Heterotachy is the variation in the evolutionary rate of aligned sites in different parts of the phylogenetic tree. It occurs mainly due to epistatic interactions among the substitutions, which are highly complex and make it difficult to study protein evolution. The vast majority of computational evolutionary approaches for studying these epistatic interactions or their evolutionary consequences in proteins require high computational time. However, recently, it has been shown that the evolution of residue solvent accessibility (RSA) is tightly linked with changes in protein fitness and intra-protein epistatic interactions. This provides a computationally fast alternative, based on comparison of evolutionary rates of amino acid replacements with the rates of RSA evolutionary changes in order to recognize any shifts in epistatic interaction.Based on RSA information, data randomization and phylogenetic approaches, we constructed a software pipeline, which can be used to analyze the evolutionary consequences of intra-protein epistatic interactions with relatively low computational time. We analyzed the evolution of 512 protein families tightly linked to mitochondrial function in Vertebrates and created “mtProtEvol”, the web resource with data on protein evolution. In strict agreement with lifespan and metabolic rate data, we demonstrated that different functional categories of mitochondria-related proteins subjected to selection on accelerated and decelerated RSA rates in rodents and primates. For example, accelerated RSA evolution in rodents has been shown for Krebs cycle enzymes, respiratory chain and reactive oxygen species metabolism, while in primates these functions are stress-response, translation and mtDNA integrity. Decelerated RSA evolution in rodents has been demonstrated for translational machinery and oxidative stress response components.http://bioinfodbs.kantiana.ru/mtProtEvol/. This resource and the devised software pipeline may be useful tool for researchers in area of protein evolution.mtProtEvol is an interactive resource focused on evolutionary analysis of epistatic interactions in protein families involved in Vertebrata mitochondria function and available at  An unevenness of the rates of molecular evolution is typical for the vast majority of functional proteins. A chance of any amino acid fixation in protein depends on their effect on the structure, function and its interactions with other proteins. This results in the observed unevenness in the rates of molecular evolution in different lineages within the same orthologous protein family and in different protein sites in various time intervals. This phenomenon is commonly recognized as heterotachy. Heterotachy is simply the variation in the evolutionary rate of the aligned sites in different parts of the phylogenetic tree, mainly due to epistatic interactions among the substitutions. Epistasis leads to coevolution of various protein regions and even different proteins as well as concerted changes –8. AnothBecause of the high complexity of epistatic interactions in proteins, it has been difficult to study the evolution of proteins, especially on deep tree branches. The common pathway to elucidate the evolution of proteins on inner tree branches is ancestral sequences reconstruction (ASR) , 13. TheNevertheless, the accurate phylogenetic estimation does not necessarily need huge computational efforts. A good example is the usability of CAT protein evolution model, that is entirely based on mixed substitution rate approach. Interestingly, recent comparison between the CAT protein evolution model and the data partitioning with site-homogeneous substitution model clearly demonstrates that the partitioning models are as accurate as the CAT evolution model in spite of >10x computational simplicity , 27. As Epistatic interactions in proteins are determined mainly by steric and physico-chemical requirements for protein folding in three-dimensional space. Therefore, we assume that the amino acid replacements characterized by large changes in solvent accessibility area, (a measure of solvent exposure of the amino acid in the 3D protein structure) are associated with abrupt changes in protein globule and are expected to be driven by epistatic changes. However, strictly said, such episodes of protein evolution are the consequence (not cause) of epistatic changes and tell nothing new about the mechanism of intra-protein epistasis. In other words, the evolution of residue solvent accessibility (RSA) is anticipated to be tightly linked with changes in protein functions and fitness. Indeed, it was shown that the evolutionary conservation of a protein site correlates with RSA of this site –35 and thttp://bioinfodbs.kantiana.ru/mtProtEvol/). We showed that site partitioning model, in contrast to heterotachy model, has limited application to the description of RSA evolution. In strict agreement with lifespan and metabolic rate data, we demonstrated that different functional categories of mitochondria-related proteins are subject to selection with accelerated and decelerated RSA evolution rates in rodents and primates. For example, in rodents accelerated RSA evolution is associated with Krebs cycle enzymes, respiratory chain proteins, ROS metabolism and mitochondrial transport, while in Primates these protein functions are stress-response components, mtDNA integrity and translational machinery.Here, using the robust randomization statistical procedures, heterotachy and site partitioning models within phylogenetic framework, we analyzed the evolution of 512 protein families on inner branches of the tree. The analyzed protein families were selected based on their association with mitochondrial function in vertebrates. We created the first web resource dedicated to analysis of the evolutionary consequences of intra-protein interactions changes in mitochondrial proteome  from ENSEMBL Compara rel. 91 [step 3} we tested all data for heterotachy effects by Procov v. 2.0 software [step 4} we predicted RSA and 8 types of secondary structure for each protein in each protein multiple alignment. We selected this software package for RSA prediction because it is one of the best for structure-aware solvent accessibility prediction [step 5}, using FASTMG software [Our pipeline has three stages: data preparation, jackknifing and data summarizing. Data preparation stage consists of database . After t rel. 91  512  frequencies of RSA type occurrence and (2) ranks of relative rates of RSA type changes from all analyzed protein families (Supplementary information 2 on the mtProtEvol site). We calculated ranks of relative rates of RSA type changes for each RSA type separately, in these calculations we filtered out near zero relative rates of substitution using three thresholds (Supplementary information 2 on the mtProtEvol site). A basic assumption of our approach is that sites in a protein-coding sequence are independent. This assumption is commonly made, and it allows huge simplification of computation, even though it is clear that sites in a protein sequence do not evolve independently. It would be challenging for future studies to include effective computations taking into consideration limited structural constraints , 35 relamodel + F*H3’ for analysis using heterotachy model [model + F’ for each edge-unlinked (‘-sp’ run option) site partition [model is the protein family REV-models in PAML text format. We placed alignment sites of both amino acids and pseudo-amino-acids into 8 categories (effectively on average not more than 4 categories (see Utility and Discussion section and Supplementary information 3 on the mtProtEvol site) by simple site diversity measure as described in [Jackknifing stage is needed for estimation of branch length variations. For each protein tree analysis we used 100 random delete-half-jackknifed alignments, namely, the pseudo-replicates generated 100 times from the data by random sampling of alignment columns without replacement from the original alignment, each pseudo-replicate being a half of the original alignment. We studied branch length variations using IQTree v 1.6 software because of computational effectiveness  and posshy model , 30 and artition , model iribed in .six computation steps. At {step 1} we gave each inner tree node a unique label. Then in {step 2} we generated two lists of branch lengths for each labelled branch: (laa) lengths based on amino acid alignments analysis, and (lrsa) lengths based on pseudo-amino-acids (or RSA) alignments analysis. In {step 3} we calculated in pairs the ratio L = lrsa/laa for each innertree branch of each protein family phylogenetic tree. In {step 4} we compared the ln(L) value set of each inner tree branch (1-set) to all ln(L) values for the all inner tree branches in protein family phylogenetic tree (0-set) using nonparametric U-test (Holm correcting for multiple comparisons [L) values than ln(L) values across all branches of the tree or, in other words, to select evolutionary events with relative gain and loss of intra-protein interaction changes. At {step 5} we checked the protein names in trees and linking protein names with species names and taxonomy using mygene.info [step 6} we summarized the results from all proteins under analysis. In doing so we filtered out all near zero internal branches (branch length < 5*10− 5) based on RSA and, finally, colorized tree branches with statistically significant accelerated and decelerated RSA change rates.Data summarizing stage is intended for robust nonparametric pairwise comparison between branch lengths, based on alignment of amino acid sequences and on alignment of pseudo-amino-acids (or RSA) for the same phylogenetic tree topology of the protein family. The relative (comparing to amino acid replacements rate) increase in the pseudo-amino-acids evolutionary rate can be a hallmark of evolutionary changes, which affect the position of amino acid residues in 3D protein structure (inner residues became outer or vice versa), while the relative decrease of this metric indicates predominant fixations, which preserve the position of amino acid residues in the 3D protein structure. Thus, our metric may suggest new point of view on evolution of proteins, pointing out branches with