Single-molecule microscopic experiments can measure the mechanical response of proteins to pulling forces applied externally along different directions . This response to external forces away from equilibrium should in principle, correlate with the flexibility or stiffness of proteins in their folded states. Here, a simple topology-based atomistic anisotropic network model (ANM) is shown which captures the protein flexibility as a fundamental property that determines the collective dynamics and hence, the protein conformations in native state.An all-atom ANM is used to define two measures of protein flexibility in the native state. One measure quantifies overall stiffness of the protein and the other one quantifies protein stiffness along a particular direction which is effectively the mechanical resistance of the protein towards external pulling force exerted along that direction. These measures are sensitive to the protein sequence and yields reliable values through computations of normal modes of the protein.ANM at an atomistic level (heavy atoms) explains the experimental (atomic force microscopy) observations viz., different mechanical stability of structurally similar but sequentially distinct proteins which, otherwise were implied to possess similar mechanical properties from analytical/theoretical coarse-grained (backbone only) models. The results are exclusively demonstrated for human fibronectin (FN) protein domains.The topology of interatomic contacts in the folded states of proteins essentially determines the native flexibility. The mechanical differences of topologically similar proteins are captured from a high-resolution (atomic level) ANM at a low computational cost. The relative trend in flexibility of such proteins is reflected in their stability differences that they exhibit while unfolding in atomic force microscopic (AFM) experiments. Proteins are often subjected to mechanical pressures in the cell, as a consequence of their interactions with other cellular biomolecules for example, within muscle fibres, in microtubules and molecular motors. These mechanical forces are functionally relevant and led to evolution of the mechanical behavior of proteins in order to fit their biological functions . RevelatSome theoretical studies have been done to gain molecular insights on the origins of anisotropic responses of proteins and the role of secondary structure composition in determining the unfolding pathways, in particular molecular dynamics (MD) simulations –10. SteeHere I present an approach that is computationally fast and analytically simple. An earlier result published by E Eyal and I Bahar effectively demonstrated the use of an analytical method (coarse-grained ANM) to construct a complete map of the mechanical response of a protein to all possible deformation directions in the protein . Howeverα atoms only . Resultgraining . The modgraining ) and theThe methods for calculation of the two spring-constant types from the normal modes of the protein are described in the next section, followed by the results obtained in the succeeding section. The reaction coordinate (N-C termini direction) defined by single-molecule AFM experiments  for the ANM provides a simple two-parameter harmonic interaction potential to generate normal modes of vibration for a protein structure . The proThe spring constants are given by,j and l run over all heavy atoms in the input protein structure, j and l. For a structure with N nodes interacting via the potential in eqn.1, only the force constant γ and the cutoff distance Rc need to be specified to construct the 3Nx3N dimensional Hessian. The Hessian is diagonalized to obtain 3 N-6 orthogonal eigenvectors  and their eigenvalues (spring constants) [1, φ2 …. φ3N-6} are sorted in ascending order of their corresponding spring constants {κ1 < κ2 < ……κ3N-6} or in descending order of their variances An atomic analysis would entail considering the indices nstants) . The set2) for all interacting nodes within the network. The force constant uniformly scales the absolute amplitudes of atomic fluctuations. Since the relative values of spring constants for two proteins are studied here, only the relative size of the amplitudes is of interest and hence, γ-value is not varied here. The other parameter The simplest implementation of uniform γ-value in the interatomic potential is used in this study and is set to unity  along a pulling direction. The deformation of the protein along the distance vector joining the Cα atoms of residues m and n is given by,This denotes the effective protein stiffness along a direction specified by the Cα) coordinates of m and n of the ith normal mode [and mentclasspt{minimaα atoms of m and n, as obtained from their position coordinates in the input X-ray crystal structure. The magnitude  of the dot product is considered in Equation 6 because this formalism only requires a set of eigenvectors  that are orthogonal to each other. The phase between the modes and hence, the eigenvector direction is arbitrary. The overall spring constant The equilibrium distance For atomistic ANM-based calculations, the cutoff distances  smaller . Since t program  has been program . The anaα atoms of these amino acid pairs in FN1 and FN2 form the respective pulling directions.Fibronectin mediates a number of cellular interactions with the extracellular matrix and has vital roles in cell adhesion, migration, growth and differentiation . The typassembly . Unfoldiassembly  are 125 Since I build upon a previous approach by Eyal and Bahar  by incluned Fig. . The FN1The For a more quantitative/numerical comparison of the theoretical results and the published experimental results, a table Table  is proviAn all-atom ANM incorporates the sequence information (amino acid composition) of the proteins and therefore, it is suitable for studying the mechanical flexibility of proteins that share the same tertiary fold. For the FN domains, the spring constant values (characterizing protein stiffness/flexibility) correlate well with the effective unfolding forces observed experimentally. The computational framework provided here can be explored in conjunction with single-molecule AFM experiments on proteins  to probe the role of native stiffness in controlling the diverse range of cellular processes mediated by such proteins.The atomic level ANM presented here reveals the utility of studying equilibrium protein dynamics which is intrinsically favored by their native folds from straightforward calculations. The estimation of protein stiffness from the two different spring-constant measures elaborated in this paper is computationally efficient and readily available for experimental tests. The effective stiffness yields an account of the anisotropic mechanical behavior of the protein which is dictated by the normal modes, helping to assess the kinetic accessibility along a certain direction in the structure. In this study, the focus completely lies on relative values of spring constants that characterize the stiffness of proteins. The theory explains the differences in flexibility of proteins (close to their native states) that share the same fold and is presumably a powerful tool in predicting the higher mechanical strength of one protein over the other. Since this theoretical framework is based on the evaluation of structural data obtained from the protein databank (X-ray or NMR structure), the framework can be extended to study the native-state characteristics of a database of proteins that differ widely in sequence yet have structural homology, and guide the experimental researchers in designing single-molecule AFM pulling experiments .The all-atom model utilizes protein native-state coordinates to predict the relative intrinsic flexibility of structurally homologous proteins. Events (non-native) far away from equilibrium are beyond the applicability of the model. The model appears to be able to predict correlations between the theoretical spring-constant measures and the spring constants computed from experimentally measured unfolding forces of proteins. In addition, it recognizes the importance of sequence in cases like the FN domains and emphasizes the crucial role of sequence (over topology) in the mechanical responses of proteins.


