, available at http://www.megabionet.org/atpid) is an integrated database resource for protein interaction network and functional annotation. In the past few years, we collected 5564 mutants with significant morphological alterations and manually curated them to 167 plant ontology (PO) morphology categories. These single/multiple-gene mutants were indexed and linked to 3919 genes. After integrated these genotype–phenotype associations with the comprehensive protein interaction network in AtPID, we developed a Naïve Bayes method and predicted 4457 novel high confidence gene-PO pairs with 1369 genes as the complement. Along with the accumulated novel data for protein interaction and functional annotation, and the updated visualization toolkits, we present a genome-scale resource for genotype–phenotype associations for Arabidopsis in AtPID 5.0. In our updated website, all the new genotype–phenotype associations from mutants, protein network, and the protein annotation information can be vividly displayed in a comprehensive network view, which will greatly enhance plant protein function and genotype–phenotype association studies in a systematical way.AtPID ( Arabidopsis thaliana as the model system for protein interactions and functional annotation. From the year 2005, we started to collect the protein–protein interactions (PPIs) from literature and released AtPID 1.0, which only included limited curated PPIs and protein functional annotations from TAIR (The Arabidopsis Information Resource)   , RAPID (RIKEN Arabidopsis Phenome Information Database), CSHL Database (the Arabidopsis Genetrap Website at Cold Spring Harbor Lab), Chloroplast Function Database, SeedGenes Database, AGRICOLA Database (Systematic RNAi knockouts in Arabidopsis), Araport  and TAIR  collected 5121 mutants with significantly observable phenotypes related to 3431 genes. In the past few years, through in-depth cooperation with Shanghai Society for Plant Biology, we collected 488 new mutants and systematically annotated all the existed and new curated mutants’ phenotypes to 167 standardized plant ontology categories Figure . ComprehFor the phenotype annotation information, we re-developed the network visualization application Figure  and C wiHere, we have made great efforts to provide a significantly improved resource for genotype–phenotype associations, which could serve as a resource for experimental design and facilitate genome-wide systematical studies in Arabidopsis. The AtPID 5.0 also provides illustrations of the functional annotation and protein network with a friendly web-based interface. We have largely extended the current annotation information by literature curation, bioinformatics predictions and also the high-throughput experimental data in the AtPID 5.0, e.g. we generated a comprehensive chloroplast proteomics dataset in Arabidopsis by large-scale proteomics experiments and indexed all the data as the evidence for subcellular localization in current AtPID. We will continue to accumulate more genome-wide data to better serve the research community.