intensive evolution of 3D protein structure. In order to discriminate these two evolutionary events we went through parisons ) and Cliparisons . We usedene.info  and newiene.info  with NCBene.info . This alhttp://bioinfodbs.kantiana.ru/mtProtEvol/), containing all analyzed data and all results. We did this using Apache web server, MySQL 5, Perl 5.24 (CGI module), HTML5, and JavaScript for generating web pages dynamically. We used special applications for interactive multiple sequence alignment  and forSAViewer ).At the last step we summarized our data in protein network framework using STRING  and GENEWith the aim to analyze evolutionary consequences of intra-protein epistatic interactions in mitochondrial proteome we focused on molecular evolution of 512 protein families, involved in mitochondrial function. For this purpose, as well as to take into consideration the importance of RSA evolution in determining intra-protein interaction shifts, we explored the evolutionary rates of amino acid replacements and RSA changes. We compared these rates, using two models of protein evolution: heterotachy and site partitioning, implemented in one software package - IQTree v. 1.6 . Our invL) measure for each inner tree branch of each protein family tree, we have shown, that reduction of number of residue type categories to 8 classes, tightly associated with increasing number of tree branches, demonstrates incongruence between length obtained from amino acid alignments analysis, and length obtained from secondary structure alignments analysis (Supplementary information 1 on the mtProtEvol site). Another reason for considering 20 residue classes is the non-zero frequencies of occurrence of all 20 classes of RSA in proteins . Thus, the reduction of residue class number is biologically unmeaningful and enhances sensitivity of phylogenetic tree topology to data variation, which in turn leads to an increase in phylogenetic tree uncertainment. Therefore, in this work we track any such reduction of the number of amino acid residue types.How many categories are enough for evolutionary analysis that is robust to data variation? To answer this question, we analyzed two types of data by random delete-half-jackknifing (see Construction and content section): amino acid residues classified in 20 classes , and amino acid residues classified in 8 classes based on protein secondary structure. Obviously, fewer residue classes makes evolutionary analysis more coarse-grained, or, in other words, fewer residue classes makes evolutionary analysis deaf to many mutations. This mutation-deafness in turn should lead (1) to the occurrence of phylogenetic tree branches with zero lengths and (2) to the whole branch lengths shortening. Yes, we demonstrated these two effects when analyzed data based on 8 classes of residues (Supplementary information 1 on the mtProtEvol site). Additionally, using the interquartile range (Q3-Q1) of the ln categories is “What is the meaning of transitions from one RSA state to another?”. To solve this problem, we carefully analyzed all matrices containing relative rates of RSA type substitutions for each protein family. Summary of these matrices is shown in Supplementary information 2 on the mtProtEvol site. It is of note, that 20 RSA categories include one uncertain RSA category (− 5(unknown) = A), that is useful for protein structure flexibility description and for describing residues, located in disordered protein regions. We demonstrated that this particular RSA category may be substituted by nearly all RSA categories that is in a full agreement with data about disordered protein regions . Frequenp < 1E-5) higher comparing with amino acid replacement-based trees, indicating that the evolution of RSA is more evolutionary fast process than the amino acid substitutions .To check the applicability of models it is necessary to analyze branch lengths distribution. We analyzed the distributions of median lengths obtained by random delete-half-jackknifing procedure on 512 protein families under analysis (see Construction and content section). The variance and asymmetry descriptions of major mode (peak) of the branch length distribution can be used as a proxy for the sensitivity of model . Figure For each class from the defined number (we used 3) of classes of the branch lengths, heterotachy model implemented in IQTree v 1.6 dynamically optimizes residue frequencies, and substitution rates , 30. SitL) value (see Construction and content section) to data variation that is simulated by random deletion half-jackknifing.Additionally, we compare site partitioning and heterotachy models in terms of sensitivity of ln(L) measure for each inner tree branch of each protein family tree and analyzed the frequencies of interquartile ranges. Ideally, the jackknife randomization will have no significant effect on the variation the ln(L) measure and, evidently, the interquartile range of the ln(L) measure for the branch should seek to zero. On the contrary, if interquartile range of the ln(L) measure is large enough, then the model is non-robust to data variation and the results should be considered with care and should be adopted by consensus only. The last is strictly a case of site partitioning model . Thus, to filter out the majority of possible methodological artifacts we took into consideration only inner tree branches with interquartile range of ln(L) lower than 6  We calculated the interquartile range Q3-Q1) of the ln(-Q1 of thL) values (see Construction and content section). In other words, in each tree, we calculated the difference between the number of inner branches with accelerated and decelerated RSA evolution. For each protein family tree, we used several Cliff’s delta thresholds . If there is an asymmetry in distribution of differences between the number of branches with positive and negative Cliff’s delta of ln(L) values than the model systematically over- or under-estimate cases with accelerated or decelerated RSA evolution. Both models have symmetrical and normal  distributions of these differences, thus there are no significant biases in both models in discriminating inner tree branches with faster or slower RSA change rate  values comparing to ln(L) values of all branches  in the tree is statistically unbiased.2) For all 512 protein family trees we calculated the difference between the number of branches with positive and negative Cliff’s delta of ln(3) Finally, we correlated natural logarithms of median lengths of inner branches between these two models. We constructed two correlations separately for the cases of amino acid replacements study and for RSA change study Fig. . Figure The mtProtEvol, to the best knowledge of the authors, is the first resource that stores the evolutionary consequences of intra-protein interactions changes in mitochondrial proteome. This time only the 512 protein families were annotated in our resource. All protein family-specific information is available through a simple interactive web interface. The results of the analysis are separated in two major groups: individual protein analysis and integrative (by-clade) analysis.L) values representing cases with accelerated and decelerated RSA changes relative rate (compared to amino acid replacements rate). Other important features deposited for each protein family are heterotachy (covarion) test results obtained by Procov 2.0 [For each analyzed protein family two interactive multiple alignments  and four phylograms  were available. The interactivity of alignments and phylograms were provided by MSAViewer  and Archocov 2.0 , resultsocov 2.0 , and varL) values; lists of proteins (protein families) in which branches are listed in Na and Nb sets; Cliff’s delta thresholds for inner branches selection and Na and Nd sets generation; model of evolution; and protein structure awareness. Clicking on the taxa (clade) name leads to generation of integrative results (GR) across all methods and Cliff’s delta thresholds. These results subdivided into three categories: the number of mitochondrial proteins and species under analysis, the two lists of protein families with accelerated and decelerated RSA evolution. The last two data categories represent: the information about the heterotachy weights ; and the measures of model sensitivity (robustness) to data variation for heterotachy and site partitioning models. The model sensitivity measures shown for each protein family. These are the median of ln(L) values and shift of mean of ln(L) values compared to median of ln(L) calculated as (mean-median)/median. The lower the median of ln(L) values, the higher the model robustness to data variation is. The higher the shift of mean of ln(L) the more the multimodal the distribution of ln(L) in protein family is. Working with the GR and ST data accompanied by the ability to generate protein lists for subsequent STRING [The integrative (by-clade) analysis is represented by interactive summary Table (ST) including: taxa (clade) name; frequency of branches with maximum (Na) and minimum (Nd) of ln(e v. 1.6 ) shown at STRING  and GENEt STRING  analysisL) for branch selection is greater than or equal to 0.7 and U-test p-value<1E-4 (see Construction and content section); in site partitioning model cases results must agree with the results from heterotachy model, Cliff’s delta of ln(L) for branch selection must be greater than or equal to 0.6 and U-test p-value<1E-4.Two clades were selected for detailed analysis. These are Rodentia and Primata, having common evolutionary origin and strictly different ecological strategies (R- and K- respectively), tightly related with housekeeping energetic metabolism carried out by the mitochondrial compartment. Here we describe the integrative results that take into consideration all data as well as data supported by available 3D protein structures only. For in-detail characterization of mitochondrial function evolution we studied two opposite evolutionary cases: cases with accelerated and decelerated RSA evolution =A, 0..5=R, 10..15=D, 20..25=E, 30..35=G, 40..45=I, 50..55=K, 60..65=F, 70..75=S, 80..95=W), as a result we found agreement for >90% proteins that were selected based on 20 RSA categories by accelerated RSA evolution – see Supplementary information 4 on