We found that the ISN is a novel robust network model to show the higher classification probability in the plots of average vertex degree (k) versus average clustering coefficient (C), both of which are typical network parameters for protein structures, and successfully distinguished between “all-α” and “all-β” proteins. On the other hand, one of the typical conventional networks, the α-carbon network (CAN), was found to be less robust than the ISN, and another typical network, atomic distance network (ADN), failed to distinguish between these two protein structures. Considering that the links in the CAN and ADN are defined by the interactions only between the main chain atoms and by the distance of the closest atom pair between the two amino acid residues, respectively, we can conclude that reflecting structural information from both secondary and tertiary structures in the network parameters improves the quantitative evaluation and robustness in network models, resulting in a quantitative and more robust description of three-dimensional protein structures in the ISN.To quantitatively categorize protein structures, we developed a quantitative coarse-grained model of protein structures with a novel amino acid network, the interaction selective network (ISN), characterized by the links based on interactions in  To compare 3D protein structures, several sophisticated automatic secondary structure assignment programs were introduced starting more than three decades ago6. DSSP (Dictionary of Secondary Structure of Proteins)7 and STRIDE 8 are the most widely used methods for the assignment of secondary structure from the atomic coordinates of proteins, which allow us to classify protein structures based on the secondary structure contents. Although the secondary structure contents provide us to the clearly defined criteria for protein structures, the relationship between the “whole” protein structures including tertiary structure, which can be defined as the “fold” or “topology” of proteins, and the secondary structure contents has not yet been clear. Considering that the protein functions highly depend on the “whole” structure, not on the secondary structure contents, classification based on “whole” protein structures would be more essential to discuss the functional significance of the protein structures.Proteins are biological macromolecules made up of liner chains of amino acid residues that fold into the corresponding unique three-dimensional (3D) structures comprising secondary structure elements, whereby they acquire their own functions regulated by their 3D structures. The search to understand protein structure geometries has led to the development of many experimental and theoretical methods13, have been widely utilized. SCOP is the oldest structural manual classification database in which the protein structures are classified into several ‘classes’ and ‘folds’. On the first level of the hierarchy, the ‘class’ is sorted into four major classes—all-α, all-β, α + β and α/β—describing the contents of these secondary structural elements in the domain. Dominant secondary structural elements—α-helix and β-sheet—are detected by the geometry of hydrogen bonds into domains14. In the newer classification database, CATH, the class assignment is automatically assigned according to the ratio of the secondary structural compositions, whereas it is manually classified in the case of the protein tertiary structures15. However, it should be noted here that quantitative comparison of the protein secondary structures cannot be available in these approaches. This is because the ratio of the secondary structural compositions shows no clear boundaries for the specific structures as they are continuously distributed. The relationship between the ratio of the secondary structure contents and the protein classes remains unclear. Moreover, because these databases characterize the protein structures by separating several hierarchies, understanding the protein 3D structures by integrating both the secondary and tertiary structures becomes difficult.Two of the most prominent protein structure classification schemes based on the “whole” protein structures, SCOP  and CATH not for analysis of protein secondary structure components, but for quantitative characterization of whole protein structures.Network characterization, which represents the protein 3D structure as an amino acid network (AAN), is one promising approach to providing quantitative insights into the classification of protein 3D structures. The network is a mathematical model describing complex structures as ‘vertices’ and ‘links’. Compared with previous classification approaches based on the protein secondary structure contents, AAN enables the quantitative characterization of protein geometry using the network parameters without estimating the secondary structure contents. In Fig. 16 by network parameters. Of the two well-known types of reported AANs, one is the Cα (α-carbon) network (CAN), in which the links are established if the distance between two Cα atoms is less than a cutoff distance, Rc, which is empirically determined from 7.0 Å to 8.5 Å17. Another previously used network is the atom distance network (ADN), where the links are defined by the atom interactions between all but the hydrogen atoms in the amino acids. The ADN provides information about all van der Waals contacts between amino acid residues18. Using these AANs to characterize protein structures, previous studies have clarified several network properties of AANs20.In this study, we constructed a new AAN and classified protein structures and structural domains deposited in the Protein Data Bank (PDB)21. Alves and Martinez, for instance, analyzed 160 low homology proteins using an AAN and classified them into four structural classes—all-α, all-β, α + β, and α/β17—but also showed that these four classes share similar network geometry, making it difficult to discriminate between protein structures consisting of different contents of the secondary structures such as α-helix and β-sheet structures. These previous AAN studies present two serious problems in applying network approaches to distinguish protein 3D structures.Many previous AAN studies have been applied to elucidate the relationship between protein domains in allosteric regulation, because the network parameters reflect the difference of global, rather than local, conformation. However, only a few studies have utilized AANs for the classification of protein structures, while an extremely limited number of studies have focused on distinguishing protein structures and classifying protein 3D structures by AANRc. Although Rc is a key factor in the development of network geometry and the characterization of network properties, in previous studies using CANs, a wide range of Rc values (mainly 7.0 Å to 8.5 Å) have been employed as the optimum value, implying ambiguity of the classification system. Previous CANs also cannot quantitatively characterize protein structures17. Links in CANs do not include the chemical properties of interactions, such as hydrogen bonds and hydrophobic or other interactions, and they are established depending only on the distance between two Cα atoms. The lack of structural information about side chains is another major problem. In CANs, the links are determined only by the distance between two Cα atoms22 and include no structural and chemical properties of side chains.One problem is the ambiguity in determining the optimum value of Rc can be uniquely determined as the distance of chemical interactions by the protein crystal structure. Although the links in ADNs are established based on the interactions involving both main chain and side chain atoms, all links are independent of the chemical properties of these interactions and thus are uniformly treated. Therefore, ADNs are also assumed to lose information about the various types of interactions. We, therefore, developed a new AAN—termed the interaction selective network (ISN)—which includes information about chemical properties of interactions, in order to quantitatively characterize and classify protein structures. The links of our ISN are determined by the distance of certain atom pairs (apart from hydrogen atoms) between two amino acid residues. We use atom pairs involved in hydrogen bonds, hydrophobic interactions, disulfide bonds, ionic interactions and covalent bonds. The ISN as a model of protein structures has the advantage that interactions involved in both main chain and side chain atoms are reflected. The ISN has thus enabled us to discriminate between all-α and all-β proteins by their geometry, not based on the ratio of the secondary structure elements. We also confirmed the difficulty of characterizing the protein secondary structures with previously used CANs and ADNs. Although we found that a CAN using Rc = 5.5 Å appears to distinguish between all-α and all-β proteins, a small deviation of Rc from 5.5 Å resulted in a less clear discrimination with a flip in the clusters of the protein structures, implying that the CAN is a less robust network than the ISN in terms of Rc. The ISN is, therefore, a more quantitative and robust AAN, which we expect will be widely used for quantitatively characterizing and classifying protein 3D geometries.On the other hand, in ADNs, the optimum 17, the network models have several common parameters13 as listed in Table Rc values for the interactions between the amino acid residues to determine the “links”. The ISN comprises five types of interactions—hydrogen bonds, hydrophobic interactions, disulfide bonds, ionic interactions and covalent bonds. For hydrogen bonds, the initial Rc value was 3.5 Å, corresponding to the maximum donor-acceptor distance, as previously used24. In the case of hydrophobic interactions, Rc was 5.0 Å between the side chain carbon atoms in the following hydrophobic residues: Ala, Val, Leu, Ile, Met, Phe, Trp, Pro and Tyr25. The initial Rc value of a disulfide bond was 2.2 Å between the sulfur atoms, while that of the ionic bond was 6.0 Å between the side chain nitrogen and oxygen atoms in the following ionic residues: Arg, Lys, His, Asp and Glu. The covalent bond is established if there are two consecutive amino acid residues on the amino acid sequence. Although we calculated correlations between all network parameters as listed in Table k) and average clustering coefficient (C)  and all-β proteins (blue) are well-separated under the conditions, Rc (hydrogen bonds) = 3.8 Å, and Rc (hydrophobic interactions) = 5.0 Å. As we expected, the distribution of the α + β and α/β proteins are overlapped with the boundary region between the all-α and all-β proteins, because α + β and α/β classes have both α-helix and β-sheet components. It should be noted here that some proteins classified into the all-α or all-β classes are located in the boundary region between the distributions of the all-α and all-β proteins in the k–C plots, as observed for the α + β and α/β proteins. The X-ray structural analysis reported that some inconsistent proteins have both α-helix and β-sheet structures26. For instance, type III antifreeze protein RD1 from an Antarctic eelpout (PDB ID: 1ucs) is classified as an all-β protein in SCOP, whereas k and C  for this protein in the ISN can be plotted in the region for α/β proteins, not all-β proteins  had relatively higher k values, as observed for the all-α proteins (mainly from 7.4 to 8.6 for k and from 0.48 to 0.59 for C), and medial C values, which were observed for the all-α proteins and the all-β proteins (mainly from 5.8 to 7.4 for k and from 0.37 to 0.50 for C). On the other hand, the α + β proteins (mainly from 6.3 to 8.3 for k and from 0.43 to 0.53 for C) showed a wide range of distribution of k and C, which overlapped with those for the all-α (overlapped range: from 7.4 to 8.3 for k and from 0.48 to 0.53 for C) as well as those for the all-β proteins (overlapped range: from 6.3 to 7.4 for k and from 0.43 to 0.50 for C). Although the different distributions of the k–C plot between the α + β and the α/β proteins were roughly proportional to the α-helix content  and the ADN (Rc = 5.0 Å) share a similar distribution of the network parameters in protein structures, regardless of the different classes as show in Fig. k–C plot is still significantly different from that of the ISN. The treatment of van der Waals interactions comprises the largest difference between