The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein–ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types . PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.The characterization of interactions in protein–ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein–ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein–ligand contacts in 3D structures, freely available at  However, the scientific community lacks freely available tools to detect frequent non-covalent protein–ligand interactions such as hydrophobic interactions, hydrogen bonds, salt bridges and π-stacking (The Protein Data Bank (PDB)  hosts nestacking . To thisstacking .PLIP is complementary to other state-of-the-art web tools such as SwissDock , GalaxySprojects.biotec.tu-dresden.de/plip-web without the need for registration or login. A short tutorial for new users as well as an extensive documentation is available on the website. The python source code is available for download on the PLIP website. Users interested in batch processing are encouraged to use the tool locally in command-line mode. A benchmark dataset of 30 literature-documented protein–ligand complexes is provided together with the source code.All interactions are listed on atom-level detail, enabling analyses of specific binding characteristics. PLIP is freely accessible at PLIP focuses on one-click processing of protein structures for the detection of interaction patterns. There are other tools, web pages and databases –20 as weThe user needs to provide a protein–ligand complex in PDB format. Any structure from the RCSB PDB server  can be aFigure The python source code of PLIP is available as open-source software and allows to run computations locally. Additional to the features of the web server, the PLIP command-line tool offers advanced settings for output files and thresholds. It enables high-throughput computation of protein structures and can be readily integrated into analysis pipelines using the machine-readable result files. The usage and options are explained in the Supplementary Data.Bacillus subtilis DegV protein binding palmitic acid (PDB ID 3FYS).PLIP uses four steps to detect and report relevant interactions: structure preparation, functional characterization, rule-based matching and filtering of interactions Figure –D. The aIn the preparation step, the input structure is hydrogenated and ligands extracted along with their binding sites. To this end, PLIP makes use of OpenBabel  for inteIn order to find interacting groups Figure , the binFollowing, putative interacting groups are matched by applying mostly geometric criteria Figure . DependiLast, filtering steps are used to eliminate redundant or overlapping interactions. As shown in Figure Detailed descriptions of the algorithm and thresholds for each interaction type are available as Supplementary Data.With the initial release of PLIP, we have included a test suite with 30 literature-validated examples (see Supplementary Data). They comprise diverse cases of protein–ligand complexes from PDB, covering all interaction types detectable by PLIP and resolutions from 1.2 to 3.3 Å. For each case, a test was implemented to check whether all interactions reported in the corresponding paper are being detected. The standard thresholds of PLIP have been carefully adapted to account for a broad range of interaction geometries while keeping the values as restrictive as possible. The test suite is available together with the source code on the PLIP website. Users are recommended to use these cases for testing when using custom thresholds and encouraged to contribute additional examples.PLIP can be used for both—structures from the PDB archive and structure files from other tools. It is therefore possible to integrate PLIP into pipelines for analyses related to protein–ligand binding, e.g. post processing of docking results or inhibitor design.swissdock.ch. While the top prediction corresponds to the pose found in the crystal structure, the first alternative pose shows a clearly different ligand conformation, but comparable SwissDock fitness scores. PLIP was used to analyze the interaction patterns in the complex from the crystal structure  with human aldose reductase Figure .Aldose reductase binds ligands via induced fit, leading to drastic conformational changes around the binding pocket . The thrAlthough all inhibitors have aromatic rings, only two form π-stacking interactions with Trp111. One of the most unique interaction patterns can be seen in the complex with zenarestat, where halogen bonds to the protein backbone are formed from both ends of the inhibitor.PLIP is the first web service to provide comprehensive analysis and visualization of non-covalent protein–ligand interactions with one-click loading of structures. With the availability of PyMOL session files and results in parsable formats, both—manual inspection and computational processing of interaction data—are possible. Furthermore, the availability of PLIP source code enables local batch processing, customization of the algorithm for special applications as well as active development of the tool in the community. Supplementary Data are available at NAR online.


Protein-protein interactions are among today’s most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more “traditional” drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of “decoys.” Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new “Talaris” update and the “pwSHO” solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta’s ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta’s ability to identify small-molecule inhibitors of protein-protein interactions. Virtual screening has become an important tool in modern drug discovery, complementing high-throughput biochemical and phenotypic screening to provide small molecules that engage target proteins important in human disease . AlreadyStructure-based (receptor-driven) virtual screening, or “docking,” is comprised of two interrelated tasks: pose generation and ranking. The former involves searching various locations and orientations of each compound in a library, to produce structural models of each potential protein-ligand complex. This search is carried out by optimization of some scoring function, and can include the internal degrees of freedom for the protein and/or for the ligand. Once models have been built for each member of the compound library, these are then ranked to identify those compounds most likely to exhibit the desired biological activity. Ranking may either be carried out using the same scoring function used in pose generation, or by using a more detailed  approach. By prioritizing a small number of compounds drawn from a much larger library, structure-based screening offers the potential to dramatically reduce the number of compounds that must be tested in the early stages of a drug discovery campaign.Both tasks are equally critical to successfully selecting active compounds. If an active compound is inappropriately positioned relative to the protein (“mis-docked”), then it is unlikely to achieve a favorable ranking. At the same time, accurately identifying the most promising compounds in the set—without an abundance of false positives—is also essential for harnessing the utility of virtual screening. In this study we focus on the latter step, and ask how recent enhancements to the Rosetta energy function affect its ability to successfully identify active compounds from among large numbers of docked “decoy” compounds.The Rosetta macromolecular modeling software suite  was origHere, we directly evaluate the performance of the Rosetta energy function in a different type of benchmark: distinguishing known small-molecule inhibitors of protein-protein interactions from large sets of “decoy” compounds. Whereas “traditional” targets for therapeutic intervention include G protein-coupled receptors, ion channels, and various enzymes , 23, theTo isolate the Rosetta energy function and examine its performance free from any complications of conformational sampling, our benchmark focuses only on the “ranking” step of virtual screening. For each of 18 non-redundant protein targets with crystal / NMR structures that have been solved in complex with a small-molecule inhibitor , we havePreviously, we described a set of 21 complexes drawn from the 2P2I  and TIMBThe underlying intention is that decoy compounds are expected not to be active against the protein target. To enhance the likelihood that this assumption holds true, we prepared a separate custom set of 2500 “presumed inactive” compounds for each protein target. Drawing from the ZINC database , 35, we To prepare for docking, we used OMEGA –39 to geBy using FRED (rather than Rosetta) to carry out this docking step, we ensured that any artifacts of the Rosetta energy function would not be inadvertently incorporated into the set of decoy complexes. As part of the benchmark experiments described below, however, each complex (decoy and native) was subjected to an equivalent energy minimization with the particular Rosetta energy function to be used for the subsequent ranking of compounds: in part, this ensures that our evaluation of a given Rosetta energy function is not simply a reflection of how well that Rosetta energy function detects potential artifacts of FRED docking (and uses these to distinguish the native complexes).not be expected to have activity; thus, these (non-native) forms of the ligand are not suitable for evaluating how well a given method identifies active compounds.The ranking step in Rosetta (described below) does not currently consider alternate tautomeric states or protonation states of the ligand. The state in the complex may, however, differ from the most likely state existing in solution. Since performance will be evaluated using the correctly-docked pose, it is critical to ensure that the Rosetta calculations are carried out using an appropriate description of the bound ligand. An alternate form of the active ligand would a priori; those particular active ligands with a configuration that differs in solution versus in the protein environment would simply be missed if the ligand configuration is not adjusted in response to the protein during screening .For this reason, we used the Protoss program  to obtaidifferences in performance—and not absolute performance—of various scoring functions.We note that this experimental design does disadvantage the decoy ligands relative to the native ligands; we therefore use this benchmark to track The entire benchmark set is available as . For eacPrior to scoring each complex, we used the “molfile-to-params” application  to assigAfter minimization, the protein-ligand interaction energy was evaluated using the desired energy function. The rank of the active compound’s interaction energy was determined relative to those of the decoy compounds, as shown in To compare the performance of two different energy functions, we considered—for each of the 18 proteins in our test set—which energy function positioned the native compound at a more favorable rank relative to the decoys, and assigned that energy function a “win” for the protein target; we then counted the total number of “wins” from each of the two energy functions. To avoid noise from very small differences in ranking, we excluded from the comparison any protein target for which both rankings were within 10% of one another.To determine the statistical significance of performance differences exhibited by two different energy functions, we started from the null hypothesis that both energy functions performed equally well. Our comparisons are between two sets of paired samples (i.e. the protein targets), and the rankings may not be normally distributed; for these comparisons we therefore applied the (non-parametric) Wilcoxon Signed-Rank test. All comparisons were applied to the logarithms (base10) of the rankings. We used the implementation