the mtProtEvol site). We used the following thresholds and limitations on the results: in heterotachy model cases, Cliff’s delta of ln category in these networks shown in Tables In order to analyze protein-protein interaction network for proteins evolved with accelerated RSA changes rate on Primata clade we did standard analysis of network topology by Cytoscape software. For this analysis, we used most full network Fig. a obtaineLet’s compare the molecular functions of some key proteins in the protein-protein interaction network composed of proteins characterized by faster RSA change rate than amino acid replacements rate on Primates clade Table . This neThe most interesting (in evolutionary case) network parts are the LONP1 protein  a mitochondrial matrix chaperone protein (ATP-dependent serine protease) and POLRMT protein . The first one mediates the selective degradation of misfolded or damaged proteins. Among its substrates, there is a very important mitochondrial DNA integrity protein helicase TWNK. Additionally it participates in the regulation of mitochondrial gene expression and maintenance of mtDNA because it was shown that it binds to DNA and RNA in a single-stranded, site-specific, and strand-specific manner. The second one (POLRMT) catalyzes the transcription of mtDNA and provides RNA primers for initiation of mtDNA replication. Another interesting network component is TUFM protein  participating in mitochondrial translation, namely in the GTP-dependent binding of aminoacyl-tRNA to the A-site of ribosomes. The MTIF2 protein  also participates in translation initiation promoting formylmethionyl-tRNA binding to the 30S ribosomal subunits.The protein-protein interaction network of proteins evolved with accelerated RSA rate in Primata have stress-response components. For example, CHCHD2 protein (Coiled-Coil-Helix-Coiled-Coil-Helix Domain Containing 2) translocates from the mitochondrial intermembrane space to the nucleus in response to stress, activates gene transcription under hypoxic conditions, and negatively regulates the mitochondria-mediated apoptosis. The C1QBP protein (Complement C1q Binding Protein) is a multifunctional protein involved in inflammation, ribosome biogenesis, apoptosis regulation, transcriptional regulation, etc. In mitochondria it is involved in translation, namely formation of 55S ribosomes. The NME4 protein (NME/NM23 Nucleoside Diphosphate Kinase 4) participates in the synthesis of nucleoside triphosphates other than ATP, it is also involved in pro-apoptotic signaling by the redistribution of cardiolipin between the mitochondrial inner and outer membrane. Additionally, HTRA2 protein (HtrA Serine Peptidase 2) induces apoptosis by binding the apoptosis inhibitory protein and relocating from endoplasmic reticulum to mitochondria.Thus, the proteins evolved with accelerated RSA change rate on Primata clade can be characterized as various enzymes participated mainly in fatty acid beta-oxidation, stress-response components and components of mtDNA integrity and protein synthesis machinery.Rodentia clade is also characterized by proteins with accelerated RSA change rate comparing to amino acid replacements rate. Figure Statistically significant enrichment of protein BP GO functions in these networks is shown in Tables In order to analyze protein-protein interaction network in details we again conducted the standard network topology analysis by Cytoscape software. We selected the most complete network Fig. a. The reIn protein-protein interaction network of proteins evolved with accelerated RSA change rate on Rodentia clade there are two tightly physically-linked central elements, the NDUFV1 protein (NADH:Ubiquinone Oxidoreductase Core Subunit V1) and NDUFA4 protein , and two peripheral elements, NDUFB6 protein (NADH:Ubiquinone Oxidoreductase Subunit B6) and NDUFA1 protein (NADH:Ubiquinone Oxidoreductase Subunit A1). All these proteins are subunits of core complex I of respiratory chain that transfers electrons from NADH and to ubiquinone. The UQCRC1 protein (Ubiquinol-Cytochrome C Reductase Core Protein 1) is another component of respiratory chain it is a component of the ubiquinol-cytochrome c reductase complex (complex III), it mediates formation of the cytochromes c / c1 complex.This protein-protein interaction network also has metabolic components. The PDHX protein (Pyruvate Dehydrogenase Complex Component X) is located in the mitochondrial matrix in pyruvate dehydrogenase (PDH) protein complex as a regulatory subunit and acts as linker-enzyme between glycolysis and Krebs cycle by catalyzing the conversion of pyruvate to acetyl coenzyme A. The ACO2 protein (Aconitase 2) catalyzes the second step of the Krebs cycle, the conversion of citrate to isocitrate. The ACAT1 protein (Acetyl-CoA Acetyltransferase 1) is the enzyme plays a crucial role in ketone body metabolism. The OXCT1 protein (3-Oxoacid CoA-Transferase 1) also involves in ketone body metabolism, it catalyzes the transfer of coenzyme A from succinyl-CoA to acetoacetate. The ME2 protein  involved in decarboxylation of malate into pyruvate.One of the most interesting part of this network are the PAM16 protein (Presequence Translocase Associated Motor 16) and PITRM1 protein . First regulates protein translocation into the mitochondrial matrix and plays a role in reactive oxygen species (ROS) homeostasis, while the second is a protease that degrades dissected mitochondrial transit peptides and other unstructured peptides.Thus, the proteins evolved with accelerated RSA change rate on Rodentia clade can be characterized as a components of respiratory chain, ROS and peptide homeostasis components and enzymes participated in Krebs cycle and ketone body metabolism.Figure Statistically significant enrichment of protein functions in these networks is shown in Table In order to analyze structure of protein-protein interaction network Fig. a composeThere are two components of respiratory chain terminal point in the network. The MT-CO1 protein  is the terminal component of the respiratory chain that catalyzes the reduction of oxygen to water. The COX4I1 (Cytochrome C Oxidase Subunit 4I1) is another protein from this terminal respiratory chain component.There are two carriers in the network the SLC25A3 protein (Solute Carrier Family 25 Member 3) catalyzes the transport of phosphate from cytosol into the mitochondrial matrix and the SLC25A5 protein (Solute Carrier Family 25 Member 5) that catalyze the translocation of cytoplasmic ADP from cytoplasm into the mitochondria and ATP from mitochondria into cytoplasm across the mitochondrial inner membrane.Thus, the list of proteins evolved with decelerated RSA change rate on Primata clade is small and proteins do not characterized by similar functions .Unlike primates, rodents clade characterized by the big number of proteins that characterized by slower RSA change rate comparing to amino acid replacements rate. Figure Statistically significant enrichment of protein functions in these networks is shown in Tables 2. The ALDH5A1 protein (Aldehyde Dehydrogenase 5 Family Member A1) catalyzes the degradation step of the gamma-aminobutyric acid (GABA) neurotransmitter.As described above, in order to analyze protein-protein interaction network Fig. a for proThis network is characterized by the presence of oxidative stress response proteins. The TXNRD2 protein (Thioredoxin Reductase 2) is a pyridine nucleotide-disulfide oxidoreductase which retains thioredoxin in a reduced state, that in turn is a well known key element of oxidative stress response. The GPX4 protein (Glutathione Peroxidase 4) is another oxidative damage protection protein. It protects cells from the toxicity of ingested lipid hydroperoxides. The ALDH7A1 protein (Aldehyde Dehydrogenase 7 Family Member A1) metabolize a number of lipid peroxidation-derived aldehydes, convert betaine aldehyde to betaine and involved in lysine catabolism.This network is also characterized by the presence of mitochondrial ribosomal protein: MRPS22 , MRPL21 , MRPS35 , MRPS11 . One of these proteins is a 39S subunit protein (MRPL21), the others are 28S subunit proteins.Thus, the list of proteins evolved with decelerated RSA change rate on Rodentia clade is characterized by the presence of mitochondrial ribosomal proteins and oxidative stress response components.We constructed a software pipeline, which allowed us to analyze evolutionary consequences of intra-protein interactions changes and implemented all the results into the web resource. We will regularly update the resource by adding (at least once a year) new mitochondrial machinery proteins and, also, by adding new quicker methods for finding evolutionary changes in intra-protein epistatic interactions. In our analyses we focused on the RSA change rate normalized by amino acid replacements rate. We have demonstrated, for the first time, that site partitioning model, in contrast to heterotachy model, has limited application for the description of RSA change rate.We tested our software pipeline on a protein family set, involved into the mitochondrial metabolism. To gain some biological insights we used two model groups of mammals with a common evolutionary ancestor: rodents and primates, different in their level of basal metabolism, body mass, longevity as well as effective population size. We observed that in rodents and primates different categories of proteins were selected towards accelerated / decelerated RSA changes. For example in rodents accelerated RSA evolution has been shown for Krebs cycle enzymes, respiratory chain, ROS metabolism and mitochondrial transport, while in primates these functions were metabolism of fatty acids, stress-response components, translational machinery and mtDNA integrity. Interestingly these categories seem to be in line with ecological strategies of the compared groups: short-lived quickly reproducing rodents optimize protein categories involved in the maintenance of high level of metabolism , while long-lived and slow-reproducing primates optimize the stability of the metabolism . If so, future large scale comparisons of ecologically different mammalian groups may shed light on causes of correlation between the life history traits and functional categories of the most optimized proteins.


