The society of proteins

Modeling protein assemblies in the proteome

Most (if not all) proteins function when associated in multimolecular assemblies. Attaining the structures of protein assemblies at the atomic scale is an important aim of structural biology. Experimentally, structures are increasingly available, and computations can help bridge the resolution gap between high- and low-resolution scales. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of experimental data; some are automated docking methods, which are computationally expensive and not applicable to large-scale proteome studies; and still others exploit the symmetry of the complexes and thus are not applicable to nonsymmetrical complexes. Our study aims to take steps toward overcoming these limitations. We have developed a strategy for the construction of protein assemblies computationally based on binary interactions predicted by a motif-based protein interaction prediction tool, PRISM (Protein Interactions by Structural Matching). Previously, we have shown its power in predicting pairwise interactions. Here we take a step toward multimolecular assemblies, reflecting the more prevalent cellular scenarios. With this method we are able to construct homo-/hetero-complexes and symmetric/asymmetric complexes without a limitation on the number of components. The method considers conformational changes and is applicable to large-scale studies. We also exploit electron microscopy density maps to select a solution from among the predictions. Here we present the method, illustrate its results, and highlight its current limitations.

DockTrina: docking triangular protein trimers

In spite of the abundance of oligomeric proteins within a cell, the structural characterization of protein-protein interactions is still a challenging task. In particular, many of these interactions involve heteromeric complexes, which are relatively difficult to determine experimentally. Hence there is growing interest in using computational techniques to model such complexes. However, assembling large heteromeric complexes computationally is a highly combinatorial problem. Nonetheless the problem can be simplified greatly by considering interactions between protein trimers. After dimers and monomers, triangular trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) are the most frequently observed quaternary structural motifs according to the three-dimensional (3D) complex database. This article presents DockTrina, a novel protein docking method for modeling the 3D structures of nonsymmetrical triangular trimers. The method takes as input pair-wise contact predictions from a rigid body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation test. Finally, it ranks the predictions using a scoring function which combines triples of pair-wise contact terms and a geometric clash penalty term. The overall approach takes less than 2 min per complex on a modern desktop computer. The method is tested and validated using a benchmark set of 220 bound and seven unbound protein trimer structures. DockTrina will be made available at http://nano-d.inrialpes.fr/software/docktrina.

"Fuzziness" in the celular interactome: a historical perspective

Some historical background is given for appreciating the impact of the empirical construct known as the cellular protein-protein interactome, which is a seemingly de novo entity that has arisen of late within the context of postgenomic systems biology. The approach here builds on a generalized principle of "fuzziness" in protein behavior, proposed by Tompa and Fuxreiter.(1) Recent controversies in the analysis and interpretation of the interactome studies are rationalized historically under the auspices of this concept. There is an extensive literature on protein-protein interactions, dating to the mid-1900s, which may help clarify the "fuzziness" in the interactome picture and, also, provide a basis for understanding the physiological importance of protein-protein interactions in vivo.

SymmRef: a flexible refinement method for symmetric multimers

Symmetric protein complexes are abundant in the living cell. Predicting their atomic structure can shed light on the mechanism of many important biological processes. Symmetric docking methods aim to predict the structure of these complexes given the unbound structure of a single monomer, or its model. Symmetry constraints reduce the search-space of these methods and make the prediction easier compared to asymmetric protein-protein docking. However, the challenge of modeling the conformational changes that the monomer might undergo is a major obstacle. In this article, we present SymmRef, a novel method for refinement and reranking of symmetric docking solutions. The method models backbone and side-chain movements and optimizes the rigid-body orientations of the monomers. The backbone movements are modeled by normal modes minimization and the conformations of the side-chains are modeled by selecting optimal rotamers. Since solved structures of symmetric multimers show asymmetric side-chain conformations, we do not use symmetry constraints in the side-chain optimization procedure. The refined models are re-ranked according to an energy score. We tested the method on a benchmark of unbound docking challenges. The results show that the method significantly improves the accuracy and the ranking of symmetric rigid docking solutions. SymmRef is available for download at http:// bioinfo3d.cs.tau.ac.il/SymmRef/download.html.