of this test in the R statistical computing environment , invokedwilcox.testrank values in the two energy-function sets, it has the advantage of taking into account the magnitude by which an energy function “wins” a given protein target; this would not be the case if we were to simply compare the number of “wins” by each energy function.Because this test uses the In light of the chronological order by which the improvements to the energy function occurred, we had a prior expectation of which version would outperform the other; for this reason, throughout this study we conducted our analyses using one-tailed tests.As a starting point for further comparisons, we began with the default Rosetta energy function from early 2013, “score12”. Prior to recent improvements, this version of the energy function had remained essentially unchanged over a period of ten years . For eacpoints below the diagonal). Nonetheless, it is important to note that in many of the cases for which score12 “wins”, neither method performs particularly well—in 5 of these 7 cases the active compound is ranked outside the top 100 by both methods. In contrast, many of Chemgauss4’s “wins” correspond to very good rankings of the active compound: in five cases, the active compound is ranked by Chemgauss4 in the top 25 (corresponding to the top 1% of the set), and in three cases the active compound is ranked first overall—ahead of every decoy complex. Both scoring functions have the same  performance on target 3ZRC, the only one ranked first overall by score12.In It is clearly important to consider the magnitude of the differences in the rankings when comparing two scoring functions, and not just the number of protein targets at which each method “wins”. To do so, we compared these scoring functions using the Wilcoxon Signed-Rank test, applied to the difference in the logs of the rankings see . Based oThis first experiment made use of score12, a “standard” Rosetta energy function intended to be used in protein-only simulations. For studies involving small molecules, an alternative energy function is typically preferred : this onThe improvement from score12 to score12_ligand is also evident when score12_ligand is compared to FRED’s Chemgauss4 . FRED’s Talaris, the updated energy function mentioned above, became available to the Rosetta community in June 2013. This major update contained multiple improvements throughout the energy function , 20, 30,Ligand-specific weights have not yet been developed for Talaris; accordingly, we compared the performance of the default Talaris to that of the default score12, since both energy functions are intended primarily for simulations of proteins alone. We find that the use of Talaris improves performance for 13 protein targets relative to score12, with decreased performance in only 4 cases (performance was unchanged for one target) . This diAn ancillary utility of this benchmark is that the detailed energetic contributions for individual complexes can help rationalize why a given energy function outperforms another. In particular, this insight can be gained from complexes—either native or decoy—that are ranked favorably by one energy function, and less favorably by the other. To do so we compared score12 and Talaris using the same (non-minimized) structures of the decoy and native complexes. This choice allowed us to directly link differences in ranking to differences in the evaluation of energy components—a connection much more difficult to draw in the case of minimized decoys, since the conformation changes slightly in response to the energy function. Two examples are provided by the decoy complexes shown in Burial of polar groups is penalized by Rosetta’s solvation model; by default this has been EEF1 . In the Upon replacing EEF1 with pwSHO in the Rosetta energy function, we find improved performance for 12 of the 13 protein targets exhibiting differences in rankings , with noCollectively, these results suggest that the enhancements to the Rosetta energy function from Talaris and pwSHO together provide improved performance relative to score12. However, it must still be recalled that the performance of score12_ligand was far superior to that of score12, since the former was parameterized specifically for protein-ligand interactions.In a previous benchmark experiment, we found that virtual screening by FRED was exceptionally effective for traditional drug targets , but much less so for identifying inhibitors of protein-protein interactions . Much liThere are seven component energy terms in Rosetta that contribute to the total intermolecular energy of a protein-ligand complex:fa_atr fa_rep lk_nonpolar (the hydrophobic part of EEF1)fa_elec (electrostatics with a distance dependent dielectric)hb_bb_sc (hydrogen bonds between the ligand and the protein backbone)hb_sc (hydrogen bonds between the ligand and the protein sidechains)occ_sol_fitted (pwSHO)These terms are combined in the energy function as follows:We fixed the weight of “fa_atr” (C1) at 0.8, its value in the Talaris 2013 energy function. We also fixed the weight of “lk_nonpolar” (C3) at 0, after preliminary runs of the optimization procedure (described in the next paragraph) converged to negative C3 values; this term favors close packing of hydrophobic groups, and is thus somewhat redundant with “fa_atr”. Developing a new weight set, then, required determining the optimal values for C2/C4/C5/C6/C7.http://www.gnu.org/s/gsl/), to optimize the values of C2/C4/C5/C6/C7; the objective function was defined to be the sum of the logs of the ranks of the native complexes relative to the corresponding decoys. Thus, each step of this simplex optimization entailed updating 45,018 energies (18 protein targets × 2501 complexes for each) using a new set of weights. Because we had pre-computed the component energies for each of these 45,018 complexes, however, updating the total energy for each complex and re-evaluating the rank of the native ligand for each protein target was trivial ; thus, the optimization could be completed in seconds. Given the stochastic nature of the Nelder-Mead simplex algorithm, we ran 5 independent optimizations, then selected the set of weights that yielded the minimum value for the objective function.For this purpose, we first extracted the component energies for each native and decoy complex in our benchmark. We then used Nelder-Mead simplex optimization, as implemented in the C++ GSL multidimensional minimization library (not intended to be used in simulations (including energy minimization) that alter the structure of the protein-ligand complex.The resulting weight set, trained on all 18 protein targets, is now available in Rosetta distributions alongside the other standard weight sets, with the name “PPI_discrimination.wts”. Examination of these weights  reveals To fairly evaluate the performance of this new energy function relative to the others we have considered so far, we used leave-one-out cross validation. For a given protein target, we first developed a unique weight set by training on the other 17 protein targets; we then applied this “custom” weight set to determine the rank of the native complex for the protein target of interest. We repeated this procedure for each of the 18 protein targets, and used the rankings obtained in this manner for the comparisons below.First, we compared the performance of these new weights to that of the Talaris+pwSHO function with standard weights . We obseIn this study we evaluated the performance of the Rosetta energy function for an important and timely virtual screening application: selecting active compounds that will inhibit protein interaction sites, from among large libraries. As we noted earlier, this is a particularly challenging task compared to virtual screening against “traditional” drug targets . This clnot intended for carrying out simulations that alter the structure of protein-ligand complexes, but rather is focused solely on the final “re-ranking” step that determines which compounds will be advanced for testing in biochemical or cell-based assays.In the course of the studies presented here, we have developed a new set of weights for the Rosetta energy function that is specifically designed for identifying candidate inhibitors to disrupt protein interaction sites. Critically, this weight set is Virtual screening can be split—both conceptually and pragmatically–into two separate tasks: docking each member of a compound library, and then re-ranking the docked complexes. Here, we focus exclusively on the latter task and have developed a benchmark experiment specifically designed to evaluate performance for this task. In doing so, however, we note that our benchmark does differ in certain key aspects from the scenario of a “true” virtual screen.all of the complexes come from docking. Indeed, we find that using FRED-docked native complexes, instead of the experimentally-derived complexes, leads to diminished performance in this benchmark . In a real virtual screening scenario, of course, enchmark . While tOur benchmark also makes use of the tautomeric state / protonation state for the known ligand that best fits the protein environment in the correctly-docked pose, as identified by Protoss. Again this assumes knowledge that would not be available in a true virtual screening scenario, and again failure to provide this knowledge  leads to diminished performance . In thisFinally, we note that each of the complexes in our benchmark are minimized using the same scoring function that is used to ranking the complexes. Carrying out this calculation for each compound in a very large library is often impractical, and thus minimization is typically only used for the top-scoring compounds after docking . In this case the minimization step does not provide extra knowledge that would not be available in a realistic scenario, however, but rather the opposite: FRED-docked decoys contain more steric clashes than the corresponding experimentally-derived complexes, and as a result it becomes nearly trivial to pick out the (non-docked) active complex . Though Using the benchmark we describe here, the performance of Rosetta for this task can be directly tracked across several recent enhancements to the energy function; all the comparisons described in the course of this study are summarized in Nonetheless, even with these enhancements both FRED's Chemgauss4 and Rosetta fail to rank the known inhibitor in the top 1% of compounds for most targets, confirming that identifying small-molecule inhibitors of protein-protein interactions remains a challenging task. Given the complexity of this task, it also remains difficult to ascertain precisely why current methods fall short. It is noteworthy, though, that considerable orthogonality remains between Chemgauss4 and Rosetta. If both methods represented slight variations of the same underlying approach, one would expect them struggle/excel on the same targets as one another. Clearly, however, this is not the case: among the five targets for which Chemgauss4 ranked the known inhibitor in the top 1%, Rosetta performed comparably in only two cases, and in one case Rosetta failed even to rank the known inhibitor in the top 50%; conversely, Rosetta had more “wins” in the same experiment . These dS1 BenchmarkFor each target protein, this includes the protein itself, the native ligand , and the decoy ligands (in the FRED-docked pose). The native ligand is provided with both the Protoss and the QUACPAC tautomeric state / protonation state.(GZ)Click here for additional data file.S1 DatasetRanking of the active compound relative to the decoys is provided for each protein target, under each of the energy functions tested in this study; these data were used in generating the figures presented in this study.(XLSX)Click here for additional data file.S2 DatasetThe weights optimized on the entire benchmark set are those deposited in “PPI_discrimination.wts”. The mean and standard deviation of the weights optimized during leave-one-out  are also reported.