At the post-synaptic density (PSD), large protein complexes dynamically form and dissociate in response to synaptic activity, comprising the biophysical basis for learning and memory. The use of detergents to isolate the PSD and release its membrane-associated proteins complicates studies of these activity-dependent protein interaction networks, because detergents can simultaneously disrupt the very interactions under study. Despite widespread recognition that different detergents yield different experimental results, the effect of detergent on activity-dependent synaptic protein complexes has not been rigorously examined. Here, we characterize the effect of three detergents commonly used to study synaptic proteins on activity-dependent protein interactions. We first demonstrate that SynGAP-containing interactions are more abundant in 1% Deoxycholate (DOC), while Shank-, Homer- and mGluR5-containing interactions are more abundant in 1% NP-40 or Triton. All interactions were detected preferentially in high molecular weight complexes generated by size exclusion chromatography, although the detergent-specific abundance of proteins in high molecular weight fractions did not correlate with the abundance of detected interactions. Activity-dependent changes in protein complexes were consistent across detergent types, suggesting that detergents do not isolate distinct protein pools with unique behaviors. However, detection of activity-dependent changes is more or less feasible in different detergents due to baseline solubility. Collectively, our results demonstrate that detergents affect the solubility of individual proteins, but activity-dependent changes in protein interactions, when detectable, are consistent across detergent types. Proteomic profiling of the PSD has revealed a highly complex and massive structure , with the number of putative PSD proteins ranging from a few hundred to nearly two thousand5. Scaffold proteins including the Disk-large proteins (e.g. PSD-95), Src homology 3 and multiple Ankyrin repeats (Shank), Homer, and Synapse-associated protein associated protein (SAPAP) families are highly abundant7. Super resolution and electron microscopy have shown that these proteins are arranged in a layered organization, providing the basic structural framework for the PSD and serving as a molecular platform onto which other proteins are recruited10. The most abundant scaffold protein, PSD-95, can bind to both N-methyl-D-aspartate receptors (NMDAR)s and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPAR)s, either directly or indirectly, stabilizing the presence of these glutamate receptors at the PSD11. Through its PDZ domain, PSD-95 interacts with SAPAP, which in turn binds to the Shank family of proteins13. Shank and its binding partner Homer then form a polymerized, mesh-like second layer of the PSD, characterized by Shank dimers binding the EVH1 domain of Homer tetramers13. Homer EVH1 domains can also bind type 1 metabotropic glutamate receptor (mGluR)s, IP3 receptors, transient receptor potential channels, dynamin, and drebrin15. Signaling enzymes such as Calcium/Calmodulin dependent Kinase II (CamKII) and the brain specific Ras GTPase, SynGAP are also highly abundant17, further increasing the complexity of the PSD Critically, the organization of the PSD is also highly dynamic. We and others have demonstrated that synaptic stimulation elicits dissociation of mGluR5-Homer-Shank scaffolds19 and dispersion of SynGAP from the PSD22, allowing for the recruitment and stabilization of AMPA receptors to the PSD and changes in synaptic strength23. It is this change in synaptic transmission efficacy that enables the PSD to regulate the flow of information between neurons and ultimately control the complex microcircuits to drive adaptive behaviors. Thus, dynamic synaptic protein co-associations at the PSD play a central role in synaptic plasticity, learning and memory.The postsynaptic density (PSD) is an electron dense specialization composed of multiprotein complexes, whose functions include mediating the apposition of pre- and post synaptic membranes, clustering glutamate receptors, and coupling the activation of receptors to downstream signaling cascades26. DOC belongs to a class of detergents often used for membrane disruption and extraction of membrane proteins, but being a strong detergent, DOC might also be expected to disrupt protein co-associations.  of lysates solubilized in NP-40, we reported the dissociation of 34 synaptic protein co-associations following acute stimulation of cultured neurons with NMDA, DHPG, or glutamate.The study of protein co-associations requires the optimization of detergent conditions such that proteins are solubilized, but co-associations are not disrupted. However, traditional biochemical analysis of the PSD has involved differential solubility in a series of detergents: crude synaptosomal fractions are solubilized in the non-ionic detergent Triton X-100 (Triton), and following high-speed centrifugation, the Triton-insoluble pellet is solubilized in the ionic, bile-acid deoxycholate (DOC) at a high pH (9.0) to yield a “PSD fraction”30. Moreover, pharmacological restoration of Homer_mGluR5 scaffolding rescues cortical hyperexcitibality in Fmr1 KO mice, suggesting direct biological relevance. Similarly, Frank et al.11 reported that PSD-associated protein super-complexes were consistent across five different detergents, suggesting that the PSD is at least partially soluble in detergents other than DOC. However, it is unclear if, or to what extent, detergents such as NP-40 solubilize protein complexes associated with the PSD, as opposed to cytoplasmic proteins or those in the PSD periphery.However, because the PSD, as it is traditionally defined, is not soluble in NP40, it is unclear if these dissociations represent proteins localized to the PSD, or a group of peri-synaptic or extrasynaptic proteins. The Huber group showed that levels of mGluR5 found in a PSD fraction are reduced in the Fmr1 knock out mouse model of Fragile X Syndrome, likely due to decreased Homer_mGluR5 co-associationHere, we sought to quantify the ability of DOC, NP-40 and Triton to solubilize PSD-associated proteins while also maintaining protein complexes, to reconcile recent results using these detergents. We first confirm that synapse-associated proteins are only partially solubilized by NP-40 or Triton, but fully soluble in DOC. While almost all protein complexes are detected across all detergent conditions, many complexes are differentially abundant in distinct detergents. For example, complexes containing SynGAP are more abundant in DOC, while Homer_mGluR5 co-associations are detected to a much greater degree in NP-40 or Triton. Importantly, activity-dependent changes in protein co-associations are consistent across detergent types, suggesting that they reflect synaptic biology. However, the detection of these activity-induced changes is more or less feasible in each detergent based upon the baseline abundance of each protein co-association. We suggest a model in which liquid-liquid phase separated synaptic proteins are partially solubilized by NP-40 or Triton, allowing for detection of high molecular weight complexes directly relevant to PSD biology. Moreover, protocols using NP-40 or Triton can result in detection of a greater number of activity-dependent interactions, and may prove beneficial for future studies.25 by dividing the S1 phase into 3 equal aliquots .While DOC (pH 9.0) best solubilized synapse associated proteins, it may simultaneously disrupt protein co-associations. To examine the effect of detergent, as well as other commonly reported lysis buffer reagents  on protein co-associations, crude synaptosomal fractions were made from the same starting material and subsequently solubilized in 10 unique lysis buffers. The relative amount of selected protein co-associations was then quantified by immunoprecipitation detected by flow cytometry  for Homer_mGluR5 co-association was detected at a significantly higher level when compared to DOC. Moreover, the fluorescent intensity histogram for Homer_mGluR5 in DOC conditions overlapped with that of the Homer_IgG control, indicating that this interaction was at the lower limit of detection in DOC  was more effective at detecting several interactions than DOC at a high pH (9.0), this lysis buffer would solidify at 4 °C if left without agitation (but maintained fluidity at room temperature). While seemingly intuitive that it is necessary to keep lysate at cold temperatures to maintain protein co-associations, we wanted to confirm that this was true. To this end, a crude synaptosomal fraction was solubilized in NP-40 at 4 °C, and allowed to warm to room temperature. The amount of Homer_mGluR5 and Homer_panShank co-association from a small sample of lysate was quantified by IP-FCM every 5 minutes for two hours. The average MFI decreased by almost 50% Fig. . This reCrude synaptosomal fractions were made from the same starting biomaterial and multiprotein complexes were separated by size using Size Exclusion Chromatography (SEC). The relative amounts of synaptic proteins in each fraction were then detected by western immunoblot Fig. . High moTo determine if the co-associations observed in previous experiments are found in large MW complexes, SEC fractionation was performed on crude synaptosomal fractions solubilized in DOC, NP-40, or Triton. The amount of co-association in pooled fractions was then quantified by IP-FCM Fig. . ConsistWe performed parallel analysis of several other co-associations. Homer_panShank co-association was detected primarily in high molecular weight fractions across all three detergent conditions Fig. . SynGAP_decreased co-association in Triton/NP40 lysates but increased co-association in DOC lysates (a ‘source-sink’ model). Alternatively, a more labile protein co-association (such as Homer_mGluR5) may be disrupted by DOC at baseline, such that activity-dependent changes would not be detectable at all. To determine the effect of detergent selection on our ability to measure activity-dependent changes in synaptic protein co-associations, we prepared hemisected cortical slices from p20-30 CD1 mice and stimulated them with either KCl or aCSF control for 5 minutes. Following homogenization, the S1 phase was divided into three equal aliquots, and the resulting crude synaptosomal fractions were homogenized in lysis buffers containing either 1% DOC, NP-40 or Triton. Selected, known-activity-dependent changes in protein co-associations were then quantified by IP-FCM .As the use of DOC vs. NP40/Triton clearly alters the abundance of specific protein co-associations, it is plausible that detergent selection also results in distinct experimental outcomes when measuring activity-dependent changes in synaptic protein co-associations. For example, recruitment of proteins into the Triton insoluble portion of the PSD might be observed as FCM Fig. . FollowiFCM Fig. . For exaTo determine if the activity-dependent changes observed were occurring in large molecular weight complexes, we prepared cortical slices from CD1 mice, stimulated with either KCl or aCSF, and homogenized the crude synaptosomal fractions in lysis buffer containing either 1% DOC or NP-40. SEC fractionation was then performed on lysates and the relative amount of selected Homer interactions were quantified by multiplexed IP-FCM Fig. . Followi18, or aCSF control. Crude synaptosomal fractions from the same starting biomaterial were solubilized in lysis buffer containing either 1% DOC, NP-40, or Triton, and network level changes among an 18-member targeted synaptic protein interaction network were quantified by quantitative multiplex co-immunoprecipitation (QMI), a novel proteomics technique that allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins31  for 5 min, a treatment previously demonstrated to produce widespread dissociation of synaptic protein complexess31 Fig. .Figure 734. Comparing unstimulated NP-40 and Triton conditions, ANC∩CNA identified 6 interactions that were significantly different  preparations that we used as our starting material are enriched for PSD associated proteins39 they also contain myelin, mitochondria, and intracellular and plasma membranes43. Furthermore, many of the PSD-associated proteins analyzed also occur in multi-protein complexes in other peri-synaptic or extra-synaptic areas of the cell45. Thus, it is possible that the solubilized proteins in any detergent condition, but especially in the NP-40 or Triton conditions, represent a pool of extra-synaptic proteins. We find this unlikely for three reasons: first, we confirmed by SEC fractionation that both NP-40 and Triton solubilize high molecular weight complexes that contain 4 well known PSD associated proteins: Homer, mGluR5, PSD-95, and SynGAP. While this does not rule out a contribution of extra-synaptic complexes, PSD proteins would be expected to reside in high molecular weight complexes in crude synaptic membrane fractions. Second, we recapitulated two well-known activity dependent interactions that have been unambiguously localized to within the PSD by molecular fractionation or electron microscopy techniques, dissociation of Homer_mGluR530 and SynGAP_PSD9522. Finally, given that NP40/Triton does not solubilize a large portion of total synaptic protein, we considered the possibility that the dissociation of proteins we previously reported18 in NP40/Triton could reflect a redistribution of proteins from a hypothetical “NP40/Triton-solubilized extrasynaptic pool” into the PSD. In this “source-sink” model, we would expect to see increases in co-associations in DOC solubilized complexes (assuming the use of DOC does not disrupt protein co-associations), reflecting recruitment of extrasynaptic proteins into the PSD. Instead, we observed similar changes (i.e. in the same direction) in protein complex abundance in all detergent conditions, although detection of activity-dependent changes was more or less feasible based on the baseline MFI of interaction in each detergent. While we cannot unambiguously conclude that the dynamic interactions described here occur at the PSD, we find it unlikely that the high molecular weight multiprotein complexes observed by SEC-western in NP-40 or Triton conditions are composed of PSD-associated proteins, and exhibit activity-dependent dynamics similar to the PSD, but are not actually part of the PSD. We therefore posit that NP-40 and Triton can solubilize a portion, albeit not all, of the PSD, and the changes in synaptic protein complexes we observe are directly relevant to PSD biology.These data beg the question, are the protein co-associations observed in NP-40 and Triton conditions localized to the PSD 46. In the LLPS model, proteins are constantly diffusing between the dense LLPS core structure and the less concentrated aqueous surrounding. We propose a model in which NP-40 and Triton detergents permit diffusion of synaptic protein complexes from a core LLPS structure into the aqueous surroundings; proteins such as SynGAP that bind with multiple components of the PSD and are tightly integrated into the LLPS structure diffuse to a lesser extent, while more peripheral proteins such as mGluR5 with single binding sites anchoring them diffuse more rapidly, leading to a greater level of solubilization. This model is consistent with the idea that we are sampling PSD-relevant protein interactions from a synaptic protein pool, as discussed above. Future work testing this hypothesis with an in vitro reconstructed system47 could clarify the origin of the multiprotein complexes observed here.Recent evidence suggests that the PSD is a liquid-liquid phase separated (LLPS) structure, in which weakly interacting proteins spontaneously segregate to form a discrete subcellular compartment, lacking a membraneIn regards to the best detergent for future studies, our data demonstrate that most interactions are detected at a higher level in NP-40 or Triton, whereas SynGAP- and NMDAR2B-containing interactions were much more abundant in DOC. Most notably, Homer_mGluR5, Homer_panShank, and Shank1_panShank were all markedly lower in DOC. It is unclear why these proteins would co-migrate in high MW fractions in DOC Fig. , but not20 and co-immunoprecipitation48, and this activity-dependent interaction is considered to be an important first step for induction of long term potentiation49. According to the “Slot hypothesis”22, the rapid dissociation of SynGAP from PSD-95 frees the PDZ-binding domain of PSD-95, allowing for the additional recruitment and binding of AMPARs, critical to short‐term potentiation50. Indeed, previous work using QMI has demonstrated that longer stimulation elicits recruitment and stabilization of AMPA receptors18, consistent with the slot hypothesis. In addition to SynGAP interactions, we also observed widespread dissociation of Homer, Shank, and mGluR5 complexes in NP-40 or Triton conditions. The co-clustering of NMDAR’s and mGluR5 through a PSD-95/Shank/Homer complex has been shown to alter the physiology of these receptors, allowing both to respond to lower activation thresholds52. As this coordination is dependent on both Homer binding to mGluR533 and PSD-95 binding to NMDAR2B51, it is possible that widespread dissociation represents a homeostatic response, designed to maintain signal strength at an appropriate level. Alternatively, widespread dissociation of scaffold proteins may allow for the competitive capture of dendritic proteins, expansion of the synapse, and an increase in synaptic strength22. Decreased association of Homer_mGluR5 itself has been crucially implicated in both normal mGluR5 signaling and the pathophysiology of Fragile X syndrome53, further supporting the physiological relevance of our findings in NP-40 and Triton conditions.When we did examine activity-dependent changes in different detergents, we observed widespread dissociation of Homer, Shank and SynGAP containing complexes. Only two interactions were detected sufficiently in all detergent conditions to reach the stringent ANC∩CNA significance criteria: dissociation of SynGAP_PSD95 and SynGAP_NMDAR1. The dispersion of SynGAP from the PSD following activity has been previously demonstrated by both electron microscopyCollectively, our data support a model in which strong synaptic stimulation elicits widespread dissociation of multiprotein complexes localized in and around the PSD. Moreover, many of the activity-dependent interactions observed involve the dissociation of Homer-containing complexes, suggesting that Homer1 plays an essential role in glutamatergic signaling. Our results also demonstrate that it is critical for researchers to acknowledge the limitations of each detergent and carefully consider how detergent selection may affect experimental outcomes. The use of non-traditional solubilization protocols may result in the identification of novel protein co-associations and a more accurate understanding of the molecular mechanisms underlying synaptic function.CD-1 (RRID:IMSR_CRL:22) mice were originally obtained from The Jackson Laboratory  and maintained in an in-house breeding colony. All mice were separated by sex, and housed with littermates in thoren cages, with no more than five mice/cage. Food and water was provided ad libitum. For slice experiments, only p21-30 mice  were used. To minimize suffering, mice were anesthetized with isoflurane and decapitated under deep anesthesia. The use and care of animals was approved by the Office of Animal Care at the Seattle Children’s Research institute (protocol# 15580) and complied with all relevant guidelines and regulations.A graphical representation of each experimental design is included in each figure. Briefly, for P2 preps, mice