Functional Bioinformatics Analyses of the Matrisome and Integrin Adhesome.

The extracellular matrix (ECM) is the noncellular compartment of living organisms and is formed of a complex network of cross-linked proteins, which is collectively known as the matrisome. Apart from providing the structure for an organism, cells interact and thereby communicate with the ECM. Cells interact with their surrounding ECM using cell-surface receptors, such as integrins. Upon integrin engagement with the ECM, cytoskeletal proteins are recruited to integrins and form a molecular protein complex known as the integrin adhesome. Global descriptions of the matrisome and integrin adhesome have been proposed using in silico bioinformatics approaches, as well as through biochemical enrichment of matrisome and adhesome fractions coupled with mass spectrometry-based proteomic analyses, providing inventories of their compositions in different contexts. Here, methods are described for the computational downstream analyses of matrisome and adhesome mass spectrometry datasets that are accessible to wet lab biologists, which include comparing datasets to in silico descriptions, generating interaction networks and performing functional ontological analyses.

Using Language Representation Learning Approach to Efficiently Identify Protein Complex Categories in Electron Transport Chain.

We herein proposed a novel approach based on the language representation learning method to categorize electron complex proteins into 5 types. The idea is stemmed from the the shared characteristics of human language and protein sequence language, thus advanced natural language processing techniques were used for extracting useful features. Specifically, we employed transfer learning and word embedding techniques to analyze electron complex sequences and create efficient feature sets before using a support vector machine algorithm to classify them. During the 5-fold cross-validation processes, seven types of sequence-based features were analyzed to find the optimal features. On an average, our final classification models achieved the accuracy, specificity, sensitivity, and MCC of 96 %, 96.1 %, 95.3 %, and 0.86, respectively on cross-validation data. For the independent test data, those corresponding performance scores are 95.3 %, 92.6 %, 94 %, and 0.87. We concluded that using feature extracted using these representation learning methods, the prediction performance of simple machine learning algorithm is on par with existing deep neural network method on the task of categorizing electron complexes while enjoying a much faster way for feature generation. Furthermore, the results also showed that the combination of features learned from the representation learning methods and sequence motif counts helps yield better performance.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

BACKGROUND: Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. RESULTS: In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. CONCLUSIONS: We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Identification of Two Distinct Classes of the Human INO80 Complex Genome-Wide.

Chromatin remodeling and histone modifying enzymes play a critical role in shaping the regulatory output of a cell. Although much is known about these classes of proteins, identifying the mechanisms by which they coordinate gene expression programs remains an exciting topic of investigation. One factor that may contribute to the targeting and activity of chromatin regulators is local chromatin landscape. We leveraged genomic approaches and publically-available datasets to characterize the chromatin landscape at targets of the human INO80 chromatin remodeling complex (INO80-C). Our data revealed two classes of INO80-C targets with distinct chromatin signatures. The predominant INO80-C class was enriched for open chromatin, H3K27ac, and representative subunits from each of the three INO80-C modules (RUVBL1, RUVBL2, MCRS1, YY1). We named this class Canonical INO80. Notably, we identified an unexpected class of INO80-C targets that contained only the INO80 ATPase and harbored a repressive chromatin signature characterized by inaccessible chromatin, H3K27me3, and the methyltransferase EZH2. We named this class Non-Canonical INO80 (NC-INO80). Biochemical approaches indicated that INO80-C and the H3K27 acetyltransferase P300 physically interact, suggesting INO80-C and P300 may jointly coordinate chromatin accessibility at Canonical INO80 sites. No interaction was detected between INO80-C and EZH2, indicating INO80-C and EZH2 may engage in a separate form of regulatory crosstalk at NC-INO80 targets. Our data indicate that INO80-C is more compositionally heterogenous at its genomic targets than anticipated. Moreover, our data suggest there is an important link between INO80-C and histone modifying enzymes that may have consequences in developmental and pathological contexts.

Global landscape of cell envelope protein complexes in Escherichia coli.

Bacterial cell envelope protein (CEP) complexes mediate a range of processes, including membrane assembly, antibiotic resistance and metabolic coordination. However, only limited characterization of relevant macromolecules has been reported to date. Here we present a proteomic survey of 1,347 CEPs encompassing 90% inner- and outer-membrane and periplasmic proteins of Escherichia coli. After extraction with non-denaturing detergents, we affinity-purified 785 endogenously tagged CEPs and identified stably associated polypeptides by precision mass spectrometry. The resulting high-quality physical interaction network, comprising 77% of targeted CEPs, revealed many previously uncharacterized heteromeric complexes. We found that the secretion of autotransporters requires translocation and the assembly module TamB to nucleate proper folding from periplasm to cell surface through a cooperative mechanism involving the ß-barrel assembly machinery. We also establish that an ABC transporter of unknown function, YadH, together with the Mla system preserves outer membrane lipid asymmetry. This E. coli CEP 'interactome' provides insights into the functional landscape governing CE systems essential to bacterial growth, metabolism and drug resistance.