TAGAP gene have been associated with many diseases including rheumatoid arthritis, multiple sclerosis and other autoimmune disorders. Identifying functional SNPs in such disease associated genes is an uphill task hence before planning larger population study, it is better to scrutinize putative functional SNPs. In this study we used various computational approaches to identify nsSNPs which are deleterious to the structure and/or function of TAGAP protein that might be causing these diseases. Computational analysis was performed by five different in silico tools including SIFT, PROVEAN, PolyPhen-2, PhD-SNP and SNPs&GO. The study concludes that mutations of Glycine → Glutamic Acid at position 120, Glycine → Tryptophan at position 141 and Valine → Methionine at position 151 are major mutations in native TAGAP protein which might contribute to its malfunction and ultimately causing disease. The study also proposed 3D structures of native TAGAP protein and its three mutants. Future studies should consider these nsSNPs as main target mutations in various diseases involving TAGAP malfunction. This is the first comprehensive study, where TAGAP gene variants were analyzed using in silico tools hence will be of great help while considering large scale studies and also in developing precision medicines for cure of diseases related to these polymorphisms. Furthermore, animal models of various autoimmune diseases and having these mutations might be of help in exploring their precise roles.Genetic polymorphisms in  Genetic polymorphisms in human genome are mostly (90%) single nucleotide polymorphisms (SNPs) which are single base pair changes in alleles and are considered to be the most common kind of variations in DNA sequence. The SNPs in coding region of human genome are of much importance and around 500,000 SNPs fall in this region . Among tTAGAP) and their effects on its structure and function. This protein, located on chromosome 6q25, acts as a molecular switch and is considered important in modulating cytoskeletal changes  [Protein Variation Effect Analyzer [http://provean.jcvi.org/index.php] [Polymorphism Phenotyping v2 [http://genetics.bwh.harvard.edu/pph2/] [Predictor of human Deleterious Single Nucleotide Polymorphisms [http://snps.biofold.org/phd-snp/phd-snp.html] [http://snps.biofold.org/snps-and-go/snps-and-go.html] [in silico tools were considered high risk nsSNPs and investigated further.We utilized five different bioinformatics tools to predict functional effects of nsSNPs recruited from dbSNP database. These algorithmic programs included: SIFT-t2.html] ,25, PROVdex.php] , PolyPheu/pph2/] , PhD-SNPnp.html]  and SNPsgo.html] . The SNPTAGAP and its amino acid substitutions were submitted. Output provides p-value where; p < 0.05 and p < 0.01 were considered as confident and very confident hypotheses, respectively.For sorting disease associated or neutral amino acid substitutions in humans, MutPred v1.2 was consulted which is a web based application tool that effectively screens amino acid substitutions . It alsoTAGAP protein sequence to predict effects of the most damaging nsSNPs on the protein. Conditions for all submissions were set at temperature 25°C and pH 7.0.To check the stability of target protein, I-Mutant 2.0 was used which is a support vector machine based web server that helps in predicting any change in stability of protein after getting mutated. The tool uses data derived from ProTherm which is currently the most comprehensive database of experimental data on protein mutations. It predicts reliability index (RI) of the results ranging from 0–10, where 10 being the highest reliability ,32. We sConSurf is a bioinformatics tool that we used to estimate evolutionary conservation of amino acid positions using protein sequence . AnalysiThe 3D models for wild type TAGAP protein and TAGAP mutated with high risk nsSNPs were generated using two homology modeling tools; Phyre2 and I-TASSER ,38–40. TTAGAP protein sequence were predicted by PSSMe and BPB-PPMS. The former tool identifies methylation sites based on information gain feature optimization method. The higher support vector machines (SVMs) probability indicates higher probability of lysine (or arginine) to get methylated. False-positive predictions were controlled by focusing on sites with stringency setting higher than 50% [TAGAP protein at serine, threonine and tyrosine residues were predicted using NetPhos 3.1and GPS 3.0. NetPhos 3.1 uses ensembles of neural networks to complete this task and residues having scores >0.5 threshold are considered phosphorylated [The putative methylation sites in the than 50% . Methodothan 50% . Likely orylated . Likewisorylated . Putativorylated  and in Borylated .We used dbSNP database to retrieve SNPs of interest as it is the most extensive SNP database . There win silico nsSNP prediction algorithms to investigate whether these SNPs have any effect on structure or function of TAGAP protein. In silico tools used for this purpose were: SIFT, PROVEAN, PolyPhen-2, PhD-SNP and SNPs&GO. According to SIFT results, nsSNPs scoring tolerance index (TI) of ≤0.05 are considered intolerant. In PROVEAN, the variants are predicted as deleterious when final score is below threshold value of -2.5 and neutral when it is above this value. PolyPhen-2 results predicted probably damaging, possibly damaging and benign nsSNPs, with probably damaging as being the most confident prediction as compared to other two. These predictions are based on position specific independent count score difference, where score 1 is considered the most damaging. The PhD-SNP predicted 95 nsSNPs as diseased while SNPs&GO revealed the most unique results showing only 18 nsSNPs as diseased .To determine whether the 9 high risk nsSNPs alter the wild type structure of ing tool . Phyre2 ing tool . We exteing tool –44. The TAGAP and its mutants. We selected only that model having minimum C-score and also had higher ERRAT values when submitted to ERRAT program . Hence these three mutant models were finally superimposed over the wild type protein model  –41 so thin model . Similarcs tools ,55. SuchTAGAP. For this, we used various in silico tools to predict probable PTM sites in the TAGAP protein.PTMs are important in regulating structures and functions of proteins hence are involved in many biological events, for example, protein-protein interactions and cell signaling etc ,57. In tMethylation of lysine residues in certain histones effect their binding with the neighboring DNA and this alters the expression of genes on that DNA. The PSSMe tool predicted a total of 21 lysine residue sites that can get methylated, whereas BPB-PPMS predicted only 6 residues that undergo methylation. Only 3 lysine residues at positions 195, 256 and 517 were common findings of both PSSMe and BPB-PPMS tools .TAGAP were predicted by using NetPhos 3.1 and GPS 3.0 servers which made predictions for 17 and 3 kinases respectively , Glycine → Tryptophan at position 141 (rs777042268) and Valine → Methionine at position 151 (rs778438807). These mutations occur in RhoGAP domain of TAGAP protein hence are of particular concern as this is the only functional domain of the protein. Therefore, these nsSNPs can be strongly considered as key candidates in causing diseases related to TAGAP malfunction and hence will help in effective drug discovery and developing precision medicines. Thorough investigations and wet lab experimentation are needed to explore the effects of these polymorphisms on structure and function of the protein. Also, various diseased animal models comprising these major mutations in the TAGAP protein might be very supportive in exploring their job in the disease.This study suggests that structure and/or function of S1 Fig(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms.In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection. Proteins seldom act alone, they tend to interact with each other and form protein complexes to perform their functions , 2. The In recent years, high-throughput experimental techniques have been developed to identify protein-protein interactions (PPI). The accumulation of PPI data facilitates the development of computational approaches for protein complex identification , 16. A PProtein domains are structural  subunits that make up proteins . The intTo address the above challenges, in this study, we introduce a novel multi-network clustering (MNC) model to exploit the shared clustering structure in PPI and DDI networks to improve the accuracy of protein complex detection. The overall framework of our algorithm is shown in Fig. In this section, we describe our multi-network clustering (MNC) model as shown in Fig. G1 with N1 proteins and a DDI network G2 with N2 domains, two nonnegative score matrices G1 and G2 respectively. Note that G1 represents a PPI network and G2 represents a DDI network, the two adjacency matrices A(1) and A(2) may have different dimensions, i.e. N1≠N2, and the relationships between nodes in G1 and nodes in G2 may be many-to-many. The domain-protein associations can be described by a N2×N1 matrix F, where Fxi=1 if protein i in G1 contains domain x in G2, and Fxi=0 otherwise. Our goal is to jointly exploit the clustering structures in PPI network G1 and DDI network G2, and infer i in the predicted k-th cluster of m-th network from each network Am).Given a PPI network Km clusters in network Gm. According to the definition of Am)( and Hm)( based on the observed data Am) is defined as FH(1), where H(1) and H(2), we introduce a nonnegative matrix H(1)=H and H(2)=FH(1)=FH.In this study, we focus on exploiting the underlying common clustering patterns of different heterogeneous networks. As an interaction between two proteins typically involves physically interacting between specific protein domains, there may be some matching relationships between the clusters in PPI networks and the clusters DDI networks. Therefore, in this study, based on the domain-protein association matrix H are chosen to make sure that all elements of H are nonnegative. Specifically, independent Half-Normal priors with zero mean and variance λ=[λk] are assigned on each column of H: Similar to , nonnegau≥0, u<0. We can find from Eq. |H) and P(A(2)|F,H) are defined in Eq. (where P(A|H) and PA(1) and A(2), the values of the model parameters H and λ can be estimated by maximizing the joint probability (With the observed networks bability . By subsA and A(2bability  and 6)  A(1) anbability , and takH≥0 means each element Hik≥0.where Hik and λk: An alternating optimization scheme is adopted to solve the objective function in Eq. . Specifiand H is initialized, we update λ and H according to Eqs.  on PPI network G1 to generate the initial value of H. We first utilize the chosen algorithm to detect G1, which involve Hinitial, where: Once  to Eqs.  and 9)  H is iniHinitial and the resulting perturbed matrix is used to feed our optimization algorithm. In practice, we stop the iteration process when the relative change of the objective function  algorithm is summarized in Algorithm 1.After obtaining the final estimator milar to , 41, we  results , so we sIn this study, we employ two heterogeneous networks for yeast, i.e., a PPI network and a DDI network, to evaluate the performance of various protein complex detection algorithms. The PPI data is downloaded from the DIP database , which ibi and a predicted complex qj, let Ti,j denote the number of proteins shared by bi and qj. Sn, PPV and Acc are defined as follows: In this study, we use two independent evaluation metrics to assess the performance of various protein complex identification algorithms. The first evaluation metric is the geometric accuracy (Acc) as introduced by Xie et al. , which ibi and qj, their overlapping score (OS) is defined as follows: where |·| counts the elements within a given set. The second evaluation metric is the fraction of matched complexes (FRAC) , which cbi and qj to be matching if OS≥ω. Similar to other researches , , , ,  are listed in Table P-value less than 1E-15, 1E-10, 1E-5 or 1E-2. Table Since the reference complexes sets are incomplete, to further validate the effectiveness of our model, we investigate the biological significance of our predicted protein complexes. Each predicted complex is associated with a d in Eq. ) for GenIn order to illustrate the benefits of integrating multiple heterogeneous networks, we introduce an example of protein complex that can be more accurately identified by MNC. GINS complex in CYC2008 involves 4 proteins, namely, YDR489W, YDR013W, YJL072C and YOL146W. Figure The joint analysis of multiple heterogeneous network data has the potential to increase the accuracy of protein complex detection. In this study, a novel multi-network clustering (MNC) model is developed to integrate multiple heterogeneous networks for protein complex detection. Our MNC model could make use of the cross-field relationships between proteins and protein domains to guide the search of protein complexes. Experiment comparisons on two real-world data sets show that our MNC outperforms other state-of-the-art protein complex detection methods in terms of two evaluation metrics with respect to three benchmark complex sets. These results show the effect of domain-domain interactions on protein complex identification, which suggests that the domain information should be used if it is available. Our model is a flexible framework, which can also be used to solve some multi-view learning problems. Regarding the future works, we would like to design an algorithm which can incorporate more types of data, including homogeneous and heterogeneous network data for protein complex detection.