(XLSX)Click here for additional data file.S1 FigA) The protein PDB ID is 4GQ4, the ligand ZINC ID is ZINC01019586. This decoy complex is ranked 10th by score12 and 34th by Talaris, with protein-ligand interface energies of -10.522 and -8.913 Rosetta Energy Units (REUs), respectively. Seventy-three percent of the difference in interface energy is due to the solvation component of the interface energy, which is +8.397 under score12 and +9.564 under Talaris. The change reflects the increase from 0.65 in score12 to 0.75 in Talaris in the weight that solvation energy has in the computation of total energy; the unweighted solvation components are indeed very similar: +12.918 for score12 and +12.752 for Talaris. The electrostatic component contributes 20% of the difference in interface energy, because the score12 “fa_pair” component is 0 REUs, whereas the Talaris “fa_elec” component is +0.315 REUs. B: The protein PDB ID is 3IN7, the ligand ZINC ID is ZINC00792906. This decoy complex is ranked 3rd by score12 and 16th by Talaris, with interface energies of -8.706 and -7.288 REUs, respectively. Here the solvation component accounts for 72% of the difference in interface energy, its value being +10.147 REUs under score12 and +11.172 REUs under Talaris. Another 26% of the difference in interface energy is contributed by the hydrogen-bond component; in particular, the hydrogen bonds between the ligand and the protein (yellow broken lines) have a total energy of -0.925 REUs under score12 and -0.558 REUs under Talaris.To appropriately compare between the two energy functions, the same conformation was scored using both energy functions (i.e. without minimization). Both images were produced using PyMOL , with th(TIFF)Click here for additional data file.S2 Figη2(64) with atom C23 in the ligand. The energetic cost for desolvating Nη2(64) with C23 is +0.12 REUs under EEF1, versus +0.22 REUs under pwSHO. In both of these cases, the pwSHO energy more strongly penalizes the fact that ligand binding reduces this ability of these protein atoms to hydrogen bond with nearby water. This image was produced using PyMOL [The protein PDB ID is 4G55 and the ligand ZINC ID is ZINC00624571. To appropriately compare between the two energy functions, the same conformation was scored using both energy functions (i.e. without minimization). This decoy complex is ranked 10th by Talaris+EEF1 and 32nd by Talaris+pwSHO, with protein-ligand interface energies of -6.611 and -6.369 REUs, respectively. The entire difference in interface energy is contributed by the polar solvation component, whose EEF1 value is +8.721 REUs and whose pwSHO value is +8.964 REUs. To help understand why pwSHO penalizes this complex more than EEF1, we present the interaction between the protein backbone carbonyl atom O(65) and atom S1 of the ligand. The energetic cost for desolvating O(65) with S1 is +0.05 REUs under EEF1, whereas it is +0.10 REUs under pwSHO. The same applies for the interaction of the protein sidechain arginine atom Nng PyMOL , with th(TIFF)Click here for additional data file.S3 Fig(A) Performance for this task is diminished significantly (p = 0.019) if the known inhibitor is docked, rather than provided in the experimentally-determined pose: with 3 versus 11 “wins”, respectively. (B) The fold-decrease in performance observed upon using the docked structure of the known inhibitor, as opposed to the experimentally determined structure, appears uncorrelated to the RMSD between the two structures. Performance decreases even in some of the cases for which the RMSD of the FRED-docked structure is less than 2 Å from the native pose, suggesting that even this accuracy may be insufficient for the known inhibitor to be selected by the downstream scoring function.Scoring in this experiment was carried out using the Talaris+pwSHO energy function. (EPS)Click here for additional data file.S4 FigPerformance for this task is diminished significantly (p = 0.0008) if the tautomeric state and protonation state of the known inhibitor are computed with QUACPAC (i.e. without knowledge of the protein environment) rather than with Protoss . There are zero “wins” using the QUACPAC ligand configuration, versus ten “wins” using the Protoss ligand configuration. Scoring in this experiment was carried out using the Talaris+pwSHO energy function.(EPS)Click here for additional data file.S5 Fig(A) Performance for this task becomes a much easier if performed on non-minimized rather than minimized complexes, due to the presence of steric clashes in non-minimized decoy complexes. The increase in performance is dramatic (p = 0.000004), with the known inhibitor being ranked 10 times as first overall, and 15 times in the top 1% of the compound library. (B) The fold-increase in performance upon using non-minimized complexes, as opposed to minimized complexes, appears uncorrelated with the RMSD between minimized and non-minimized structures of the known inhibitor. This is consistent with the expectation that minimization makes the decoy complexes more competitive, and thus the change in performance is unrelated to how minimization affects the active complex. RMSD was computed over the inhibitor’s heavy atoms, after superposition of the protein residues within 10 Å of the inhibitor.Scoring in this experiment was carried out using the Talaris+pwSHO energy function. (EPS)Click here for additional data file.S6 FigA: The PDB ID of this active complex is 4GQ4. The known inhibitor is ranked 3rd by FRED Chemgauss4, but 23rd by Talaris+pwSHO. Both scoring functions agree that the inhibitor has moderately favorable hydrogen-bonding and polar-solvation energies. A prominent difference between the two scoring functions is in the evaluation of non-polar solvation: while Chemgauss4 has no explicit term to model non-polar solvation, Talaris+pwSHO ranks the inhibitor's “lk_nonpolar” energy term very unfavorably (2461st out of 2501). B: The PDB ID of this active complex is 2YEL. The known inhibitor is ranked 1st by Talaris+pwSHO, but 371st by Chemgauss4. Under Chemgauss4 the inhibitor has unfavorable shape complementarity (ranking 682nd by combined “CG3:Steric” and “CG3:Clash” scores) and unfavorable intermolecular hydrogen bonding (434th by “CG4:HB” score). By contrast, under Talaris+pwSHO the inhibitor has excellent shape complementarity (3rd by combined “fa_atr” and “fa_rep” energies), and favorable hydrogen bonding (42nd by combined “hb_bb_sc” and “hb_sc” energies).To appropriately compare between the two energy functions, the same conformation was scored using both energy functions (i.e. without minimization). Both images were produced using PyMOL , with th(TIFF)Click here for additional data file.


Saccharomyces cerevisiae with a partner from each of the major cellular compartments and quantitatively assessed the effects upon growth. This analysis reveals that cells have a remarkable and unanticipated tolerance for forced protein associations, even if these associations lead to a proportion of the protein moving compartments within the cell. Furthermore, the interactions that do perturb growth provide a functional map of spatial protein regulation, identifying key regulatory complexes for the normal homeostasis of eukaryotic cells.To understand the function of eukaryotic cells, it is critical to understand the role of protein-protein interactions and protein localization. Currently, we do not know the importance of global protein localization nor do we understand to what extent the cell is permissive for new protein associations – a key requirement for the evolution of new protein functions. To answer this question, we fused every protein in the yeast DOI:http://dx.doi.org/10.7554/eLife.13053.001 Our actions often depend on who we interact with: parents, teachers, friends, colleagues. So it is for proteins in the cell: their function depends on which other proteins they work with. If a protein interacts with new partners or ends up in a new neighborhood of the cell, it can perform an entirely unexpected role, rewiring how that cell works.There are millions of possible protein-protein interactions, but it is not known how cells behave if their proteins are forced into new associations. For example, how many of these associations affect how well the cell can grow?Using budding yeast, Berry et al. were able to associate every protein in the cell with proteins from each of the major areas of the cell such as the nucleus, cell membrane or mitochondria. These new associations and relocations were then examined to see how many of them caused problems, slowing the cell’s growth or killing it.Unexpectedly, most forced associations had no detectable effect, indicating that the cell is remarkably tolerant of new protein-protein interactions. This contradicts a common idea that proteins are very fussy about their partner proteins, and will not work properly if they are forced into new interactions.The associations that do cause a growth defect are often between proteins that normally work together, indicating that their association is normally carefully controlled during the normal growth of cells. In some cases these forced associations identified previously unknown regulators of cell behavior.Proteins that interact with the wrong partners or are in the wrong place within cells cause a number of diseases. Future forced association experiments will allow us to examine such interactions and possibly search for drugs that will correct the problem.DOI:http://dx.doi.org/10.7554/eLife.13053.002 Post-translational protein modifications such as phosphorylation or ubiquitylation often alter the affinity of one protein for other proteins or cellular components, which drive their movement within the cell . ProteinSynthetic Physical Interaction (SPI) system  system  to creat) system . The SPISelective Ploidy Ablation technique (The SPI system uses a GFP-binding protein (GBP) derived from an alpaca antibody , which wechnique  to introechnique . In eachechnique , such asechnique . Thus, fechnique  do not affect the growth of cells . These dIn cases where fluorescent imaging was able to detect protein relocalization, we confirmed that ~72% of interactions do occur. Since the GBP tag is linked to red fluorescent protein (RFP), we were able to assay colocalization with GFP. We examined 552 GBP-GFP combinations - each of the 23 GBP-tagged target proteins separately combined with a random selection of 24 GFP-tagged query proteins - using live cell imaging . Of the The microscopy analysis also allows us to examine whether the GBP-tagged target protein recruits the GFP protein to its location or vice versa. We anticipated that each binary protein association would create a ‘tug-of-war’ between the target protein and the query protein. The image data support this notion; where it is possible to distinguish the location of two proteins in the cell, we observed that there are roughly equal instances of the GBP protein recruiting the GFP protein as the reverse . HoweverAround 2% of the forced interactions restrict growth . HoweverR2 values ≤0.004). To address the issue further we grouped all GFP strains into eight categories based upon the abundance of their GFP proteins, each group has 421 proteins. We then plotted the proportion of GFP strains within each group that produced SPIs with a given GBP target  terms include RNA metabolism , mRNA polyadenylation , cytoplasmic and nuclear transport , microtubule nucleation , and spindle pole body . We have previously shown that these interactions are mostly suppressed by having an untagged copy of the query protein present in the cell . Also, this notion is true in specific cases, particularly for nuclear proteins. For example, SPIs with a nucleolar protein, Nop10, are enriched for nucleolar components  or SPIs with the microtubule-associated kinetochore component Nuf2, which are enriched