Growth condition dependency is the major cause of non-responsiveness upon genetic perturbation.

Investigating the role and interplay between individual proteins in biological processes is often performed by assessing the functional consequences of gene inactivation or removal. Depending on the sensitivity of the assay used for determining phenotype, between 66% (growth) and 53% (gene expression) of Saccharomyces cerevisiae gene deletion strains show no defect when analyzed under a single condition. Although it is well known that this non-responsive behavior is caused by different types of redundancy mechanisms or by growth condition/cell type dependency, it is not known what the relative contribution of these different causes is. Understanding the underlying causes of and their relative contribution to non-responsive behavior upon genetic perturbation is extremely important for designing efficient strategies aimed at elucidating gene function and unraveling complex cellular systems. Here, we provide a systematic classification of the underlying causes of and their relative contribution to non-responsive behavior upon gene deletion. The overall contribution of redundancy to non-responsive behavior is estimated at 29%, of which approximately 17% is due to homology-based redundancy and 12% is due to pathway-based redundancy. The major determinant of non-responsiveness is condition dependency (71%). For approximately 14% of protein complexes, just-in-time assembly can be put forward as a potential mechanistic explanation for how proteins can be regulated in a condition dependent manner. Taken together, the results underscore the large contribution of growth condition requirement to non-responsive behavior, which needs to be taken into account for strategies aimed at determining gene function. The classification provided here, can also be further harnessed in systematic analyses of complex cellular systems.

Protein-protein binding affinity prediction from amino acid sequence.

MOTIVATION: Protein-protein interactions play crucial roles in many biological processes and are responsible for smooth functioning of the machinery in living organisms. Predicting the binding affinity of protein-protein complexes provides deep insights to understand the recognition mechanism and identify the strong binding partners in protein-protein interaction networks. RESULTS: In this work, we have collected the experimental binding affinity data for a set of 135 protein-protein complexes and analyzed the relationship between binding affinity and 642 properties obtained from amino acid sequence. We noticed that the overall correlation is poor, and the factors influencing affinity depends on the type of the complex based on their function, molecular weight and binding site residues. Based on the results, we have developed a novel methodology for predicting the binding affinity of protein-protein complexes using sequence-based features by classifying the complexes with respect to their function and predicted percentage of binding site residues. We have developed regression models for the complexes belonging to different classes with three to five properties, which showed a correlation in the range of 0.739-0.992 using jack-knife test. We suggest that our approach adds a new aspect of biological significance in terms of classifying the protein-protein complexes for affinity prediction.

Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches.

Protein-protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein-protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein-protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein-protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein-protein complexes into high and low affinity groups based on their Kd values. Our results showed a 10-fold cross-validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions.

Dihedral-based segment identification and classification of biopolymers II: polynucleotides.

In an accompanying paper (Nagy, G.; Oostenbrink, C. Dihedral-based segment identification and classification of biopolymers I: Proteins. J. Chem. Inf. Model. 2013, DOI: 10.1021/ci400541d), we introduce a new algorithm for structure classification of biopolymeric structures based on main-chain dihedral angles. The DISICL algorithm (short for DIhedral-based Segment Identification and CLassification) classifies segments of structures containing two central residues. Here, we introduce the DISICL library for polynucleotides, which is based on the dihedral angles ε, ζ, and χ for the two central residues of a three-nucleotide segment of a single strand. Seventeen distinct structural classes are defined for nucleotide structures, some of which--to our knowledge--were not described previously in other structure classification algorithms. In particular, DISICL also classifies noncanonical single-stranded structural elements. DISICL is applied to databases of DNA and RNA structures containing 80,000 and 180,000 segments, respectively. The classifications according to DISICL are compared to those of another popular classification scheme in terms of the amount of classified nucleotides, average occurrence and length of structural elements, and pairwise matches of the classifications. While the detailed classification of DISICL adds sensitivity to a structure analysis, it can be readily reduced to eight simplified classes providing a more general overview of the secondary structure in polynucleotides.