Protein complexes are the key molecular entities to perform many essential biological functions. In recent years, high-throughput experimental techniques have generated a large amount of protein interaction data. As a consequence, computational analysis of such data for protein complex detection has received increased attention in the literature. However, most existing works focus on predicting protein complexes from a single type of data, either physical interaction data or co-complex interaction data. These two types of data provide compatible and complementary information, so it is necessary to integrate them to discover the underlying structures and obtain better performance in complex detection.In this study, we propose a novel multi-view clustering algorithm, called the Partially Shared Multi-View Clustering model (PSMVC), to carry out such an integrated analysis. Unlike traditional multi-view learning algorithms that focus on mining either consistent or complementary information embedded in the multi-view data, PSMVC can jointly explore the shared and specific information inherent in different views. In our experiments, we compare the complexes detected by PSMVC from single data source with those detected from multiple data sources. We observe that jointly analyzing multi-view data benefits the detection of protein complexes. Furthermore, extensive experiment results demonstrate that PSMVC performs much better than 16 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques.https://github.com/Oyl-CityU/PSMVC.In this work, we demonstrate that when integrating multiple data sources, using partially shared multi-view clustering model can help to identify protein complexes which are not readily identifiable by conventional single-view-based methods and other integrative analysis methods. All the results and source codes are available on The online version of this article (doi:10.1186/s12859-016-1164-9) contains supplementary material, which is available to authorized users. Proteins play an important role in the functioning of the cell. Most proteins perform their functions by collaborating with other proteins. Protein complexes, which are groups of proteins that physically interact with each other, carry out almost all the functional processes in the cell . For insRecent advances in high-throughput screening (HTS) techniques  method , 6 and aAs PI data and TAP data are collected from different HTS techniques, they provide different views to describe the co-complex propensities among proteins, which can further be used to predict protein complexes . A numbeIn recent years, several approaches of learning from multiple data sources have been proposed , 33–36. With these motivations, in this study, we regard the PI and TAP data as different views of the underlying co-complex associations and propose a novel multi-view clustering algorithm, called Partially Shared Multi-View Clustering model (PSMVC), to carry out such a multi-view analysis. The overall framework of PSMVC is shown in Fig. In this section, we first introduce the data sources used in this study. Then we formulate our problem and describe the details of the proposed Partially Shared Multi-View Clustering model (PSMVC).Saccharomyces cerevisiae [In this study, two data sources of yeast are used in the experiment, namely, PI data and TAP data. The PI data is compiled from BioGRID database (version 3.4.125) with all physical interactions determined by yeast two-hybrid assays (Y2H) and protein-fragment complementation assay (PCA), and the entire high-quality binary interactions in the HINT database (version 8/21/2015). The PI data contains 19331 interactions among 5082 proteins. We use a combined set of purifications from two independent large-scale screens in revisiae , 38 as oi and protein j.Two scoring methods, namely, FSWeight  and PE shttp://interactome-cmp.ucsf.edu/ and the PE score matrix for TAP data is denoted by i and protein j.The Purification Enrichment (PE) scoring scheme proposed by Collins et al.  uses logN proteins are represented by 2-view representations, i and protein j, derived from PI data and TAP data, respectively. Our goal is to integrate these multi-view relationships into the task of multi-view clustering and infer m=1,2, which represents the weight of protein i in the predicted k-th complex for m-th view, from each score matrix Wm)( from the observed data Wm)( from Wm)( by minimizing Eq. (H(1)=H(2)), our algorithm jointly exploits the properties of consistency and complementarity. That is, we assume that only partial latent factors are shared by both two views and the other latent factors are embedded in particular views. Therefore, in this study, each Hm). The Hc reflects the consistent information which is common for both two views and H is composed of the common part Hc and the specific parts Kc is the common latent factor dimension and Ks is the specific latent factor dimension for each network. Thus, Km)( cannot be larger than the number of clusters Km)(. As we have no prior knowledge on Km)( is thus needed during estimating Am)(∥∗ as a relaxation of the low rank constraint [Am) with the following objective function: λ≥0 is the tradeoff parameter that controls the balance between the two factors.where Hc, Hc, In this section, we present the learning algorithm to solve the optimization problem in Eq. . As the Θm)(=θm)(θm)T,,Θ(m)=θ and O(N2Ks). If the number of iterations is Iter, the overall time cost of PSMVC is O(Iter(N2Kc+2N2Ks)).As we have discussed above, the overall protein-complex membership matrix is To measure whether the predicted complexes match with known experimentally determined protein complexes, we employ three benchmark complex sets as our gold standards. They are derived from CYC2008 , MIPS 44 and SGDbi and a predicted complex qj, let ni denote the number of proteins in bi and Ti,j denote the number of proteins shared by bi and qj. bi by its best-matching predicted complex, and Sni over all complexes. qj predicts that a protein belongs to its best-matching complex, and PPVj over all clusters  stands for the union of bi∩qj over i). Acc is defined as follows: In this paper, we use three independent quality metrics to assess the similarity between a set of predicted complexes and a set of reference complexes. The first metric we use is the geometric accuracy (Acc) as introduced by Xie et al. , which iUsing Acc is better than Sn and PPV individually, as it can provide a balanced view of the prediction performance.bi and qj, we consider them to be matching if ω as 0.25 in our experiments). Let TP (true positive) be the number of predicted complexes that are matched by the known complexes, and FN  be the number of known complexes that are not matched by the predicted complexes, and FP  be the number of predicted complexes minus TP. Precision, Recall and F-measure are then defined as follows: When evaluating the predicted complex set over a reference set, other commonly used evaluation metrics include Precision, Recall and F-measure. Given We note that the data set used in our study contains 5,944 proteins, while the three gold standard sets  cover 1329, 1178 and 1153 proteins. That is, the reference complex sets are far from complete. Therefore, predicted protein complexes that do not match with reference complexes are not necessarily undesired results and they would probably be novel protein complexes , 31. As The third metric we use is the fraction of matched complexes (FRAC) , which iB is the set of benchmark complexes and Q is the set of predicted complexes.where V| proteins. For a given predicted complex qj and a functional group F, let |qj| and |F| denote the number of proteins in the predicted complex and in the category, respectively. If the predicted complex qj contains x proteins in the functional group F, the probability of observing x or more proteins annotated by F by pure chance is then given by: The above three metrics are independent and can work together to evaluate the performance of a complexes detection approach. Due to the fact that the gold standard protein complexes are incomplete, we also test the functional homogeneity of predicted complexes, following the method of Nepusz et al. . The hypSmaller P-value indicates that the predicted complex is not accumulated at random and is more significant biologically than one with a larger P-value. The functional annotation is obtained from Gene Ontology, which provides three types of annotations: molecular function, biological process and cellular component .η, K and λ in our model, where K is the total number of latent factors, η is the common factor ratio and λ controls the effects of the low rank constraint. In this study, the value of η is set to 0.5 and we do not tune the value of η for a particular data source , we may be able to filter out the irrelevant dimensions of H. If so, we can fit our model with a large value of K as our model is able to determine the number of complexes adaptively. Furthermore, for different species, biologists have already collected several protein complexes. Although the number of known protein complexes are still far from complete, we can use some of the known complexes to test the effect of parameters. Therefore, in this study, we use MIPS reference set to test how these parameters affect the performance of our model. Note that most of the previous protein complex detection algorithms have several parameters that need to be tuned. We also use MIPS reference set to select the optimal parameters for these algorithms.There are three parameters In this section, we will present detailed experimental results.K and λ in our model that need to be tuned. In particular, we first keep η=0.5, and run PSMVC with different combination values of λ  and K , and assess how well the predicted complexes match with MIPS reference set. To understand how η affect the performance of PSMVC, we fix the values of K and λ which result in the best performance, and study the effect of η by setting η={0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1}, respectively.As mentioned above, there are two parameters K, as the value of λ increases, the Acc increases initially and decreases after reaching the maximum. We can also find that for a fixed value of K, as the value of K increases, the Acc increases initially and decreases after reaching the maximum. Based on the above analysis, K=2500 and λ=4 would be the optimal setting for parameters K and λ with respect to MIPS. On the other hand, as shown in Fig. η. Overall, PSMVC achieves the best performance when η=0.5. In the following experiments, we keep η=0.5, K=2500 