for microtubule components (described below). This pattern was typical of nuclear proteins, but not evident for other proteins: for example, the SPIs with the mitochondrial protein Om45 did not include any mitochondrial proteins. However, it should be noted that although there are more SPIs between proteins in the same compartment, SPIs produced by proteins in different compartments tend to give a greater growth defect . This is visualized by overlaying all the confirmed SPI query proteins onto a graph of the yeast physical interaction dataset (HINT database (Cutoff Linked to Interaction Knowledge tool (CLIK), which examines quantitative data for interaction density , specifically the Nup84 complex, which functions in specialized types of DNA repair (-13). Nuf2 is an outer kinetochore protein whose calponin-homology domain directs microtubule binding ; hence cellular Dad4 protein levels are not sufficient to explain the aberrant Dad4 foci seen in most hmo1∆ cells  at 300 dpi resolution in transmission mode. These images were processed and analyzed using the ScreenMill suite of software , which aColonies arrayed on agar plates often grow faster on one side of the plate than the other. This growth effect can be caused by temperature or humidity gradients within incubators, variable thickness of agar (and hence concentration of nutrients), or uneven pinning pressure during plate copying. These anomalies can result in one side of the plate producing an overall higher z-score than the other. To correct for these type of biases, algorithms adjust colony size data to reflect overall even growth across a plate . The ScrTo examine the levels and location of tagged proteins within the cells, we used epifluorescence microscopy. Log phase cells were embedded in 0.7% low melting point agarose dissolved in the appropriate growth medium. The depth of agarose between the slide and coverslip is fixed at 6–8µm, slightly larger than the diameter of the average yeast cell, which maintains a consistent distance from the coverslip to the cell nucleus. Cells were imaged with a Zeiss Axioimager Z2 microscope , using a 63x 1.4NA oil immersion lens, illuminated using a Zeiss Colibri LED illumination system . Bright field contrast was enhanced with differential interference contrast (DIC) prisms. The resulting light was captured using either a Hamamatsu ORCA ERII CCD camera containing an ER-150 interline CCD sensor with 6.45 µm pixels, binned 2x2  or a Hamamatsu Flash 4 Lte. CMOS camera containing a FL-400 sensor with 6.5 µm pixels, binned 2x2. The exposure times were set to ensure that pixels were not saturated and were identical between control and experimental images. All images were acquired using either Axiovision or Zen software from Zeiss. Images shown in the figures were prepared using Volocity imaging software  and control and experimental images have identical linear contrast adjustments unless otherwise stated.https://sourceforge.net/projects/berry-et-al/files/RFP_quantitation.assf/download).To quantify the relative amount of RFP in cells containing GBP-RFP tags we used custom scripts for the Volocity image analysis software (Perkin Elmer Inc. USA). Briefly, red fluorescence regions were identified within the three-dimensional images based upon an intensity threshold. These regions were then dilated by a fixed amount (~600 nm) in each direction to ensure that we assay all of the red fluorescence signal. The regions were further dilated (2.4 µm) to create an outer background region, which was subtracted from each fluorescence measurement .To quantify the relative levels of Dad4-YFP and Mtw1-YFP kinetochore proteins within kinetochore foci, we employed a custom ImageJ script . To quanMichael Eisen’s cluster program (version 3.0) was used to cluster the SPI data . We used In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Randy Schekman as the Senior and Reviewing Editor.Thank you for submitting your work entitled "Synthetic protein interactions reveal a functional map of the cell" for consideration by The following individuals involved in review of your submission have agreed to reveal their identity: Stanley Fields and Nevan Krogan (peer reviewers).The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The authors present a screen using their newly developed SPI system, which allows for the creation of artificial interactions between pairs of proteins. They create binary interactions between each of the ~6,000 yeast proteins and 23 target proteins that represent the major cellular compartments. The effects of these interactions on growth are assayed by measuring colony sizes. In spite of these interactions often leading to protein relocalization, they find only a small fraction lead to a measurable growth phenotype, suggesting that the cells are tolerant of both protein movement and association. The authors highlight how their method can be used to discover new regulatory relationships and to provide structural information on large cellular complexes.eLife as a tool/resource. However, there are a number of important points that need to be addressed.The SPI system provides an exciting complement to PPIs and genetic interactions, and the scale of the collected dataset is impressive. The manuscript is well written and for the most part the methods are clearly described. Together, the importance and quality of the work makes it suited for publication in Essential revisions:1) The analysis on a limited number (24) of GFP-fusion proteins suggests that for only roughly ~20% of time when they co-express a GFP-fusion query protein with a target protein do they see mislocalization of the GFP fusion outside of where it is normally found. But even this may be a large over estimation of the degree to which their system is causing protein mislocalization. First, they do not evaluate what fraction of the GFP-fusion is mislocalized. Second, cleavage of the GFP from the fusion protein will result in mislocalization of the GFP domain but not of the rest of the protein. Finally, they report on ~6000 individual GFP fusion proteins obtained from the library developed in Huh et al. (2003). But in that paper only ~4500 of the fusion proteins were validated and observable. Thus unless the remaining 1500 strains were made and validated separately, they are suspect. In the end because of these and other concerns, I think it is not possible evaluate from these data what fraction of the proteins tolerate being effectively mislocalized to a cellular location that is different from where it is naturally found. This limitation must be discussed and explained.hmo1 and Sgf29, show increase levels of a kinetochore protein Dad4). This would be much more compelling if the authors had data indicating some direct physical interaction (IP/mass spec) or biochemical rational for how hmo1 could regulate the kinetochore to support their claim that SPIs can identify functional regulators- this could easily be a case where hmo1 regulates transcription of Dad4 directly or indirectly. If so, please include this in the revised version or at least comment on relevant data in support of a direct and/or functional interaction.2) The potentially most interesting result reported here is the detection of Synthetic Physical Interactions (SPIs), where a forced interaction does cause a growth phenotype. But it's not entirely clear that one learns all that much from these results. The one example of an SPI that they do examine in more detail in 3) The clustering of complexes  is not t4) Results, second paragraph: “we found that 98% of GBP-GFP combinations […] do not affect the growth of cells.” Do 98% not affect growth at all, or are they using a z-score threshold? If z-score, this should be clarified, as it is not necessarily the same as "no effect on growth".5) Results, third paragraph and 6) Results, fifth paragraph: “Around 2% of the forced interactions restrict growth […]” Specify z-score cutoff used to qualify as restriction in main text.7) Results, fifth paragraph: “[…]of the 727 SPI proteins […]” -> of the 727 SPI query proteins.8) Results, fifth paragraph: “[…] whose sequestration to another compartment is lethal […]” Did they show it's actually lethal or just decreases growth (i.e. sick)?9) Results, fifth paragraph: Discuss why there is a difference in suppression of interactions between the frequent and non-frequent SPI groups.10) Results, sixth paragraph and 11) Results, seventh paragraph and 12) Results, seventh paragraph and 13) Results, eighth paragraph: Describe or reference the gene ontology enrichment analysis.14) Results, eighth paragraph: Was fluorescently tagged Nuf2 examined in these cells as well? How is Nuf2 affected?15) 16) 17) Results, last paragraph: “[…] SPI data can be used to predict protein complexes […]” Substantiate this claim. Show if the SPI data actually have predictive power with a ROC or precision recall curve.18) Results, last paragraph: Try to interpret why the DAM/DASH complex segregates into distinct clusters.19) Discussion: I suggest explicitly writing "physical interactions" instead of "interactions" to not confuse with SPIs.20) Discussion: “[…] and derive quaternary structure […]” ‘provide information on’ would be more appropriate than ‘derive’.21) Discussion: “[…] 54% (394) are conserved in human cells.” May be worth discussing how this compare to the conservation of the complete yeast genome to human?eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Randy Schekman as the Senior and Reviewing Editor. Stanley Fields and Nevan Krogan have agreed to share their identity. There remains one concern with the language in your Abstract. Please adjust this according to the suggestion of reviewer #1.Thank you for submitting your article "Synthetic protein interactions reveal a functional map of the cell" for consideration by Reviewer #1:The authors have largely addressed the concerns I raised and put in appropriate caveats in their revised manuscript. I am still concerned that their Abstract is misleading in claiming to establish that proteins have an "unanticipated tolerance for forced protein associations and consequently their relocation". I think it is likely that in many, perhaps the large majority, of the cases where apparent relocation of a protein does not disrupt function has to do with at least partial retention of the protein in its correct locations. It would indeed be quite surprising if most nuclear proteins could function in the cytosol or most organellar localized proteins could function outside of their native organelle. I do not think this is what the authors intend to say (and certainly is not what they have shown) but I could easily see how a casual reader of the Abstract could be left with this impression.Reviewer #2:I find the revised version acceptable.Reviewer #3:I am happy with the revisions and support publication. Essential revisions: 1) The analysis on a limited number (24) of GFP-fusion proteins suggests that for only roughly ~20% of time when they co-express a GFP-fusion query protein with a target protein do they see mislocalization of the GFP fusion outside of where it is normally found. But even this may be a large over estimation of the degree to which their system is causing protein mislocalization. First, they do not evaluate what fraction of the GFP-fusion is mislocalized. Second, cleavage of the GFP from the fusion protein will result in mislocalization of the GFP domain but not of the rest of the protein. Finally, they report on ~6000 individual GFP fusion proteins obtained from the library developed in Huh et al. (2003). But in that paper only ~4500 of the fusion proteins were validated and observable. Thus unless the remaining 1500 strains were made and validated separately, they are suspect. In the end because of these and other concerns, I think it is not possible evaluate from these data what fraction of the proteins tolerate being effectively mislocalized to a cellular location