Protein-protein docking by shape-complementarity and property matching

We present a computational approach to protein-protein docking based on surface shape complementarity ("ProBinder"). Within this docking approach, we implemented a new surface decomposition method that considers local shape features on the protein surface. This new surface shape decomposition results in a deterministic representation of curvature features on the protein surface, such as "knobs," "holes," and "flats" together with their point normals. For the actual docking procedure, we used geometric hashing, which allows for the rapid, translation-, and rotation-free comparison of point coordinates. Candidate solutions were scored based on knowledge-based potentials and steric criteria. The potentials included electrostatic complementarity, desolvation energy, amino acid contact preferences, and a van-der-Waals potential. We applied ProBinder to a diverse test set of 68 bound and 30 unbound test cases compiled from the Dockground database. Sixty-four percent of the protein-protein test complexes were ranked with an root mean square deviation (RMSD) < 5 A to the target solution among the top 10 predictions for the bound data set. In 82% of the unbound samples, docking poses were ranked within the top ten solutions with an RMSD < 10 A to the target solution.

Understanding and predicting protein assemblies with 3D structures

Protein interactions are central to most biological processes, and are currently the subject of great interest. Yet despite the many recently developed methods for interaction discovery, little attention has been paid to one of the best sources of data: complexes of known three-dimensional (3D) structure. Here we discuss how such complexes can be used to study and predict protein interactions and complexes, and to interrogate interaction networks proposed by methods such as two-hybrid screens or affinity purifications.

Integrative structure modeling of macromolecular assemblies from proteomics data

Proteomics techniques have been used to generate comprehensive lists of protein interactions in a number of species. However, relatively little is known about how these interactions result in functional multiprotein complexes. This gap can be bridged by combining data from proteomics experiments with data from established structure determination techniques. Correspondingly, integrative computational methods are being developed to provide descriptions of protein complexes at varying levels of accuracy and resolution, ranging from complex compositions to detailed atomic structures.

Inferential optimization for simultaneous fitting of multiple components into a CryoEM map of their assembly

Models of macromolecular assemblies are essential for a mechanistic description of cellular processes. Such models are increasingly obtained by fitting atomic-resolution structures of components into a density map of the whole assembly. Yet, current density-fitting techniques are frequently insufficient for an unambiguous determination of the positions and orientations of all components. Here, we describe MultiFit, a method used for simultaneously fitting atomic structures of components into their assembly density map at resolutions as low as 25 A. The component positions and orientations are optimized with respect to a scoring function that includes the quality-of-fit of components in the map, the protrusion of components from the map envelope, and the shape complementarity between pairs of components. The scoring function is optimized by our exact inference optimizer DOMINO (Discrete Optimization of Multiple INteracting Objects) that efficiently finds the global minimum in a discrete sampling space. MultiFit was benchmarked on seven assemblies of known structure, consisting of up to seven proteins each. The input atomic structures of the components were obtained from the Protein Data Bank, as well as by comparative modeling based on a 16-99% sequence identity to a template structure. A near-native configuration was usually found as the top-scoring model. Therefore, MultiFit can provide initial configurations for further refinement of many multicomponent assembly structures described by electron microscopy.

Modifying the DPClus algorithm for identifying protein complexes based on new topological structures

Background

Identification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.

Results

Based on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density. Following the approach of that of the previously proposed clustering algorithm DPClus which expands clusters starting from seeded vertices, we present a clustering algorithm IPCA based on the new topological structure for identifying complexes in large protein interaction networks. The algorithm IPCA is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm IPCA recalls more known complexes than previously proposed clustering algorithms, including DPClus, CFinder, LCMA, MCODE, RNSC and STM.

Conclusion

The proposed algorithm based on the new topological structure makes it possible to identify dense subgraphs in protein interaction networks, many of which correspond to known protein complexes. The algorithm is robust to the known high rate of false positives and false negatives in data from high-throughout interaction techniques. The program is available at .