these two models. In the ADN, all van der Waals interactions are counted as links, but the links in the ISN are defined only by van der Waals interaction between hydrophobic residues. The ISN, therefore, detects only critical interactions as links, without recognizing weakly interacting contacts with the neighbor residues in their 3D structures. This property enables us to discriminate the difference in geometry between protein structures.To confirm the validity of the ISN for characterizing protein structures, we compared the network properties of the ISN with those of the CAN and the ADN. For protein structures, NVertices, and links, NLinks. As clearly shown in Fig. NVertices and NLinks. The slopes of NLinks – NVertices plots are summarized in Table NLinks implies a more crowded network, corresponding to larger k and C values, consistent with the fact that the hydrogen bond network of the α-helix is a spatial structure with many interacting atoms, whereas the β-sheet is a more planar structure with a lower number of interacting atoms. On the other hand, in the case of the CAN and the ADN, it is difficult to detect the correlations between the slopes and the protein classes, as shown in Fig. Other notable network parameters are the numbers of vertices, Rc = 7.0 Å)20. Their results display a clear separation of all-α and all-β proteins in the L–C plots, in contrast to our result  and we show the k–C and L–C plots in Fig. Rc values, disconnected components were not obtained because these Rc values were larger than the distance between the corresponding adjacent α-carbon atoms. The distributions of k and C indicate that all-α and all-β protein structures were not discriminated at Rc = 7.0 Å , but the L–C plots of the CAN (Rc = 7.0 Å) we calculated here indicate that the distribution of all-α and all-β proteins are overlapped as shown in Fig. 20, whereas we used 247 all-α and 557 all-β protein structures in our study  proteins over the number of all-α  proteins. In this analysis, the common data set for the three network models (Table Rc = 3.8 Å), the CAN (Rc = 8.5 Å) and the ADN, null hypothesis that the two distributions of the average degree (k) in both all-α proteins and all-β proteins are identical was tested with a significance level of 0.05. The hypothesis was rejected for the ISN (Rc = 3.8 Å) with p-value < 2.2 × 10−16, also rejected for the CAN (Rc = 8.5 Å) with p-value = 0.01588, but not rejected for the ADN with p-value = 0.1445. Null hypothesis that the two distributions of the mean clustering coefficient (C) are identical was also tested. The hypothesis was rejected for the ISN (Rc = 3.8 Å) with p-value < 2.2 × 10−16, but not rejected for the CAN (Rc = 8.5 Å) with p-value = 0.331, and rejected for the ADN with p-value = 3.7 × 10−7. By the results of statistical test, all-α protein was distinguished from all-β protein by significantly smaller p-values for each distribution of k and C only in the ISN (Rc = 3.8 Å). On the other hand, the null hypothesis was rejected with p-value < 2.2 × 10−16 for both k and C for the CAN (Rc = 5.5 Å) as observed for the ISN (Rc = 3.8 Å). These results show that both the ISN and the CAN (Rc = 5.5 Å) on the learning dataset are significantly different from previously studied AAN, the ADN and the CAN (Rc = 8.5 Å), and all-α proteins and all-β proteins are classified by the logistic regression on the ISN (Rc = 3.8 Å) and the CAN (Rc = 5.5 Å). The optimum classification line for the ISN (Rc = 3.8 Å) in Fig. k–C plane, and, for the CAN (Rc = 5.5 Å) in Fig. Rc = 3.8 Å), the classification probabilities on all-α and all-β proteins are 0.9323 and 0.9843, respectively. On the other hand, those on all-α and all-β proteins in the CAN (Rc = 5.5 Å) were 0.8906 and 0.9641, respectively. For both the ISN (Rc = 3.8 Å), and the CAN (Rc = 5.5 Å), the classification probabilities were over 0.88, implying that the classifications of all-α and all-β proteins in the k–C plots of the ISN and the CAN are statistically significant.As shown in the results, significant differences between all-α and all-β proteins were detected in average vertex degree, ls Table  were utiRc = 5.5 Å) were able to distinguish between all-α and all-β protein structures, the ISN and the CAN differ in terms of three points, as follows: the robustness, the average degree for all vertices, k, and the distribution of the network parameters. Robustness in network models is defined by the capability of maintaining similar network geometry with a wide range of parameters. We, therefore, use ‘robustness’ as the capability of distinguishing all-α and all-β protein structures within a wide range of Rc values, corresponding to a stable model, while less robust networks are unstable models drastically perturbed by small deviations of the network parameters. A notable difference in the robustness between the CAN and the ISN resides in the comparison between the distributions of the k–C plots. The all-α and all-β proteins were distinguished with a wide range of Rc from 3.4 Å to 5.0 Å in the ISN, while the CAN was able to distinguish between all-α and all-β protein structures only around Rc = 5.5 Å. The CAN lost clear discrimination when Rc was lower than 5.0 Å and flipping was observed for the clusters of the two protein structures, where C became approximately 0 and k was between 2 and 3  ranged from 3 to 5  include interactions only in main chain atoms. In the case of the ISN, five to nine links were exhibited per vertex , wherein links only reflect the secondary structures from interactions in the main chain atoms.A comparison of the o 5 Fig. , thus extex Fig. , implyinRc = 5.5 Å) is based on the information about protein secondary structures reflecting hydrogen bonds between main chain atoms. On the other hand, the ISN includes additional information about interactions involved in the side chain atoms. Since the side chain atoms often mediate van der Waals contacts and hydrophobic interactions between two residues located on separated positions on the primary sequence, thus reflecting the tertiary structures of proteins, the links in the ISN include the structural information from the tertiary structures of proteins. The scattered distribution in the k–C plots in the ISN, compared to that in the CAN, also suggests that, not only the structural information from the secondary elements, but also additional structural information from the tertiary structures are reflected in the distribution. The CAN (Rc = 5.5 Å), therefore, describes the protein structures only according to the secondary structures, whereas the ISN categorized protein structures based on both their secondary and their tertiary structures.As discussed above, the CAN  and the second direction as the principal component two (PC2). In the CAN (Rc = 5.5 Å), the standard deviations of PC1 and PC2 are calculated to 0.1662 and 0.03614, respectively. The proportion of the variance in PC1 and PC2 is, therefore, 0.9549 and 0.04512, respectively, indicating that most of the information (more than 95%) to discriminate the protein structures on the k–C plots in the CAN is primarily based on PC1. Thus, the discrimination of protein structures in the CAN depends on only one parameter of the protein structures, corresponding to the secondary structures. On the other hand, the standard deviations of PC1 and PC2 in the ISN are 0.1181 and 0.06049, respectively, and the proportion of variance in PC2 was enhanced to 0.20773, while that in PC1 was decreased to 0.7923. In the ISN, over 20% of the information is, therefore, included along PC2, and the second principal component significantly contributed to the discrimination of the k–C plots in the ISN, supporting the contribution of the information of the tertiary structure to the correlation between k and C in the ISN. Considering the stability of the model depends on the number of the principal components, the significant contribution of the second principal component to the discrimination of protein structures on the k–C plots also suggests that the ISN is a more stable and robust network model than the CAN.To get further insights into the mathematical basis for the discrimination of protein structures in the two network models, we performed one of the typical multivariate analyses, principal component analysis, of these network models. To apply the method to the k and C, enabled us to distinguish between all-α and all-β protein structures. By exploring the optimum Rc for the discrimination of protein structures, we determined that the ISN is more robust than the CAN, while the wider range of the distribution of k and C and the multivariate analysis in the ISN suggests that the ISN has additional structural information from protein tertiary structures. On the other hand, as clearly shown in Figs k–C and Nlink–Nvertices plots, regardless of the Rc value. The distributions of other network parameters including L, kmax, and vertex assortativity also indicated no clear differences between all-α and all-β protein structures , if the distance between their Cα atoms is less than Rc. The links of the ADN are determined by the distance of the closest atom pairs between two amino acid residues. Because data sets of the protein structures determined by X-ray crystallography do not contain the atomic coordinates of hydrogen atoms, hydrogen atoms are ignored. If the distance is less than Rc = 5.0 Å, these two amino acid vertices are connected; otherwise, they are disconnected. Rc is defined as the distance of van der Waals contacts29 in the ADN. In our novel introduction of the ISN, the links are determined by the distance as defined in the ADN. However, to establish the links, we only use atom pairs involved in hydrogen bonds, hydrophobic interactions, disulfide bonds, ionic interactions and covalent bonds. Hydrogen atoms, as in the ADN, are ignored. Rc in the ISN is determined based on the cutoff value used in Protein Interaction Calculator (PIC)30. PIC is a server computing various interactions in a protein structure based upon the coordinate set of the 3D structure of the protein.The AANs we examined here use the structural data of native-state protein, structural domains, and some partial protein structures, as available in PDB14, as shown in Table k, average clustering coefficient C, and average path length L, as shown in Fig. 33. The k value of a protein structure is defined asN is the number of amino acid residues, and the vertex degree, ki, is the connected number of amino acid residues, i, with other vertices in a protein structure. Ci, the clustering coefficient of the amino acid residue, i, is defined as the fraction of links that exist among the nearest neighbors of the amino acid residue, i, to the maximum number of possible links among them. Therefore,ni is the number of links that actually exist, and ki (ki −1)/2 is the number of all possible links for the nearest neighbors. C of a network can be calculated byThese AANs are characterized by following several parametersL is defined asLij is the number of links on the shortest path between the ith and jth amino acid vertices. We also calculated additional parameters in network, assortativity and maximum vertex degree (kmax). Assortativity is the correlation of the degree between vertices adjacent to each other34. kmax is the largest k in the network.16. Our study focused on four broad protein structural classes—all-α, all-β, α + β and α/β, according to the SCOP classification10. SCOP11 is the oldest and manual-based approach and classifies a single αβ class of CATH13 into two classes, α + β and α/β. To acquire detailed information about relationship between structural classes and network properties, we used the SCOP classification data to examine the difference between α + β and α/β classes by the network parameters. All-α proteins comprise predominantly α-helices, while all-β proteins, β sheets. α + β proteins have α-helices and β-strands that are largely segregated, whereas α/β proteins are largely interspersed10. To ensure the quality of structural data used for AAN construction, we selected high-resolution (higher than 2.0 Å) X-ray crystallographic structures of proteins as classified all-α, all-β, α + β and α/β in SCOP. In low resolution protein structures (less than 2.0 Å), considerable ambiguity in the atomic coordinates was found, where chemical groups such as phenyl rings and carboxylates) were not resolved35. We also omitted the protein and domain structures including any ligands or nucleic acids, due to the difficulty in definition of such non-amino acid components in AAN. In Table We obtained protein structures from the PDB databaseThe difference in the number of structures in data sets mainly arises from the different requirement of the specific atomic coordinate for AANs Table . In cons36 were also excluded from the leaning data set. Table To construct the learning data set to estimate the classification probability, two selection criteria are added to the criteria for the data listed in Tables Supplementary Materials