that is different from where it is naturally found. This limitation must be discussed and explained.We agree that it is possible that the imaging overestimates the frequency of mislocalization. We have now discussed these limitations . The amount of a given protein that is mislocalized will depend in part on stoichiometry of the GBP and GFP tagged proteins, which is why we examined the effects of stoichiometry in 2) The potentially most interesting results reported here is the detection of Synthetic Physical Interactions (SPIs), where a forced interaction does cause a growth phenotype. But it's not entirely clear that one learns all that much from these results. The one example of an SPI that they do examine in more detail in hmo1∆ mutants in more detail, we examined whether the Hmo1-Nuf2 SPI is specific to Nuf2 or more general to the kinetochore, we have now tested 10 other kinetochore GBP-target proteins and find that in addition to Nuf2, both Mif2 and Ctf19 give a SPI, whereas Kre28, Mtw1, Dad2, Ctf3, Chl4, Skp1, Cnn1 and Cbf1 do not . However, to examine this in more detail we have quantified the total cellular levels of Dad4 in an hmo1∆ strain and find that they are elevated in approximately 30% of cells. However, nearly half of hmo1∆ cells have normal Dad4-YFP levels (quantitation provided in hmo1∆ cells in To characterize the effect of  not new  – sugges3) The clustering of complexes  is not tWe agree that structural information is best achieved using other methods, such as protein-protein interactions (PPIs) identified by, for example, mass spectroscopy. We realize that the SPIs are distinct from PPIs and have attempted to clarify this, see point 17 below. In our original submission we gave the impression that SPIs predict structure which is misleading, rather SPIs identify proteins which behave similarly when associated with specific other proteins, in some cases reflecting characterized functional complexes. We think that the power of the SPI technology is as stated in the Discussion:“These data illustrate that SPIs can be used, like physical interactions, to reveal the functional organization of the cell. However, since the readout of SPIs is phenotypic, in this case cell growth, the SPIs indicate functional interactions rather than physical interactions per se.”Some structural complexes are inferred from the SPI data, however this is likely via a common functional association and the SPI data cannot be used as structural evidence. We have now made clear that the SPIs do not constitute structural data – this is discussed more in point 17 below. Nevertheless, we feel that the cluster visualization allows readers to clearly see that SPI data for certain groups of proteins are more similar than for other groups. We have simplified “Hence, although SPIs do not substitute for physical interaction data they indicate a common phenotype produced by specific protein-protein associations.”4) Results, second paragraph: “we found that 98% of GBP-GFP combinations […] do not affect the growth of cells.” Do 98% not affect growth at all, or are they using a z-score threshold? If z-score, this should be clarified, as it is not necessarily the same as "no effect on growth".We do not use a defined z-score or log growth ratio (LGR) as a cutoff for interactions. One reason for not using a z-score cutoff is that although within a screen the LGR between experiment and controls shows a linear correlation with z-score, this is not true between screens as z-scores were calculated on the data from each screen individually, not by combining the LGRs from the whole dataset. Thus a z-score in one screen does not equate to a z-score in another screen. We could have used an LGR cutoff, however, this assumes that SPIs with one target protein would produce a comparable growth phenotype with SPIs from a different target protein, which is an assumption we were not comfortable with. We felt that the best way to confirm SPIs was to sequentially retest the strongest interactions (as judged by z-score) from every screen. Thus the strongest SPIs were retested with 16 replicates and any target-query interaction that gave an LGR that was consistently more than control strains (a non-GFP strain) was considered a SPI. We stopped this retest process when the false discovery rate reached 40% . It is r5) Results, third paragraph and We have amended the 83% figure to 72% by excluding the 40% of samples that were already colocalized, the sentence now reads:“In cases where fluorescent imaging was able to detect protein relocalization, we confirmed that ~72% of interactions do occur.”This is now re-iterated in the legend of “[…] note that the colocalized category includes 210 combinations where the target and query proteins are within the same compartment and so protein-protein association will not be apparent from this microscopy analysis.”6) Results, fifth paragraph: “Around 2% of the forced interactions restrict growth […]” Specify z-score cutoff used to qualify as restriction in main text.We have now clarified this as described in point 4 above.7) Results, fifth paragraph: “[…]of the 727 SPI proteins […]” -> of the 727 SPI query proteins.We have included this specific change and also several other instances of ‘SPI proteins’ throughout the manuscript.8) Results, fifth paragraph: “[…] whose sequestration to another compartment is lethal […]” Did they show it's actually lethal or just decreases growth (i.e. sick)?We were wrong to state ‘lethal’ when the data show a growth defect (i.e. sick). We have amended the text .9) Results, fifth paragraph: Discuss why there is a difference in suppression of interactions between the frequent and non-frequent SPI groups.This issue relates to point 8 also. We have moved the section concerning suppression of frequent SPI query proteins up to follow on from the characterization of the SPI query proteins . We have expanded the explanation which now concludes as follows:2008)).”“Thus we conclude that these frequent SPI query proteins are predominantly those whose essential function is location-dependent and whose sequestration to another compartment results in a growth defect  Results, sixth paragraph and p-value, but importantly, we note that this is not true for all compartments.The relationship between compartments and z-scores is interesting. We find that in some cases, z-scores correlate between two screens in which the target proteins were from the same compartment, for example Pus1 and Rad52 target proteins, which both localize to the nucleus . We now In contrast to the number of SPIs within a compartment, when we compare the log growth ratios (i.e. the degree of growth restriction) of strains in which the query and target proteins are from the same compartment, the growth restriction is less than that for associations between different compartments. We have now included a description of this in the text, see Results, seventh paragraph and also included the box plot of same vs. different compartment LGRs in 11) Results, seventh paragraph and p value should have been <2.2E-16 (the limit provided by the R function we had used). The p value of the Wilcoxon rank-sum test is similarly small. We failed to specify in the figure legend that the top of the box plot was cut off, hence some outliers were excluded. We have changed the y axis of this plot to a Log scale to allow all the data to be included and changed the box plots to include notches that indicate 95% confidence intervals of the median  Results, seventh paragraph and  We have now described the CLIK analysis in more detail .13) Results, eighth paragraph: Describe or reference the gene ontology enrichment analysis.We have included a reference for the GOrilla algorithm for ontology enrichment.14) Results, eighth paragraph: Was fluorescently tagged Nuf2 examined in these cells as well? How is Nuf2 affected?To characterize the effect of Hmo1 on the kinetochore in more detail, we have now created ten additional kinetochore proteins fused with GBP (two of which are tagged at both the N- and C- termini) and tested the effect of introducing these into an Hmo1-GFP strain; we have now discussed this in the text  and in a new 15) See point 14 above.16) p-values from two-tailed unpaired t-tests to Wilcoxon’s rank-sum tests – comparing the foci intensities of WT kinetochore foci with those of the mutants . The distribution of foci fluorescence intensities (right panel), particularly for hmo1∆ cells, is not a normal/Gaussian one, hence we wanted to show the distribution of the fluorescence data so that readers can get a clearer picture of these data (the right panels in We have amended the 17) Results, last paragraph: “[…] SPI data can be used to predict protein complexes […]” Substantiate this claim. Show if the SPI data actually have predictive power with a ROC or precision recall curve.This point follows on from point 3, we do not wish to claim the SPIs substitute for PPIs. We realize that this was an over-statement of our data to say that SPI data can predict protein complexes or would substitute for protein-protein interaction data, since SPI data is not structural. Rather, we want to indicate that when specific proteins are associated with the same protein complex they may produce a similar SPI phenotype. We have changed this section to“It is important to note that SPIs are not a substitute for physical interaction data, but rather represent a common phenotype in response to forced association. […] Thus although the proteome-wide SPI data themselves do not directly give structural information, the SPI data does group query proteins within these known protein complexes.”We have also amended the quaternary structure section to reflect this;“We nest asked whether the SPI data would correlate with the quaternary structure of multi-protein complexes, since protein associations with one part of a complex may give a similar growth phenotype that contrasts with a different part of that same complex.”This section now fits with our speculation of the different phenotypes given for members of the DAM1 complex . We speculate here why some protein complexes may not cluster together using SPI data.Furthermore we conclude the Results section with the sentence“Hence, although SPIs do not substitute for physical interaction data they indicate a common phenotype produced by specific protein-protein associations”.18) Results, last paragraph: Try to interpret why the DAM/DASH complex segregates into distinct clusters.We have expanded this section of the text to speculate on why the DAM/DASH complex splits into two clusters based upon high-resolution structural models of this complex, see Results, last paragraph. This addresses issues raised in point 3 and 17.19) Discussion: I suggest explicitly writing "physical interactions" instead of "interactions" to not confuse with SPIs.We have changed these in the Discussion.20) Discussion: “[…] and derive quaternary structure […]” ‘provide information on’ would be more appropriate than ‘derive’.We have changed this in the Discussion.21) Discussion: “[…] 54% (394) are conserved in human cells.” May be worth discussing how this compare to the conservation of the complete yeast genome to human?p-value = 3.32x10-23).We have amended these values utilizing data from ‘Yeastmine’ (yeastmine.yeastgenome.org). 3766 out of 6604 yeast ORFs have human homologs (57%), 549 out of the 727 SPIs have human homologs (~76%)  but I could easily see how a casual reader of the Abstract could be left with this impression.Reviewer #1 had asked for one change to the Abstract, we have changed:“This analysis reveals that cells have a remarkable and unanticipated tolerance for forced protein associations and consequently their relocation.”to“This analysis reveals that cells have a remarkable and unanticipated tolerance for forced protein associations, even if these associations lead to a proportion of the protein moving compartments within the cell.”