and λ=4 as the default values of PSMVC. Nevertheless, it is worthy to mention that for a particular data set, better performance will obtained if the value of K is changed in proportion with the size of input data, and the value of λ is selected over best tuned.We can see from Fig. In order to demonstrate the benefit of integrating multiple views of data, we apply PSMVC on each individual data source and evaluate its performance with respect to three benchmark complex sets. For convenience, the results of applying PSMVC on PI data (FSWeight) and TAP data (PE score matrix) are denoted by PSMVC-FS and PSMVC-TAP respectively. For a fair comparison, optimal parameters are also set for PSMVC-FS and PSMVC-TAP to generate their best results.Figure We can find from Fig. In this section, we compare PSMVC with 9 existing state-of-the-art graph clustering algorithms that detect protein complexes from PI data, which include CMC , ClusterTable http://go.princeton.edu/cgi-bin/GOTermFinder). Additional file In Table http://www.ntu.edu.sg/home/zhengjie/data/InteHC/. Figure Ensemble clustering, which integrates the clustering results generated by various clustering algorithms, is able to improve the detection of protein complexes , 56, 57.As shown in Fig. Besides protein interactome , InteHC integrated gene expression profiles and functional annotations to predict protein complexes. Furthermore, they utilized a supervised model to learn the weights assigned to various data source. We can find from Fig. In this section, to illustrate the benefits of jointly exploring the shared and specific information inherent in different types of data, we introduce two examples of protein complexes that are more accurately identified by PSMVC.http://geneontology.org/), it is a putative integral membrane protein which is a member of DUP240 gene family and may be closely related to the functional process of mitochondrial inner membrane protein insertion complex.Mitochondrial inner membrane protein insertion complex in SGD catalog is a multi-subunit complex embedded in the mitochondrial inner membrane that mediates insertion of carrier proteins into the inner membrane. Figure NuA4 histone acetyltransferase complex is a complex having histone acetylase activity on chromatin, as well as ATPase, DNA helicase and structural DNA binding activities. In yeast, this complex has thirteen subunits. Figure The fast generation of high-throughput technologies makes it possible to study protein-protein interactions in a computation-intensive manner. During the past years, we have witnessed the rapid advances in developing the effective algorithms for protein complex detection. However, until now, methods of detecting protein complexes mostly mine the clusters from one type of data, such as physical interaction network or TAP data, and miss the information inherent in other type of data. Different types of data may reveal the relationships between proteins from different perspectives. For example, physical interactions represent the direct biophysical interactions between proteins and co-complex interactions indicate the co-complex relationships between proteins. Physical interactions may take place between proteins belong to different complexes, while proteins within same complexes may not have physical interactions. Integrating different types of data may help to improve the accuracy of protein complex detection. In this paper, we propose a novel multi-view clustering algorithm, called the Partially Shared Multi-View Clustering model (PSMVC), to carry out such a multi-view analysis. Unlike previous multi-view learning algorithms that focus on one type of dependent structure among multiple views, i.e., either consistency or complementarity, our method can jointly explore the both properties of consistency and complementarity for multi-view data. The analysis on real biological data shows that our proposed PSMVC significantly outperforms existing state-of-the-art protein complex detection algorithms.Saccharomyces cerevisiae since it is well studied and the comprehensive PI and TAP data and reference sets are available. Recently, several other related data sources are becoming available, including a collection of genomics, functional genomics, genetics studies and their corresponding result datasets. As such, in our future work, we will study how to incorporate other biological evidences for multi-view learning and protein complex detection.Applying our proposed PSMVC method on multiple heterogeneous networks could effectively improve the accuracy of complex prediction and provide a new biological knowledge and insight about the molecular systems. In this study, we use FSWeight and PE score to assess the likelihood of physical or co-complex interactions between proteins. Besides these two techniques, other methods are also capable of undertaking this task. We choose these two techniques just because they are popular methods to deal with this problem. Other methods can also be used to undertake this task, and evaluate the performance of various data pre-processing techniques is not the focus of this study. Furthermore, we test our model on


Overall we demonstrate a clear relationship between protein function and homomer symmetry that has important implications for understanding protein evolution, as well as for predicting protein function and quaternary structure.Approximately half of proteins with experimentally determined structures can interact with other copies of themselves and assemble into homomeric complexes, the overwhelming majority of which (>96%) are symmetric. Although homomerisation is often assumed to a functionally beneficial result of evolutionary selection, there has been little systematic analysis of the relationship between homomer structure and function. Here, utilizing the large numbers of structures and functional annotations now available, we have investigated how proteins that assemble into different types of homomers are associated with different biological functions. We observe that homomers from different symmetry groups are significantly enriched in distinct functions, and can often provide simple physical and geometrical explanations for these associations in regards to substrate recognition or physical environment. One of the strongest associations is the tendency for metabolic enzymes to form dihedral complexes, which we suggest is closely related to allosteric regulation. We provide a physical explanation for why allostery is related to dihedral complexes: it allows for efficient propagation of conformational changes across isologous ( One of the fundamental challenges in the biological sciences is in understanding the relationship between protein structure and function. This problem is highly relevant not only to the understanding of the evolution of a protein’s biological role, but for protein structure and function prediction, protein design, and the prediction of the phenotypic impact of mutations.3. In the dynamic and crowded environment that is a living cell5, proteins are in constant contact with each other and often carry out their functions as part of larger protein complexes9. The way that the subunits are organised to form the quaternary structure of a protein complex is a crucial piece of the protein structure-function relationship puzzle, alongside sequences, folds and domains.Within protein families there is enormous functional diversity, and sequences, folds and domains can all code for different functionalities depending on their surroundingsi.e. protein complexes that are formed by the assembly of multiple copies of a single type of polypeptide chain. Analysis of published X-ray crystal structures shows that roughly 45% of eukaryotic proteins and 60% of prokaryotic proteins can form homomeric complexes10. Whilst the high fraction of homomers does reflect biases in protein structure determination, and the fraction of heteromeric complexes (i.e. those formed from multiple distinct polypeptide chains) within cells is probably higher, homomerisation is clearly an extremely common biological phenomenon.Most of the structural information on protein complexes that we have available today is for homomers, 14. For example, the construction of a large complex from a number of smaller units, rather than one large peptide chain, provides a means for regulation, as the kinetics of homomer assembly is heavily dependent on monomer concentration. Homomerisation can also provide both coding efficiency and error control, as a smaller genetic space is used compared to a monomeric protein of a similar size. From a purely physical perspective, the formation of a homomeric complex can potentially lead to higher stability, as a larger proportion of protein surface area is buried, minimizing any energetically unfavourable solvent interactions.Why do proteins assemble into homomeric complexes? Multiple benefits have been proposed15 and it has been suggested that, as symmetry contributes to the form of the underlying energy landscape, a symmetric organisation of protein subunits can be associated with increased stability in protein complexes17. Recent large-scale simulations and directed evolution experiments have concluded that symmetric assemblies are in fact the most energetically stable and favourable, at least for homodimers, and therefore more likely to overcome the entropic cost of complex formation. Hence there is an overrepresentation of symmetric assemblies that are available for evolution20. The drive towards formation of symmetric structures is also evident when considering ordered protein complex assembly pathways24. Protein assembly seems to follow an analogous mechanism to protein folding, as the most common homomer assembly intermediates are in their most energetically favourable, often symmetric, conformations21.Another notable feature of homomeric structures is that the large majority are symmetric. This is striking, considering the irregularity of individual protein molecules and the lack of any underlying symmetry of the individual amino acids. Symmetry is routinely used as a tool to predict both structure and reactivity of many chemical systems25. As the formation of a protein complex leads to a modification of residue interactions, it also alters the conformational space available, leading to a change in the overall protein dynamics. Access to more conformations can lead to new cooperative mechanisms that facilitate allosteric changes at active sites located at different units in the assembly26. The textbook example is haemoglobin, where the oxygen affinity at a specific active site