Interaction of proteins with charged macromolecules is involved in many processes in cells. Firstly, there are many naturally occurred charged polymers such as DNA and RNA, polyphosphates, sulfated glycosaminoglycans, etc., as well as pronouncedly charged proteins such as histones or actin. Electrostatic interactions are also important for “generic” proteins, which are not generally considered as polyanions or polycations. Finally, protein behavior can be altered due to post-translational modifications such as phosphorylation, sulfation, and glycation, which change a local charge of the protein region. Herein we review molecular modeling for the investigation of such interactions, from model polyanions and polycations to unfolded proteins. We will show that electrostatic interactions are ubiquitous, and molecular dynamics simulations provide an outstanding opportunity to look inside binding and reveal the contribution of electrostatic interactions. Since a molecular dynamics simulation is only a model, we will comprehensively consider its relationship with the experimental data. The importance of electrostatic interaction for biological processes is clear since cells contain a lot of charged molecules . First, The second class of naturally occurred charged macromolecules is comprised of nucleic acids. Electrostatic interactions are important for the interaction of DNA with histones and chromatin condensation , interacMany of the aforementioned systems are extremely difficult to study in vivo and even in vitro. From this point of view, molecular modeling provides an outstanding opportunity to look at the protein interaction, with numerous macromolecules moving to the atomistic level ,23. ClasHomopolymers and simple  copolymers of charged repeat units, mainly synthetic polymers, can be considered as a simplified model to study electrostatic interactions in experiments as well as in modeling according to the assumption that such interactions are generally charge-driven. Computer modeling of such model systems can be performed on a different detailing level, i.e., using different approaches: Monte Carlo ,29,30,311-42 fibrils [Several groups adopted molecular dynamics simulations approach to investigate protein interaction with charged polysaccharides because of high biological impact of polysaccharides as important components of extracellular matrix . Thus, a fibrils . In addi fibrils ,50, and  fibrils  are nece fibrils . For sho fibrils ,45 or so fibrils . In addi fibrils ,45,46.Computational studies of the protein interaction with polyphosphates, another class of natural polyanions, are less numerous . However, MD simulations were adopted to investigate atomistic details of specific  and non-Next, we should mention modeling of the cationic or anionic dendrimers, which are extensively studied as multi-functional drug carriers ,57 in coComprehensive investigations of the protein complexes with charged model polymers revealed the regularities and key factors of these interactions. The first lesson learned from the study of such model systems is a deep understanding of patch-based interaction of similarly charged macromolecules. Indeed, proteins have both negatively and positively charged patches on the surface in a wide pH range, which can interact with oppositely charged regions of the polymers even if total charges of the protein and the polymer are the same ,59,67. TSome technical caution is required to obtain a realistic model of protein interaction with highly charged polymers. In principle, electrostatic interactions are handled as non-bonded interactions of charged atoms (or atom groups). Long-range electrostatic interactions can be expressed using the particle mesh Ewald (PME) technique , which iAccording to experimental data and theoretical calculations, one of the key factors in the formation of inter-polyelectrolyte complexes is counterion release ,83,84,85However, there are many good examples of realistic modeling of the inter-polyelectrolyte interaction accompanied by counterion release or binding, and even attempts to compare different counterions . As an eNucleic acids belong to one more important class of polyanions present in a cell. Obviously, the binding of proteins with nucleic acids is determined by many of the following factors, but electrostatic interactions are important for the binding . GeneralElectrostatics plays a significant role in protein‒protein interactions ,106. AmoHow can MD simulations help in a study of electrostatically driven protein‒protein interactions? A “simple” (providing fewer quantitative atomistic details than MD) analysis of electrostatic potential can suggest potential binding sites ,116,117.There are a lot of other examples of MD simulations’ utilization for modeling of charged protein behavior since electrostatic interactions are important for almost all systems. In the next two sections, we focus on two specific cases, where the impact of electrostatic interactions is very high: intrinsically disordered proteins and proteins undergoing post-translational modifications associated with charge changes.MD simulations are a powerful approach to study unfolded proteins  and their complexes with other proteins  because Thus, electrostatic interactions and the formation of intra-molecular and inter-molecular salt bridges is an important factor for amyloid aggregation . This tyFormation of salt bridges is also important for the interaction of intrinsically disordered proteins with other proteins. Thus, α-synuclein and Aβ peptide are known to interact and form co-aggregates , and eleTo conclude, MD simulations can provide a detailed model of the interaction, aggregation, and sometimes folding of the unfolded proteins, which can then be experimentally verified. It is useful, for example, in comparative investigation of point mutations effect, which is important in the case of amyloidogenic peptides associated with neurodegenerative diseases ,147. NotPhosphorylation is one of the most common protein post-translational modification, and it is associated with charge change . Though direct modulation of protein‒protein interaction via changes in local electrostatics and formation or disruption of new inter-molecular contacts. Phosphorylation can lead to a significant change in the protein surface’s properties [changes in protein conformation induced by the formation of new intra-molecular contacts , was proposed to be a key factor in many cases and seems to be much more widespread. Thus, electrostatic interaction between phosphorylated serine and arginine located in different domains destabilizes the closed conformation of DNA polymerase β and consequently inhibits its activity, as was suggested using MD simulations and corroborated with experiments [The related papers can be separated into two groups according to the mechanism of the phosphorylation contribution revealed by MD simulations. The first one is operties  and theroperties . The samoperties . Howeveroperties . This seeriments . On the eriments . Phosphoeriments . Phosphoeriments . Phosphoeriments . Accordieriments  and initeriments .In conclusion, phosphorylation usually leads to “cosmetic” changes such as the formation of a salt bridge, which is nevertheless important for the protein behavior. It suggests a high capability of the MD simulations to provide insights into the mechanism of such signal transduction from the local protein area to the overall structure and function. The main conclusions  obtained using MD simulations can and should be experimentally verified.The appearance of a charged group is of special importance for sulfated proteins. Protein sulfation usually occurs at tyrosine  and someUnfortunately, the atomistic details of the role of sulfation in the behavior of the aforementioned proteins and peptides are scant. Based on our data about protein affinity to sulfate- and phosphate-based polymers, we hypothesized that sulfation occurs when a strong interaction is required, whilst phosphorylation is preferable for less tight but reversible interactions . This hySummarizing this scarce information, we suggest MD simulations as a powerful tool to understand the role of sulfation in particular cases of protein‒protein interaction. We wonder why the use of this approach is so rare and expect that the growing usage of MD simulations will provide insight into the role of protein sulfation in other numerous cases.Glycation of positively charged residues (lysine and arginine) is a common non-enzymatic process occurring in living cells  as well might influence protein function, but cannot prove how it actually does influence it.Modeling of glycated proteins is very complicated because of a large diversity of probable intermediate and end products  for each residue and almost statistical distribution of the glycation sites among lysine and arginine residues ,7. FurthIntrinsically disordered amyloidogenic proteins are a beautiful example of the glycation effect on charge-based interactions revealed using MD simulations. For example, according to the results of modeling of Aβ peptide self-oligomerization , glycatiThiol groups of cysteine can be subjects of oxidation up to formation of charged groups such as sulfenic and sulfonic acids ,210,211.Summarizing the reviewed data, we conclude that electrostatic interactions are critically important for protein behavior since they control interaction with other protein, glycosaminoglycans, polyphosphates, and nucleic acids. The molecular dynamics simulations approach provides an outstanding opportunity to probe the atomistic details of these interactions. Furthermore, it is the method of choice for comparative study of the role of point mutations and post-translational modifications, as well as for drug design. It is of special interest for intrinsically disordered protein, including amyloidogenic proteins and peptides, since many amyloidosis-related diseases are associated with point mutations. Notably, electrostatic interactions and the formation of intra- or inter-molecular salt bridges are important to the behavior of amyloidogenic proteins. In addition, these unfolded proteins do not have a rigid structure involved in molecular recognition. Hence, the modeling of electrostatic interactions helps to elucidate the mechanism of their interaction with other macromolecules present in cells. There are still challenges to modeling the aforementioned systems, including proper sampling and development of realistic force fields. Finally, many important post-translational modifications associated with changes in the protein local charge are difficult to study using experimental approaches, and molecular dynamics simulations of such systems will elucidate the role of these modifications on protein‒protein interactions. In the case of phosphorylation, molecular modeling can be considered a routine approach, while in the case of sulfation, glycation, or polyphosphorylation, it should provide insight into the molecular mechanism of the unclear impact of the modification. We expect that the number of papers employing molecular dynamics simulations for post-translational modifications investigation will increase. Specifically, this approach should force an understanding of the role of intrinsically disordered proteins post-translational modifications in human pathologies such as diabetes, aging, neurodegenerative diseases, etc. There is a large body of evidence for the interconnection of the listed diseases’ development and progression and protein post-translational modifications, but the mechanism underlying this relation is unclear. Notably, this combination, i.e., unfolded proteins and non-enzymatic post-translational modifications, is the most complicated for in vivo study, and therefore the capabilities of molecular modeling would be outstandingly useful.


Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure. Therefore, tools for validating many criteria have been developed, including Ramachandran plot outliers, all-atom clash scores, deviations from bonding geometry, and rotamers. These criteria are good indicators of the local structure’s correctness. Implementation of Huber’s rule that the structure is correct when it fits the electron density and is correct locally and globally relies on human assessment5; however, algorithms and their software implementations are lacking.Insight into the three-dimensional structures of macromolecules resolved to atomic detail is crucial for our understanding of biological processes. As only the correct structures can be used in earnest to address biological questions, the validation and quality assessment of three-dimensional structures is an important issue in structural biology6. In the last decade, protein structures have been modelled as networks numerous times9. Interacting amino acids, presented as nodes and edges, have small-world properties10 with relatively short characteristic path lengths and high clustering coefficients. From this perspective, the interacting amino acids are like other self-organized networks, such as World Wide Web pages11, biological signaling pathways12, metabolic networks13, and scientific collaboration networks14. Protein and residue network models have been successfully used for predictions of catalytic sites17, examinations of protein structures from modeling18, investigations of protein dynamics19, analysis of protein-protein interaction networks21, graph theoretical analysis of protein pathways22, and assignments of residues that play crucial roles in protein folding25. Surprisingly, to date, residue network characteristics have not been used to validate three-dimensional protein structures deposited in Protein Data Bank and, Protein Graph Repository. Here, we demonstrate the use of complex network analysis for macromolecule model quality assessment and for differentiation between incorrect and correct protein structures.The informational abstraction of three-dimensional macromolecular structures into residue networks provides a means to address this issue. The analysis and exploration of a complex networks approach are still expanding across various research areas19.We analysed more than 50,000 residue networks and evaluated the dependence of node degree (ND) on (i) resolution and (ii) residue network size. The ND parameter indicates how an average node is connected to the other nodes. The distribution of ND clearly shows that ND does not depend on resolution Fig. , inset a023 Fig. . FurtherBecause ND does not depend on resolution and its relation to protein size is rather weak, we investigated the general applicability of complex network parameters for validation and quality assessment. To address the global correctness of the model, we analysed the shortest path length and graph energy of nine pairs of previously studied correct and incorrect protein models  that shows the largest increase of node degree Fig.  and grap19 Fig. .Figure 3(i).26. The ND analysis showed that the correct trace of the mini-chain is linked by 14 body residues, whereas the incorrect trace is linked by 11 body residues  has an eight-residue-long propeptide, termed a mini-chain, with a disulphide bond link to the main-chain. There are two alternative chain traces of the mini-chain. Using kick maps, the authors decided on the correct chain traceues Fig. . Moreove(ii).10 and α11 helices but had scores below 0.6 for rest of the structure 8 barrel and the α10 and α11 helices in dimer formation .L-lactate dehydrogenase27 (PDB id: 1LTH) revealed that the region with the largest discrepancies involves residues Ala17, Pro89, Pro126, Ile229 and Ile230 . Molecules in a very similar environment are expected to have similar, but not identical, three-dimensional structures. The ND differences between two pairs of the same protein structure were calculated by subtracting the ND of chain A from the ND of chain B. The correct pair (PDB id: 1CEL) of structures exhibited insignificant discrepancies in ND along the chain Fig. , whereas230 Fig. . This inNext, we inspected three cases with local problems: (i) an eight-residue-long bound peptide that can be traced in two alternative directions, (ii) a register error, and (iii) differences between non-crystallographic symmetry-related molecules.28 and Sali Lab29 decoy datasets. A total of 110 targets and their corresponding 7,800 models were taken in to our analysis. Energy statistical functions34 and Machine learning technique39 are often used on the decoy discrimination problem.In addition to the nine models that had been deposited in the PDB but later found to be incorrect we ranked the CASP11-stage140, Root Mean Square Distance (RMSD) of Cα atoms and Native Overlap (NO)41 at a 3.5 Å cutoff. Note, the similarity metrics GDT-TS, RMSD and NO are part of the CASP11-stage1 and Sali Lab decoy datasets. Figure Here we introduce simple, but efficient ranking which is rather similar to the comparison of important global quality indicators in the wwPDB validation report. By using the Z-score of mean node degree we classified decoys in to four quality groups: (i) Good: absolute value of the Z-score in the interval , (ii) Fair: absolute value of the Z-score in the interval , (iii) Poor: absolute value of the Z-score in the interval , (iv) Very poor: absolute value of Z-score is greater than 3. The Z-score of mean node degree was compared against three structural similarity metrics: global distance total score (GDT-TS)38, which is a single model quality assessment method, on the CASP11-stage1 dataset shows that DeepQA correlates (R = 0.64) better with GDT-TS, than residue graph parameters ND Z-score (R = 0.41) and shortest path (R = −0.39), see Supplementary Table 38. Furthermore, the correlations of DeepQA training feature spanned from 0.37 (Euclidean compact score) up to 0.63 (Qprobe score). It follows that ND and shortest path  correlate better than the lowest DeepQA feature38 on the CASP11-stage1. In this respect, the results show that residue graph parameters could be used for ranking and selecting protein models as a single quality assessment method or as an individual feature of a deep belief network.Moreover, the evaluation of the presented method with standard metrics (average per-target correlation), shows that ND Z-score and shortest path strongly correlate with RMS . By transformation of the three-dimensional protein models into the 2D graphs, each amino acid is abstracted as a Cα atom. The Cα atom of an amino acid residue is a node, and an edge is created when the distance between Cα atoms is equal to or below a threshold value of 7 Å43.Node degree and clustering coefficient against protein size and resolution was analysed using 50,249 residue networks. Residue network data were retrieved from the Protein Graph Repository44. Poor and long graphs were then decomposed into connected components, i.e., subgraphs.In an incorrect model graph, edges between nodes are constructed when the distance between any two nodes (Cα atoms) is between 3.0 and 3.7 Å. The edges in a long graph are constructed only when sequential nodes in the primary structure of protein model were more than 3.9 Å apart from each other. The threshold distances and poor/long terminology followed terminology introduced by Kleywegt, 199745 classes A (α proteins), B (β proteins), C (α/β proteins), D (α + β proteins), E (multi-domain proteins) or F (membrane and cell surface proteins and peptides), see Supplementary Material for details on the raw data sets.The structures from the Protein Graph Repository were selected according to the following criteria: (i) the protein chain was longer than 50 residues, (ii) the resolution of the crystallographic experimental data was beyond 4.0 Å and (iii) the protein was a member of the Structural Classification of Proteins (SCOP)An objective comparison between two residue networks can only be made when they have the same number of nodes, which excludes comparisons of models with different numbers of missing residues. Before performing a detailed analysis of the network parameters, we constructed Cα atom matches between the incorrect and correct models. The incorrect and correct primary structures were aligned, and only the residues present in both models were used to construct the residue network.46, Bio3D (2.3.3) and igraph (1.0.1) R package (3.4.0), see Supplementary Material for details on R scripts.All network parameters were calculated using online tool for Network based Analysis of Protein StructuresCrystal structures of SarA, a pleiotropic regulator of virulence genes in S. aureus47 (PDB id: 1FZN), an erratum was published. Later, in 2006, the correct model (PDB id: 2RH) was deposited into the PDB database48. The X-ray structures of EmrE (PDB id: 2F2M and 3B5D) was published by Chang and co-workers. The structure published in 2006 (PDB id: 2F2M) was later retracted, and the correct model was published in 200749 (PDB id: 3B5D).Protein models (PDB ids) 1PHY, 1PTE, 1ENL, 2FD1, 3XIA and 179l are obsolete in the Protein Data Bank (PDB) and have been superseded by 2PHY, 3PTE, 3ENL, 5FD1, 1XYA, and 177l, respectively. The structure of aspartyl protease from human immunodeficiency virus HIV-1 (PDB id: 2HVP) is partly incorrect and was later corrected and refined as chemically synthesized HIV-1 protease (PDB id: 3HVP). Structures (PDB id) 1FZN and 2F2M are both obsolete but have not been superseded. In 50. The crystal structure of a class II fructose-1,6-bisphosphate aldolase (PDB id: 1ZEN) is partly incorrect and contains a registry error. This case was analysed during method validation for the removal of model bias52. Furthermore, this partly incorrect model was rebuilt, refined and used to show the efficacy of free kick refinement52. The comparison and presentation of the correct model (PDB id: 1CEL) and the model with unusual features (PDB id: 3SDP) related by non-crystallographic symmetry was a case in Validation of protein crystal structure1.The three additional cases were selected from our previous works in which all three protein models were analysed. The structure of Cathepsin H with both the correctly and reversely built mini-chain (PDB id: 8PCH) was analysed by validation of averaged kick maps, which can remove model bias from a protein modelG = G consists of a set of vertices (nodes) V = v1, v2, … vn and a set of edges E = e1, e2, … em. Two vertices vi and vj of a graph G are said to be adjacent if there is an edge eij connecting them. The vertices vi and vj are then said to be incident to the edge eij. Two distinct edges of G are adjacent if they have at least one vertex in common.A graph v, denoted d(v), represents the number of nodes adjacent to v. The average node degree of a graph G is the average value of the degrees of all nodes in G. The average node degree is formally written asd(vi) represents the degree of the node vi and N is the total number of nodes in a graph G. Another way of expressing the average node degree is