In this study, we filter compounds of traditional Chinese medicine (TCM) using bioactivities predicted by three distinct prediction models before the virtual screening. For the top candidates, the molecular dynamics (MD) simulations were also utilized to investigate the stability of interactions between ligand and PPAR-γ protein. The top two TCM candidates, 5-hydroxy-L-tryptophan and abrine, have an indole ring and carboxyl group to form the H-bonds with the key residues of PPAR-γ protein, such as residues Ser289 and Lys367. The secondary amine group of abrine also stabilized an H-bond with residue Ser289. From the figures of root mean square fluctuations (RMSFs), the key residues were stabilized in protein complexes with 5-Hydroxy-L-tryptophan and abrine as control. Hence, we propose 5-hydroxy-L-tryptophan and abrine as potential lead compounds for further study in drug development process with the PPAR-γ protein.The peroxisome proliferator-activated receptors (PPARs) related to regulation of lipid metabolism, inflammation, cell proliferation, differentiation, and glucose homeostasis by controlling the related ligand-dependent transcription of networks of genes. They are used to be served as therapeutic targets against metabolic disorder, such as obesity, dyslipidemia, and diabetes; especially, PPAR- They have different tissue distributions and responses to different ligands [γ is the most extensively investigated isoform for the treatment of dyslipidemic type 2 diabetes [γ has been served as a brain protector against ischemic cerebral infraction [The peroxisome proliferator-activated receptors (PPARs) belonged to the nuclear receptor superfamily of ligand-inducible transcription factors. They are “fatty acid sensors” related to regulation of lipid metabolism, inflammation, cell proliferation, differentiation, and glucose homeostasis by controlling the related ligand-dependent transcription of networks of genes –3. There ligands –6. PPARsdiabetes –10. It idiabetes –13. In rfraction .Nowadays, increasing numbers of drug are designed with a target protein against a specific disease –18, as iα, PPAR-δ, and PPAR-γ [γ and filter TCM compounds using bioactivities predicted by three distinct prediction models before the virtual screening. The molecular dynamics (MD) simulations were also utilized to investigate the stability of interactions between ligand and PPAR-γ protein in the docking pose under dynamic conditions. We attempt to identify the potent TCM compounds with higher bioactivities and binding affinity for PPAR-γ protein and discuss the functional group of these candidates and common binding residues of PPAR-γ protein in their docking pose.In the former study, we aim to detect potential candidates from TCM compounds as agonists targeting PPAR-d PPAR-γ . HoweverAfter TCM compounds from TCM database, Taiwan , were fiγ) protein was obtained from RCSB Protein Data Bank with PDB ID: 3K8S [γ protein was used as target protein for virtual screening, and T2384, cocrystallized in PPAR-γ protein, was used as control.The X-ray crystallography structure of the human peroxisome proliferator-activated receptor gamma (PPAR-ID: 3K8S . After p50 (log(1/EC50)) value of 20 compounds from Rikimaru et al.'s study [R2). Cross-validation test was used to validate the prediction model. For three distinct prediction models, multiple linear regression and Bayes network toolbox were performed using MATLAB, and support vector machine was performed using LibSVM developed by Chang and Lin [For the prediction of biological activity for the TCM compounds, three distinct prediction models were constructed with the pEC's study  of DS 2. and Lin .For virtual screening, LigandFit protocol  in DS 2.Before the molecular dynamics simulation by Gromacs , each prThe genetic approximation algorithm determined the six optimum molecular descriptors for constructing prediction models with 20 compounds of training set. The selected descriptors were ES_Sum_sssCH, ES_Count_aaN, BIC, IAC_Mean, CHI_3_P, and JY. These six optimum molecular descriptors can be broadly divided into two groups, which are electronic and special topological descriptors. For electronic topological descriptors, it includes ES_Sum_sssCH, ES_Count_aaN for calculating the sums of the electrotopological state (E-state) values and the counts of each atom type, respectively. For special topological descriptors, BIC and IAC_Mean are bonding information content and mean information of atomic composition, which both belong to Graph-Theoretical InfoContent descriptors . CHI_3_PR2) of training set for MLR, SVM, and BNT models are 0.8442, 0.8536, and 0.7612, respectively. These prediction models are acceptable for predicting activity of PPAR-γ protein.The support vector machine (SVM) and Bayes network toolbox (BNT) models were also constructed with the identical training set and descriptors. The correlation of predicted and observed activities shown in The potent compounds, which have acceptable predicted activities in all three prediction models, have been virtual screening with the target protein. After filtering by the absorption properties, the top TCM candidates ranked by Dock score were listed in γ protein was defined by the volume and position of control, T2384  with residues Cys285 and Lys367, and hydrophobic contact with other nine residues.For the docking simulation, the binding site of PPAR-l, T2384 . We visul, T2384 , 5-hydro Figures .  Figureγ protein, the top three TCM candidates have been docked with similar docking poses. Due to the molecular size of three TCM compounds, none of them have interaction with Phe264 as T2384. Except saussureamine C, both of 5-hydroxy-L-tryptophan and abrine have π interaction with residue Phe363 as control. However, saussureamine C still has hydrophobic contact with residue Phe363. All top three candidates have similar H-bond with residue Lys367 and hydrophobic contacts with some common residues, such as Leu330 and Met364. Except that 5-hydroxy-L-tryptophan has hydrophobic contact instead of H-bond with residue Cys285, the other two candidates have the similar H-bond with residues Cys285 as T2384. In addition, abrine and saussureamine C also have H-bond with Ser289 and Met364, respectively.Compared with T2384 in PPAR-γ protein is fixed during the progress of docking simulation. As this reason, the molecular dynamics (MD) simulations for each protein-ligand complex were performed to investigate the stability of interactions between ligand and target protein in the docking pose under dynamic conditions and investigate the possible variations for each protein-ligand complex after docking.The docking poses in the docking simulation illustrate that the top three TCM candidates have similar interactions with the target proteins as T2384. However, the structure of PPAR-γ protein in the complex with T2384 and top three TCM candidates over the course of 40 ns MD simulation. γ protein and secondary structural feature ratio over the course of 40 ns MD simulation for each complex with T2384 and top three TCM candidates. It indicates that docking with three TCM candidates may not cause the significant differences from docking with the control in the secondary structure of PPAR-γ protein.The root mean square deviations (RMSDs) and radii of gyration for each protein and ligand in the complexes were illustrated in γ protein. In addition, the ligand interaction diagram also indicates that T2384 has interactions with common residues in docking simulation. For 5-hydroxy-L-tryptophan, it keeps the H-bond with residue Lys367 in a nonstatic condition and also has an H-bond with residue Ser289 as the docking pose of abrine in the docking simulation. Similarly, abrine has H-bonds with residues Ser289 and Lys367 as well as has an H-bond and π interaction with residue Tyr327. The docking pose of saussureamine C in the docking simulation is not stable in a nonstatic condition. In the representative structures after MD simulation, it has H-bonds with residues His449 and Leu476, as well as a π interaction with residue Phe282.The representative structures of each complex after MD simulation were identified by the cluster analysis with a RMSD cutoff of 0.1 nm. In γ protein in each complex are shown in γ protein are formed by its sulfonamide group. 5-Hydroxy-L-tryptophan and abrine form H-bonds with residue Lys367 by the carboxyl group. They form H-bonds with residue Ser289 by the indole group in the beginning of MD simulation, but the H-bond for abrine has shifted from the indole group to secondary amine group after 5 ns of MD simulation. In addition, the carboxyl group of abrine also forms a stable H-bond with residue Tyr327 after 7 ns of MD simulation. For saussureamine C, the docking pose in the docking simulation had changed after MD simulation. The H-bonds formed by the carboxyl group are shifted from residue Lys367 to residue Ser289 after 5 ns of MD simulation. In addition, it forms stable H-bonds with residue His449 by its sulfonamide group and heterocycle group after MD simulation.The H-bonds occupancies for key residues of PPAR-The root mean square fluctuations (RMSFs) shown in γ complexes is shown in γ protein, the docking pose is stable during 40 ns of MD simulation. For 5-hydroxy-L-tryptophan, the docking pose which is also stable during 40 ns of MD simulation except for the hydroxyl group in the indole ring has a 180-degree shift after MD simulation. For abrine, the variation of torsions 10 and 11 at the initial period of MD simulation may be the reason that the H-bond has shifted from the indole group to secondary amine group, and carboxyl group forms a stable H-bond with residue Tyr327 after MD simulation. Torsions 14 and 16 for saussureamine C indicate that the docking pose of saussureamine C has a fluctuation during 15–30 ns of MD; it can also be seen in the ligand RMSD  based on their ligand characteristics. After docking simulation, the docking poses of top TCM compounds ranked by the scoring function were validated by the MD simulation. For the top three TCM candidates, both of 5-hydroxy-L-tryptophan and abrine have an indole ring and carboxyl group to form the H-bonds with the key residues of PPAR-γ protein. The secondary amine group of abrine also stabilized an H-bond with residue Ser289. The key residues were stabilized in protein complexes with 5-Hydroxy-L-tryptophan and abrine as control. For saussureamine C, the interactions of docking pose in the docking simulation are not stable after MD simulation. Hence, we propose 5-hydroxy-L-tryptophan and abrine as potential lead compounds for further study in drug development process with the PPAR-γ protein.This study aims to investigate the potent TCM candidates for PPAR-


Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN  to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called  Cellular processes are typically carried out by protein complexes formed by groups of proteins interacting with each other, rather than by individual protein. Large-scale protein-protein interaction data being produced along with high-throughput techniques such as yeast two-hybrid Y2H) provide maps of molecular networks for several organisms , therebyH provideWhile significant progress has been made in those computational analysis of proteome-scale cellular networks, the inherent dynamics of protein interactions within these networks are often overlooked . CellulaNevertheless, the protein interaction networks derived from high throughput processing techniques could not enable us to discern temporal and contextual signals. Fortunately, gene expression data provide a complementary view by their ability to monitor changes in RNA concentration in thousands of genes simultaneously , 17. ThuDe Lichtenberg et al. constructed a dynamic PIN over the yeast mitotic cell cycle . For theDeviation Degree, to recognize the active time point of protein according to the deviation degree of its expression values from their arithmetic average. Then a time-evolving protein interaction network  is constructed by mapping active proteins into the original static PIN. TEPIN could more closely imitate the dynamic evolutionary procedure of protein interaction. Experimental results show that TEPIN greatly improves the prediction of temporal protein complexes in terms of match degree, sensitivity, specificity, F-measure and function enrichment, which indicates that our method not only successfully surmounts the drawback mentioned above, but also outperforms three-sigma method in practice for recognizing protein activity.We propose a novel method—Further, we present a weighted approach for PINs, which is applied on TEPIN to quantify the degree of interactions among proteins based on connected affinity  and geneProtein interaction network: We use yeast protein interaction data derived from DIP  (VersionGene expression data: Gene expression data over three successive metabolic cycles are available from GEO (Gene Expression Omnibus)  with accftp://ftpmips.gsf.de/), which contains 1063 protein complexes through a series of preprocessing, excluding the ones containing only one protein.Known protein complex dataset: MIPS Complex-Catalogue is probably one of the most comprehensive public datasets of yeast complexes available and allows precise standardized functional descriptions of genes . It is oTypically, the dynamics of protein interactions are indirectly reflected in the active time points of proteins. Therefore, the construction of a time-evolving dynamic PIN is determined by the identification of these active time points.Deviation Degree is a method created to identify the active time points of each protein according to the deviation degree of its expression values from their arithmetic average. For a gene i at time point t , only if the positive deviation degree of its expression value at this time point is greater than the standard deviation of the gene’s expression values over time points 1 to n, we consider it to be active at this time point. Let expit denote the expression value of gene i at time point t, then the gene’s arithmetic average (ui) and standard deviation (σi) of its expression values over time points 1 to n can be formulated as Eqs i as n is 36. We manage to achieve the time-evolving active protein sets under n time points. To begin with, each protein’s active threshold value is calculated according to (3). Then, for a time point t , each of the proteins is determined to be active or not by comparing its expression value at this time point with its active threshold value. As a result, we obtain the active protein set at time point t, which is denoted as ActiveProteinsTt. After the traversal of n time points, a sequential collection containing n active protein sets is generated, which is denoted as {ActiveProteinsT1,…, ActiveProteinsTn}.The expression values of each gene/protein fluctuate in a certain range, meaning they rise and fall around their arithmetic average value. Deviation Degree method, which is capable to recognize the active time points of proteins correctly, including the ones with very low or high expression levels.When a single global threshold is used to identify proteins’ active time points, the proteins with low expression levels will be filtered out even if they are always active during the whole metabolic cycle; while the ones with high expression levels during the whole metabolic cycle will be considered as active proteins at all the time points, even if their activities never appear. Although three-sigma method overcomes these drawbacks , it brinActually, the PINs in the real world are changing over time, environment and different stages of cell cycle. The assembly processes of almost all eukaryotic complexes are just-in-time, contrary to the just-in-time synthesis observed in bacteria . Just-inn snapshots, each of which is a subset of the original static PIN. For a time point t , the proteins in ActiveProteinsTt and their interactions in static PIN are reserved to form a temporal PINTt, namely, a snapshot of the dynamic PIN at time point t. After the traversal of n time points, we generate a TEPIN which is denoted as a sequential collection {PINT1,…,PINTn}. TEPIN reveals the dynamic evolutionary procedure of protein interactions.As the gene products involved in the gene expression data cover 97.8% of the proteins in the original static PIN, it is reasonable to construct TEPIN by combining these two datasets. A TEPIN behaves as In this section, TEPIN is converted into a weighted network in which the edge-weights represent the degree of protein interactions.CAC) to enhance the biological character of PIN [Pi and Pj, their relationship RPij should be closer when the complex contains more proteins but slighter when it includes more interactions [CCij standing for how large possibility to connect the proteins Pi and Pj in one protein complex is defined for RPij as Nk and Rk represent the number of proteins and interactions within protein complex k respectively. Considering the fact that proteins interacting with each other are often subordinate more than one complex simultaneously, Connected Affinity Coefficient CACij standing for the likelihood of that two proteins Pi and Pj could interact with each other is inferred from a protein complex set [Mij is the number of the known protein complexes which include the interaction connecting Pi and Pj. The value of CACij thus depends on two factors: the number of the protein complexes including interaction RPij and their individual values of CCij.For the one hand, it has been noticed that the interactions among proteins shouldn’t be treated equally. But owing to the neglect of biological nature, only Boolean values “1” and “0” can be employed to denote whether two proteins could interact or not in PIN. To resolve this issue, Li et al. defined connected affinity coefficient —can diminish the impacts of the inherent false negatives and false positives in PINs [x =  and y = . PCCxy can be denoted as x and gene y respectively. The values of PCCxy range from -1 to 1.For the other hand, the protein interaction data are not absolutely convincing due to the limitations of the associated experimental techniques. Interestingly, the integration of gene co-expression—which is usually measured by Pearson Correlation Coefficient  of TEPIN, thereby generating a weighted TEPIN denoted as {WDPINT1,…,WDPINTn}. Interactions with positive weight are deemed to be positive interactions and reserved within weighted PIN, while the others are eliminated as false positives. The ratio of eliminated interactions varies between 0.021 to 0.134  across 36 time points.Our weighted approach is applied on each temporal Deviation Degree method. To accomplish this goal, we employ several classic and state-of-the-art algorithms to mine protein complexes from our TEPIN, DPIN (constructed with three-sigma method based on the same datasets) and SPIN. Markov Cluster algorithm (MCL) [n temporal snapshots. Therefore, n groups of predicted protein complex are generated, which are finally merged into one group. The predicted protein complexes containing only one protein will be wiped out. Besides, inner kernel extension threshold and outer kernel extension threshold involved in CAMSE algorithm need to be adjusted to render the best performance.As we shall demonstrate in that following section, WTEPIN provides a more reliable basis for detecting temporal protein complexes. As three-sigma method has been demonstrated to be superior to the other dynamic PIN construction methods and has been widely accepted in academic circle as a state-of-the-art method to date , it is uhm (MCL) , which ihm (MCL)  and CAMShm (MCL)  are two hm (MCL)  is a famWe need to filter the redundant complexes from predicted protein complex set due to the high overlap ratio within them. To be more specific:All the predicted protein complexes are sorted in descending order by their size;Cu, we compare it separately to the other undiscarded ones with smaller or the same size (denoted by {Co}). Among the complexes in {Co}, the one whose similarity with Cu is greater than a very high similarity threshold will be discarded.For each of the undiscarded protein complexes Such a filter operation reduces the number of predicted protein complexes and retains the correct ones, which is helpful to the analysis of experimental results. The similarity threshold is set to 1.0 for ClusterONE and MCL algorithms, and 0.8 for CAMSE algorithm , 16.OS) [pc and a known protein complex kc:pc∩kc| represents the number of the proteins involved in both complexes pc and kc; |pc| and |kc| represent the number of proteins involved in complex pc and complex kc respectively. Two protein complexes are considered to be matched if their overlapping score is greater than or equal to a given threshold, which is set to 0.2, the same as many other researches [OS = 1 indicates that the two complexes pc and kc match perfectly. The predicted protein complex sets identified from various networks are separately compared against the known protein complex set.Overlapping Score (OS)  Eq (8) isearches . ParticuSn) and Specificity (Sp) are typically employed to evaluate the detection of protein complexes [TP) denote the number of predicted protein complexes that match with known complexes, false positives (FP) denote the number of unmatched predicted complexes, and false negatives (FN) denote the number of known protein complexes which match with none of the predicted protein complexes, then Sn and Sp can be defined as Eqs Sn and Sp, also known as F-measureSn to some extent indicates that more known protein complexes could be recognized, while higher Sp shows that higher percentage of predicted protein complexes match with known protein complexes.Sensitivity —in terms of the average scale and network density. As is shown in In the rest of this section, we’ll confirm the validity of our TEPIN and its weighted strategy by assessing their overall performances with three classic evaluation metrics See .OS threshold ranges from 0.2 to 0.4. Particularly, MCL algorithm obtains 47.5% as its percentage from TEPIN, which is 28% and 49% greater than that achieved from DPIN and SPIN respectively as OS threshold is set to 0.2 , which is shown in . The fravely see  at OS thvely see . TEPIN iypl235w to which only one protein node connects. In addition, these three protein complexes share the identical Gene Ontology terms such as RNA polymerase activity | AmiGO with p-values 5.01e-45 ; the frely (see ). In shoSn 0.794 and F-measure 0.650 from WTEPIN; MCL achieves 0.481 as its F-measure from WTEPIN, which is 6% and 46% higher than that achieved from WDPIN and WSPIN respectively; in addition, MCL achieves 0.353 as its F-measure from TEPIN, which is 12% and 49% higher than that achieved from DPIN and SPIN respectively; ClusterONE algorithm also achieves the highest Sn and F-measure from TEPIN. Although the values of Sp obtained from TEPIN (WTEPIN) are little lower, which is mainly due to their higher #PC, the values of MKC are always greater than those achieved from DPIN (WDPIN). Obviously, our Deviation Degree method is superior to the state-of-the-art three-sigma method in practice for recognizing the active time points of proteins.We use several metrics for evaluating the performance of TEPIN including Sensitivity, Specificity and F-measure See . Table 2F-measure obtained from WTEPIN, WDPIN and