Transcription factors achieve specificity by establishing intricate interaction networks that will change depending on the cell context. Capturing these interactions in live condition is however a challenging issue that requires sensitive and non-invasive methods.Drosophila transcription factors for performing Bimolecular Fluorescence Complementation (BiFC). The multicolor BiFC library can be used to probe two different binary interactions simultaneously and is compatible for large-scale interaction screens. The library can also be coupled with established Drosophila genetic resources to analyze interactions in the developmentally relevant expression domain of each protein partner. We provide proof of principle experiments of these various applications, using Hox proteins in the live Drosophila embryo as a case study. Overall this novel collection of ready-to-use fly lines constitutes an unprecedented genetic toolbox for the identification and analysis of protein-protein interactions in vivo.We present a set of fly lines, called ‘multicolor BiFC library’, which covers most of the  Proteins are distributed in various compartments within the cell, acting in a crowded environment and establishing hundreds of molecular contacts that will eventually dictate cellular function. These molecular contacts are often highly dynamic, may be of weak affinity, and depend on the cell context. Capturing these versatile interactions is therefore a key challenge to better understand the molecular cues underlying protein function in vivo.Two types of experimental strategies are classically used for high-throughput screening of protein-protein interactions (PPIs): yeast two-hybrid (Y2H) and tandem affinity purification coupled to mass spectrometry (TAP-MS). Y2H detects PPIs in an automatable way in live yeast cells, while TAP-MS is based on co-immunoprecipitation (co-IP) and subsequent MS analysis of the different constituents of the complex , which provides a direct readout of the candidate PPI in the cell . A majorAmong the few methods that are compatible for PPI analysis in live conditions is Förster (or Fluorescence) resonance energy transfer (FRET), which relies on the transfer of a virtual photon between two fluorescent chromophores upon excitation. This transfer occurs within a small distance (less than 10 nm) and can thus be used to validate the close proximity between two candidate proteins, or to capture a conformational change . FRET reDrosophila embryo  appears much more convenient since it is based on a visible fluorescent signal. BiFC relies on the property of monomeric fluorescent proteins to be reconstituted from two sub-fragments upon spatial proximity (in a similar range of distance as FRET). This method has been used in different plant and animal model systems and with various types of proteins . In parta embryo , and theproteins .Moreover, the bright intrinsic fluorescence of BiFC allows analysis of PPIs using commonly available fluorescent microscopes and with normal protein expression levels. Originally established with the Green Fluorescent Protein (GFP ), BiFC hDrosophila transcription factors (TFs) (corresponding to around 65% of annotated TFs) for performing BiFC in a tissue- and developmental stage-specific manner in vivo. This genetic repertoire is called multicolor BiFC library and consists of a collection of fly lines that complement the previously established FlyORF collection . The muDrosophila wing imaginal disc (560 open-reading frames (ORFs) among the 3000 actually present in the FlyORF library code for TFs. These ORFs are under the control of upstream activation sequences (UAS sites) and fused in frame to a hemagglutinin tag (3xHA) sequence at their 3’ end . The ORFnal disc .Drosophila embryo  fused to the VC complementary fragment , and whose interaction status was also validated by co-IP experiments . Among tDrosophila TFs (around 65% of annotated TFs), making a total of 579 fly lines due to several TFs fused with different VN and/or CC versions. Among the 450 TFs, 127 are available as VN fusion constructs, 221 as CC fusion constructs, and 105 as VN- and CC-fusion constructs  and AbdA . These tUbx- or abdA-expression domain, by using Ubx-Gal4 or abdA-Gal4 driver, as previously described  and 57% (149/260) of the tested TFs could interact with Ubx or AbdA, respectively . Among aAlthough Ubx and AbdA shared a number of common interactions, our BiFC screen also revealed interactions that were specific for only one of the two Hox proteins. These interactions represent 20% and 13% of their overall interactome, respectively  was obtained from a previous candidate gene screen based on competitive BiFC with a set of 80 TFs . This hiUbx- or abdA-expression domain by expressing the TFs using Ubx-Gal4 or abdA-Gal4 driver. True positive interactions are expected to occur between TFs that are co-expressed in the same spatial expression domain. To test this, we considered the extent of the overlap between the expression domain of Ubx or AbdA and each TF . To this end, we used annotations from  when compared to all positive interactions (−2) is also observed in the dorsal vessel . The rational was that affecting the expression of a positive TF acting as a Ubx cofactor in the haltere primordium should increase the haltere-to-wing phenotype. Reversely, a negative TF should have no effect.Since BiFC revealed a number of potential interactions, we asked whether hits from the large-scale interaction screen could somehow be confirmed to contribute to Hox protein function. To this end, we searched for a sensitive genetic background where any subtle modification in Hox protein function could lead to a quantifiable phenotype. In this context, the loss of a Hox cofactor could affect the sensitized Hox protein function and lead to a stronger phenotype. No sensitive Hox-dependent phenotypes are known in the embryo, but several exist in the adult, like eye reduction  or antenormation . Importaormation . In thiske shape . This reUbx heterozygous mutant haltere discs. Slightly more than half of the tested TFs  in homothorax (hth) in TFs were selected based on their known expression and/or function in the haltere  of the selected TFs that give an interpretable phenotype were correlated to the BiFC interaction status. Thus, results obtained from the BiFC screen in the embryo could be reproduced at the functional level in another developmental context.Drosophila embryos . Exd belongs to the PBC class of TALE (Three Amino Acids Loop Extention) TFs . PBC proomplexes .abdA-Gal4 driver, as previously described  and Bagpipe (Bag)) that were positive with AbdA and Exd and present in the ORF-CC library to perform bicolour BiFC. An additional TF, Empty spiracles (Ems), which was positive with AbdA . BiFC with sfGFP is described to rely on the complementation property between a long N-terminal fragment of 214 residues (sfGFPN) and a short C-terminal fragment of 19 residues . In addHere, we tested the complementation between sfGFPN and CC fragments by considering five TFs that were positive with Exd in our previous BiFC analyses. The sfGFPN fragment was fused at the N-terminus of Exd, making a sfGFPN-Exd fusion protein with the same fusion topology as previously done with the VN fragment . BiFC was analyzed with excitation and emission wavelengths of the sfGFP. Results showed that the sfGFPN-Exd fusion protein could complement with the CC fragment in the case of all five tested TFs . MoreoveThe multicolor BiFC library is under the control of UAS sequences. Pilot tests were performed as previously described , using aDBD) and Gal4-activation  domains. This property can be used to activate any UAS-driven gene in cells that will independently co-express the Gal4DBD and Gal4AD moieties, for example under the control of two different enhancers . In addition, Mimic transposons have also been designed to be compatible with the split-Gal4 system  . We usedUbx-Gal4 . This coUbx-Gal4 . In any Drosophila.We present a new fly line library called multicolor BiFC library that currently allows using 450 TFs for testing PPIs in vivo. This library contains two different sets of fly lines. The first set contains 232 TFs fused to VN and derives from previous work  or from The second set of the multicolor BiFC library consists of 326 ORF-CC constructs that are compatible with the first set of ORF-VN fly lines for doing Venus-based BiFC. ORF-CC fly lines are also compatible for doing Cerulean- or sfGFP-based BiFC with a CN- or sfGFPN-fused bait protein, respectively. This property allows multicolor BiFC experiments, which was here demonstrated with the analysis of interaction properties of different TFs in the context of the AbdA/Exd partnership. The multicolour BiFC system provides an unprecedented opportunity to dissect cell-specific regulatory mechanisms involving three interacting proteins in vivo.Drosophila  were common to the two Hox proteins, which reflects their ability to control a number of identical developmental processes during embryogenesis. This hypothesis is supported by the observation that common interactions were not so frequent between Ubx and a more divergent Hox protein like Sex combs reduced (40% of common interactions among 35 tested TFs ), or betHox-Gal4 driver and therefore in conditions that do not reproduce the expression profile of the tested ORF. Despite this, we found that TFs that are endogenously more frequently co-expressed with Ubx and AbdA during embryogenesis were significantly enriched among the positive interactions.The diversity of positive TFs reflects the likely high potential of Hox proteins to engage in many different types of interactions in vivo, as previously shown for Ubx . In addiSpecificity of the BiFC screen was further demonstrated by the identification of several interactions that were exclusive to either Ubx or AbdA. Interestingly, the AbdA-specific interactome was significantly enriched in TFs that are expressed in tissues  or cells (oenocytes) with known AbdA-specific functions.Finally, the functional analysis of a selected pool of TFs expressed in the haltere disc showed that 80% of the interpretable phenotypes (25/31 TFs) were consistent with the interaction status found by BiFC in the embryo.Altogether these results confirm that the multicolor BiFC library is suitable for performing large-scale interaction screens and revealing specific interactomes even for two closely related bait proteins.P-Gal4 insertion that will affect the expression of the bait-encoding gene, as done here with the Ubx-Gal4 or abdA-Gal4 driver. This genetic context allows expressing the bait protein at normal levels while eliminating one dose of endogenous competitive gene product. Furthermore, expressing the cofactor in cells that do not contain the corresponding endogenous gene product allows doing BiFC in the absence of competition, therefore increasing assay sensitivity. Here interactions were analyzed in the epidermis, which is an easily accessible tissue with low fluorescent background. The epidermis was previously shown to be tolerant for interactions with many different types of TFs  for valWe showed that the interaction could be confirmed in the relevant developmental context in vivo by doing BiFC with the split-Gal4 system, In particular, the split-Gal4 system was used to visualize BiFC specifically in the overlapping expression domain of Ubx and Twi. The large number of fly lines compatible with the split-gal4 system makes this system very attractive for the future use of the multicolor BiFC library. Along the same line, the Janelia and Mimic collections propose hundreds of fly lines compatible with the LexAop/LexA-AD induction system. This expression system is also compatible with the multicolor BiFC library since UAS sequences of ORF-VN and ORF-CC constructs are swappable with LexAop sequences upon FLP/FRT-mediated recombination . The twoDrosophila and the multicolor BiFC library enable selection of biologically relevant conditions that better recapitulate the endogenous expression profile of each candidate partner, thus maximizing