on a monomer in the tetramer is heavily dependent on the coordination of oxygen at the analogous active sites in the complex27.From the perspective of the specific functions carried out by protein complexes, there are several benefits that could drive the evolution of symmetric homomers. One of the more obvious functional aspects is when active sites are located directly at homomeric interfaces. There are a number of examples of shared active sites, such as that of the homotetrameric dihydropicolinate synthase29. A recent coarse-grain modelling study also shows an example of oligomerisation without any obvious functional benefits, emerging purely as a side-effect of the thermodynamic stability of the assemblies30. Clearly there is an important open question as to how much of protein homomerisation can be attributed to evolutionary adaptation.As is the nature of evolutionary questions, there are many difficulties in trying to determine what aspects contribute to homomeric quaternary structures, and functional advantages are often assumed without any direct evidence. Lynch has in fact suggested that the observed distribution of homomer quaternary structures could be explained by purely stochastic, non-adaptive processes29.Here we take advantage of the large amount of data that is now available on both protein structure and function, and carry out a systematic analysis on the functional determinants of protein assembly into symmetric homomers. We demonstrate strong relationships between the structural symmetry of complexes and the functions they perform. We also present results suggesting that complex symmetry is linked to specific allosteric functions. These results have important implications for understanding the evolution of protein complexes, as well as potential for being utilised in both the characterisation and prediction of cellular functions in homomeric complexesHomomeric protein complexes often assemble into energetically stable structures that are symmetric at the quaternary structure level. This means that group theory can provide a useful metric to characterise and separate most homomeric proteins with respect to a closed or helical symmetry group.34.The Gene Ontology (GO) project is a large bioinformatics-based initiative with the goal to provide terms for the description of function and characteristics of gene products. The functional annotations of genes in the GO database are based on experiments reported in many different studies, and as such, it is not likely to be uniformly represented across gene products and organisms. However, despite these limitations, investigations of over- and under-representation of GO terms have become one of the main uses of the GO project to date and enrichment analysis are used routinely on sets of genes from various sources with excellent resultsi.e. monomers and homomers). Below we first introduce the closed symmetry groups that are commonly seen in homomeric complexes and discuss the top most significantly enriched functions associated with each group. Importantly, we only consider protein structures with at least one GO-term assignment in the Uniprot Gene Ontology Annotation (Uniprot-GOA) Protein Data Bank (PDB) dataset31. We determine enrichment of GO terms within the set of annotated structures in order to avoid bias in functionalities that are more highly associated with the PDB than the overall ontology. The enrichments of all GO terms in each symmetry group are provided in Table Here we investigate the enrichment of GO terms in a large set of non-redundant protein structures containing only a single type of polypeptide chain , and cyclic assemblies with higher-order rotational symmetry ). The logic behind this distinction is that C2 dimers contain isologous (i.e. symmetric or head-to-head) interfaces, whereas cyclic homomers form ring-like structures via heterologous (i.e. asymmetric or head-to-tail) interfaces that allow them to adopt closed ring-like structures  with C2 symmetry.In our analysis, we separate the protein complexes with a single axis of rotational symmetry into those with twofold symmetry . This term is defined as the energy demanding conversion of small molecules to more complex substances. One example of the latter is the heterocycle biosynthetic process, which is also significantly associated with the C2 symmetric structures in our set .The CDNA-templated transcription” . Although the DNA macromolecule clearly does not qualify as a “small” substrate, many transcription factors, like nuclear receptors, bind to double-stranded DNA at palindromic sequences or inverted repeats37, which have local twofold symmetry. Thus, a twofold symmetric homomer provides an ideal mechanism to recognise such a site. This is consistent with, for example, the modular view of transcription factors, where the specific DNA-binding regions can only bind to DNA once a transcription factor dimer has been formed38. In Fig. 2 symmetrical DNA-binding domain of human HSF2 is used as an illustration39.Interestingly, the twofold symmetric structures are also significantly associated with “n(n > 2)) assemblies, comprising 8.9% of homomers. All of the most significantly enriched functionalities in cyclic complexes presented in Fig. e.g. “passive transmembrane transporter activity” . This is unsurprising, as the tendency for membrane proteins to adopt cyclic symmetries has previously been noted13. As the cell membrane is effectively two-dimensional, it makes structural sense to find that the cyclic homomers are enriched in this group, forming a defined channel or pore through the membrane with the symmetry axis in its centre. An axis of rotation perpendicular to the membrane plane would imply that the different subunits would be in the same relative position with respect to the membrane bilayer. This set-up has been suggested to be a simplification of the insertion process of the protein in the membrane13, although membrane complexes have also been observed to form co-translationally40. Whilst the much larger set of C2 complexes is found to associate with a large variety of functions, processes and structures, higher-order cyclic complexes are significantly specialised towards functions that require some type of directionality and/or the formation of a channel structure. The recently reported structure of the IP3R1 ion channel41 is used to illustrate an example in Fig. Our dataset contains 652 higher-order cyclic , the most significantly enriched GO terms are all related to metabolic processes. For example, more than 60% of the dihedral complexes are associated with the GO term for “single-organism metabolic process” .Apart from the functional term “42. The bringing together of many subunits also leads to a generally large enzyme complex being formed from relatively small units. A large protein complex is more likely to be able to provide the correct functional groups for catalysis, have the correct complementarity to its substrate, and also be bulky enough to provide a low dielectric environment for its catalytic process if needed43. While large complexes with cyclic or cubic symmetries are possible, it is likely that a dihedral homomers with a given number of subunits is easier to form from an evolutionary perspective23.Why might metabolic enzymes have such a strong tendency to form dihedral complexes? One possible reason is that there are advantages to co-localizing multiple different enzymes within a single complex, and dihedral symmetry is a relatively easy way to form homomers with four or more subunits. For example, a greater number of subunits within a complex can provide a higher concentration of active sites. This can be directly beneficial to the kinetics of its enzymatic function. If the substrate concentration is high, the rate of enzyme activity is a function of not only its rate-constant and enzyme concentration, but also the number of active sites present45. In order to address this important issue, we also investigated our dataset with respect to allostery, as discussed later in the manuscript.One crucial aspect of enzymatic activity and dihedral complexes that must be mentioned is that it is often beneficial for the enzyme to be able to undergo conformational rearrangements that not only facilitate the process itself, but also change the affinity of the substrates if needed. Bringing together a large number of subunits and forming active sites at interfaces could facilitate this type of allosteric control mechanismscellular iron ion homeostasis” is still the most significantly enriched functional term  symmetry, fourfold, for octahedral (O) symmetry, or fivefold, for icosahedral (I) symmetry. These large complexes are assembled from 12, 24 and 60 subunits respectively. These symmetry groups make up only 1.1% of the homomers in our dataset, including 56 tetrahedral, 22 octahedral and 4 icosahedral complexes . The top results from our enriched functions in the cubic set are all associated with homeostasis and metal ion binding in general. This is because, despite our filtering for sequence redundancy, the set contains several octahedral ferritins from evolutionarily diverse organisms. However, even when all but one of the ferritin structures are removed in our dataset filtered for redundancy at the domain level, “49. The rest (260 or 3.6%) are asymmetric (C1) and contain no rotational axes, nor helical symmetry elements.In our non-redundant dataset of homomers with functional associations, the vast majority (96.2%) belong to a closed symmetry group. Of the exceptions, a tiny fraction (15 or 0.2%) have open helical (H) symmetry, where the symmetry elements contain a rotational axis as well as a translational element along the direction of the rotational axis. These are often found in proteins involved in the formation of long fibres, such as microtubules and actin filamentsnon-bijective according to the nomenclature of the recently published “Periodic Table of Protein Complexes”, as sequence-identical subunits are required to exist in topologically non-equivalent positions23. Interestingly, however, the majority of these non-bijective homomers are the result of quaternary structure assignment errors50, often due to the PDB biological assembly erroneously being defined to be the same as the asymmetric unit. Thus, we must be cautious interpreting enrichments in this group, as most quaternary structures are likely to be erroneous. Despite this, it is interesting to note the most significantly enriched functional term for these complexes is “signal transducer activity” . This can be rationalised by the 2:1 stoichiometry of the interaction