Integrating diverse data for structure determination of macromolecular assemblies

To understand the cell, we need to determine the macromolecular assembly structures, which may consist of tens to hundreds of components. First, we review the varied experimental data that characterize the assemblies at several levels of resolution. We then describe computational methods for generating the structures using these data. To maximize completeness, resolution, accuracy, precision, and efficiency of the structure determination, a computational approach is required that uses spatial information from a variety of experimental methods. We propose such an approach, defined by its three main components: a hierarchical representation of the assembly, a scoring function consisting of spatial restraints derived from experimental data, and an optimization method that generates structures consistent with the data. This approach is illustrated by determining the configuration of the 456 proteins in the nuclear pore complex (NPC) from baker's yeast. With these tools, we are poised to integrate structural information gathered at multiple levels of the biological hierarchy--from atoms to cells--into a common framework.

M-TASSER: an algorithm for protein quaternary structure prediction

In a cell, it has been estimated that each protein on average interacts with roughly 10 others, resulting in tens of thousands of proteins known or suspected to have interaction partners; of these, only a tiny fraction have solved protein structures. To partially address this problem, we have developed M-TASSER, a hierarchical method to predict protein quaternary structure from sequence that involves template identification by multimeric threading, followed by multimer model assembly and refinement. The final models are selected by structure clustering. M-TASSER has been tested on a benchmark set comprising 241 dimers having templates with weak sequence similarity and 246 without multimeric templates in the dimer library. Of the total of 207 targets predicted to interact as dimers, 165 (80%) were correctly assigned as interacting with a true positive rate of 68% and a false positive rate of 17%. The initial best template structures have an average root mean-square deviation to native of 5.3, 6.7, and 7.4 A for the monomer, interface, and dimer structures. The final model shows on average a root mean-square deviation improvement of 1.3, 1.3, and 1.5 A over the initial template structure for the monomer, interface, and dimer structures, with refinement evident for 87% of the cases. Thus, we have developed a promising approach to predict full-length quaternary structure for proteins that have weak sequence similarity to proteins of solved quaternary structure.

Reporter gene imaging of protein-protein interactions in living subjects

In the past few years there has been a veritable explosion in the field of reporter gene imaging, with the aim of determining the location, duration and extent of gene expression within living subjects. An important application of this approach is the molecular imaging of interacting protein partners, which could pave the way to functional proteomics in living animals and might provide a tool for the whole-body evaluation of new pharmaceuticals targeted to modulate protein-protein interactions. Three general methods are currently available for imaging protein-protein interactions in living subjects using reporter genes: a modified mammalian two-hybrid system, a bioluminescence resonance energy transfer (BRET) system, and split reporter protein complementation and reconstitution strategies. In the future, these innovative approaches are likely to enhance our appreciation of entire biological pathway systems and their pharmacological regulation.

A procedure to analyze surface profiles of the protein molecules visualized by quick-freeze deep-etch replica electron microscopy

Quick-freeze deep-etch replica electron microscopy gives high contrast snapshots of individual protein molecules under physiological conditions in vitro or in situ. The images show delicate internal pattern, possibly reflecting the rotary-shadowed surface profile of the molecule. As a step to build the new system for the "Structural analysis of single molecules", we propose a procedure to quantitatively characterize the structural property of individual molecules; e.g. conformational type and precise view-angle of the molecules, if the crystallographic structure of the target molecule is available. This paper presents a framework to determine the observed face of the protein molecule by analyzing the surface profile of individual molecules visualized in freeze-replica specimens. A comprehensive set of rotary-shadowed views of the protein molecule was artificially generated from the available atomic coordinates using light-rendering software. Exploiting new mathematical morphology-based image filter, characteristic features were extracted from each image and stored as template. Similar features were extracted from the true replica image and the most likely projection angle and the conformation of the observed particle were determined by quantitative comparison with a set of archived images. The performance and the robustness of the procedure were examined with myosin head structure in defined configuration for actual application.

Target selection for complex structural genomics

Most cellular processes are carried out by macromolecular assemblies and regulated through a complex network of transient protein-protein interactions. Genome-wide interaction discovery experiments are already delivering the first drafts of whole organism interactomes and, thus, depicting the limits of the interaction space. However, a complete understanding of molecular interactions can only come from high-resolution three-dimensional structures, as they provide key atomic details about the binding interfaces. The launch of structural genomics initiatives focused on protein interactions and complexes could quickly fill up the interaction space with structural details, offering a new perspective on how cell networks operate at atomic level. Clear target selection strategies that rationally identify the key interactions and complexes that should be first tackled are fundamental to maximize the return, minimize the costs and prevent experimental difficulties.

Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations

A map of protein-protein interactions provides valuable insight into the cellular function and machinery of a proteome. By measuring the similarity between two Gene Ontology (GO) terms with a relative specificity semantic relation, here, we proposed a new method of reconstructing a yeast protein-protein interaction map that is solely based on the GO annotations. The method was validated using high-quality interaction datasets for its effectiveness. Based on a Z-score analysis, a positive dataset and a negative dataset for protein-protein interactions were derived. Moreover, a gold standard positive (GSP) dataset with the highest level of confidence that covered 78% of the high-quality interaction dataset and a gold standard negative (GSN) dataset with the lowest level of confidence were derived. In addition, we assessed four high-throughput experimental interaction datasets using the positives and the negatives as well as GSPs and GSNs. Our predicted network reconstructed from GSPs consists of 40,753 interactions among 2259 proteins, and forms 16 connected components. We mapped all of the MIPS complexes except for homodimers onto the predicted network. As a result, approximately 35% of complexes were identified interconnected. For seven complexes, we also identified some nonmember proteins that may be functionally related to the complexes concerned. This analysis is expected to provide a new approach for predicting the protein-protein interaction maps from other completely sequenced genomes with high-quality GO-based annotations.

Structural flexibility of small GTPases. Can it explain their functional versatility?

Multiple interactions with many different partners are responsible for the amazing functional versatility of proteins, especially those participating in cellular regulation. The structural properties that could facilitate multiple interactions are examined for small GTPases. The role of cellular constraints, compartmentation and scaffolds on protein-protein interactions is considered.

Ten thousand interactions for the molecular biologist

Previous studies have suggested that nature is restricted to about 1,000 protein folds to perform a great diversity of functions. Here, we use protein interaction data from different sources and three-dimensional structures to suggest that the total number of interaction types is also limited, and estimate that most interactions in nature will conform to one of about 10,000 types. We currently know fewer than 2,000, and at the present rate of structure determination, it will be more than 20 years before we know a full representative set.

Transcriptomic and proteomic responses of human renal HEK293 cells to uranium toxicity

The industrial use of uranium, in particular depleted uranium, has pin-pointed the need to review its chemical impact on human health. Global methodologies, applied to the field of toxicology, have demonstrated their applicability to investigation of fine molecular mechanisms. This report illustrate the power of toxicogenomics to evaluate the involvement of certain genes or proteins in response to uranium. We particularly show that 25% of modulated genes concern signal transduction and trafficking, that the calcium pathway is heavily disturbed and that nephroblastomas-related genes are involved (WIT-1, STMN1, and STMN2). A set of 18 genes was deregulated whatever the concentration of toxicant, which could constitute a signature of uranium exposure. Moreover, a group of downregulated genes, with corresponding disappearing proteins (HSP90, 14-3-3 protein, HMGB1) in two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), are good candidates for use as biomarkers of uranium effects. These results reveal a cross-checking between transcriptomic and proteomic technologies. Moreover, our temporal gene expression profiles suggest the existence of a concentration threshold between adaptive response and severe cell deregulation. Our results confirm the involvement of genes already described and also provide new highlights on cellular response to uranium.

A structural perspective on protein-protein interactions

Structures of macromolecular complexes are necessary for a mechanistic description of biochemical and cellular processes. They can be solved by experimental methods, such as X-ray crystallography, NMR spectroscopy and electron microscopy, as well as by computational protein structure prediction, docking and bioinformatics. Recent advances and applications of these methods emphasize the need for hybrid approaches that combine a variety of data to achieve better efficiency, accuracy, resolution and completeness.