specificity and providing an unprecedented combination of possibilities for large-scale and/or in depth analysis of PPIs in a live animal organism.In conclusion, the versatile inducible expression systems for thousands of enhancers in yellow marker insert) was digested with BamHI-NotI and the shortened FRT2-VNm9short fragment was inserted. Finally, the yellow marker gene was inserted via the XhoI site downstream of the fragment, resulting in pTSVNM9short.attB  and reverse primer tub-R2 (5’-ACACTGATTTCGACGGTTACC-3’), thereby eliminating a stretch of 12aa between FRT2 and VNm9 and introducing flanking restriction sites BamHI and NotI. Plasmid pTSeGFP.attB (without the ails see ).The N-terminal part of Cerulean (CN) including the FRT2 site was PCR-amplified from the Cerulean cDNA  with forTo generate this plasmid, the 3 HA tag from pGW-HA.attB  was replDrosophila transcription factor collection in the pDONR221 vector  was PCRFor all germline transformation experiments we used the ΦC31 integrase-mediated site-specific integration method.TSVNm9short-86Fb, TSCN-86Fb strains: The constructs pTSVNM9short.attB and pTSCN.attB were injected into line ΦX-86Fb. Transgenic offspring was made homozygous for these transgenes and combined with an X chromosome-linked hsp70-flp construct. These strains provide either the Venus N-terminal tag or the Cerulean N-terminal tag for a swapping event at the 3’end of any UAS-ORF strain from FlyORF that is based on cloning with pGW-HA.attB.PSlexO-21F strain the pPSlexO.attB plasmid was injected into line ΦX-21F, again followed by balancing the strain for the transgene and providing the hsp70-flp construct on the X-chromosome. This strain can be used to exchange the UAS-regulated promotor of the TF-ORF-CC library (at location 21F) for a lexA operator (lexO) promotor . Pool injection, transgene identification and stock generation were described in detail previously  can easily be tracked by screening for a specific marker combination, i.e. eviously .https://flyorf.ch/index.php/orf-collection)Fly lines generated for the project will be deposited in the FlyORF library and are available upon request to FlyORF . RNAi against TFs are from Bloomington and were expressed with the MS1096 driver combined or not with UAS-dicer in individuals heterozygous for the abxpbxbx mutation, which affects Ubx expression in the haltere disc (http://www.flyorf.ch).The sly used . UAS-ORFsly used . Ubx-Galere disc . The traWilcoxon statistical tests were performed by using Python and R in-house scripts. Quantification of ectopic wing-like hairs in the haltere was shown by boxplot representation using RSoftware. Boxplot depicts the smallest value, lower quartile, median, upper quartile, and largest value for each condition. 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.Drosophila" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by K VijayRaghavan as the Senior/Reviewing Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Generation of a versatile BiFC ORFeome library for analyzing protein-protein interactions in live 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:Drosophila fly lines called "multicolor BiFC library", which covers most of the Drosophila transcription factors and thus allows to capture transcription factor (TF) interactions using the Bimolecular Fluorescence Complementation (BiFC) in live conditions. Importantly, the multicolor BiFC can be used to simultaneously probe two binary interactions, is compatible for large-scale interaction screens and can be combined with the existing Drosophila tools, for example, the split-GAL4 system and thus allows a visualization of potential interactions only in domains where the proteins of interest are co-expressed.In this manuscript, a new resource is presented, a set of eLife and these are listed below.Overall, the manuscript is very solid and provides an important genetic toolbox for the identification and analysis of protein-protein interactions. Although BiFC has a couple of disadvantages, this resource is nonetheless very valuable. Our initial enthusiasm on reading the manuscript was somewhat tempered on careful reading. Several important issues need to be addressed before publication in Essential revisions:1) The authors have described before the usability of BiFC to monitor interactions in vivo. What is new and exciting in this paper is the use of this system to show interactions of not only two but also of three interaction partners. The authors show that they can detect interactions between Abd-A and a third partner and Exd and the same third partner . However, the co-occurrence of blue and green signals in the same cell is not proof that the three proteins appear in a complex. These could be simply independent interactions in the same cell. The issue of whether using BiFC to study interactions between three proteins provides information about tripartite complexes is important. The authors appear to be aware of the issue that the assay only directly reports on binary interactions, describing "analyzing two different PPIs simultaneously" and in Figure 6 legend "potentially in the context of…complexes". The relationship between simultaneous reporting of two binary interactions and information about tripartite complexes should be clarified. The authors should drop the term "tripartite" from the Abstract and instead generally make it clear that the advance is to study two binary interactions in the same cell. This may provide information on potential complexes and they should present how the binary information might be used; e.g. where they suggest that "BiFC with Exd is dependent on the concomitant interaction with AbdA" since TF-Exd BiFC (green) is only found in cells that also have TF-AbdA BiFC (blue). They should also remove in Figure 6A the 'tripartite complex' or indicate somehow that this is just a possibility but not shown in the paper.2) For showing the functional relevance of the identified interactions, the authors used the haltere-to-wing transformation and a sensitized mutant background (Ubx heterozygous condition). This is an elegant experiment, however, the pictures in Figure 5 are not good enough. The authors need to show higher magnifications and better images of the bristles. In addition, quantification of the phenotypes for all genotypes is needed.3) The authors introduce the super-folded GFP as an additional GFP derived protein useable for BiFC and show that this variant behaves similarly to VN fusions. Their reasoning for introducing this variant was that it has shorter maturation times and distinct spectral properties from Venus and Cerulean. However, the authors do not specifically test the usability of this variant for detecting interactions with unstable proteins. Thus, the data as presented just show that the sfGFP variant works similarly well as the VN variants for more stable proteins, which is good but not surprising. If the authors could show an interaction using an unstable TF comparing the performance of the Venus and sfGFP variant, this would strengthen this point very much. In any case, the authors should move the sfGFP part to the supplement if they cannot show its usability for a more unstable protein.Ubx-GAL4 or twi-GAL-4 drivers . Careful quantification of the fluorescence signal is required for the split-GAL4 line described in Figure 8, as the fluorescence intensities are quite different. Does this result also mean that BiFC should be in principle only be done with the split-GAL4 system? This needs to be carefully discussed.4) The use of the split-GAL4 system is an excellent addition, however, it also raises the question why the strong interaction of Ubx and Twi in the visceral mesoderm, which is clearly visible with the split-GAL4 system (8C') is not detectable with the 5) Figure 3 is unreadable. Could the authors think of better ways to represent the interactomes? For example, they could show the overlap of Abd-A and Ubx interactors in a circle diagram etc. They show that HD TF are enriched, any other class? What about the function of these TFs: repressors, activators? The same is true for Figure 4, the information could be better and more intuitively presented.Drosophila embryos”) is followed by a number of approaches seeking to "validate" the observed interactions. However, these approaches do not provide straightforward validation, demonstrating corroboration based on expected outcomes, but rather lead to novel findings. Although these novel findings, such as the difference in IDR and SLiM occurrence between Ubx- and AbdA-specific interactomes and differential effects of HX mutations on Ubx and AbdA, are intriguing, they do not provide clear validation of the BiFC observations. We would recommend deleting the sections beginning "The multicolor BiFC library reveals Hox-specific…" and "A short conserved Hox protein motif…".6) The paper could be considerably shortened. The presentation of the large-scale BiFC analysis of interactions with Ubx and AbdA  The authors have described before the usability of BiFC to monitor interactions in vivo. What is new and exciting in this paper is the use of this system to show interactions of not only two but also of three interaction partners. The authors show that they can detect interactions between Abd-A and a third partner and Exd and the same third partner . However, the co-occurrence of blue and green signals in the same cell is not proof that the three proteins appear in a complex. These could be simply independent interactions in the same cell. The issue of whether using BiFC to study interactions between three proteins provides information about tripartite complexes is important. The authors appear to be aware of the issue that the assay only directly reports on binary interactions, describing "analyzing two different PPIs simultaneously" and in Figure 6 legend "potentially in the context of…complexes". The relationship between simultaneous reporting of two binary interactions and information about tripartite complexes should be clarified. The authors should drop the term "tripartite" from the Abstract and instead generally make it clear that the advance is to study two binary interactions in the same cell. This may provide information on potential complexes and they should present how the binary information might be used; e.g. where they suggest that "BiFC with Exd is dependent on the concomitant interaction with AbdA" since TF-Exd BiFC (green) is only found in cells that also have TF-AbdA BiFC (blue). They should also remove in Figure 6A the 'tripartite complex' or indicate somehow that this is just a possibility but not shown in the paper.We agree that “tripartite complex” is an over-interpretation and we have removed the term form the Abstract. We now present this aspect as a possibility, both in the new Figure 6A (with a dotted arrow) and in the corresponding legend.2) For showing the functional relevance of the identified interactions, the authors used the haltere-to-wing transformation and a sensitized mutant background (Ubx heterozygous condition). This is an elegant experiment, however, the pictures in Figure 5 are not good enough. The authors need to show higher magnifications and better images of the bristles. In addition, quantification of the phenotypes for all genotypes is needed.abxpbxbx background).Illustrative phenotypes of the different categories were de novo acquired with a scanning electron microscope and higher magnifications of ectopic bristles are also shown in the new Figure 5. Statistical quantification of the number of ectopic bristles is also provided for each positive RNAi  The authors introduce the super-folded GFP as an additional GFP derived protein useable for BiFC and show that this variant behaves similarly to VN fusions. Their reasoning for introducing this variant was that it has shorter maturation times and distinct spectral properties from Venus and Cerulean. However, the authors do not specifically test the usability of this variant for detecting interactions with unstable proteins. Thus, the data as presented just show that the