WSPIN are 36%, 18% and 28% higher than that achieved from TEPIN, DPIN and SPIN, respectively. Applying MCL algorithm, we find a 28% reduction (in contrast to TEPIN) in the number of the predicted protein complexes identified from WTEPIN, which is mainly due to the removal of the edges with negative weight. Nevertheless, the F-measure obtained from WTEPIN, WDPIN and WSPIN are 36%, 44% and 39% higher than that obtained from TEPIN, DPIN and SPIN, respectively. In short, our weighted approach dramatically enhances the efficiencies of the PINs, which greatly improves the accuracy of protein complexes identification.In conclusion, the time-evolving dynamic network TEPIN constructed with our new method can reveal the dynamic evolutionary procedure of protein interactions more precisely than the other networks, which naturally leads the prediction of temporal protein complexes get significantly improved. Moreover, the weighted TEPIN offers powerful support for revealing the biological properties of protein interactions, which further optimizes the detection of protein complexes.Deviation Degree method. Using the tool GO::TermFinder (http://www.yeastgenome.org/cgi-bin/GO/goTermFinder.pl), we calculate the p-values of the predicted protein complexes identified from WTEPIN and WDPIN by CAMSE algorithm. The other predicted protein complexes are left out in this section for the reason that they have relatively weaker performance according to previous analyses. Besides, owing to the inconvenience of dealing so many predicted protein complexes, here, only the ones containing at least 20 proteins account for our analysis, which still ensures the fairness of comparisons. As a result, we get 301 and 329 predicted complexes from WTEPIN and WDPIN respectively.We manage to implement the function enrichment analysis to validate the efficiency of our p-values falling into the corresponding intervals, while percentages denote the ratio of those complexes to the total predicted complexes. The proportion of predicted protein complexes with biological significance detected from WTEPIN is up to 98.7%. Despite of an 8.5% reduction in the total number of predicted protein complexes (denoted by #PC), the number of the complexes with p-values falling into interval [0, E-15) obtained from WTEPIN advances 40% in contrast to WDPIN; while the number and proportion of predicted protein complexes with no or weak biological significance derived from WTEPIN are evidently less than that derived from WDPIN. In short, our WTEPIN has a distinct advantage in statistically significant, indicating our Deviation Degree method outperforms three-sigma method in practice for identifying the activities of proteins.As is shown in p-values identified from WTEPIN. In each row, the proteins shown in bold are involved in the known protein complex that matches best with the predicted complex, while the additional uninvolved proteins within the predicted protein complex probably share the similar functions with this complex. For instance, for the No.1 predicted protein complex, 6 proteins are not involved in its matched known protein complex, of which 4 proteins  share the similar annotations—DNA-directed RNA polymerase—with the real protein complex. The No.6-10 predicted protein complexes are detected from WTEPIN but excluded from WSPIN. We obtain 774 extra predicted protein complexes from our WTEPIN in total, of which 706 (91.2%) with p-value less than 0.01, explaining our network is more helpful to analyze the protein interaction networks. Given the incompleteness of known protein complex set, the predicted protein complexes with small p-values are highly likely to be true protein complexes, and our weighted TEPIN provides many novel biological knowledge that cannot be detected from the original SPIN.p-values—under three different time points, meaning this predicted complex can perform five different biological functions. We analyze the active time points of 21 proteins involved in this predicted protein complex. After the disassembly of the complex at time point 9  which eliminates the disadvantages in other methods for constructing dynamic PINs. Further, we weight the TEPIN to depict the biological properties of protein interactions, as well as to diminish the impacts of the inherent false negatives and false positives in PINs. The experimental results show that the predictions of protein complexes on TEPIN outperform those on the other networks in terms of various evaluation measurements, which indicates the approach can reveal the dynamic evolutionary procedure of protein interactions more correctly than the other networks. Moreover, the weighted TEPIN further optimizes the detection of protein complexes. We obtain huge amount of predicted protein complexes with strong biological significance and provide helpful biological knowledge to the relate researchers. In addition, our analysis of the dynamic evolutionary procedure of a predicted temporal protein complex verifies the fact that protein complexes are assembled just-in-time.In this paper, we develop a Time-evolving dynamic PIN eliminates the noises which exist in static PIN and provides increased reliability for uncovering the dynamic protein assembly progress for cellular organization . Therefo


The yeast two-hybrid (Y2H) system exploits host cell genetics in order to display binary protein–protein interactions (PPIs) via defined and selectable phenotypes. Numerous improvements have been made to this method, adapting the screening principle for diverse applications, including drug discovery and the scale-up for proteome wide interaction screens in human and other organisms. Here we discuss a systematic workflow and analysis scheme for screening data generated by Y2H and related assays that includes high-throughput selection procedures, readout of comprehensive results via next-generation sequencing (NGS), and the interpretation of interaction data via quantitative statistics. The novel assays and tools will serve the broader scientific community to harness the power of NGS technology to address PPI networks in health and disease. We discuss examples of how this next-generation platform can be applied to address specific questions in diverse fields of biology and medicine. Networks of protein–protein interactions (PPIs) govern essentially all biological processes and mechanisms, such as receptor-ligand recognition, immune responses, intracellular and extracellular signaling, growth regulation, and development. Early on, PPI networks or “interactomes” were recognized as the next frontier in biomedicine after the completion of the human genome project . The rolThe charting of interactomes is in many ways more challenging than that of genomes. Proteins are encoded by multiple transcript isoforms and are localized in diverse cellular compartments with distinct milieus. Moreover, variations in amino acids and post-translational modifications affect and determine PPIs. Hence, from a practical perspective, working with proteins is more demanding than working with DNA. For these reasons, to this date, our technical capabilities for systematic approaches toward PPI networks remain limited, when compared with the routine deciphering of genomes, transcriptomes, and exomes at high efficiency and low cost by next-generation sequencing (NGS) technologies .in vivo screening of binary interactions to identify direct binding partners  is the standard method to identify protein complexes . On the partners . These aSince its inception , Y2H hasin vivo assays for screening binary PPIs can be considered alternative implementation of Y2H principles, such as split ubiquitin system for membrane proteins . NGIS procedures can be applied to every available Y2H and Y2H variant setup for binary interaction screens.We believe that the perceived shortcomings of Y2H such as inconsistent or non-reproducible results, lack of quantitation, laborious procedures, and above all, high rates of false positive results can be traced to the lack of an adequate readout system. With next-generation interaction screening (NGIS), we developed an innovative concept and methodology to harness the power of NGS technologies for the exploration of PPIs. The application of NGS removes the main restrictions on Y2H imposed by the cost of DNA sequencing. Replacing conventional Sanger sequencing with NGS leads to a massively increased throughput while reducing the cost of sequencing per screen to a small fraction of the conventional readouts . Currently we are providing screening services for clients that include experimental work, data analysis, and the use of a cloud-based platform , the maximum information can be extracted out of the assays, scoring false positives and also taking into account the occurrence of false negatives and the reproducibility of the screening results.The technical principle of NGIS is shown in a priori. This closes an existing gap to other technologies, such as AP-MS for which control datasets for background contaminants are routinely applied to distinguish bona fide interactors from non-specific contaminants  often makes up a majority of all hits in a Y2H screen . Hence, aminants . Importaaminants . Quantitaminants .Table 1). The primary goal in most Y2H screens is to define the function of proteins by identifying their molecular neighborhoods and to find specific targets that are relevant in diseases, e.g., proteins with functions in cancer or host receptors for pathogen effector proteins in microbial pathogenesis. NGIS interaction profiles and gene enrichment analysis help to understand the function of proteins of interest and the search for relevant interaction targets. An approach related to ours, Quantitative Interactor Sequencing (Qi-Seq), applied the split-ubiquitin system and Illumina NGS to screen for plant host targets for the HopZ2 effector protein that is secreted by the Gram-negative bacterial pathogen Pseudomonas syringae, and identified the Arabidopsis thaliana MLO2 protein as a target . Considering that protein domains rather than full-length proteins are at the basic level of proteome organization, screening for protein fragments often reveals specific interaction sites and also PPIs that are masked in full length-proteins by steric hindrance. The value of fragment-based Y2H approaches was demonstrated previously (Other areas in which NGIS could have an impact are protein engineering and target discovery for small molecule drugs (see eviously . NGS witeviously . We can Within the proper framework, NGIS could also, in principle, be applied for Y2H-based protein-drug interactions, such as Y3H to screen for novel protein targets that bind to known drugs , or to aBy providing quantitative measurements, reproducibility by repeat assays, background controls for false positives, streamlined scoring and statistical analysis, NGIS overcomes existing bottlenecks of Y2H, thus providing a valuable technology and service platform. In addition, reconstruction of the components for Y2H fusion expression and reporter selection could increase accuracy, speed, automation, and cost-effectiveness for Y2H screens. A wide repertoire of sequence elements and well-characterized parts is now available for this purpose, although less attention had been paid to PPI and interaction affinities than to transcription parameters . RegulatBS developed NGS for Y2H screens is responsible for content and wrote the article. J-HM and SD-K are scientific collaborators and advisors for the NGIS technology, XZ does the bioinformatics and codeveloped the concept. GP and AM, helped develop the Y2H procedures. All authors read and commented the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.