were deeply anesthetized with Isofluorane, brains were removed, and tissue was homogenized in 0.32 M sucrose in 5 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer with protease/phosphatase inhibitor cocktails using 12 strokes of a glass-teflon homogenizer. The homogenate was immediately centrifuged x1,000 G for 10 minutes at 4 °C. The S1 was then divided into the appropriate number of aliquots, and spun at 12,000 G for 15 minutes at 4 °C. The resulting P2 pellet was then resuspended in the appropriate lysis buffer × 15 minutes  for 10–15 min at 32–34 °C, then transferred to a modified HEPES holding solution  for an additional 60–90 min recovery at room temperature using the protective recovery method55. CML latex microspheres  were activated with EDAC (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide HCl; Pierce, USA), and coupled to 50 µl of 0.5 mg/ml antibody for 3 hours at room temperature. Probe antibodies were biotinylated at 0.5 mg/ml with EZ-link Sulfo-NHS-Biotin . Following solubilization of P2 pellets in distinct lysis buffers, protein concentrations were normalized by BCA assay and 2.5 × 104 antibody-conjugated beads were added to the lysate and incubated overnight at 4 C with rotation. The following day, beads were washed (x3) in Fly-P buffer (50 mM Tris (pH 7.4), 100 mM NaCl, 1% bovine serum albumin, and 0.02% sodium azide), biotinylated probe antibodies were added for 2 h on ice, followed by washing (x3), and incubation with 1:200 streptavidin‐phycoerythrin  for 30 m. CML beads were then analyzed on a flow cytometer (Novocyte). MFI values and bead distributions were used for analysis. To validate the specificity of the detected interactions, each IP/detergent was probed with a mouse IgG control (ThermoFisher 10400 C). No significant differences in MFI values were observed across any IP/detergent condition. Values presented are the average negative IgG control across all detergent conditions for each experiment.IP-FCM was performed as described previouslyFollowing solubilization in distinct lysis buffers, lysate protein concentrations were normalized by BCA. Lysates were then injected in a Superose 6 Increase 10/300 GL with flow rate of 1 mL/min in the appropriate lysis buffer. Fractions were advanced at 1.5-min intervals. While absorption was monitored, the presence of 1% detergent in the flow through increased absorption making detection of proteins in the effluent impossible. For western blot analysis, proteins in each fraction were then concentrated using methanol precipitation. For detection of protein co-associations, 4 samples were pooled for IP-FCM analysis.34; all procedures were performed at 4 °C or on ice. Briefly, a master mix containing equal numbers of each antibody‐coupled Luminex bead was prepared and distributed into post‐nuclear cell lysate samples, in duplicate. Protein complexes were IP’d from samples containing equal amounts of protein overnight at 4 °C, washed twice in ice‐cold Fly‐P buffer and distributed into twice as many wells of a 96‐well plate as there were probes, on ice. Biotinylated detection antibodies were added and incubated for 1 h, with gentle agitation at 500 rpm in a cold room (4 °C). For antibody panel details, see18. Following incubation, microbeads and captured complexes were washed three times in Fly‐P buffer using a Bio‐Plex Pro II magnetic plate washer at 4 °C. Microbeads were then incubated for 30 min with streptavidin‐PE on ice, washed three times, and resuspended in 125 μL of ice‐cold Fly‐P buffer. Fluorescence data were acquired on a customized, refrigerated Bio‐Plex 200 using Bio‐plex Manager software (version 6.1). The instrument was calibrated and routinely validated according to the manufacturer’s recommendations. Data files were exported in both Microsoft Excel and XML formats for further processing. Each experiment generated a 17 × 20 matrix of capture and detection antibodies, measuring 378 protein co‐associations.QMI experiments were performed as described previouslyFor each well from a data acquisition plate, data were processed to (i) eliminate doublets on the basis of the doublet discriminator intensity , (ii) identify specific bead classes within the bead regions used, and (iii) pair individual bead PE fluorescence measurements with their corresponding bead regions. This processing generated a distribution of fluorescence intensity values for each pairwise measurement. XML output files were parsed to acquire the raw data for use in MATLAB. No specific analysis was performed on the data to test for outliers.34. As previously reported, we required that hits must be present in >70% of experiments  at an adjusted p < 0.05. The α‐cutoff value required per experiment to determine statistical significance was calculated to maintain an false discovery rate of 0.05 (adjusted for multiple comparisons with Bonferroni correction), with further empirical adjustments to account for technical variation34. No assessment of normality was carried out as ANC analysis is a non‐parametric test.Adaptive non‐parametric analysis with empirical alpha cutoff (ANC) is used to identify high‐confidence, statistically significant differences (corrected for multiple comparisons) in bead distributions on an individual protein interaction basis. ANC analysis was conducted in MATLAB (version 2013a) as described in57. Data were filtered to remove weakly detected interactions , and batch effects were removed using the COMBAT function for R57 with ‘experiment number’ as the ‘batch’ input. Post‐Combat data were log2 transformed prior to CNA analysis. Soft thresholding using a power adjacency function was used to determine the power value resulting in the best approximation of scale‐free topology, and the minimum module size was set to between 5 and 10, with the goal of generating a manageable number of modules . Protein interactions whose behavior was tightly correlated across experiments were assigned to arbitrary color‐named modules by the weighted gene correlation network program. Modules whose eigenvectors significantly (p < 0.05) correlated with a given experimental trait  were considered significantly correlated with the specific trait, and protein interactions belonging to modules of interest were defined as those with a probability of module membership (p.MM) < 0.05, as defined in the weighted gene correlation network program56. All P values were adjusted for multiple comparisons to maintain a false discovery rate of 0.05.Weighted correlation network analysis (CNA) is a second, independent statistical method to identify co‐varying modules of protein interactions, and to then correlate those modules to experimental variables. Importantly, CNA relies on different assumptions than ANC, querying not what an individual interaction is doing, but what coordinated sets of interactions do together, as a unit. Remarkably, the two different approaches converge on a set of ‘high‐confidence’ protein interactions. Bead distributions used in ANC were collapsed into a single median fluorescent intensity (MFI), which was averaged across technical replicates and input into the weighted gene correlation network analysis package for R studio (version 3.4.1)34 to produce a high‐confidence set of interactions that were both individually, statistically significantly different in comparisons between experimental groups, and that belonged to a larger module of co‐regulated interactions that was significantly correlated with experimental group.To ensure reporting of a core, high‐confidence group of protein interactions, ANC data and CNA data were merged as described previously2 transformed post‐combat data were clustered using the Ward’s method with a Euclidean distance matrix using the statistical package flashClust in R studio. Principal component analysis was performed on the same data in R studio using the prcomp function.For hierarchical clustering, log2 fold change value was selected for visualization34. For heatmap visualization of fold change data, Heatmap.2 in R was used. For heatmap visualization of MFI values of CNA module members, log2 transformed data were input into the Heatmap program in R studio, which normalized the data by row for visualization of multiple analytes spanning a 3‐log range.QMI maps were generated to visualize only ANC ∩ CNA merged significant hits for all experiments using the open network resource Cytoscape (version 3.6.1). Mean fold changes, calculated from replicate experiments with significant fold change differences , were used to generate diagram edges. For protein pairs with significant changes in multiple measurements with different epitope combinations, the measurement with the greatest absolute logFor all western blot experiments, gels were transferred onto polyvinylidene difluoride (Millipore) membranes, blocked in 4% milk or bovine serum albumin in TBST  Tween20) for 60 min at room temperature, and primary antibodies were applied overnight at 4 °C in blocking medium. Primary antibodies for western blots were diluted as follows: GluR1 , Homer1 , mGluR5 , NMDAR1 , PSD-95 , SynGAP  After washing and probing with the appropriate species-specific secondary horseradish peroxidase conjugated antibodies, blots were imaged using Pierce Femto detection reagents in a Protein Simple western blot imaging system.Supplementary Info