sfGFP variant works similarly well as the VN variants for more stable proteins, which is good but not surprising. If the authors could show an interaction using an unstable TF comparing the performance of the Venus and sfGFP variant, this would strengthen this point very much. In any case, the authors should move the sfGFP part to the supplement if they cannot show its usability for a more unstable protein.There is some confusion about the usability of sfGFP-based BiFC for detecting interactions with unstable proteins. The sfGFP protein has a faster maturation time than other GFP-derived proteins, and is therefore more appropriate for monitoring protein dynamics. However, BiFC with sfGFP- or Venus-derived fragments requires several hours for maturation, which forbids tracking protein interaction dynamics. To date, there is no fluorescent protein with a complementation maturation time that could be compatible with the analysis of protein interaction dynamics in vivo. In addition, sfGFP- or Venus-based BiFC stabilizes the interaction once it is revealed. In conclusion, the novel combination of fragment complementation between the sfGFPN and CC fragments just adds to the fluorescence repertoire of the multicolor BiFC library. It will not provide access to protein stability or interaction dynamics. We tried to reformulate more clearly this aspect to avoid any confusion with this regard. The corresponding results are also included in the supplement , as suggested by reviewers.4) The use of the split-GAL4 system is an excellent addition, however, it also raises the question why the strong interaction of Ubx and Twi in the visceral mesoderm, which is clearly visible with the split-GAL4 system (8C') is not detectable with the Ubx-GAL4 or twi-GAL-4 drivers . Careful quantification of the fluorescence signal is required for the split-GAL4 line described in Figure 8, as the fluorescence intensities are quite different. Does this result also mean that BiFC should be in principle only be done with the split-GAL4 system? This needs to be carefully discussed.Ubx- and Twi-Gal4 drivers lead to expression and interaction of Ubx and Twi in different parts of the embryo, including the visceral mesoderm. BiFC in the visceral mesoderm was not obvious in the previous Figure 8 of the first version, due to strong fluorescent signals in the epidermis or somatic mesoderm, respectively. We now present maximum intensity projections of confocal sections centered on the visceral mesoderm to better highlight BiFC in this tissue with each individual driver . In addition, we quantified the intensity of BiFC in the visceral mesoderm with the split-Gal4 system, in comparison to the BiFC intensity observed in the same tissue with the Ubx-Gal4 driver. Values are in accordance with the known diminished activation activity of the reconstituted Gal4 when compared to full length Gal4 (which is now mentioned in the main text). Finally, we discussed why and how the split-Gal4 system should be used with the multicolor BiFC in the Discussion section .Because of this interesting comment on the split-Gal4 system, we decided to better describe the interaction profile with each different driver. The use of the split-Gal4 system with the multicolor BiFC library was also more carefully discussed. Practically, the 5) Figure 3 is unreadable. Could the authors think of better ways to represent the interactomes? For example, they could show the overlap of Abd-A and Ubx interactors in a circle diagram etc. They show that HD TF are enriched, any other class? What about the function of these TFs: repressors, activators? The same is true for Figure 4, the information could be better and more intuitively presented.Interactomes of Ubx and AbdA are now presented differently, in a way that each TF name could be read upon higher magnification in the new Figure 4—figure supplement 4 and Figure 4—figure supplement 10. As suggested by reviewers, interactomes are also presented in a simplified manner to better illustrate the repartition of the main TFs families in the different Ubx and AbdA interactomes . Of note, the distribution of the different BiFC fly lines was voluntary switched from the supplement to the new Figure 3 to directly provide the global information that will evolve in the future.6) The paper could be considerably shortened. The presentation of the large-scale BiFC analysis of interactions with Ubx and AbdA  is followed by a number of approaches seeking to "validate" the observed interactions. However, these approaches do not provide straightforward validation, demonstrating corroboration based on expected outcomes, but rather lead to novel findings. Although these novel findings, such as the difference in IDR and SLiM occurrence between Ubx- and AbdA-specific interactomes and differential effects of HX mutations on Ubx and AbdA, are intriguing, they do not provide clear validation of the BiFC observations. We would recommend deleting the sections beginning "The multicolor BiFC library reveals Hox-specific…" and "A short conserved Hox protein motif…".The entire parts dedicated to the analysis of intra-molecular features of Ubx and AbdA interactors have been deleted according to the reviewers’ suggestion.


The docking methods used in structure-based virtual database screening offer the ability to quickly and cheaply estimate the affinity and binding mode of a ligand for the protein receptor of interest, such as a drug target. These methods can be used to enrich a database of compounds, so that more compounds that are subsequently experimentally tested are found to be pharmaceutically interesting. In addition, like all virtual screening methods used for drug design, structure-based virtual screening can focus on curated libraries of synthesizable compounds, helping to reduce the expense of subsequent experimental verification. In this review, we introduce the protein-ligand docking methods used for structure-based drug design and other biological applications. We discuss the fundamental challenges facing these methods and some of the current methodological topics of interest. We also discuss the main approaches for applying protein-ligand docking methods. We end with a discussion of the challenging aspects of evaluating or benchmarking the accuracy of docking methods for their improvement, and discuss future directions.  In the not-so-distant past, the effects of drugs on disease were known only by empirical observation. A century of subsequent research has revealed many intricacies in the working of cellular receptors and other drug targets, and likewise the methodology of finding small molecules that bind to specific targets has become increasingly complex. This development has been marked by the realization that the interacting surfaces of cellular receptors are chemically active and often flexible, and that these properties tend to be critical to the biological effects of the small molecules, or ligands, that bind to these receptors. Within this climate of complexity, the field of rational drug design emerged to play an important role in the search for new medications . An important early step in rational drug design is the identification of a biological target of interest . This tade novo drug design, in which a novel compound is designed from scratch, and virtual database screening, in which computational methods are used to search through libraries of small molecules, in order to find those that are predicted to be the most likely to bind to a drug target of interest [De novo drug design has the advantage of versatility; only the imagination and the need to synthesize the compound in question limit its conceptual possibilities. However, this advantage can also be a disadvantage. New compounds can prove difficult or expensive to synthesize, constraining the number of new compounds that may be subsequently analyzed by experiment. In addition, predicting the interactions of entirely novel compounds is inherently difficult. The other category, virtual database screening, helps mitigate the synthesis problem by focusing on large databases of synthesizable compounds. Rational drug design aims to use knowledge of the biological target of interest to optimize the process of finding new medications. It may be divided into two broad categories: interest . De novoIn virtual database screening, computational techniques are used to search databases of compounds for small molecules predicted to bind to a drug target . Such prIn principle, the methods used in virtual screening may be applied to any conceivable compounds, but in practice one usually focuses on curated libraries of purchasable or synthesizable compounds, or close analogues of such compounds. Some examples include Accelrys Available Chemicals Directory , eMolecules Database , and the free ZINC database . There are two general types of virtual screening: ligand-based virtual screening and structure-based virtual screening. In ligand-based virtual screening, properties of a set of ligands known to bind to the drug target of interest are used to build a model for the common features believed to be important for a ligand’s biological effects. This model can then be used to find new ligands that share these common features . In strude novo. Protein-ligand docking is not usually applied to the whole surface of a protein to predict a ligand’s binding site. Instead, separate methods or biological information about the protein target are used to determine the primary binding site, and the subsequent docking is restricted to this site of interest. Nevertheless there are a few combined methods in which the binding site search and docking procedure are performed simultaneously  [Structure-based virtual screening typically employs docking software that is designed to explore the possible binding modes of a ligand within a binding site of interest and scoring functions that are used to estimate the affinity of the ligand for the binding site of interest ,9,10,11.docking) ,16. There is a great variety of software packages available for performing protein-ligand docking. Some popular ones include DOCK ,20,21,22In summary, we will review the application, methodologies, and evaluation of the protein-ligand docking approaches. In As aforementioned, protein-ligand docking software attempts to sample the possible ways a ligand can be positioned in a protein receptor of interest, and typically provides an estimate of the binding affinity and binding mode of a ligand for the protein receptor ,9,10. DoAn essential component of docking methods is the scoring function. In protein-ligand docking, the scoring function typically assesses the overall favorability of a protein-ligand complex and is meant to be comparable to the free energy of binding of the protein and ligand ,40. TherComputing the binding affinities of protein-ligand complexes cannot yet be done very accurately by a general scoring function. The calculation is especially challenging due to the combinatorial explosion of possible conformational states of the flexible protein and ligand, and of the surrounding water molecules and ions. In addition, the binding process involves a balance between many different physical interactions: a flexible ligand may gain favorable interactions upon binding, while simultaneously suffering a substantial entropic penalty as a result of binding. For example, charged polar groups may gain favorable electrostatic interactions when binding, while simultaneously losing favorable interactions with the solvent. Even when polar groups are solvent-exposed, as they like to be, this energetic favorability is partially tempered by the loss of entropic freedom of nearby water molecules. The contribution of each of these interactions can be substantial, yet they tend to cancel each other out; therefore the total binding free energy involves a delicate balance, and inaccuracies in the computation of any one type of interaction can lead to substantial inaccuracies in the computation of total binding free energies . Here we discuss the three main types of scoring functions