Native protein complexes in the cytoplasm of red blood cells.

Despite decades of advancements, the investigation of the red blood cell (RBC) cytosolic proteome still represents a challenging task because of the overwhelming abundance of hemoglobin. Besides, the separation method is one of the main limiting factors when investigating protein complexes. In this study, we performed for the first time a 2D-clear native (CN)-SDS-PAGE followed by mass spectrometry-based identification to screen multiprotein complexes (MCPs) in the cytosol of human RBCs. Upstream to 2D-CN-SDS-PAGE, we applied a recently developed native pre-enrichment strategy that allows discriminating and separately collecting three distinct fractions, one of which is highly enriched for hemoglobin. Such prefractionation strategy is conservative, in that it makes soluble native-complex analyses amenable without loss of biological information. Because of the resolution of native gel electrophoresis techniques, we could observe and describe 55 potential hetero-oligomeric MPCs from the RBC native cytosolic proteome, among which ultratetrameric hemoglobin. The detected protein complexes were characterized by proteins mainly involved in oxygen transport, antioxidant responses, metabolism, and protein degradation cascades, in agreement with recent in silico models. Metabolic enzyme oligomers also interacted with complexes of proteins involved in oxidative stress responses, thus suggesting a functional relationship between metabolic modulation and antioxidant defenses.

A survey of computational methods for protein complex prediction from protein interaction networks.

Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. Their identification is therefore necessary to understand not only complex formation but also the higher level organization of the cell. With the advent of "high-throughput" techniques in molecular biology, significant amount of physical interaction data has been cataloged from organisms such as yeast, which has in turn fueled computational approaches to systematically mine complexes from the network of physical interactions among proteins (PPI network). In this survey, we review, classify and evaluate some of the key computational methods developed till date for the identification of protein complexes from PPI networks. We present two insightful taxonomies that reflect how these methods have evolved over the years toward improving automated complex prediction. We also discuss some open challenges facing accurate reconstruction of complexes, the crucial ones being the presence of high proportion of errors and noise in current high-throughput datasets and some key aspects overlooked by current complex detection methods. We hope this review will not only help to condense the history of computational complex detection for easy reference but also provide valuable insights to drive further research in this area.

Mechanical stress affects glucagon fibrillation kinetics and fibril structure.

Mechanical stress can strongly influence the capability of a protein to aggregate and the kinetics of aggregation, but there is little insight into the underlying mechanism. Here we study the effect of different mechanical stress conditions on the fibrillation of the peptide hormone glucagon, which forms different fibrils depending on temperature, pH, ionic strength, and concentration. A combination of spectroscopic and microscopic data shows that fibrillar polymorphism can also be induced by mechanical stress. We observed two classes of fibrils: a low-stress and a high-stress class, which differ in their kinetic profiles, secondary structure as well as morphology and that are able to self-propagate in a template-dependent fashion. The bending rigidity of the low-stress fibrils is sensitive to the degree of mechanical perturbation. We propose a fibrillation model, where interfaces play a fundamental role in the switch between the two fibrillar classes. Our work also raises the cautionary note that mechanical perturbation is a potential source of variability in the study of fibrillation mechanisms and fibril structures.

The mitochondrial proteome of the model legume Medicago truncatula.

Legumes carry out special biochemical functions, e.g. the fixation of molecular nitrogen based on a symbiosis with proteobacteria. At the cellular level, this symbiosis has to be implemented into the energy metabolism of the host cell. To provide a basis for future analyses, we have characterized the protein complement of mitochondria of the model legume Medicago truncatula using two-dimensional isoelectric focussing (IEF) and blue-native (BN)-SDS-PAGE. While the IEF reference map resulted mainly in resolution of those proteins associated with the mitochondrial matrix, the BN proteomic map allowed separation of protein subunits from the respiratory chain protein complexes, which are located in the organelle's inner membrane. The M. truncatula mitochondrial BN reference map revealed some striking similarities to the one from Arabidopsis thaliana but at the same time exhibited also some special features: complex II is of increased abundance and additionally represented by a low molecular mass form not reported for Arabidopsis. Furthermore three highly abundant forms of prohibitin complexes are present in the mitochondrial proteome of M. truncatula. Special features with respect to mitochondrial protein complexes might reflect adaptations of legumes to elevated cellular energy requirements enabling them to develop symbiotic interactions with rhizobial bacteria.

The Protein Ontology: a structured representation of protein forms and complexes.

The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry.

An active dimanganese(III)-tyrosyl radical cofactor in Escherichia coli class Ib ribonucleotide reductase.