CCAAT enhancer-binding protein beta (C/EBPβ) is a pioneer transcription factor that specifies cell differentiation. C/EBPβ is intrinsically unstructured, a molecular feature common to many proteins involved in signal processing and epigenetics. The structure of C/EBPβ differs depending on alternative translation initiation and multiple post-translational modifications (PTM). Mutation of distinct PTM sites in C/EBPβ alters protein interactions and cell differentiation, suggesting that a C/EBPβ PTM indexing code determines epigenetic outcomes. Herein, we systematically explored the interactome of C/EBPβ using an array technique based on spot-synthesized C/EBPβ-derived linear tiling peptides with and without PTM, combined with mass spectrometric proteomic analysis of protein interactions. We identified interaction footprints of ∼1,300 proteins in nuclear extracts, many with chromatin modifying, chromatin remodeling, and RNA processing functions. The results suggest that C/EBPβ acts as a multi-tasking molecular switchboard, integrating signal-dependent modifications and structural plasticity to orchestrate interactions with numerous protein complexes directing cell fate and function.  •PRISMA yields a high-resolution SLiM- and PTM-based C/EBPβ interactome•Disclosure of intrinsically disordered protein hub functions and interaction networks•New connections between C/EBPβ and RNA processing, transcription elongation, MLL, NuRD•C/EBPβ interaction with TLE3 is regulated by arginine methylation Proteomics; Systems Biology; Transcriptomics Prototypic C/EBPβ is widely expressed, highly regulated at the post-transcriptional level, and integrated in many signaling events communicating extracellular cues to epigenetic changes, examples of which include adipogenesis, hematopoiesis, innate immunity, female fertility, skin function, apoptosis, and cellular senescence  are basic leucine zipper (bZip) transcription factors that regulate chromatin structure and gene expression by dimerization and binding to nescence .trans-differentiation and accelerates acquisition of the induced pluripotent state by the Yamanaka set of reprogramming transcription factors  by alternative translation initiation of the single-exon C/EBPβ transcript. As consecutive C/EBPβ start sites are positioned in the same reading frame, the isoforms vary in their gene regulatory N-terminal extensions but retain the same C-terminal dimerization and DNA-binding bZip domain . The divtrans-regulatory bZip network including C/EBP, AP-1, and ATF transcription factors. The N-terminal two-thirds region of the C/EBP primary structure is predicted as an intrinsically disordered region (IDR)  . Phylogeon (IDR) . Experimon (IDR) . Screenion (IDR) .protein interaction screen on peptide matrices (PRISMA), revealed hundreds of C/EBPβ- and PTM-specific protein interactions that left footprints on C/EBPβ-derived peptides. Based on comparison with other affinity enrichment approaches, 45 protein complexes were predicted and several novel interactions with proteins and complexes were experimentally confirmed.Based on previous observations of the modular structure and biological functionality of C/EBPβ peptide regions, we developed a novel technique to systematically explore the C/EBPβ interactome. Briefly, C/EBPβ was deconstructed into small linear peptides of 14 amino acids immobilized on a solid-phase matrix. The starting point for each peptide was shifted by four amino acids, creating a tiled peptide array covering the entire amino acid sequence of C/EBPβ. PTM-containing peptides were included according to recorded modifications and their position in the primary C/EBPβ structure. The matrix was incubated with nuclear extracts, and protein enrichment on individual peptide spots was identified and quantified by mass spectrometry. The assay, termed The occurrence of novel PTMs on endogenous C/EBPβ was investigated by mass spectrometry of C/EBPβ immunoprecipitates derived from the human anaplastic lymphoma cell line SU-DHL1 that critically depends for growth and survival on C/EBPβ . Over 90To examine the linear CEBPβ interactome, two replicates of the peptide matrix were incubated with nuclear extracts of HeLa cells. HeLa cells express CEBPβ, and commercially available nuclear extracts have been used successfully by many research groups for the purification of biologically active proteins and protein complexes involved in gene regulation B. IndiviEnrichment efficiency by PRISMA was examined by comparing the PRISMA intensity distribution with the total proteome of the nuclear extract. Approximately 5,100 proteins were identified and quantified in the nuclear cell extract using the intensity-based absolute quantification method . The copAn initial inspection of the two replicate datasets revealed signal intensity variation of the interacting proteins. The two datasets were therefore integrated to increase robustness, as outlined in To assess the biological significance of data derived by PRISMA, we compared PRISMA data  to previously identified C/EBPβ interactome data, as shown in The global protein interaction profile obtained from the C/EBPβ peptide matrix is depicted as a non-hierarchically clustered heatmap in Clusters of protein-peptide interactions tended to co-localize with regions predicted to undergo disorder-to-order transition during interaction with binding partners and coincide with the profile of conserved C/EBPβ regions (CR1−7), interaction and MoRF predictions , as showNext, PRISMA-derived data were compared with previously mapped C/EBPβ-interacting proteins. As shown in The multi-subunit Mediator (MED) complex  has alsoCR1 of the LAP* C/EBPβ isoform was reported to specify conjugation by SUMO3  and in a−128). RNA-binding proteins that contain an RNP-1 RNA-binding domain fall into three classes that preferentially interact with the C/EBPβ bZip domain and the adjacent Fork and BA motifs, whereas a third group interacts with the CR2 region. A similar binding pattern was found for DEAD box helicases, a group of enzymes involved in ATP-driven conformational adjustment of ribonucleoprotein assembly.PRISMA data (SET1 and SET2) were inspected for enrichment of Gene Ontology (GO) terms, protein families, and protein domains. As shown in Protein domain enrichment using InterPro and PFAM databases revealed nucleotide-binding proteins (102 hits), RNA-binding proteins (80 hits), armadillo, WD40, histone fold, and DEAD box domain proteins as major C/EBPβ interaction partners. The number of domain hits obtained using standard algorithms to search protein domain databases may be underrepresented, because manual curation of PRISMA data increased the number of hits for WD40 domain proteins  from 32 to 40, and the number of DEAD box proteins  from 20 to 24.The C/EBPβ structure is predicted to fold back onto itself, permitting intramolecular signaling, or to stretch out to allow contact with several interaction partners simultaneously . A stronDiscrete parts of the C/EBPβ primary structure have previously been assigned to different biochemical or cellular functions. Protein binding data from different C/EBPβ regions were extracted and GO term analysis performed for each of the regions to map structure-function relationships, as shown in Next, PRISMA data were analyzed for potential enrichment of large protein complexes interacting with CEBPβ. In addition to anticipated C/EBPβ-interacting complexes such as the basic RNA polymerase II (RNAPII) transcription machinery or MED, previously unknown interrelations became apparent. These included potential complexes involved in the RNA transcript processing machinery responsible for transcript capping, splicing, termination, and polyadenylation. Interactions included 3'- end processing, cleavage, and polyadenylating factor , and several components of the Integrator complex involved in small nuclear RNA (snRNA) expression and the RNA exosome . Furthermore, many components of the nuclear pore and associated adapter complexes were identified. In addition, components of the transcript export THO-TREX complex, including THO1, 2, 3, 5, 6, 7, Aly, DDX39, and the THO-TREX-associated mRNA export factors Nxf1-Nxt1 were identified, along with a large number of heterogeneous nuclear ribonucleoproteins that may couple transcript splicing, maturation, and the formation of messenger ribonucleoprotein particles. Taken together, these data imply the existence of a previously unknown connection between CEBPβ and several steps involved in transcript generation, processing, maturation, and transport .Proteins co-occurring in both the PRISMA and SU-DHL1 proteomic data were then systematically explored to identify soluble protein complexes listed in the CORUM database  using the g:Profiler bioinformatics toolkit . A list Next, conventional protein pull-down, immunoprecipitation, and immunoblotting analysis were used in combination with CEBPβ mutants to validate data from the PRISMA-derived interactome . AccordiThe histone acetyltransferase GCN5 of the SAGA complex and the DNA methyltransferase-associated protein DMAP1 of the NuA4/TIP60 acetyltransferase complex are involved in a wide variety of developmental and genome regulatory activities, including transcription, DNA repair, DNA methylation, and chromatin remodeling . DMAP1 aDetection of PTM-dependent protein interactions in linear C/EBPβ peptides was a major objective for the development of the PRISMA screening method. Protein binding to each PRISMA peptide and its PTM-modified versions were therefore examined. The binding signal for each protein was normalized against the signal from the corresponding unmodified peptide to compare enhanced or reduced binding. Interacting proteins were then clustered, as shown in The transactivation region CR3 (residues 53–68: AIGEHERAIDFSPYLE) contains an arginine residue that is conserved in C/EBPβ, but not in C/EBPα,δ,ɛ, and was previously found to be methylated . The PRIin vitro differentiation cocktail . Fat cell differentiation was evident by up-regulation of the adipogenic genes ADIPOQ, CFD, and FABP4 (Both C/EBPβ and TLE3 had been described as important regulators in adipogenesis . As showOf particular interest was the observation that many proteins forming part of the machinery involved in RNAPII pausing and elongation were included in the PRISMA data. Components of the Trithorax MLL/Set1/COMPAS histone H3 lysine 4 (H3K4) methyltransferase complexes  were alsImmunoprecipitation