between a dimeric DNA-binding protein (e.g. transcription factor) and the double-stranded DNA, which will be inherently asymmetric unless there is twofold symmetry at the DNA level10.Both helical and asymmetric complexes are i.e. structures made up of single peptides that do not assemble into homomers  and “protein metabolic process” . We investigated this further by calculating the enrichment of monomeric structures within each hydrolase subclass, according to the Enzyme Commission classification scheme53  and positively enriched for glycosylase activity (P = 1.58 × 10−4). Overall, this suggests a preferential binding to large hydrolase substrates such as peptides and oligosaccharides.The most significantly enriched functions in monomers are “3 Figure . Monomermacromolecule modification” , which refers to modification of a biological macromolecule, such as a polypeptide or polysaccharide, which alters its properties. Thus, the terms most significantly associated with monomers are all clearly related to processes, components and functionalities that involve large macromolecular substrates. This makes logical sense from a structural point of view, especially comparing with complexes of higher-order symmetry, as a monomer would be more likely to be able to accommodate large substrates. The only functionality that involved binding to a large molecule that was enriched in the twofold symmetric homomers was “DNA binding”, which is related to the dimeric and often symmetric nature of DNA. In contrast, if we look through our full enrichment analysis for “RNA binding”, we do indeed find it ranked #13 of the positively enriched functions in the monomeric structures .Another strongly enriched term in monomers is “Interestingly, the most significantly negatively enriched functions in the monomer set were related to metabolic processes involving small molecules and organic substances Table . This reFinally, because monomers comprise such a large fraction of our dataset, we also repeated all enrichment analyses for the different homomers symmetry groups with a dataset that excluded monomer structures Table . In gene56. Therefore, we also performed an analysis where we split the homomers from each symmetry group into two equally sized sets: one containing complexes with larger interfaces and one with smaller interfaces. We then compared the relative enrichment of each functional term between the two sets  model58, here we are interested in proteins that have been actually noted as being allosteric, typically due to the biochemical observation of cooperative regulatory behaviour. To do this, we divided our set of structures into those that are putatively allosteric and those that have not been annotated as being allosteric. As there are no GO annotations for allostery, we used the protein complexes included in the AlloSteric Database (ASD)59 as well as a keyword search to identify further putative allosteric proteins (see Methods). The proteins in the ASD are defined as those that are known in the literature to have their function and/or structure at one site modulated in some fashion by the binding of a ligand at a distant site. While these text mining approaches are not perfect, it does allow us to define a set of proteins that should be highly enriched in allostery. We then assessed whether allostery is associated with different symmetry groups, as presented in Fig. 2 homomers. Importantly this overall enrichment profile of allostery is still evident when we control for metabolic enzymes, as illustrated in Fig. First, we need a way to identify putative allosteric proteins. Although it has been suggested that all dynamic proteins are allosteric to an extent2, complexes. Interestingly, these two symmetry groups are both associated with isologous protein interfaces, whereas cyclic complexes have only heterologous interfaces. Therefore, to further investigate whether there is a relationship between isologous interfaces and allostery, we separated the dihedral homomers into tetramers (D2), which have exclusively isologous interfaces, and those with six or more subunits (Dn(n > 2)), which can possess a mixture of both isologous and heterologous interfaces, as illustrated in Fig. 2 set ). Since they can have a mixture of isologous and heterologous interfaces, we wondered whether allosteric complexes would tend to contain a greater proportion of isologous interface than non-allosteric complexes. For every Dn(n > 2) homomer, we calculated the fraction of the total amount of intersubunit interface formed that is isologous and plotted the distributions for allosteric and non-allosteric complexes in Fig. P = 0.045, Wilcoxon rank-sum test), further supporting the idea that isologous interfaces are conducive to allostery.We can also look specifically at the dihedral complexes with six or more subunits  might be related to the homomeric symmetry groups studied here. Any function that is specifically associated to a symmetry group in this study therefore also has the potential to help characterise, for example, asymmetric heteromeric complexes with a symmetric catalytic site and complexes with pseudosymmetry.Finally, this work also shows that the formation of isologous interfaces between subunits of the protein complex can provide specific functional advantages. Essentially, these types of interfaces provide a structural mechanism for transmitting cooperative conformational changes across multiple subunits of the complex, which can contribute to allosteric mechanisms. These results provide an added degree of information that can have future practical advances when it comes to the prediction of both function and malfunction of protein complexes.i.e. all of the monomers and homomers. Viral structures, which were dominated by capsids with icosahedral symmetry, and structures with known quaternary structure misassignments50 were excluded. For each structure, we used the first biological assembly (i.e. pdb1 file). In order to compile a non-redundant set of structures the chains were clustered into groups according to their sequence similarities and selecting one representative of each cluster. Monomers and homers were then clustered at the level of 50% sequence identity, and only a single structure was used from each cluster. This resulted in a set of 13353 non-redundant structures of either monomers or homomers with functional assignments. Symmetry assignments were taken directly from the PDB. For the comparison of complexes with larger vs smaller interfaces, complexes were split into two equally sized sets for each symmetry group, bigger than and smaller than the media per-subunit interface size. Interface symmetry was calculated from the dihedral homomer structures as was done previously60. Briefly, interfaces were classified as isologous if the correlation between the residue-specific buried surface area for each subunit in an interacting pair was >0.7. All complexes are provided in Table Starting with the full set of protein crystal structures in the PDB on 2015-03-19, we selected all of those containing only a single type of polypeptide chain containing at least 30 residues, 61 instead of sequence identify. SUPERFAMILY predicted assignments were used rather than SCOP assignments because they are available for many more structures. Analysis of this dataset containing 5431 structures revealed enrichment of similar functionalities, and is presented in Table For an even more strictly filtered dataset, we clustered structures using SUPERFAMILY domain assignments31. In this dataset, a GO term is associated to a chain that maps with at least 90% identity to a UniProt knowledgebase entry. Calculating GO enrichment only within the structure annotated GO term set avoids bias against type of functional terms that are enriched in the PDB. This set was then extended by us to include all associated GO terms (terms that include the is_a and part_of association in the ontology) from the core ontology database, as curated by the Gene Ontology Consortium. Each GO term was then analysed with respect to the non-viral structures in our set, and the relative ratio of enrichment of protein symmetry groups within each functional term of our set  were evaluated and tested for significance using Fisher’s exact test, where the null hypothesis is that the odds ratio equals one. The standard package for Fisher’s test in R, fisher.test, was used throughout. Because there are many very similar GO terms, we filtered the GO terms for redundancy, removing terms that share an association with >50% of the same proteins from our structural set. All GO terms, including those we classified as redundant, are provided in Table P-values of a Fisher’s exact test62.Functional assignments were taken from the Uniprot Gene Ontology Annotation (Uniprot-GOA) PDB dataset (2014-09-27 release), which provides GO functional annotations to PDB chainshttp://www.ncbi.nlm.nih.gov/structure). We also accessed all the proteins associated with allostery in the AlloSteric Database59. Allosteric proteins are listed in Table To identify putatively allosteric proteins, we searched PubMed for the terms “allostery”, “allosteric” and “allosterism”, and then identified PDB structures associated with these abstracts through the NCBI Structure site (Supplementary InformationTable S1Table S2Table S3