Escherichia coli class Ib ribonucleotide reductase (RNR) converts nucleoside 5'-diphosphates to deoxynucleoside 5'-diphosphates and is expressed under iron-limited and oxidative stress conditions. This RNR is composed of two homodimeric subunits: alpha2 (NrdE), where nucleotide reduction occurs, and beta2 (NrdF), which contains an unidentified metallocofactor that initiates nucleotide reduction. nrdE and nrdF are found in an operon with nrdI, which encodes an unusual flavodoxin proposed to be involved in metallocofactor biosynthesis and/or maintenance. Ni affinity chromatography of a mixture of E. coli (His)(6)-NrdI and NrdF demonstrated tight association between these proteins. To explore the function of NrdI and identify the metallocofactor, apoNrdF was loaded with Mn(II) and incubated with fully reduced NrdI (NrdI(hq)) and O(2). Active RNR was rapidly produced with 0.25 +/- 0.03 tyrosyl radical (Y*) per beta2 and a specific activity of 600 units/mg. EPR and biochemical studies of the reconstituted cofactor suggest it is Mn(III)(2)-Y*, which we propose is generated by Mn(II)(2)-NrdF reacting with two equivalents of HO(2)(-), produced by reduction of O(2) by NrdF-bound NrdI(hq). In the absence of NrdI(hq), with a variety of oxidants, no active RNR was generated. By contrast, a similar experiment with apoNrdF loaded with Fe(II) and incubated with O(2) in the presence or absence of NrdI(hq) gave 0.2 and 0.7 Y*/beta2 with specific activities of 80 and 300 units/mg, respectively. Thus NrdI(hq) hinders Fe(III)(2)-Y* cofactor assembly in vitro. We propose that NrdI is an essential player in E. coli class Ib RNR cluster assembly and that the Mn(III)(2)-Y* cofactor, not the diferric-Y* one, is the active metallocofactor in vivo.

Prediction of protein-protein interaction types using association rule based classification.

BACKGROUND: Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. RESULTS: This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content. CONCLUSION: The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at http://bioinfo.ssu.ac.kr/~shpark/picasso/

The centrosome is a polyfunctional multiprotein cell complex.

Contemporary knowledge about centrosome proteins and their ensembles, which can be divided into several functional groups--microtubule-nucleating proteins, microtubule-anchoring proteins, centriole-duplication proteins, cell cycle control proteins, primary cilia growth regulation proteins, and proteins of regulation of cytokinesis--is reviewed. Structural-temporal classification of centrosomal proteins and the scheme of interconnection between the different centrosomal protein complexes are presented.

Morphological aspects of oligomeric protein structures.

Features of multimeric proteins are reviewed to shed light on the formation of protein assemblies from a structural perspective. The features comprise biochemical and geometric properties. They are compiled on new low-redundancy sets of crystal structures of homomeric proteins with different symmetry and subunit multiplicity, as well as on a set of heteromeric proteins. Crystal structures of likely monomers provide a control group.

The ESC-E(Z) complex participates in the hedgehog signaling pathway.

Polycomb group (PcG) genes are required for stable inheritance of epigenetic states throughout development, a phenomenon termed cellular memory. In Drosophila and mice, the product of the E(z) gene, one of the PcG genes, constitutes the ESC-E(Z) complex and specifically methylates histone H3. It has been argued that this methylation sets the stage for appropriate repression of certain genes. Here, we report the isolation of a well-conserved homolog of E(z), olezh2, in medaka. Hypomorphic knock-down of olezh2 resulted in a cyclopia phenotype and markedly perturbed hedgehog signaling, consistent with our previous report on oleed, a medaka esc. We also found cyclopia in embryos treated with trichostatin A, an inhibitor of histone deacetylase, which is a transient component of the ESC-E(Z) complex. The level of tri-methylation at lysine 27 of histone H3 was substantially decreased in both olezh2 and oleed knock-down embryos, and in embryos with hedgehog signaling perturbed by forskolin. We conclude that the ESC-E(Z) complex per se participates in hedgehog signaling.

PDBML: the representation of archival macromolecular structure data in XML.

SUMMARY: The Protein Data Bank (PDB) has recently released versions of the PDB Exchange dictionary and the PDB archival data files in XML format collectively named PDBML. The automated generation of these XML files is driven by the data dictionary infrastructure in use at the PDB. The correspondences between the PDB dictionary and the XML schema metadata are described as well as the XML representations of PDB dictionaries and data files.