used for docking. Firstly, there are the force-field-based approaches, which attempt to exhaustively model the many types of interactions involved in protein-ligand binding using physics-based functional forms and parameters that are derived from experiments or quantum mechanical simulations. Secondly, there are empirical approaches, in which regression or machine learning methods are used to associate the desired prediction, typically the binding affinity of the complexes, with general features of those complexes such as the number of hydrogen-bonding pairs. Finally, there are statistical potentials, in which energy-like terms are assigned to structural features of protein-ligand interactions based on the frequency with which those features occur in a training set of protein-ligand complexes. Force-field-based potentials can be used in protein-ligand docking as well as molecular dynamics (MD) simulations. They generally include a number of terms representing the various kinds of physical interactions that dominate protein-ligand binding. There are many popular force-field-based potentials in use for various applications, but most of them are quite similar in functional form. The main differences between them are which terms are included in the functional form and which specific values are used for the parameters in those terms. These parameters can be derived for experiment or fitted based on quantum mechanical simulations . ConsequThe AMBER force fields take the following functional form .EAMBER Eangle and Ebond are harmonic approximations of the bond angle and strain energies, respectively, and Edihedral is an energy term associated with the dihedral angles of linearly-bonded sets of four atoms . The term Enon−bonded aggregates the non-bonded interactions: a Lennard-Jones 6-12 potential which approximates the van der Waals attraction and Pauli repulsion [epulsion , and an The ff94 force field, which uses the AMBER functional form, has been very popular for simulating proteins , as haveThe CHARMM force fields are similar the AMBER force fields, but include some additional terms.bond, Eangle, Edihedral, and Enon−bonded have functional forms like those in the AMBER force fields, but the parameter values may differ. The Urey-Bradley term, EUB [Eimproper term provides an energy penalty for improper dihedral angles and helps to control the interconversion of stereocenters. The parameters in EUB and Eimproper can be optimized based on vibrational spectra [The terms Eerm, EUB , is base spectra . The CHA spectra . The mor spectra . In addition to functional forms and parameterization, the force-field-based approaches are also distinguished by the method of simulating the solvent. The most obvious approach is to model explicitly all of the water molecules in the vicinity of a ligand-receptor and their interactions with the protein and ligand. There are a variety of explicit water models, which are distinguished by the number of sites used to represent the charge distribution of each water molecules: TIP3P uses 3-sites to represent the charge of the oxygen and two hydrogens, TIP4P splits the oxygen into two sites to better represent the charge distribution, and so on . Due to To simulate some of the effects of the solvent while reducing the computational expense, implicit solvent approximations were introduced. These approximations generally start with the assumption that the solvent can be treated as a continuous dielectric medium with a charge distribution and a resulting electrostatic potential that obeys the Poisson-Boltzmann (PB) equation ,52,53,54To further decrease the complexity, there are empirical solvent methods. In these methods, the electrostatic forces between the protein and ligand are modulated by an empirical distance-dependent parameter that roughly models the tendency of water to screen the electrostatic forces between charged atoms. For one example, this approach was used in DOCK . Finally, it is worth noting that the force-field-based potentials mentioned above give estimates of the internal energy of the protein-ligand system in a specific microstate rather than the free energy of binding. In principle, one can use direct integration of the partition function to compute the free energy. Computing the full partition function is typically intractable, but sometimes approximations of the partition function are used . In pracGiven their relatively complex functional forms, the force-field-based approaches described in the previous subsection are computationally intensive. In order to provide a higher-speed alternative, researchers introduced empirical scoring functions ,71. LikeEmpirical scoring functions combine features such as hydrophobic contacts, hydrophilic contacts, or number of hydrogen bonds, and parameterize these features as favorable or disfavorable based on regression or machine learning methods. Typically the parameters are optimized to predict the binding affinities of a set of protein-ligand complexes that are used as a training set ,71. In ti, protein atom type j, at a distance of rk (the distance of the k-th bin), can be computed as follows [ρij(rk)/ρij,ref  is the relative radial density for atom pair typeij within the training set and is a function of the binned distance rk. The density ρij,ref  associated with the reference state may be computed using an ideal gas approximation or other approaches [Besides the empirical scoring functions, there is another type of scoring function that uses a simpler functional form than the ones given in Equations (1) and (2). Statistical potential-based scoring functions  assign energy-like quantities to structural features, based on the frequency with which those features are found to occur in a training set of suitable examples, such as a set of protein-ligand complexes, relative to a reference state ,77,78,79 follows .(1)Theproaches ,82. The derivation of statistical potentials using the inverse-Boltzmann relation is not necessarily physically rigorous, and typically involves some false assumptions. One example is the assumption that the occurrences of features in the training set are conditionally independent of each other, given the energies associated with those features. In applications such as protein interactions and protein structure prediction, the interdependencies neglected by the derivation can manifest in the form of problems such as the excluded volume problem . Howeveret al. introduced an iterative method of deriving a statistical potential that helps avoid the need to define a specific reference state. In this method, which was developed for protein folding, the interaction potentials between residues were iteratively adjusted based on the difference between the residue pair frequencies in the native structures and the residue pair frequencies in a Boltzmann-weighted ensemble of decoy conformations [Another open problem in the implementation of statistical potentials is the reference state problem ,82,85,86rmations . The extrmations ,90,91,92i.e., clashes) [Finally, there is also the sparse data problem. The inverse-Boltzmann relation in Equation (3) maps the observed frequency of features in a training set to the energies assigned to those features; for features that occur infrequently in the training set, the deriving energies are inaccurate or undefined. Even when very large training sets are available, the problem persists due to physically disallowed states such as very close atom pair distances (clashes) ,94. Given the many approaches that have been used to tackle the problems mentioned above and the diverse applications, there are many examples of statistical potentials. Some of the popular ones for protein-ligand interactions include DFIRE , DrugScoThe force-field-based potentials, statistical potentials, and empirical scoring functions each offer specific advantages and consequently tend to be used for different applications. Force-field-based potentials separate the types of physical interactions within the system into separate terms, and can therefore provide information on the contribution of these interactions to the internal energy of the system ,46,48. FEmpirical scoring functions ,71 use aLike empirical scoring functions, statistical potentials use a simple functional form allowing for faster implementations than are typically possible with force-field-based potentials ,77,78,79There have been some efforts to combine different scoring functions in a way that provides a compelling combination of the advantages in each scoring function. An early example of a consensus scoring function may be found in . Other eThe other fundamental challenge facing protein-ligand docking methods is sampling. Protein-ligand binding involves changes in the relative orientation and conformation of the ligand, as well as possible conformational changes to the protein. Docking software attempts to sample these possible changes with varying degrees of exhaustiveness . The simplest approach for sampling the possible ligand binding modes is rigid docking; the docking software can simply explore the six degrees of translational and rotational freedom and filter those with poor shape complementarity before final scoring. This approach was used by older versions of DOCK  as well There are also docking approaches that sample the possible ligand conformations on-the-fly. One method of on-the-fly sampling is the incremental construction method, also known as the anchor-and-grow method. A rigid central portion of the ligand is placed in the binding site, and the rest of the ligand is incrementally grown from this rigid anchor, filtering out those possibilities that clash with the protein receptor during the process ,107. DOCAnother approach for sampling ligand conformations is the hierarchical docking method. In this approach, low-energy conformations for each ligand are pre-computed and aligned so that as many atoms as possible are identically-positioned. Each ensemble of pre-generated ligand conformations is organized into a hierarchy so that similar conformations are similarly positioned within the hierarchy. Then, for each possible translation and rotation of the ligand, the docking software makes use of the hierarchical data structure to simultaneously prune or filter sets of conformations that are not sterically possible for the given translation and rotation. For example, if an atom near the rigid center of the ligand is found to clash with the protein in a given rotation/translation, the method can confidently reject all of the descendent conformations in the hierarchy for that rotation/translation, because the descendants must contain the same clash, without having to sample each descendant conformation individually . The GliIn addition to these methods of sampling ligand conformations, there are methods to handle protein flexibility. One simple approach is to rigidly dock the ligands to several putative conformations of the protein, to represent some of the protein’s conformational variability ,109,110.While sampling and scoring constitute the fundamental challenges in docking, much research focuses on more specialized topics. We do not attempt to exhaustively review all of the active topics, but instead sample a few topics that have received a large amount of recent attention. et al., modeling structural water molecules was found to increase the accuracy of docking simulations, up to a binding mode success rate of 67% [As mentioned in e of 67% . Other we of 67% . In one e of 67%  to reproe of 67% . Docking methods improved by structural water simulation have seen practical applications, such as an inverse docking application in . et al. noted the regulatory recommendation that all new drug candidates be tested for their potential to block human Ether-a-go-go Related-Gene (hERG) potassium channel, given the substantial risk of cardiotoxic side effects such as arrhythmias [in silico screening to be a useful step in identifying cardiotoxic leads before they are given larger investments. It has been well-recognized that drugs may bind to many targets