Protein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution.Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively.In this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including In the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies.The online version of this article (doi:10.1186/s12859-017-1585-0) contains supplementary material, which is available to authorized users. Our understanding of a protein's role in a biological system strongly depends on its placement in a network of protein-protein interactions (PPIs), or interactome. Recently, interactome data sets involving proteins from various microbial species have been constructed using experimental , 2 and iE. coli is the only bacterial species with a comprehensive interactome that has been studied by both Y2H [E. coli PPIs roughly 1800 were found outside of known protein complexes [Nearly all published bacterial interactomes have been created using either the yeast two-hybrid (Y2H) system or affinity purification followed by mass spectrometry analysis (AP/MS). Although both Y2H  and AP/Mboth Y2H  methodolA way to overcome such problems is to combine not only different datasets from the same species but also data from different species. Although cross-species interactome approaches have been recently presented for human and yeast protein sets  no comprOne of the most promising applications of interactomics is in the analysis of protein function. In a “guilt by association” approach , 16, PPIHere, we combine experimentally-derived, previously published PPIs from 349 bacterial species and strains to form a consensus meta-interactome, using orthologous groups (OG) of proteins to combine all known interactions into a single network. Notably, we observe that such a network shares characteristics of single species interactomes. Furthermore, the augmentation of single species interaction networks with a bacterial meta-interactome boosts our ability to predict functions of the underlying proteins, given its dramatically increased information content. Finally, we utilize such a bacterial meta-interactome to predict interactome sizes of species for which incomplete interaction data is available.Mesorhizobium loti [E. coli and C. jejuni , suggesting that the augmentation of the underlying network with interactions from other bacteria significantly improved the quality of functional predictions . Based on our random samples, we calculated the fraction of correctly predicted functions of OGs as a function of the degree in the underlying OG networks of E. coli and C. jejuni , the counts provided by the first option are very similar to the third. Considering a set of reference proteomes  screens using ~90% of the ORFs, or 1477 in total [M. loti as reported by Shimoda et al. [M. loti interactome, the full genome was used as yeast two hybrid preys but only 1542 of 7281 genes were used as baits. This subset was curated as per the goals of the study and therefore represents a conscious technical difference between interactomes.Published interactomes vary in size and composition across different studies and species, rendering them difficult to compare. In the case of in total . By conta et al.  includesinfer interactions from co-purification through a spoke model approach  while two hybrid methods generally screen for binary interactions only. We have previously estimated that the spoke-model approach over-estimates the number of PPIs by about 3-fold [The comparison of interactomes also reveals unavoidable methodological discrepancies. More than half of the PPIs contributing to the meta-interactome were observed using two hybrid methods, offering some methodological consistency, yet these methods may vary in technical implementation details such as protein expression conditions, growth conditions, or even the exact yeast or bacterial strains used. As we have shown previously, even when exactly the same protein pairs are tested by Y2H assays, small differences in the experimental protocol can yield dramatically different results . Inclusit 3-fold .E. coli and M. pneumoniae, both of which have been subjects of full protein complex surveys). In the meantime, we believe a cross-species approach is helpful for identifying expected PPI in interactomes. As seen in Fig. In this study, we have attempted to reduce the impact of technical differences between interaction studies by focusing on the subset of interactions that we pooled from multiple species. This approach is especially effective for minimizing the influence of potentially erroneous spoke model interactions, as the bulk of these interactions in the meta-interactome are from just two species , fewer than 10 are seen in more than 2 different species Fig. . Highly This list omits broadly-conserved self-interactions, such as those among histidine kinases (ENOG4105BZU). An orthology-based approach is more informative when used with interactions among proteins in different groups  than with interactions among proteins of the same OG as individual protein identities are ignored in the consensus meta-interactome. We have made the assumption that cross-OG interactions are more likely to indicate cross-function interactions and are therefore of great relevance to functional context. This idea is illustrated by the MdB and NDH-1 complexes:H. pylori in its host [H. pylori, flagellin C (UniprotKB: P96747) from C. jejuni, and chemotaxis protein CheW (UniprotKB: P0A964) from E. coli K-12. We suggest that the colonization phenotype is related to its motility rather than oxidative stress. In fact, motility is critical for initial colonization of H. pylori in its host cells [C. jejuni [(ENOG4105NF4) proteins figure prominently in the meta-interactome. MdaB was first identified as modulator of drug activity  and is sits host . Its mutits host . Howeverst cells . FlaC in. jejuni .2 uptake [Synechocystis 6803, usually called L, L’, MS, and MS’. Each complex is composed of a basal complex  associated with variable subcomplexes of Ndh and Cup subunits  and ndhF  gene families. It is possible that with sudden changes in CO2 levels, cyanobacteria can flexibly use the NDH-1 M basal subcomplex and change contents of its variable subcomplex to form MS and L complexes [Interactions between components of a protein complex can be reconstructed from the meta-interactome interactions. The cyanobacterial NDH-1 membrane protein complexes provide a good example: these proteins belong to widely-conserved family of energy converting NAD(P)H: Quinone oxidoreductases which are unique to organisms capable of photosynthesis. Many distinct NDH-1 complexes may coexist in cyanobacteria to carry out different functions like respiration, cyclic electron transfer and CO2 uptake –42. At lits Fig. . Each coSynechocystis 6803 and T. elongatus BP-1 is shown in Fig. E. coli. CupA (ENOG4107YAI) has been found to interact with NdhF (ENOG4106TXZ), NdhD1-D4 (ENOG4105C8S), and an unknown protein (ENOG410906A) to form the NDH-IS (NdhD/F/CupA/CupS) sub-complex. Members of ENOG410906A, though coding for protein of unknown function, have sequence similarity to Fasciclin superfamily proteins associated with cell adhesion in plants and algae species. Korste et al. (2015) [Synechocystis 6803 and Q8DMA1 in T. elongatus BP-1 and designated it as CupS, a small subunit of the NDH-1MS complex [An example of the NDH-1MS  network in . (2015)  found a  complex . The NMRE. coli data point (at the top of the figure) nearly does not fit the trend and most of the PPI predictions we can make originate with E. coli data. Additionally, predicted interactome sizes are limited by the number of unannotated or highly unusual genes in a genome. The largest genomes in this set, from P. aeruginosa and M. loti, contain ~310 and 737 genes without orthology predictions, respectively. Further annotation of these genes or interactions among their products may allow for interaction predictions more like those for other species. Total counts of OG vs. OG interactions corresponding to each taxon in the meta-interactome are provided in Additional file If we assume that the average degree of a protein remains the same, independent of the proteome, then interactomes should grow linearly with proteome size and thus with genome size Fig. . HoweverProteome size is likely just one trait contributing to the overall complexity of a species  and we mThe meta-interactome approach is an intentional abstraction. It is intended to underscore the bacterial cross-species commonality and conservation of protein interactions among currently available interaction data. As a result, this approach is limited by at least three main factors: limitations of protein-protein interaction screens, limitations of publicly-available data, and constraints on orthology prediction. All experimental interactomes are inherently incomplete and may include numerous false positives and otherwise erroneous results. The authors of these studies employ different filtering approaches and likely interpret their results based on expectations . Most of the available interaction data for bacterial proteins has focused on just a handful of species. Additional screens of proteins from more diverse sources across the bacterial tree of life will reveal a universe of yet unknown functions, just as gene sequences did for genetic diversity.Bacillus subtilis and Salmonella enterica. We also employ the meta-interactome as a predictive tool to assign functions to uncharacterized proteins. These efforts and the methods presented here will allow researchers pursuing new interactome studies to easily predict the potential scope of their own results. As more bacterial interactomes reach completion, the interactions occupying prominent locations in a meta-interactome will likely reveal novel, broadly-conserved biological phenomena and appealing anti-microbial targets.In this work, we have assembled a set of more than 52,000 unique PPIs between bacterial proteins to perform cross-species interactome comparisons. The combined set, or meta-interactome, allows us to define a set of interactions observed across multiple species. Though this set is much smaller than expected, this result highlights the ongoing challenge of duplicating results of interactome screens. In an effort to address this challenge, we use the meta-interactome as a model for bacterial species without comprehensive interactome results, such as H. pylori [C. jejuni [Synechocystis [M. loti [T. pallidum [E. coli [M. pneumoniae [M. tuberculosis [S. aureus [The initial stages of this project required assessment of whether comparisons of bacterial interactomes were common in the interactome literature. A list of 11 publications, each describing a single bacterial protein-protein interactome, was assembled as a representative set of the bacterial protein-protein interactome literature, namely those of . pylori , 46, C. . jejuni , Synechohocystis , M. loti[M. loti , T. pallpallidum , E. coli[E. coli , 9, M. peumoniae , M. tuberculosis , and S. . aureus . The fulMesorhizobium loti [The full set of interactions was obtained from the IntAct database . To prodium loti . Where pium loti ; proteinNetwork_umbra (available at https://github.com/caufieldjh/network-umbra). This program parses interaction data files in PSI-MI TAB 2.7 format  and facilitates all further methods described in this study.PPI sets were obtained and filtered using a set of scripts developed for the purpose, The full set of PPIs sourced from IntAct constitutes the starting data set for meta-interactome construction . We defiThe full meta-interactome is provided in Additional file A further subset of the meta-interactome was prepared such that this set merged all interactions on the basis of shared interactors , Caulobacter crescentus CB15 (190650), Escherichia coli K-12 (83333), Helicobacter pylori 26695 (85962), Mesorhizobium loti MAFF303099 (266835), Mycoplasma genitalium G37 (243273), Pseudomonas aeruginosa PAO1 (208964), Salmonella enterica subsp. enterica serovar Typhi (90370), Staphylococcus aureus subsp. aureus NCTC 8325 (93061), Synechocystis sp. PCC 6803 substr. Kazusa (1111708), and Treponema pallidum subsp. pallidum str. Nichols (243276).We utilized the consensus meta-interactome of OG-OG interactions to generate predicted interactomes for a given bacterial species. Given a list of UniprotAC identifiers we assigned each to an OG and constructed a set of interactions among those OGs based on their presence in the consensus network. In most cases, predictions are general and unverified: if a pair of OGs is present in the consensus network they are predicted to interact in any context. Reference proteomes for the following species and strains were used, with NCBI taxonomy IDs in parentheses: σ of a protein i as a Potts model [S. pneumoniae using COG classes. All proteins without a functional annotation as well as proteins that were either classified as ‘unknown’ or had a ‘general function’ were randomly assigned a function out of the remaining 23 classes. In particular, we minimized the following global function, We modeled the prediction of a functional class ts model . In partJij is the adjacency matrix of the interaction network for the unclassified proteins. In particular, Jij = 1 if unclassified proteins i and j interact and vice versa.  