with the ratioe(G) represents the total number of edges in a graph G, and N(G) is the number of nodes in a graph G.The degree of a node G = G be a graph containing n vertices and m edges, with the set of vertices V = v1, v2, … vn and the set of edges E = e1, e2, … em. The adjacency matrix A(G) =  of G is then a n × n matrix defined byLet vi and vj, is the minimal number of edges that lie between two given nodes. For computing the shortest path between a pair of nodes, we used Dijkstra’s algorithm53.Since protein graphs are simple graphs without loops or multiple edges, the adjacency matrix of any protein graph is symmetric with zeros on the diagonal. The shortest path between two nodes, A(G) of an undirected simple graph is symmetric and therefore has a complete set of real eigenvalues and an orthogonal eigenvector basis. The set of eigenvalues of a graph is the spectrum of the graph. Eigenvalues are denoted as λ1 ≥ λ2 ≥ … λN. The eigenvalues are obtained as the roots of the characteristic polynomial of matrix A; that is, we look for the solutions of the equationI is the identity matrix. For every eigenvalue, we can identify at least one vector x for which it holdsx is called a corresponding eigenvector of the given eigenvalue λ, and A is an adjacency matrix. The Perron–Frobenius theorem asserts that a real square matrix with positive entries has a unique largest real eigenvalue and that the corresponding eigenvector can be chosen to have strictly positive components. The vth component of the vector that corresponds to the largest eigenvalue (λ1 = LEV) gives the relative centrality score of the vertex v in the network.The adjacency matrix E(G) of a graph G on N vertices with M edges is defined as the sum of the absolute values of all eigenvalues λ1,λ2…, λN of the adjacency matrix A(G), formally written asThe graph energy Supplementary materialDataset 1


At least one third of soluble proteins are predicted to exist in a stable oligomeric state. However, the compositions of the vast majority are unknown. This paper describes a biochemical method to predict protein complex composition based on orthogonal chromatographic separations and label-free protein correlation profiling. The validated method predicts hundreds of novel homo- and heterooligomeric complexes, and provides a new way to analyze protein complexes in any organism with a well-annotated proteome. HighlightsEndogenous protein complex composition was predicted using orthogonal protein separations, protein correlation profiling, and novel data filtering scripts.The validated method accurately identifies homo- and heterooligomeric complexes.AIMP1 mutant validated the discovery of a t-RNA synthetase-clustering complex.Profiling of the  Information on the composition of protein complexes can accelerate mechanistic analyses of cellular systems. Protein complex composition identifies genes that function together and provides clues about regulation within and between cellular pathways. Cytosolic protein complexes control metabolic flux, signal transduction, protein abundance, and the activities of cytoskeletal and endomembrane systems. It has been estimated that one third of all cytosolic proteins in leaves exist in an oligomeric state, yet the composition of nearly all remain unknown. Subunits of stable protein complexes copurify, and combinations of mass-spectrometry-based protein correlation profiling and bioinformatic analyses have been used to predict protein complex subunits. Because of uncertainty regarding the power or availability of bioinformatic data to inform protein complex predictions across diverse species, it would be highly advantageous to predict composition based on elution profile data alone. Here we describe a mass spectrometry-based protein correlation profiling approach to predict the composition of hundreds of protein complexes based on biochemical data. Extracts were obtained from an intact organ and separated in parallel by size and charge under nondenaturing conditions. More than 1000 proteins with reproducible elution profiles across all replicates were subjected to clustering analyses. The resulting dendrograms were used to predict the composition of known and novel protein complexes, including many that are likely to assemble through self-interaction. An array of validation experiments demonstrated that this new method can drive protein complex discovery, guide hypothesis testing, and enable systems-level analyses of protein complex dynamics in any organism with a sequenced genome. There are important roles for “omics” technologies to generate systems level data to inform strategies for trait engineering , 2. Info3Using the plant model Arabidopsis, it is estimated that about one third of the cytosolic proteins exist as a subunit of a stable complex ; however1325Protein correlation profiling is an attractive method to analyze endogenous protein complexes as a function of their elution profiles. Protein complex composition prediction is based on the premise of “guilt by association” in which subunits of stable protein complexes coelute independent of the purification strategy. The method is enabled by the parallel protein quantification inherent to modern protein mass spectrometry and the availability of high-quality proteomes. Increased protein coverage and accurate quantification is being driven by improvements in mass spectrometry instrumentation and data analysis pipelines , 31–33.31In practice, protein complex composition prediction is challenging because the cell extract is a mixture of thousands to tens of thousands of monomers and complexes. Using size-based separations it is possible to measure the apparent mass of hundreds to thousands of proteins in a single experiment, and the subset that is likely to exist in an oligomeric state , 34, 35.Our goal here is to develop a protein correlation profile workflow in which imperfect but highly useful protein complex composition predictions can be made based on LC/MS profile data alone. Soluble Arabidopsis leaf extracts, enriched in soluble cytosolic and chloroplast proteins, were separated by SEC and IEX chromatography to generate thousands of elution profiles. Automated data filtering of biological replicates was used to identify and combine reproducible profiles and subject them to distance-based clustering analyses to identify groups of proteins with similar elution profiles. The intrinsic information content of the dendrogram and the behaviors of selected known proteins complexes were used to divide the dendrogram and generate specific protein complex composition predictions. An array of biochemical and genetic validation experiments demonstrates the utility of this dataset and the potential use of this method to generate systems-level knowledge about protein complex composition and dynamics.aimpl1 mutant were analyzed on Q Exactive mass spectrometer. For CoIP-MS pull downs three replicates were performed with antibodies against the protein of interest and negative controls and were analyzed on Q Exactive mass spectrometer.For LC-MS/MS profiling two biological replicates were used based on the high level of reproducibility between replicates. In previous studies most of the proteins had a reproducible peak between biological replicates , 21, 32.thaliana ecotype Colombia was grown in tissue culture under continuous light  for 21 days after germination 2, 1 mm EDTA, 1 mm EGTA, 1 mm DTT, 2 mm phenyl methyl sulfonylfluoride and 1% (v/v) inhibitor mixture  for homogenization. Two 10 s bursts of a polytron  homogenized the tissue. Debris was removed by filtration of the homogenate through four layers of cheesecloth. Differential centrifugation enriched the soluble proteins by spinning at 1000 × g  for 10 min, 4 °C. The supernatant was enriched by pelleting membranes by ultracentrifugation at 200,000 × g for 20 min, 4 °C (Beckman Optima Ultracentrifuge). The remaining supernatant contained the crude cytosolic proteins. RuBisCO was depleted from the crude cytosolic fraction using Seppro RuBisCO spin columns according to the manufacturer's specifications .Arabidopsis mination . Two gram HEPES-KOH pH 7.8, 100 mm NaCl, 10 mm MgCl2, 5% glycerol and 1 mm DTT] and flow rates were 0.6 ml/minute for the 10/300 column and 1 ml/min for the 16/600 column. Protein loading was 0.5 ml  for the 10/300 and 2 ml  for the 16/600 column. The columns were calibrated using the gel filtration kit 1000  using standards ranging from 669 to 29 kDa and the void was determined using blue dextran as previously described  using either a Superdex increase 200 10/300 GL  or HiLoad 16/600 Superdex 200 pg column (GE Life sciences). The mobile phase was  then eluted with a 35 min linear gradient to increase the mobile phase to 50% buffer A and 50% Buffer B  and over the final 5 min the buffer composition was ramped to 25% Buffer A and 75% Buffer B. Sixty-five 500 μl fractions were collected.For separation by charge using ion exchange chromatography a buffer exchange was required for effective protein binding to the solid phase. Buffer exchange was performed using Amicon ultra-15 50 ml centrifugal filters  to exchange into 20 mProteins were separated by SDS-PAGE and visualized with Coomassie blue staining using standard procedures. Proteins were loaded by equal proportions in 1× Laemmli buffer  onto 10% gels and stained with Coomassie blue .For mass spectrometry analysis, proteins were digested to peptides as described in . Brieflyet al.  with the normalized collision energy of 27 eV. MS/MS scans were acquired at a resolution of 15,000 at 200 m/z. The dynamic exclusion was set at 20 s to avoid repeated scanning of identical peptides.For the Thermo Fisher Q Exactive high field mass spectrometer, samples were analyzed by reverse-phase HPLC-ESI-MS/MS using the Dionex UltiMate 3000 RSLC nano System coupled to the Q Exactive High Field (HF) Hybrid Quadrupole Orbitrap MS (Thermo Fisher Scientific) and a Nano- electrospray Flex ion source (Thermo Fisher Scientific). Peptides were loaded onto a trap column (300 mm x 5 mm) packed with 5 mm 100 Å PepMap C18 medium and washed using a flow rate of 5 μl/minute with 98% purified water/2% acetonitrile (ACN)/0.01% formic acid (FA) for 5 min. Peptides were separated using a reverse phase Acclaim PepMap RSLC C18 analytical column using a 120-min method at a flow rate of 300 nl/minute. The analytical column was packed with 100 Å PepMap C18 medium (Thermo Fisher Scientific). Mobile phase A consisted of 0.01% formic acid in water and a mobile phase B consisted of 0.01% FA in 80% ACN. The peptides were separated over a linear gradient started at 5% B and reached 30% B in 80 min, 45% B in 91 min, before the column was washed and regenerated. The sample was injected into the QE HF through the Nanospray Flex™ Ion Source fitted with an emission tip from Thermo Scientific. Column temperature was maintained at 35 °C. MS data was acquired with a Top 20 data-dependent MS/MS scan method. The full scan MS spectra were collected over 300–1,650 m/z range with a maximum injection time of 100 milliseconds, a resolution of 120,000 at 200 MaxQuant software (v. 1.5.3.28)  