with significant affinities, and that this drug promiscuity gives rise to a complex polypharmacology with clinical relevance to the toxicity and side effects of pharmaceuticals ,121. Thehythmias . They coet al. point out that this is particularly important for non-traditional drug targets, such as a target within the interface of protein-protein interactions. For such targets, compounds that bind are often inadequate, if they do not bind in a sufficiently buried manner to achieve good ligand efficiency [et al. introduced a ligand-based approach that uses shape matching to identify promiscuous ligands [Besides the prediction of toxicity and side effects, the tendency of ligands to bind to several sites presents another challenge: if a ligand binds tightly somewhere, even near the desired binding site, it may still not substantially affect the drug target in question. Gowthaman ficiency . Perez-N ligands . et al. review the chemogenomics approaches in which a spectrum of a ligand’s interactions with many drug targets is predicted by structure-or ligand-based methods. Using these approaches, one can attempt to increase those interactions within the spectrum that are desired while simultaneously reducing unwanted interactions [One the other hand, particularly for multifaceted diseases such as cancers or metabolic disorders, it may be desirable for a drug to bind to multiple targets. Peng ractions . Docking studies often employ comparative (or homology) models of the protein target that are based on the crystal structures of homologous proteins. The methodology behind building homology models is outside the scope of this review, but here we remark on the popularity of the approach in practice. It is notable that a number of recent successful virtual screening projects used homology models of the protein receptor, and for some the best template for homology modeling was fairly divergent from the target structure, in terms of percent sequence identity ,131,132.et al. investigated the accuracy of predicting ligand binding modes in comparative models of G-protein coupled receptors. The researchers found that for the best models with template structures over 50% sequence identity, the accuracy of binding mode prediction was within 2.9 Å RMSD (root-mean-squared standard deviation) from the native experimental structure on average. In cases of low sequence similarity, it is challenging to produce a homology model with sufficient accuracy to use as a basis for virtual screening [Nguyen creening ,134, butcreening . Having introduced structure-based drug design and the methodologies of protein-ligand docking, we will now sample the most common research approaches in which such methods have been applied. Docking methods have a long and successful history of identifying new protein inhibitors and enriching compound databases in structure-based virtual screening. Here we discuss some examples of this common application. et al. used structure-based virtual screening to identify new inhibitors of maternal embryonic leucine zipper kinase (MELK), an important kinase target known to be involved in several types of cancer. As signaling molecules, kinase targets tend to be challenging for docking methods due to their tendency to undergo major conformation changes induced by ligand binding [Recently, Mahasenan  binding . Their t binding . et al. performed a virtual screening study of Gloeobacter violaceus ligand-gated ion channel (GLIC), a bacterial homolog of GABAA receptors, to search for compounds that bind to the same site as the anesthetic propofol. Among a database of commercially available compounds, 29 compounds were experimentally tested of which 16 were found to exhibit significant inhibition of GLIC relative to dimethyl sulfoxide. The active compounds were further tested on GABAA receptors. One of the compounds, like propofol, was found to inhibit both GLIC and GABAA receptors, suggesting that the GLIC receptor may be a plausible model system for GABAA receptor ligands. In another recent work, Heusser et al. used MODELLER [In a third example, Tahir MODELLER  to buildMODELLER . In an eMODELLER  for new MODELLER . In a final example, a series of substituted heteroaromatic piperazine and piperidine derivatives were found through virtual screening based on the structure of human enterovirus 71 capsid protein VP1. The preliminary biological evaluation revealed that two of the compounds (8e and 9e) have potent activity against EV71 and Coxsackievirus A16 with low cytotoxicity . The structure-and ligand-based methods of performing virtual database screening are not simply competing alternatives to perform the same task. They each have unique strengths and weaknesses and can therefore play a complementary role in the drug design process and other applications. Such hybrid approaches have become increasingly popular . Here ar et al. In this work, the binding profiles of the spherical C 60 version of fullerene and its derivatives were investigated. Aside from the remarkable physicochemical characteristics of these molecules, fullerene and its derivatives are increasingly investigated for their unique biological effects [et al. included quantum-mechanical calculations, protein-ligand docking and QSAR. They used quantum-mechanical calculations to determine geometries, dipole moments, orbital energies, and other parameters of the fullerene derivatives. They used protein-ligand docking software including AutoDock Vina [One example of a hybrid approach may be found in Ahmed effects . The hybock Vina  and Schrock Vina ,29 to seock Vina . Another work that used a hybrid approach of structure-and ligand-based methods can be found in a study identifying four inhibitors of heat shock protein 90 (Hsp90), which is an important chaperone protein and anticancer drug target . In thiset al. used a hybrid approach to screen a database of commercially available compounds for their predicted ability to bind G-quadruplex structures. Before this work, there were already a variety of knowns binders for G-quadruplex structures. They first screened over one million compounds from the ZINC database [The last example of the hybrid approach that we will mention here involved DNA G-quadruplex structures, which are found in some critical positions within the genome such as near the telomeres and gene promoter regions. Unsurprisingly, they are involved in cellular aging and cancers . Alcaro database  using lidatabase . Virtual database screening studies do not always start with the identification of a drug target of interest. Often, one is interested in a compound that is known to have an important biological effect, but for which the underlying molecular mechanism is unknown . ConsequA recent application of inverse docking may be found in . Some plBenchmarking plays an important role in the development and improvement of docking methodologies . Public It would be ideal for docking scoring functions, sampling schemes, and other methodologies to be tested in prospective studies in which the targets of the benchmark were unknown at the time the methodology was developed. Such prospective evaluations are not always available when new methods are introduced. In such cases, a rigorous experimental design can help ensure trustable evaluations, especially with regard to the independence of the benchmark from the development of the methods. Examples of prospective evaluations of protein-ligand docking methods include CSAR, the Community Structure Activity Resource ,155, andIn practice, docking methods are unable to rely on the availability of structurally accurate knowledge of the bound, native conformation of a protein binding site and ligand, due to the conformational changes that occur during binding. A realistic evaluation of docking would require the method to dock an arbitrary conformation of the ligand to either a ligand-free crystal structure of the protein, or if none are available, then a crystal structure bound to a different ligand than the one being docked. This allows the docking software to be tested for the ability to either simulate the induced fit of binding, or test the success of a smoother scoring function designed for soft docking. Examples of recent evaluations of docking methods that included unbound evaluations may be found in ,158. In addition to the change in protein conformation associated with the induced fit of protein-ligand binding, docking methods also have to deal with the flexibility of ligands. To evaluate scoring function performance in flexible binding mode predictions, one approach is to pre-generate many decoy binding modes for a ligand in the vicinity of the binding site, and test the ability of the scoring function to distinguish between the native pose and decoys. This approach was used for decoy sets that extend the CSAR benchmark . et al. suggested that this approach of testing docking scoring functions with predefined sets of decoy ligands is not adequate for distinguishing a scoring function that performs well in practice from one that performs poorly. The reason is simple: in docking, sampling that is sufficient to identify the native pose and conformation must be very thorough, and so in practice many more diverse poses and conformations are considered during docking than are typically generated for the decoy poses generated for scoring function evaluation [Korb aluation . It seemaluation . et al. addresses this problem by using the Hungarian algorithm. The Hungarian algorithm can be used to find the optimal mapping of two graphs under a cost function, and in addition to other applications in chemical informatics, has recently been used by Allen et al. to find the optimal mapping between two molecules in RMSD calculations, ensuring that a correct binding mode prediction will be recognized as such [Another challenge in evaluating docking methods is the need for special handling of symmetric molecules when evaluating binding mode predictions. The binding mode predictions of a docking method are commonly evaluated using the root-mean-squared standard deviation (RMSD) of atom positions between the known native binding mode of a ligand and its predicted mode according to the docking method. However, comparing the atom positions between two structures of a ligand requires mapping the atoms in the native ligand conformation to the atoms in the docked conformation of the ligand. Due to the symmetry of entire molecules, or substructures within them, these mappings can be ambiguous and a naïve treatment of binding mode evaluations can consider a perfect binding mode prediction to be a poor prediction. Recently, work by Allen  as such . This me as such  and we aThe methods used to simulate the binding of proteins and small molecules face substantial challenges. Two of the fundamental challenges are sampling and scoring . ProteinAnother important component of protein-ligand binding that is sometimes neglected is the effect of entropy. Rigorous computation of the entropic contribution to binding free energy is intractable for large molecular systems such as protein-ligand complexes. Some approximations have been introduced to deal with entropy ,162,163,Despite all the challenges, protein-ligand docking has a long and successful history of practical applications including newly discovered enzyme inhibitors, receptor antagonists and agonists, ion channel blockers, as well as the subsequent approval of new drugs discovered with the help of structure-based drug design. Docking methods have provided new mechanistic insights into protein-ligand binding mechanisms, and have also helped investigate the influence of protein mutations on ligand binding, offering clues regarding the mutations that enable the robust survival of drug-resistant pathogens. As the continued increases in computational power expand the practical applications of molecular models, the field is quickly advancing, with approximations that offer a better tradeoff between accuracy and computational cost. These efforts will undoubtably lead to many more intriguing applications into the future.