and immunoblotting were performed to validate several of these novel connections. As shown in The Mi2/NuRD corepressor complex is widely expressed and maintains chromatin in a closed state by ATP-dependent chromatin remodeling and histone tail deacetylation. Some of the NuRD components (CHD3/4(Mi2), HDAC1, RBBP4/7, MTA1,2, GATAD2A/p66α, MBD2, MBD3) identified by PRISMA, SU-DHL1 IP, and other approaches  are sharIn conclusion, the PRISMA technique reveals a comprehensive and PTM-dependent interactome of the disordered C/EBPβ transcription factor hub and permits the detection of footprints of protein interactions and protein complexes. The results presented in this article suggest that C/EBPβ is involved at the nexus between enhancer and promoter regulation, in many aspects of pre- and post-transcriptional control, including initiation and pausing control, splicing and exosomal RNA degradation, polyadenylation, and RNA maturation, and in nuclear export. Previously, we described an array peptide screening system (APS) for PTM-dependent protein interactions that relied on fluorophore-based detection of peptide interactions with bacterially expressed human proteins immobilized on macroarrays. Although it is difficult to compare APS with PRISMA because of major differences in peptide choice, peptide length, biochemical conditions, local concentration of interaction partners, signal-to-noise ratio, or complexity of the protein expression library and requirement of a renaturation step, 35 common APS/PRISMA interaction partners, including Brg1/SMARCA4, were found with an N-terminal APS peptide and a comparable section covered in PRISMA that contained CR1 .The high local concentration of peptides in matrix spots may limit free diffusion and overcome the dissociation problem of low-affinity interactions . These fPRISMA was developed for analysis of the interactome of proteins with a high degree of intrinsic disorder. Such proteins lack a defined 3D structure, and protein interactions and complex formation are based on SLiMs, PTMs, and intrinsic flexibility. IDRs in conjunction with PTMs may facilitate structural transitions in partner protein exchanges . ComprehProtein interactions can be mediated by binding domains or by smaller interaction motifs. One key aspect of the study is the identification of the interactome of IDRs and SLiMs and binding regulation by PTMs. As the PRISMA technique is based on the use of peptides, the detection of interactions is limited to those that are based on structurally variable SLiMs, as binding directly depends on the amino acid sequence properties. The high local concentrations of the peptides on the membrane permit the detection of low-affinity interactors and leave components of their corresponding protein complexes intact under mild washing conditions. For interactions that are based on recognition of three-dimensional structures, however, the method will probably not be applicable.All methods can be found in the accompanying


Large-scale datasets of protein structures and sequences are becoming ubiquitous in many domains of biological research. Experimental approaches and computational modelling methods are generating biological data at an unprecedented rate. The detailed analysis of structure-sequence relationships is critical to unveil governing principles of protein folding, stability and function. Computational protein design (CPD) has emerged as an important structure-based approach to engineer proteins for novel functions. Generally, CPD workflows rely on the generation of large numbers of structural models to search for the optimal structure-sequence configurations. As such, an important step of the CPD process is the selection of a small subset of sequences to be experimentally characterized. Given the limitations of current CPD scoring functions, multi-step design protocols and elaborated analysis of the decoy populations have become essential for the selection of sequences for experimental characterization and the success of CPD strategies.users and developers, being easily integrated in analysis workflows. For users, it offers the ability to profile and select decoy sets, which may guide multi-step design protocols or for follow-up experimental characterization. rstoolbox provides intuitive solutions for the visualization of large sequence/structure datasets (e.g. logo plots and heatmaps) and facilitates the analysis of experimental data obtained through traditional biochemical techniques (e.g. circular dichroism and surface plasmon resonance) and high-throughput sequencing. For CPD software developers, it provides a framework to easily benchmark and compare different CPD approaches. Here, we showcase the rstoolbox in both types of applications.Here, we present the rstoolbox, a Python library for the analysis of large-scale structural data tailored for CPD applications. rstoolbox is oriented towards both CPD software rstoolbox is a library for the evaluation of protein structures datasets tailored for CPD data. It provides interactive access through seamless integration with IPython, while still being suitable for high-performance computing. In addition to its functionalities for data analysis and graphical representation, the inclusion of rstoolbox in protein design pipelines will allow to easily standardize the selection of design candidates, as well as, to improve the overall reproducibility and robustness of CPD selection processes. The fast-increasing amounts of biomolecular structural data are enabling an unprecedented level of analysis to unveil the principles that govern structure-function relationships in biological macromolecules. This wealth of structural data has catalysed the development of computational protein design (CPD) methods, which has become a popular tool for the structure-based design of proteins with novel functions and optimized properties . Due to Deterministic algorithms are aimed towards the search of a single-best solution. The OSPREY design suite, which combines Dead-End Elimination theorems combined with A* search (DEE/A*) , is one Heuristic algorithms, such as those based on Monte Carlo (MC) sampling , use stoab initio or docking may require to generate up to 106 decoys to find acceptable solutions [4 to 106 decoys, depending on the number of residues for which sequence design will be performed. Due to the large decoy sets generated in the search for the best design solution, as well as the specificities of each design case, researchers tend to either generate one-time-use scripts or analysis scripts provided by third parties [For Rosetta, as with other similar approaches, the amount of sampling necessary scales with the degrees of freedom  of a particular CPD task. Structure prediction simulations such as olutions , 14. Simolutions  may reac parties . In the Here, we present rstoolbox, a Python library to manage and analyse designed decoy sets. The library presents a variety of functions to produce multi-parameter scoring schemes and compare the performance of different CPD protocols. The library can be accessed by users within three levels of expertise: a collection of executables for designers with limited coding experience, interactive interfaces such as Ipython  for desirstoolbox has been implemented extending from pandas , one of ab initio folding and loop closure protocols. Derived from pandas.DataFrame [Additionally, rstoolbox contains 3 table-like data containers defined in the rstoolbox.components module Fig. : I) DesiataFrame , all theThe DesignFrame is the most general data structure of the library. It allows fast sorting and selection of decoys through different scores and evaluation of sequence and structural features. It can be filled with any tabulated, csv or table-like data file. Any table-formatted data can be readily input, as the generation of parsers and integration into the rstoolbox framework is effortless, providing easy compatibility with other CPD software packages, in addition to Rosetta. Currently, rstoolbox provides parsers for FASTA files, CLUSTALW  and HMMEThe components of the library can directly interact with most of the commonly used Python plotting libraries such as matplotlib  or seaboA typical metric to assess the quality of protein backbone conformations is by comparison of the backbone dihedral angles with those of the Ramachandran distributions . Such evab initio protocol is typically used [ab initio predictions. rstoolbox provides analysis and plotting tools to evaluate the similarity of fragment sets to a target structure . When redesigning naturally occurring proteins, it also presents a function to generate reversions to wild-type residues (generate_wt_reversions) to generate the best possible design with the minimal number of mutations. These functions will directly execute Rosetta, if installed in the system, but can also be used to create input files to run the simulations in different software suits. Code example for these functionalities is shown in Table rstoolbox allows the user to exploit the data obtained from the analysis of designed populations in order to bias following design rounds. When using rstoolbox, this process is technically simple and clear to other users, which will improve the comprehension and reproducibility of iterative design pipelines.Mamestra brassicae . The deno-target (binding-target absent), static , pack (binding-target with side-chain repacking) and packmin (binding-target with side chain repacking and backbone minimization) and evaluated the performance of each simulation. Specifically, we analysed how the design populations performed regarding energetic sampling : Tested on Linux and macOS.Programming language: Python.Other requirements: python2.7 or python3.4+. Non-standard Python libraries required are automatically installed during setup with pip.License: MIT.Any restrictions to use by non-academics: None.