was usedTo increase the number of peptides that can be used for peptide extracted ion chromatogram (XIC)-based quantification and protein quantification and relative abundance profiling across SEC fractions, the “match between runs” function was enabled in a search containing all raw files with a maximum retention time window of 1 min . This “mProteins with reproducible profiles were determined by the optimized Gaussian fitting algorithm described in . To summapp. In rare cases in which multiple peaks were present in the SEC and IEX profiles for the same protein there would be uncertainty regarding the correct correspondence between the SEC and IEX peaks. In these instances, the peak with the largest Mapp was used because this corresponds to the protein peak that is most likely to participate as a complex subunit. IEX profiles that contained multiple peaks were deconvoluted and split into multiple entries with a sequential suffix based on the peak number. The dendrogram results were made available at a wide range of divisions with different cluster numbers to enable individuals to search for candidate proteins in nearby clusters with similar elution profiles. A specific protein complex composition was generated by analyzing the dendrogram at a cluster number that was designed to have a high cluster number that decreased false positives, but not too high to separate subunits of known complexes and increase false negatives. This was achieved by analyzing the intactness and purity of known complexes and by analyzing the inherent resolution of the combined datasets. To analyze the intrinsic resolution of the data, we calculate the distance within a cluster. A cluster center is first obtained as the average profile of all proteins in the cluster. The distance within the cluster is the average distance of proteins from the cluster center. The average within cluster distance is calculated as a function of increasing cluster number. Orthologs to known metazoan complexes were used as to identify the small subset of known complexes that were likely to be fully assembled  knock out and wild-type (Col-0). The methods were as described above but were analyzed using the Thermo Q Exactive with improved sensitivity and reproducibility compared with the AB Sciex 5600. The aimp1 knock out Gabi Kat 220E08 line was confirmed by PCR to contain a T-DNA insertion in exon 2 of AT2G40660 using PCR  was unavoidable because chloroplasts were broken during homogenization. To solve this problem an antibody column was used to deplete RuBisCO to the extent that it was no longer the most prominent protein . The RuBisCO-depleted crude cytosolic fraction was separated by SEC and IEX, and fractions were collected for LC-MS/MS profiling. Profiling was performed on two biological replicates of 38 SEC fractions and 65 IEX fractions to identify over 1500 and 2300 proteins in both biological replicates for SEC and IEX, respectively . There are tradeoffs between sample processing costs, mass spectrometer instrument time, and experimental replication. A previous study relied on a combination of replicates, single runs, and a high number of different separation strategies  for both the SEC and IEX separations . The overlap between the SEC and IEX datasets was good with ∼1390 proteins being detected in all four replicates, and this subset was used for further analysis.Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a highly abundant chloroplast protein that confounds quantitative proteomics studies because it suppresses the signals of coeluting peptides and caused artifactual peak splitting in profiling experiments , 21. In Fig. 2rategies . For thirategies . Supplemet al., 2014. In our dataset 417 chloroplast proteins were subjected to clustering analysis . 64 additional proteins were removed because they contained one or more transmembrane domains and appeared to be proteolytic fragments of integral membrane protein, leaving ∼700 cytosolic proteins for the clustering analysis.Proteins that reside in completely distinct cellular compartments cannot form a protein complex. Although enriched in cytosolic proteins, our sample contains hundreds of chloroplast proteins . ChloropBsupplemental Fig. S2). The R2 analyses of the fitting outcomes, which can be interpreted as the square of the correlation between the observed values and the fitted values are displayed in a boxplot, and again a strong correlation was observed with the mean of the boxplot being near 1.0 .In this study, only proteins with a reproducible peak in both replicates were used for protein complex predictions. The use of biological replicates is justified because SEC MS profiling was shown to be highly reproducible in previous studies , 21, andC). Using these reproducibility data filters, 727 cytosolic and 402 chloroplast protein profiles were reproducibly measured across all separations , and this subset was used for protein complex composition predictions.The Gaussian-fitting algorithm fitted 81%  of the proteins in the SEC profiling experiment and 97%  in the IEX profiling experiment. In this study, the peak locations in the SEC and IEX separations were reproducible between replicates because 84% of proteins profiled in the SEC had ≤ 2 column fraction shift and 94% in the IEX had ≤ 4 fraction shift C. Using Dsupplemental Fig. S2). Fourteen proteins had multiple peaks on the SEC column. Seventy-six proteins had multiple peaks only on the IEX. This higher number for the IEX likely reflects both the increased resolution of the column and/or the possibility that less stable complexes could partially disassemble during the high salt elution. However, peak locations among 70 cytosolic proteins with multiple IEX peaks were not correlated with high salt concentration, because only 24 of 140 peaks resided in the last third of the column fractions containing the highest salt concentration.Not all proteins had a single peak in the SEC and IEX separations, and we did not want to ignore the plausible and biologically relevant possibility that a protein could have combinations of physical associations with itself (homo-oligomerization) and/or unrelated proteins (hetero-oligomerization). The deconvolution of complex elution profiles into individual peaks enabled a protein to have multiple oligomerization states and multiple protein complex predictions . In thisFour proteins had multiple peaks in both the SEC and IEX, making it impossible to accurately pair the deconvoluted SEC and IEX peaks in a clustering-based composition prediction. For these 4 we selected only the one SEC peak with the largest apparent mass, duplicated it, and concatenated these profiles with the deconvoluted peaks from IEX column. In most cases the secondary SEC peak corresponded to the expected mass of the monomeric form making it less important for protein complex prediction anyway. Each of the proteins with multiple peaks in the IEX were given multiple entries (multiple data rows in the profile database) and labeled with a unique “_peak number” suffix. In this way proteins with multiple peaks could be clustered into multiple protein complexes.A is an example clustering result using only one biological replicate. The red color represents column fractions with maximal relative protein abundance which were normalized from 0 to 1 allowing proteins to cluster independent of their abundance. Well-resolved protein peaks were distributed across the SEC and IEX column fractions. One highly useful way to validate the clustering result is to test for the coelution of known protein complex subunits. For conserved known protein complexes, the major subunit pool does not exist in a fully assembled state in this profiling workflow . Subunits of the coatomer complex (B) and 20S proteasome core particle (B) coeluted on both columns. The value of the orthogonal IEX separation to resolve complexes that coeluted in the SEC was clear. The coatamer and 20S proteasome complexes coeluted on the SEC, but were clearly resolved on the IEX column . Similar increased resolution was evident for the EIF3 and CCT complexes . Coelution of multiple subunits from a known complex indicates that all steps of our workflow were reliable because errors in protein identification, quantification, or clustering analysis would generate scattered elution profiles for known complex subunits.Protein complex predictions assume that stable subunits of a protein complex will coelute under any chromatography condition. The heatmap in Fig. 3workflow . Nonetheworkflow  was mine complex B and 20Sparticle B coeluteB). The plots showed the average distance within clusters was high when the dendrogram was divided into 20 clusters and decreased to zero with increasing cluster number. The within cluster distance approached zero because many of the clusters contained a single protein, meaning there was no distance measured with that cluster. In the boxplot for SEC and IEX alone, the minimum value of the third quartile approached zero at ∼180 clusters (B). Concatenating the SEC+IEX profile data from both replicates increased the resolution and the lowest values in the third quartile did not reach zero until ∼300 clusters (B), suggesting that cutting the dendrogram of the concatenated IEX and SEC datasets at or near 300 clusters would capture most of the resolving power of the profile data.A variety of metrics were developed to analyze the resolution of our datasets and choose a cluster number to divide the dendrogram and generate a specific set of protein complex predictions. One way to analyze the intrinsic resolving power of a clustering result is to calculate the average within cluster distance (described in the previous section) as a function of increasing cluster number. When the average distance within a cluster is high, the elution profiles of the proteins are not as similar compared with when the distance is small. A boxplot showing the mean, first, and third quartiles of the within cluster distance for all clusters in the cytosolic protein dataset was constructed as a function of increasing cluster number B. The plclusters B. Concatclusters B, suggesC). All complexes are expected to be grouped within a cluster at low cluster number, but as cluster number increases too far, an increasing number of subunits would be expected to fall into nearby clusters, reducing the “intactness” of the prediction. As expected, the concatenated SEC and IEX profiles outperformed SEC and IEX alone because the 20S proteasome, coatomer, and EIF3 stayed assembled at a higher number of clusters . The CCT complex was the exception because it remained fully assembled beyond 600 clusters in SEC only and intactness drops at ∼480 clusters in SEC+IEX. This reflects the distinct behaviors of knowns.Additional metrics were developed to analyze the “intactness” and “purity” of known protein complexes as a function of increasing cluster number . IntactnFig. 