Arabidopsis thaliana at the end of the day (ED) and the end of the night (EN). We show that size-fractionation of native protein complexes by Clear-Native-PAGE (CN-PAGE), coupled with mass spectrometry can be used to establish abundance profiles along the molecular weight gradient. Furthermore, by deconvoluting complex protein abundance profiles, we were able to drastically improve the clustering of protein profiles. To identify putative interaction partners, and ultimately protein complexes, our approach calculates the Euclidian distance between protein profile pairs. Acceptable threshold values are based on a cut-off that is optimized by a receiver-operator characteristic (ROC) curve analysis. Our approach shows low technical variation and can easily be adapted to study in the complexome in any biological system.The majority of cellular processes are carried out by protein complexes. Various size fractionation methods have previously been combined with mass spectrometry to identify protein complexes. However, most of these approaches lack the quantitative information which is required to understand how changes of protein complex abundance and composition affect metabolic fluxes. In this paper we present a proof of concept approach to quantitatively study the complexome in the model plant  An additional layer of complexity is added by the interaction of different protein complexes in so called super-complexes or metabolons2. The formation and rearrangement of super-complexes and metabolons has an influence on metabolic fluxes and helps plants to adapt metabolic pathways in response to environmental changes3. Therefore, global and quantitative analysis of protein complexes is required to understand how the organization of proteins into functional units is connected to the regulation of metabolic pathways.Protein function and activity are highly regulated within living cells. To become functional, proteins commonly depend on their interactions with other molecules. These molecules are known to include other proteins, which readily interact to form protein complexes. The majority of cellular processes are dependent on protein-protein interactions (PPI’s)5. The Y2H system, although scalable to high throughput, is limited by the large number of false positive and false negative results that are generated7. Further, it often has a rather limited binary readout for a theoretical possible interaction, without discrimination of interaction stability, subcellular localization and subunit stoichiometry. AP-MS strategies often require modifications of the biological system, which not only alters the native structure of the protein by the introduction of a tag, but also the quantity as a result of overexpression. Furthermore, this method is labour intensive and limited to studying a small number of proteins in parallel9. Moreover, the ability to obtain quantitative data on protein complex abundance and composition from AP-MS experiments is very limited.Studying protein complexes is a technically challenging and time-consuming task. The most common experimental approaches used to identify protein complexes rely on the classical yeast two-hybrid (Y2H) system or affinity purification in combination with mass spectrometry (AP-MS). Each of these have advantages and limitations, especially concerning the sensitivity and specificity of the method, and its potential for scalability12. One-dimensional fractionation is not only less laborious than 2-D PAGE, but it also provides more reproducible protein separation at a high resolution.In an attempt to overcome the challenges of the aforementioned methods, researchers combined classical methods of protein fractionation, such as polyacrylamide gel electrophoresis (PAGE) or column based chromatography with quantitative mass spectrometry. One such method, 2-D PAGE remains an accessible method for separating and studying protein complexes. In contrast to the previously described approaches, it can provide quantitative data on hundreds of proteins. More recently, researchers have used one-dimensional fractionation of protein complexes by size-exclusion chromatography (SEC) or blue-native PAGE (BN) to obtain size-distribution profiles for up to 3400 proteins in a single experiment13.To achieve the goal of studying protein complexes within the complexome comparatively, the currently used methods need to be extended to include quantitative and proteome-wide analyses of protein complexes. Here we propose a method for the isolation and identification of protein complexes using native-PAGE (NP) for the separation of protein complexes in their native state. Our approach works to overcome the limitations of other methods and provides high quality qualitative and quantitative data. To minimize technical variation, the samples from different biological replicates and conditions were processed in parallel, thereby minimizing technical variation. The importance of minimization of technical variation implementation has been also recently highlighted in complexome studiesArabidopsis thaliana at end of day (ED) and end of night (EN). The highly reproducible nature of the data across biological replicates facilitates the identification of changes in protein complex abundance between ED and EN. This method is a step towards comparative analyses of protein complexes in different conditions or genotypes. Our approach can easily be adapted for any biological system.We show that size-distribution profiles of proteins that participate in different complexes can be deconvoluted, in turn isolating peaks and improving the clustering of our data. Using the proteasome as a benchmark protein complex we show that our native PAGE analysis effectively separates and identifies the two proteasomal subunits with a high coverage of subunits. Furthermore, we analyse the protein complexome of 14. Great care should be taken to select the appropriate methods that are used for protein complex extraction, fractionation and sample preparation for mass spectrometry, any or all of which could serve as the source of technical variation. With this in mind we established a workflow to allow processing of native protein extracts for the comparative analysis of the plant complexome at different diurnal time points. In this approach the isolated protein extracts are separated by native PAGE fractionation, followed by a modified in gel digestion procedure and analysis by tandem mass spectrometry , when plants are photosynthetically active, and the end of the night (EN), when plant growth relies on internal stored carbon reserves. Native proteins and intact protein complexes were extracted from four biological replicates at 4 °C in a detergent-free buffer and separated using 1D native PAGE. The native gel prepared for this study showed highly reproducible protein separation for all lanes. This was essential for producing 20 fractions for each of the eight biological samples by cutting across the full width of the gel  along the molecular weight gradient for each protein using the turnpoints function (pastecs package) in R Fig. . The loc18. This provided us with an overview of the oligomerization state (Rapp) of all the protein peaks identified in our experiments. The distribution of the apparent masses of identified proteins was visually summarized using scatterplots  in our data peaked in fractions corresponding to size ranges larger than their monomeric mass. Fewer than 11% of the peaks were identified in a monomeric range and proteins experienced minimal degradation as indicated by the small number of peaks with a Rapp of less than 0.5. This data shows that most of the proteins are found in complexes. As an additional proof of protein complexes in the dataset we used a histogram to summarize the distribution of monomeric masses in comparison to the observed size ranges of each local maximum . If the abundance of a fraction exceeds the abundance of the previous one the profile is considered on the rise towards a peak and the next fractions is analysed. If the abundance decreases to < 70% of the maximal abundance in the current peak, the script considers that the profile is entering the downward slope and the peak fraction is fixed. A peak is defined as finished when the signal crosses the minimal considered signal or the relative abundance in a following fraction is increased by > 30% in comparison to the previous one. Finally, the putative peak has to fulfil two more criteria to be accepted by the deconvolution script: Firstly, the recorded peak fraction has to be annotated as local maxima (see above), and secondly, the relative abundance at the peak position has to be > 20% of the maximum abundance in the total profile. Next, we removed the deconvoluted peaks representing the monomeric state by comparing the size of each peak with the monomeric molecular weight of the respective protein according to the TAIR10 database.16. To circumvent the problem of peak shape and multiple peaks per protein profile, researchers focused their analysis on the position of the maximal protein abundance in a profile and considered proteins with similar maxima as putative interaction partners. This solution does offer insight into protein complex formation, but it excludes the possibility of identifying co-eluting protein complexes in the remaining fractions. It has previously been reported that the deconvolution of the elution profiles are a possible solution and we applied our own method of deconvolution to address the problem12. Using a two-fold molecular weight cut-off to exclude deconvoluted peaks representing monomeric proteins, we found 1768 (ED) and 1758 (EN) proteins in a size range indicating protein complex formation  as an example Fig. . After dase Fig. . The putase Fig. . It is h20.The majority of proteins in the datasets were found to exist as protein assemblies Fig.  and 4. AIn order to evaluate the quality of our dataset, we selected proteins from biologically relevant pathways and reconstructed the protein complexes using deconvoluted peaks.16. This complex is present in all eukaryotic cells and responsible for the degradation of proteins in the cytosol. The proteasome is comprised of a catalytic 20S core particle (CP) and a 19S regulatory subunit (RP)21. Represented visually as a heatmap, we were able to detect both the CP and the RP in easy to discern clusters  proteins of the CP and 3 proteins (9.3%) of the RP. We identified 20 (87%) and 22 (95%) co-migrating proteins of the CP at the ED and the EN, respectively  cycle to this end. The TCA cycle is present in the mitochondria and drives cellular respiration under oxidative conditions. It has been postulated that enzymes in the TCA cycle are able to form supramolecular complexes with weak and transient interactionsapp) of more than two, indicating complex formation  were also observed in our analysis of interactions between TCA cycle enzymes29  and ketoglutarate dehydrogenase (ODC1-1) were predicted to interact in our dataset. Finally, we predicted the interaction between citrate synthase subunit (CSY4) with ACO1  and the three ATP citrate lyase (ACL) subunits, that play an important role in the reductive TCA cycle32 Fig. . While f33. However, we could not observe any change in the oligomerization state of TCA cycle related enzymes between the two time points  was calculated for all identified proteins, based on the deconvoluted migration profiles. Using the Euclidean distance cut-off obtained by ROC curve analysis . Plant material was harvested into liquid nitrogen at two time points. The first time point was at the end of the 12 hour light period and the second time point was at the end of the 12 hour dark period. Whole rosettes from up to 5 distinct plants were combined to create a biological replicate. A total of four biological replicates were collected at each time point. Plant tissue was ground to a fine powder in liquid nitrogen and proteins were extracted on ice in a native protein extraction buffer ). Protein estimation was performed using a BCA assay kit (Thermo Scientific) according to the manufacturer’s instruction.