is the discrete hi(σi) is the number of classified interaction partners of protein i with function σi.where E we applied a simulated annealing approach that features an effective temperature T. After initially assigning random functions to all unclassified proteins, we randomly selected a protein, changed its function to a different class and determined the energy of the new configuration. If the difference of energies ΔE ≤ 0, the new configuration was accepted. If ΔE > 0, the new configuration was accepted with probability p = eΔE/T− . To obtain stabilized functional configurations we repeated such a Monte-Carlo step 10,000 times. Subsequently, we increased the inverse of T by 0.01 in each step and repeated such Monte-Carlo steps. Since minimum energy solutions are not unique, we repeated such runs of simulated annealing 100 times, and considered the fraction of times an unclassified protein i was observed in a certain functional state σ as an estimate of the probability that protein i belongs to class σ.To minimize i, j we determined its probability,Focusing on a set of PPIs that connect proteins in orthologous groups (OG), we counted the occurrence of different class combinations. For each combination of classes N is the total number of interactions between classes. As a null-model, we determined an expected probability of interactions between classes i, jwhere vi is the number of viable proteins in class i (i.e. proteins of class i that are involved in at least one interaction in the underlying set), and Ji,j is the number of genes that are involved in both classes. Combining these probabilities, we determined a log-odds ratio,Specifically, σ2 = nij− 1 + (N − nij)− 1 + a− 1 + (b − a)− 1For large samples, we estimated the variance of the odds distribution as whereIn particular, we calculated a Z-score representing the significance of a link between two classes by \documentE. coli and C. jejuni and the complete network of orthologous groups (OG) we calculated the fraction of correctly predicted functions in bins of OGs with a given number of interaction partners in the underlying networks obtained with the mentioned bacterial species. Since each OG was assigned to a functional class with a certain probability, we labeled each group with the most probable function. We defined the enrichment of accuracy in a given bin of degree k asfk is the fraction of correctly predicted functions of OGs with degree k in the original networks. In turn, fk,m reflects the rate of correctly predicted functions using the consensus meta-interactome.To compare the prediction results we obtained with the original networks that were based on interactions in


Structural disorder is an essential ingredient for function in many proteins and protein complexes. Fuzzy complexes describe the many instances where disorder is maintained as a critical element of protein interactions. In this minireview we discuss how intramolecular fuzzy interactions function in signaling complexes. Focussing on the Src family of kinases, we argue that the intrinsically disordered domains that are unique for each of the family members and display a clear fingerprint of long range interactions in Src, might have critical roles as functional sensor or effectors and mediate allosteric communication via fuzzy interactions. A large majority of proteins are build from domains, classically defined as functional autonomous folding units. In a typical divide-and-conquer approach, the structure-function analysis proceeds through the characterization of the individual domains followed by the study of their mutual interactions. This approach makes a clear distinction between the “functional” domains and the linkers separating them.The same strategy is taken in the analysis of the also very abundant multiprotein complexes, in which the individual proteins are considered the building blocks . Key components of multiprotein complexes are scaffolding proteins, which would play the role of linkers in multidomain proteins.The current view of protein-protein interactions is quite dynamic and intrinsically disordered regions (IDR) are increasingly recognized as key players.In this minireview we shall summarize some important aspects of (intermolecular) protein binding by disordered proteins and extend them to the case of interdomain  binding using the c-Src family of kinases as an example.Intrinsically disordered proteins (IDP) or proteins with long IDR form a significant portion of the proteome of eukaryotes and are specially prevalent in signaling and regulation complexes .An entropic cost for binding an IDR is intuitively expected, however, the surprising result is its relatively small value suggesting compensatory mechanisms are an important component of IDR interactions. The importance of entropy compensation is highlighted by the similarity in the distribution of ΔH° values, a very interesting result in itself that emphasizes the underlying short-range similarities between protein-protein interactions involving folded and disordered proteins.Flock et al.  have revThe formation of a stable complex between two proteins  can be achieved by optimizing the enthalpy gain (ΔH° < 0), increasing the entropic gain (–T ΔS° < 0 = > ΔS° > 0) or minimizing the entropic loss (–T ΔS° ≈ 0). A fundamental aspect of the interplay between enthalpy and entropy components is their “locality.”Enthalpy effects usually reflect local short-range interactions and may be considered additive and proportional to the contact surface. Thus, large enthalpy components are usually associated to large contact surfaces, although these interfaces do not have to be necessarily continuous. Electrostatic contributions to the enthalpy, however, are long range. They often drive the partners together  and, in the formed complex, enable dynamic interactions that minimize the entropy loss upon complex formation. A recent example of a picomolar interaction between two IDPs without a significant loss of flexibility is driven by electrostatics  the effect of regions that can remain highly flexible in the complex , (ii) the possible preexistence of long range intramolecular contacts restricting the conformational freedom in the free IDR (therefore minimizing the loss of entropy upon complex formation), and (iii) the configurational entropy arising from multiple alternative binding poses (“microstates”) contributing to the bound state.The first two situations reduce the entropic cost of binding through IDRs and correspond to the strategies of not to pay (i) or pre-pay (ii). The third situation actually contributes an entropic gain.In the interacting regions, pre-pay strategies may take the form of preformed structural elements retained in the complex , measured by NMR in the entirely protein, explain the dramatic changes in affinity observed in CAP variants that form complexes with identical interfaces  play an important role in IDR interfaces. Although the definition of LM is based on bioinformatic studies, they can be interpreted using structural and dynamic concepts. LM are often formed by hydrophobic residues grafted onto a maleable template  along the sequence induced by one or several paramagnetic tags  the disordered region remains highly dynamic, as seen by NMR, (ii) its overall dimension is affected by the presence of the SH3 domain, as seen by Small Angle X-ray Scattering, (iii) the local perturbations sensed by chemical shifts are affected by modifications in distant, well defined parts of the protein, and (iv) mutations in the disordered region cause strong functional effects in the entire protein.Classical descriptions of multidomain signaling proteins distinguish between regulatory/sensor and catalytical/effector domains. IDR can act as linkers, effectors or sensors Figure . IDR-medIDRs can act as linkers through which information is propagated to distant regions. This can occur without concomitant structuration through remodeling of the protein free energy landscape affecting the conformer populations and causing specific functional outputs  studied by our group suggests an additional class of IDR allostery, in which the disordered region acts a sensor but the connecting element is a folded SH3 domain.Mutations in the Unique domain of c-Src induce strong phenotypes in Src-dependent colorectal cancer cells (Arbesú et al., Recent NMR data (Tong et al., In spite of their large sequence divergence, the IDR regions of the various SFK show coevolution with their respective SH3 domains, suggesting that a fuzzy interaction such as the one found in c-Src may be a functional element in all SFKs (Arbesú et al., The ΔPRE method is a simple and robust analytical approach to generate a fingerprint of the long-range interactions within intrinsically disordered domains. The complete processing tools are part of the Farseer software (Teixeira et al., Simulations based on fuzzy logic recapitulate many features of a kinase network (Aldridge et al., This is a mini-review article based on the Ph.D. thesis of MA, supervised by MP and with contributions from GI, HF, and JT working in fuzzy complexes of Src Family Kinases.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer AJDQ and handling Editor declared their shared affiliation.