3C). The GRF/14–3-3/GRF subunits had a sharp decrease intactness near a cluster number of 170. However, this did not necessarily reflect disassembly of GRF subunit containing complexes, because several of the GRFs had multiple peaks on the IEX column, and a small subset of GRFs  coeluted at a distinct peak location and clustered as a distinct population of putative heteromeric GRF complexes . These distinct predicted GRF complexes may have distinct charge distributions or stabilities on the IEX column that allow them to be cleanly separated. EIF3 and the coatomer also suffered hits to their intactness at increasing cluster number, indicating that in many cases the final predictions will have some false negatives because of a subset of subunits either having a reduced stability in the complex or a more variable elution profile compared with other subunits that remained together at higher cluster numbers.In the clustering result obtained with the concatenated datasets, the intactness and purity responses of the known complexes were variable. The CCT complex and 20S proteasome were extremely stable and had an intactness of one until the dendrogram was cut into >500 clusters C. The GRD), but for most of the known complexes perfect purity could not be attained, pointing to the unavoidable problem of chance coelution and false positives in our prediction. The intactness and purity indexes suggest the dendrogram be cut between 200 and 500 clusters (C and 3D). Considering all of the above, the dendrogram from cytosolic protein data was cut into 300 clusters (termed cytosol300) and supplemental Table S2 (cytosolic tab) provides the protein composition of each cluster. A cluster number of 300 is a somewhat arbitrary selection. To enable readers to scan the cytosol clustering more broadly for candidate interactors, supplemental Table S3 provides the protein cluster assignment for dendrograms at cluster numbers ranging from 20 and 600 at 10 cluster increments.A purity metric was developed to analyze the effect of increasing cluster number on the frequency of false positive prediction. Complexes are intact but false positive are high at low cluster numbers. The purity increased greatly from 20 to 200 clusters for all complexes D, but foclusters C and 3D.200) was chosen because in the distance boxplot the mean is flat and third quartile approaches zero at ∼170 clusters . The clustering result for the chloroplast proteins was useful because the large and small subunits of RuBisCO, which are known to physically interact . At a cluster number of 500, which reflects the resolution used for the individual cytosolic and chloroplast datasets, ∼80% of the clusters were pure containing only cytosolic or chloroplast proteins. This provides an estimate for the false discovery rate for chance coelution.To estimate the false discovery rate a clustering analysis was performed on the SEC+ IEX profiles using the both the cytosolic and chloroplast data sets. The assumption is that chloroplast and cytosolic proteins do not physically interact because of distinct compartmentalization, and any cluster that contains a mixture of cytosolic and chloroplast proteins would contain at least one false positive. The percent of clusters containing a mixture of cytosolic and chloroplast proteins was calculated at a range of cluster numbers . There were examples in which subunits of known complexes were highly enriched within a single cluster. For example, coatomer is an heterooligomic protein complex that associates with organelle membranes to promote cargo selection and vesicle trafficking . The EIF3 complex recruits the mRNA to the 40 S ribosome, and is required for translation initiation . The 20S proteasome is a large complex that degrades proteins . The GRF proteins are signaling proteins that can bind to phosphorylated effector proteins or form mixed hetero- and homo-dimers, depending on their subcellular localization .The analyses above generated a specific prediction for 300 cytosolic and 200 chloroplast-localized complexes. At 300 clusters the number of cytosolic proteins in a cluster ranged from 16 (1 instance) to 1 (90 instances) . The chloroplast-encoded large subunit of RuBisCO and multiple nuclear-encoded RuBisCo small subunits had multiple peaks and each was given 2 protein profile entries. The large and small subunits were assigned to two distinct clusters (clusters 109 and 172) containing only these proteins. In plants thioredoxins are known redox regulators . Along similar lines, among the 19 nonself-interacting pairs of Arabidopsis proteins that were in the Biogrid database . The low degree of overlap and Biogrid data is somewhat expected because it is derived using methods that often detect a different array of physical interactors compared with those found using affinity-based capture . This level of similarity is not due to chance. When cluster IDs were randomly drawn for the predicted interactors zero pairs were matched in over 70% of the simulations , and we never observed more than 4 matched pairs in any of the simulations. These analyses indicate that there is significant agreement between our clustering predictions and the Biogrid database.We wanted to determine if existing large-scale datasets on gene coregulation or protein-protein interactions were consistent with our chromatography data and had potential use to refine our protein complex predictions. First subunits of known protein complexes were tested for coexpression across a wide array of microarray and RNAseq experiments that were conducted using Arabidopsis  and humadatabase  and in o capture , 55. NonC and 3D). Parameters like chance coelution of unrelated proteins and complex disassembly during purification contribute to false positives and false negatives, respectively. If the method was perfect and if all subunit stoichiometries were 1:1 , then one would expect the summed monomeric masses of all proteins in a cluster  to equal the apparent mass (Mapp) of the protein measured using SEC. The plot of Mappversus Mcalc revealed an overall weak correlation between these two measurements (D), and only about ∼25% of the complexes fell near the diagonal with less than a 40% difference between the Mapp and Mcalc.The results sections below include a wide array of validation studies that demonstrate the utility of these predictions. However, we want to emphasize that this protein complex prediction method is imperfect and contains many false positives and false negatives. Selecting a cluster number of 300 for the cytosol is somewhat arbitrary and differentially affects the purity and intactness of protein complexes C and 3D.urements D, and on300 and chloroplast200 predictions. Clusters and individual proteins were divided into defined classes based on the number of proteins in the cluster, the summed mass of proteins in the cluster , and the measured apparent masses of the individual proteins (Mapp). See supplemental Table S2, column M, for the classification scheme and the category definitions. For example, single protein clusters were defined as “degraded” when the Rapp (Rapp = Mapp/Mmonomer) < 0.5 or “monomeric” when 0.5 ≤ Rapp ≤ 1.6. Homo-oligomerization is commonly evolutionary phenomenon  were classified as “possible homo- or hetero-oligomer/high subunit stoichiometry”. About 30% of the cytosol300 clusters and 35% of the chloroplast200 clusters fell into the homomer/heteromer/high subunit stoichiometry categories.Therefore, to facilitate judicious use of the prediction results we developed a simple classification scheme to categorize the reliability of the cytosolenomenon , and somcalc values that were similar to Mapp of the proteins in the cluster. If a cluster had 2 or more proteins and Mcalc was within 40% of the average Mapp (Mapp-avg) of the cluster, then proteins in the cluster were classified as “putative intact complex.” 15% of the cytosol300 and 12% of chloroplast200 clusters fell into this category. Proteins in this cluster type could also correspond to unstable subunits of large complexes that disassociate during purification on the IEX. There were examples in which a subset of proteins of known complexes had multiple peaks in the IEX and were clustered into a second small cluster compared with subunits of the intact complex. These proteins tended to reside in the upper left sector of the graph because all multiple peak proteins were referenced to a single maximal SEC peak (as explained above). Any protein for which a subset of the IEX peaks fell in the upper left quadrant ) was classified as “subcomplex or high subunit stoichiometry.” Multiple peak proteins in which all peaks had an Mapp > 1.4* Mcalc were classified as “partial complex/false negatives”.Another likely reliable prediction class had MD. A cluster containing two or more proteins was flagged for false positives if Mcalc > 1.4 * Mapp. The Rapp of individual protein in this cluster type was used to distinguish putative complex subunits from the false positive. If a protein within this cluster type had an Rapp ≥ 1.6, it was classified as “putative complex clustered with false positives”. Predicted complexes with the highest Mcalc to Mapp ratios are the least reliable among our cytosol300 and chloroplast200 predictions. A protein within this cluster type was flagged as “likely false positive: monomer” if it was expected to be monomeric (Rapp < 1.6) (e.g. see proteins in cluster 107 of the cytosol300 prediction). Fourteen percent of the cytosol300 and 19% of chloroplast200 clusters fell into this least reliable category in which the cluster was comprised entirely of predicted monomeric proteins. These metrics can serve as benchmarks for future complex prediction studies.False positives because of chance coelution are the most common source of errors in our predictions, and the least reliable clusters fell in the extreme lower right sector of Fig. 4A) (A and 5B). The coelution and homology to known subunits provides strong evidence that these two Ntn hydrolases were indeed unannotated proteasome subunits showing that 15 of the 16 proteins in cluster 34 belong to the proteasome.Our dataset appears to contain useful predictions for hundreds of unannotated proteins and novel complexes. For example, our analysis of the 20S proteasome showed that known subunits of the 20S proteasome formed a nearly pure cluster. The 20S proteasome falls into cluster 34 and contains 16 proteins with 13 being known subunits A 9, 56), 56A) (9A) A and 5B.app greatly exceeded Mcalc. These behaviors are expected for homooligomers and heterooligomers with high subunit stoichiometries. This sector of the graph is populated by several known homomers. Examples include PYRIDINE BIOSYNTHESIS 1.1 . This cluster class included phosphofructokinase (PFK) an important enzyme in central carbon metabolism that promotes carbon flux into the glycolytic pathway. The vertebrate PFKL isoform forms filaments of stacked tetramers that are easily resolved by EM and cluster into distinct puncta in living cells , and it was located in a high confidence cluster with 4 other proteins (two tRNA synthetases (LYSYL-tRNA SYNTHETASE 1 (LYSRS), ISOLEUCINE-tRNA LIGASE (ILERS)), a ribosomal subunit (40S RIBOSOMAL PROTEIN S8–1 (RPS8A)), tubulin (β-6 TUBULIN (TUBB6) (A). In a previous publication AT2G40660 was identified as a likely