® II xi cell from Biorad. Following protein separation the gel was stained for 1 hour at room temperature in a Coomassie Blue solution  and then destained for another 2 hours at room temperature in a destaining solution (50% [v/v] methanol and 10% [v/v]). Each lane was fractionated into 20 pieces and the slices were transferred to 96 well plates for the in-gel tryptic digestion procedure17. Gel slices were washed three times for 1 hour at 37 °C with a destaining buffer  and subsequently subjected to the in-gel tryptic digest with minor modifications to the sample volumes. The resulting peptides were desalted by reverse-phase chromatography on Finisterre C18 SPE columns (Teknokroma), dried in a vacuum centrifuge and stored at −80 °C.Approximately 400 µg of protein from each biological replicate was separated in a large format handcast Tris-glycine gel  under native conditions using a PROTEAN−1 in a buffer composed of 3% (v/v) acetonitrile and 0.2% (v/v) formic acid. An acetonitrile concentration gradient (5% to 40% [v/v] acetonitrile) was used to elute peptides over 70 minutes at a flow rate of 0.5 µL min−1. The column was then washed for ten minutes with 80% (v/v) acetonitrile at a 0.25 µL min−1 flow rate to avoid potential memory effect as it is crucial for quantification study were small number of remaining proteins would have an effect on obtained results. Scan parameters were configured to detect ions in a full scan from 300 to 2000 m/z at a resolution of 35’000. Following detection data dependent tandem mass spectrometry scans were performed for the 7 most abundant ions , 35%). Dynamic exclusion was applied for 30 secs to peptides for which MS/MS spectra was recorded. MS data was acquired in Proteomics Facility of the ETH Zurich.Prior to analysis peptides were resuspended in 40 µl of resuspension buffer . Peptides were separated online by reverse phase liquid chromatography using a NanoLC 1D (Eksigent) before being measured on a LTQ-Orbitrap XL ETD (Thermo Scientific). An in-house made capillary column  was packed with Magic C18 AQ beads  and used for sample loading. Samples were loaded at a flow rate of 0.5 µL min42. Peptide identification by the Andromeda search engine was based on the Arabidopsis thaliana TAIR10 protein sequence database 18. The following parameters were applied to the analysis: 10 ppm peptide mass tolerance; 0.8 Da MS/MS tolerance; a maximum of two missed cleavages were allowed; a decoy database search with a 1% FDR cutoff on the protein level; carbamidomethylation of cysteine was set as a fixed modification, while the oxidation of methionine was set as variable modification. The “label-free quantification” and “match between runs” settings were also highlighted in the software. Valid peptides were expected to have a minimum length of six amino acids. Peptide quantitation was performed for proteins identified with at least two peptides (a minimum of one unique and one razor) unmodified peptide. Peptides intensity was taken and further normalize by LFQ algorithm. Known contaminants were removed from the analysis. MaxQuant was used to produce the “mean” complexome dataset by combining replicates within the software.Raw MS/MS spectra was imported into MaxQuant (version 1.6) for protein identification and quantitation42 partner repository with the dataset identifier PXD010919.The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDESingle linkage clustering of the data was performed using the in R software. The distance between protein profile peaks were calculated using the Euclidean distance formula in R. Heatmaps were produced in R by applying the hclust function to the data.The deconvolution procedure starts by identifying the local maxima in a protein profile by applying the turnpoints function from the pastecs package in R. After the local maxima have been defined, each maxima is filtered to retain only the peaks that exceed 20% of the highest relative abundance. To be considered as part of a peak after deconvolution, the signal needs to be at least 5% of the maximum abundance as measured in the total profile. The deconvoluted peaks were obtained by applying a decision tree at each local maxima, which was repeated for each analysed fraction. A deconvoluted peak was considered to terminate when the signal fell below 70% of the maximum relative abundance in the total profile. If this behaviour is not observed the profile is investigated in the next fraction until the signal enters the downward slope and reaches the termination threshold. Alternatively a peak can also be terminated when the relative abundance in a downstream fraction exceeds 70% of the relative relative signal of the previous peak. Peaks obtained by deconvolution are finally crosschecked against the initial local maxima, and where the peaks are in agreement, a verified deconvoluted peak is obtained.19 annotations for Arabidopsis as a validated set of true interactions. The set of true negative interactions were generated using random combinations of proteins annotated in the database, as well as proteins from the dataset which are not annotated to belong to any protein complex in the GO Slim database. To reduce the risk of accidentally selecting proteins that really interact, only protein pairs that were predicted to have distinct subcellular localizations were accepted in the TN set. The subcellular localization was determined using the SUBA3 database43.Receiver-operator characteristics for all experiments were calculated using the GO Slim19 was used a reference to group all of the proteins that belong to the same protein complex. Next, at each of the time points, these groupings were inspected to determine which fraction contained the highest number of coeluting protein peaks. The protein peaks within this fraction were then combined with those of the neighboring fractions (+1/−1) to create a set of all protein peaks representing the protein complex at the investigated size range. Subsequently, the Euclidean distance was calculated between all of the aforementioned profiles within the protein complex. A filtering step was then performed. Only the protein profiles that satisfied the Euclidian distance cutoff of 0.3, as calculated by the ROC curve analysis, were kept. Additionaly, to be retained for the analysis, each of the proteins belonging to the protein complex were expected to have similar profiles in at least three of the biological replicates. Finally, the protein complexes that had the same protein composition at both time points were selected for the complexome comparison. The area under the curve (AUC) was calculated for each of the proteins in the protein complex and the resulting mean values were used for the comparison. A student’s t-test was applied to determine whether changes were statistically significant.Deconvoluted protein profiles, prepared as previously described, were used for the complexome comparison. Firstly, the GO Slim databaseSupplementary Dataset 1


E. With this experimental setup, we characterized the effect of the tic56-3 mutation in the 1-MDa inner envelope translocase (TIC) on the assembly of the chloroplast proteome. We show that the tic56-3 mutation results in a reduction of the 1-MDa TIC complex to approximately 10% of wildtype levels. Hierarchical clustering confirmed the association of malate dehydrogenase (MDH) with an envelope-associated FtsH/FtsHi complex and suggested the association of a glycine-rich protein with the 1-MDa TIC complex. Depletion of this complex leads to a reduction of chloroplast ATPase to approx. 75% of wildtype levels, while the abundance of the FtsH/FtsHi complex is increased to approx. 140% of wildtype. The accumulation of the major photosynthetic complexes is not affected by the mutation, suggesting that tic56-3 plants can sustain a functional photosynthetic machinery despite a significant reduction of the 1-MDa TIC complex. Together our analysis expands recent efforts to catalogue the native molecular masses of chloroplast proteins and provides information on the consequences of impaired accumulation of the 1-MDa TIC translocase for chloroplast proteome assembly.We report a systematic analysis of chloroplast high-molecular mass protein complexes using a combination of native gel electrophoresis and absolute protein quantification by MS Stable protein interactions allow protein complex analysis and identification by classical biochemical techniques such as native gel electrophoresis or gel permeation chromatography [Protein/protein interactions determine complex biosynthetic activities such as the synthesis of metabolites, DNA, RNA and proteins. Interactions between proteins can be transient and conditional and depend on the presence of ligands. These protein interactions are difficult to assess with biochemical approaches making a functional tography , 2. Withtography . Using ttography  and a fltography .In chloroplasts, several approaches characterized the native protein complex proteome by classical biochemical techniques such as BN PAGE, colorless native (CN-) PAGE, gel permeation chromatography and/or glycerol density gradient centrifugation –12. AparRecent efforts to map the high molecular mass chloroplast proteome suffered from low resolution because there is a tradeoff between resolution and scalability in large-scale mass spectrometric analyses. Consequently, the number of samples is reduced to keep instrument time low to allow the analysis of replicates under a variety of different experimental or biological conditions. For example, Olinares and colleagues used five sample bins for the identification of soluble protein complexes in a molecular mass range from 800 kDa to >5 MDa  used BN-tic56-3 mutation on the 1-MDa TIC complex and the assembly of the plastid high-molecular mass proteome. The 1-MDa TIC complex consists of four known subunits comprising Tic214 (i.e. YCF1), Tic100, Tic56 and Tic20, and it has been reported as the general protein import pathway across the inner envelope membrane [tic56-3 is expected to provide new information about its functional context [With the study reported here, we expand these recent efforts by combining BN-PAGE separation of protein complexes with absolute protein quantification by MSE. MSE is a data-independent