protein complex subunit with an Rapp of ∼12 that coeluted with several tRNA ligases including GLUTAMINYL-tRNA SYNTHETASE and ISOLEUCINE-tRNA SYNTHETASE  A. In a pNTHETASE .supplemental Fig. S6). The primary function of tRNA synthetases occurs in the nucleus and is to charge tRNAs with the appropriate amino acid  and yeast tRNA-aminoacylation cofactor ARC1 (ARC1p), two proteins that form a cytosolic complex with multiple tRNA ligases –77. The ino acid . A subseino acid –81. For ino acid  that is ino acid  and inflino acid . We willSALK-220E08 that contains a T-DNA in the second exon and is predicted to generate a strong loss of function allele. This study was conducted using a Thermo Q Exactive High Field mass spectrometer that had improved sensitivity compared with the AB Sciex 5600. As expected AIMP1L had a single peak and an apparent mass of ∼540 kDa in the wild-type control replicates but was not identified in the mutant . We first focused on predicted AIMP1L-interactors in cluster number 64. ISOLEUCINE-tRNA LIGASE (IIERS) had an Mapp of 541 kDa in the wild type. Unexpectedly, in aimp1l ILERS had an increased apparent mass of 683 kDa, a subtle ∼1 fraction shift compared with the corresponding wild-type control . A similar pattern was observed for RPS8A, which was shifted to a higher apparent mass in the mutant . Glutamyl-tRNA ligase (GluRS) was not reproducibly detected in the original clustering experiment, but it did coelute in a previous profiling publication . These subtle differences were not because of random fluctuations in tRNA ligase elution profiles because five other tRNA ligases that did not cluster with AIMP1L had elution profiles that were nearly identical in the wild-type and aimp1l . In the absence of AIMP1L, a subset of tRNA-ligases may assemble into distinct larger complexes that are independent of AIMP1L function. Alternatively, several AIMP1L complex subunits may dynamically rearrange among multiple complexes, and in the absence of AIMP1L, they preferentially interact with other large protein/protein complexes.SEC-MS profiling was performed on wild-type and homozygous knockout line lication . In thisapp of ∼270 kDa and TUBB6 had an Mapp of ∼110 kDa (B). However, LysRS and to a lesser degree TUBB6 displayed evidence for multiple peaks and a tendency toward a reduced oligomerization state in aimp1l compared with the wild type. Alanine tRNA ligase (AlaRS) was not predicted to be a member of the AIMP1L-complex based on our clustering analysis. However, in this both aimp1l replicates there was a slight shift of AlaRS to a smaller apparent mass and a clear secondary peak centered at ∼150 kDa (C). Perhaps LysRS and AlaRS complexes are indirectly affected by the removal of AIMP1L.The results with LysRS and TUBB6 were less clear. Both were predicted to interact with AIM1P but in our validation experiments neither proteins clearly coeluted with AIMP1. LysRS had an M∼110 kDa B. Howeve∼150 kDa C. Perhapet al., 2017 performed CoIP analysis to identify a novel chloroplast-localized complex that contained NITRILASE1 (NIT1), CHAPERONIN 60 SUBUNIT BETA 2 (CPN60B2) and CHAPERONIN 60 SUBUNIT BETA 1 (CPN60B1). When the chloroplast200 prediction was queried, NIT1 (cluster 121), was found very close to CPN60B1 and CPN60B2 in cluster 123 (A). The profiles indicate NIT1, CPN60B have nearly identical peaks in SEC but, there is a slight fraction shift in the IEX separation that is driving NIT1 and the CPN proteins into slightly different clusters (B). This shows the utility of searching nearby clusters for putative interactors.Another approach to validate protein complex predictions is CoIP-MS. Antibody-based purification should identify the same stable protein complexes that we detect here; however, this is unlikely to be true in all cases as different antibodies to the same protein can identify widely varying sets of interacting proteins . In a rester 123 A. The prclusters B. This sAsupplemental Fig. S7). CoIP of YFP:GAPC identified the target protein and the ACTIN 2 and ACTIN 7 isoforms. The actin and GAPC complex was also identified in the actin CoIP. There was considerable coelution on the SEC column of actin, GAPC, and many of the additional actin-interacting proteins identified by CoIP. For example, both ACTIN isoforms and six additional proteins coeluted at ∼950 kDa (C). GAPC and the actin isoforms also coeluted on the SEC column . However, there was very little coelution on the IEX column . We suspect that these actin-containing complexes are relatively unstable, and perhaps the combination of the TRIS-buffer exchange and high salt elution that was associated with the IEX separation caused artifactual disassembly of subsets of protein complexes. Clusters containing this type of false negative would have a relatively small number of proteins in the cluster, but the individual proteins would have a large apparent mass.Additional CoIP experiments revealed potential artifacts likely caused by protein complex disassembly during separation on the IEX column. CoIP experiments were performed in triplicate using antibodies specific to ACTIN and GFP )  and a no∼950 kDa C. GAPC aProtein complexes integrate metabolism, transport, and signal transduction to enable complex behaviors , 86–90. 8622A). Although there is coverage cost with concatenation of the SEC and IEX profile data, it decreased noise and generated dendrograms with an increased resolving power (B). Orthogonal separations enabled us to provide a highly useful dataset on predicted protein complex compositions in leaf cells.Our “guilt by association” method is based on the expected coelution of subunits of stable protein complexes. The parallel size- and charge-based separations generated highly reproducible elution profiles with peaks distributed widely across all column fractions A. Althoung power B. Orthogsupplemental Table S3, which provides protein groupings at a range of cluster numbers. There are also many instances of false positives because of chance coelution and false negatives because of inadequate protein coverage or noise in the profile data (D). Complex instability during high salt elution from the IEX likely disrupted actin-containing complexes and may have revealed the relative instability of GRF1, GRF6, and GRF9 subunits. Therefore, metrics for the reliability and type of each cluster were provided for data users in supplemental Table S2, column M).Validation experiments showed that many clusters were highly enriched for subunits of known protein complexes –5. Subunile data D. CompleAIMP1L mutant identifies true- and false-positive subunits of a novel aminoacyl tRNA synthetase containing complex. AIMP1L has weak homology with a vertebrate ARS complex subunit and was clustered with two class I tRNA ligases, the ribosomal protein RPS8A, and TUBB6. Removal of AIMP1L caused unexpected behaviors of predicted subunits: RPS8A, ILERS, and GLURS  had subtle increases in apparent mass in the mutant. The predicted interactors LYSRS and TUBB6 coeluted in the SEC column in the clustering dataset, but did not coelute in the mutant profiling experiment, suggesting they are false positives or more labile subunits. However, the oligomerization states of LYSRS and ALARS (another tRNA ligase that was reproducibly detected in the profiles aimp1 with the Thermo QE) were altered in aimp1l (B and 6C). These tRNA ligases may be indirectly influenced by loss of AIMP1L and physically interact with proteins that have altered abundance or protein binding activities in aimp1l. This altered elution pattern in aimp1l was not observed with 5 other tRNA ligases that did not coelute with AIMP1L (D). The data are pointing to a broad AIMP1L-dependent protein interaction network involving many proteins protein translation. Although the aimp1l plant has no obvious whole-plant phenotype, this profiling analysis is a new type of phenotyping tool that can be used to develop hypotheses about gene function.Our validation studies using a n aimp1l B and 6C.h AIMP1L D. The daapp. For example, the PHOSPHOFRUCTOKINASE7 (PFK7) had an Rapp of 18. The vertebrate PFK-L ortholog has a very high degree of polymerization, and forms filaments at the ∼100 nm spatial scale. The oligomerization of PFK7 likely has a structural importance in addition to its enzymatic function. The homomer classification also flagged NAP1 as a cytosolic homo-hexamer that may control the flux of newly synthesized histones into the nucleus. Glucosinolate production is an important form of plant chemical defense against herbivory. Based on the ability of PYK10-binding protein 1 (PYKBP1) to sediment the glucosinolate hydrolysis activity of PYK10 in vitro, PYK10BP1 was hypothesized to oligomerize . Perhaps the oligomerization state and/or binding partners of COR47 change in response to environmental stress. These selected examples were chosen to illustrate how this dataset can be used to better understand the evolution and importance of self-interaction in a wide array of physiological contexts.Homo-oligomerization is a common method of enzyme regulation , 91 and gomerize . In our omplexes , and CORomplexes . Our datB and 6C). This demonstrates the utility of this method to analyze the dynamics of systems of protein complexes in response to mutation, changing environmental conditions, or developmental programs. We hope that these protein complex predictions will be used by the research community to test hypotheses and provide a more complete assessment of the reliability of the dataset. Certainly, there is room for improvement. The IEX separation needs to be optimized to eliminate the buffer exchange, and better coverage will come from the continued use of the Thermo QE instrument. Additional orthogonal separations and separations done in series will help to reduce the primary technical challenge of chance coelution and occurrences of false positives. Efficient cell fractionation and the analysis of organelles will also decrease sample complexity and increase the coverage and accuracy of protein complex predictions. Our mass spec data, data filtering scripts (https://github.com/dlchenstat/ProteinComplexPredict), and final results  are all publicly available, with the hope that these data and this method gain wide use to analyze the systems-level behaviors of endogenous protein complexes.Here we predict the composition of hundreds of novel protein complexes from Arabidopsis leaves. The endogenous protein correlation profiling method requires no gene cloning or tagging and can be applied to any organism with an accurate proteome. The response of putative AIMP1L-containing protein complexes to subunit removal was analyzed B and 6C.https://github.com/dlchenstat/Gaussian-fitting). The code used to generate the clustering analysis was made publicly available at (https://github.com/dlchenstat/ProteinComplexPredict). The raw mass spectrometry data was made accessible at JPOST (https://jpostdb.org/) (Identifier: PXD012601). The mass spectra are available at Protein Prospector  (http://prospector.ucsf.edu/prospector/).The Gaussian fitting code described in McBride et. al., 2017 and deposited at (