acquisition method that rapidly switches between MS and MS/MS acquisition modes, thereby providing high-resolution quantitative information for all fragmented precursor ions in a sample. This enables absolute protein quantification by setting the intensity read-out of proteotypic sample peptides in relation to those of an external standard (e.g. 10 fmol of glycogen phosphorylase B). The correlation of peptide signal intensity and protein concentration is used to infer the absolute abundance of proteins in the sample –20. Withmembrane . This vitic56-3, we could not identify significant differences in the assembly of protein complexes at the thylakoid membrane, with the ATPase complex as only exception. We could show that a recently identified FtsH/FtsHi complex at the inner envelope membrane accumulates independently of the 1-MDa TIC complex, even though they form a supercomplex during protein translocation in wildtype chloroplasts [Our analysis provides a robust identification and quantification of the major chloroplast proteins complexes and provides new information on their compositions. Despite a significant reduction in the abundance of the 1-MDa TIC complex in roplasts . We discArabidopsis thaliana lines were used: tic56-3 (FLAG579H12), Ecotype Wassilewskija (Ws) was grown as wild-type control for the tic56-3 wildtype comparison. Plants were grown under short-day conditions (8 h/d light at 21°C) on 1% (w/v) agar plates containing 0.5 × concentrated Murashige and Skoog (MS) medium supplemented with 0.8% (w/v) sucrose.The following Chloroplasts were isolated by Percoll density gradient centrifugation as described previously and washed three times to remove residual Percoll contaminations . After tA. thaliana database  containing common contaminants (ftp://ftp.thegpm.org/fasta/cRAP/crap.fasta) and rabbit glycogen phosphorylase B (P00489) was used as internal quantification standard [http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [http://mev.tm4.org).Dried peptide pellets were dissolved in 2% (v/v) ACN and 0.1% (v/v) FA and injected into an ACQUITY UPLC System connected to a Synapt G2-S mass spectrometer . Nano-LC separation (140 min gradient) and HD-MSE data acquisition was performed as described previously , 24. Datpository . To asseAntibodies against atTic20-I (At1g04940) were described previously .tic56-3 mutants and solubilized 200 μg chloroplast proteins prior to BN-PAGE in a 1% digitonin-containing buffer   can be taken as a quality indicator for the solubilization efficiency and the stability of the complexes in BN PAGE. As can be seen in We first assessed the abundance of protein complexes in chloroplasts using the wildtype data as a reference . The mede (pTAC) . The thyThe distribution and migration profiles of proteins within a complex is exemplified for the pTAC complex in tic56-3 comparison. A tight clustering is observed for the subunits of the thylakoid membrane complexes PSI, PSII, the ATPase  and the thylakoid-associated FtsH proteases (chloroplast stem-loop binding protein of 41 kDa) and two RNA-binding proteins  family protein) form a small sub-cluster that is associated with the cluster of the small subunits of the ribosome . In chloroplasts, hetero-oligomeric complexes that are involved in the assembly maintenance of a functional thylakoid membrane system have been described [Arabidopsis chloroplasts contain nine FtsH proteases and five presumably inactive enzymes that lack the consensus Znescribed , 29–31. escribed . We idenIn addition to the thylakoid FtsH proteases, several FtsH and FtsHi enzymes localize to the envelope membrane system. Our data clearly separate the envelope from the thylakoid FtsH enzymes by their molecular mass and their abundance. While the envelope proteases form very large protein complexes with molecular masses of around 2 MDa, the thylakoid FtsHs are smaller and accumulate to higher abundance  S1 Tabl, S2 Fig.We identified several known members of the translocon at the outer chloroplast envelope membrane (TOC), including Toc159, Toc33, Toc34 and Toc75. These proteins form complexes during the import process but the complex is unstable under the BN PAGE conditions reported here. In contrast, the recently identified 1-MDa translocon at the inner envelope membrane forms a stable complex that is identified by the unsupervised hierarchical cluster . In additic56-3 plastids. To this end, we summed up the fmol values from every gel slice to result in a single fmol value for every protein. Based on four replicates, we calculated p-values from a two-sided t-test to test for the rejection of the null hypothesis, i.e. that the protein abundances are identical in wildtype and the mutant , among them six subunits of the chloroplast ATPase, three subunits of the 1-MDa TIC complex, Grp23 (At2g43630), that clusters with the 1-MDa complex  and Grp23. Thus, the strongest effect of the tic56-3 mutation on the plastid proteome is the downregulation of the 1-MDa TIC complex and of ATPase. None of the photosynthetic complexes at the thylakoid membrane is significantly affected in the tic56-3 mutant  and an almost stoichiometric abundance distribution (ATPase), we think that solubilization efficiency is not a systematic problem.In conclusion, we report here a detailed analysis of chloroplast protein complexes by BN PAGE. We found 1% digitonin (w/v) suitable for solubilization of chloroplast proteins, but other detergents or combinations thereof may as well be suitable for this purpose and deliver partially complementary results . While ONonetheless, despite the overall good data quality several other critical points require attention. First, the fmol values we report are only predictive for subunit stoichiometry in the case of stable protein complexes. Complexes that dissociate during BN PAGE may give rise to inaccurate quantification results that are manifested as far spreading of protein abundances in protein complexes. This was observed for the photosynthetic complexes and the ribosomes  1 interaction network [2+-binding motif in the M41 peptidase domain and are thus catalytically inactive [The latter aspect is supported by the association of the FtsH/FtsHi complex with the plastid import machinery , that wa network . The ori network . For rea network , 40. Theinactive  with theinactive . It is tinactive .S1 FigNote that the data are not normalized.(PPTX)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigThe BN-PAGE gel slice number is provided on the x-axis, the abundance  is provided on the y-axis.(PPTX)Click here for additional data file.S1 TablePresented data are fmol values that were determined by compariosn to 10 fmol glycogen phosphorylase b as previously described –20.(XLSX)Click here for additional data file.S2 TableThe paired t-test was performed on ppm-normalized data.(XLSX)Click here for additional data file.


PMD), a novel method of penalized matrix decomposition for the identification of protein complexes  was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMDpc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition ( The identification of protein complexes is highly beneficial for the investigation of all kinds of organisms to understand biological processes and determine inherent organizational structures within cells . The draAnother type of method used to detect protein complexes employs an intelligent optimization algorithm, which seeks the optimal solution of PPI based on a heuristic concept . For larpc, an approach used to identify the protein interaction network of protein complexes was originally proposed. First, the adjacent matrix of the protein interaction network was normalized. Second, the normalized matrix was decomposed into three factor matrices. Finally, the PMDpc algorithm and several classical algorithms were executed from the well-investigated human PPI network. The experimental results show that our approach achieved satisfactory performance in terms of F-measure, ACC, and maximum matching ratio (MMR).Inspired by Ref. , PMDpc, pc is applied to identify the protein complexes in PPI network, the parameters of When PMD2=0.25×p .k on the effect on the experimental results, we repeated the execution of algorithm and studied how the algorithm behaves in terms of F-measure and let To study the parameter of The value of the F-measure increases gradually until Five classical protein complex algorithms, namely, CFinder , ClusterFrom To void the advantage of some evaluation metric, the composite score  is emploOur method is applied to detect the protein complexes in the human PPI dataset downloaded from Ref. , in whicConsider a sample dataset that consists of size n×p . WithoutThe decomposition of sparse matrix is executed by imposing additional constraints on lated as (2)argmiof forms .Let ments δr (3)12‖η−Therefore, when Moreover, The optimization problem in Equation (4) can be applied to the following biconvex optimization :(5)argmaEquation (5) satisfies Equation (4) based on the appropriate value of c . EquatioAlgorithm 1. Calculating the single factor of PMD.Step1. Initialize Step2. Interate until convergence: (i)(ii)Step3.Equation (2) is computed repeatedly to obtain other PMD factors. The corresponding algorithm is described in Algorithm 2.Algorithm 2. Calculating the Step1.Step2. For (i)The single factor PMD (Algorithm 1) is executed on the matrix of (ii)The constraint is imposed on Let Theorem 1. Considering the optimization problems.t.The solution is  in Ref. . The anaAlgorithm 3. The optimization process of the single factor PMD.Step1. Initialize Step2. Iterate until convergence:(i)(ii)Step3.To obtain the sparse factors of For comprehensive discussion, discovered protein complexes and gold standard dataset are matched. The following evaluation measures are employed in this study.F-measure. Two protein complexes, namely,  follows :(8)osF−meaACC . ACC is used to quantify the quality of detected protein complexes, which is the geometric means of sensitivity and positive predictive value, PPV. The corresponding formulas are described as follows :(10)ACC=Sep . To void the case wherein proteins of a gold standard complex are matched with several identified protein complexes, Sep is used to measure the one-to-one correspondence between generated protein complexes and gold standard protein complexes. The formula is described as follows :(11)SepgMMR (Maximum Matching Ratio). MMR is used to describe the maximum one-to-one matching between the identified and gold standard protein complexes, which are defined as follows :(12)MMR(MCC (Matthews Correlation Coefficient). MCC is widely used in bioinformatics as a performance metric that can handle imbalanced data. The formula is described as follows :(13)MCC=E is a set of edges (protein pairs), and A PPI network is usually modeled as an undirected weight graph  follows :(14)Sim(According to The identification of protein complex helps us to discover and understand the cellular organizations and biological functions in PPI networks. Previous computational approaches mainly identified protein complexes in dense PPI networks, which had inferior performances in sparse PPI networks. In this work, The performance of our method,