Electron microscopy studies on DNA recognition by DNA-PK

Advances in transmission electron microscopy coupled to increasingly powerful biocomputing techniques are opening enormous possibilities to understand the structure and function of complex biological processes performed by large multi-protein assemblies. This is an exciting time for electron microscopists because we can combine our efforts with X-ray crystallographers and NMR spectroscopists to reach the prospect of studying the structure and dynamics of the so-called 'molecular machines'. One of these fascinating systems is the macromolecular complex formed around double-stranded DNA breaks (DSBs). Non-homologous end-joining (NHEJ) is the main DSBs repair pathway in mammalian cells, where a collection of proteins interact to rejoin two broken DNA ends. During NHEJ, DNA-dependent protein kinase (DNA-PK) binds damaged DNA with high affinity and acts as the main scaffold for other repair factors. Several studies have made use of the electron microscope to reveal the three-dimensional architecture of DNA-PK and the structural basis for the recognition of damaged DNA and the activation of DNA-PK's kinase activity.

Cryo-electron microscopy of vitreous sections of native biological cells and tissues

Cryo-electron microscopy of vitreous sections (CEMOVIS) is, in principle, the ultimate method of specimen preparation. It consists in ultra-rapid cooling of a sizable sample of biological material that is cut into thin sections. These are subsequently observed at low temperature in their fully hydrated vitreous state. Here, we show that CEMOVIS reveals the native state of cells and tissues with unprecedented quality and resolution. What is seen differs considerably from what conventional electron microscopy has shown previously and it is seen with more details. Our findings are demonstrated with images of cyanobacteria and skin.

Cryo-electron microscopy of vitreous sections

Since the beginning of the 1980s, cryo-electron microscopy of a thin film of vitrified aqueous suspension has made it possible to observe biological particles in their native state, in the absence of the usual artefacts of dehydration and staining. Combined with 3-d reconstruction, it has become an important tool for structural molecular biology. Larger objects such as cells and tissues cannot generally be squeezed in a thin enough film. Cryo-electron microscopy of vitreous sections (CEMOVIS) provides then a solution. It requires vitrification of a sizable piece of biological material and cutting it into ultrathin sections, which are observed in the vitrified state. Each of these operations raises serious difficulties that have now been overcome. In general, the native state seen with CEMOVIS is very different from what has been seen before and it is seen in more detail. CEMOVIS will give its full potential when combined with computerized electron tomography for 3-d reconstruction.

Proteomics in pancreatic disease

Proteomics represents a novel methodological approach to investigate the expression of all proteins by a cell or organism in its entireness, similar to global strategies for DNA (genomics) and RNA (transcriptomics). This review focuses on the history of protein analysis, which made up the golden age of pancreatic physiology, the current methodology for proteomics (2D gel electrophoresis, mass spectrometry) and the few published experiences with proteomics in the field of pancreatology until now. Finally, potential applications of proteomics for the pancreas, in concert with other techniques, are cited.

The relationship between sequence and interaction divergence in proteins

There is currently a gap in knowledge between complexes of known three-dimensional structure and those known from other experimental methods such as affinity purifications or the two-hybrid system. This gap can sometimes be bridged by methods that extrapolate interaction information from one complex structure to homologues of the interacting proteins. To do this, it is important to know if and when proteins of the same type (e.g. family, superfamily or fold) interact in the same way. Here, we study interactions of known structure to address this question. We found all instances within the structural classification of proteins database of the same domain pairs interacting in different complexes, and then compared them with a simple measure (interaction RMSD). When plotted against sequence similarity we find that close homologues (30-40% or higher sequence identity) almost invariably interact the same way. Conversely, similarity only in fold (i.e. without additional evidence for a common ancestor) is only rarely associated with a similarity in interaction. The results suggest that there is a twilight zone of sequence similarity where it is not possible to say whether or not domains will interact similarly. We also discuss the rare instances of fold similarities interacting the same way, and those where obviously homologous proteins interact differently.

Bioinformatics analysis of experimentally determined protein complexes in the yeast Saccharomyces cerevisiae

Many important cellular functions are implemented by protein complexes that act as sophisticated molecular machines of varying size and temporal stability. Here we demonstrate quantitatively that protein complexes in the yeast Saccharomyces cerevisiae are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or nonessential), and share identical functional classification and cellular localization. This core is surrounded by a functionally mixed group of proteins, which likely represent short-lived or spurious attachments. The results allow us to define the deletion phenotype and cellular task of most known complexes, and to identify with high confidence the biochemical role of hundreds of proteins with yet unassigned functionality.

From words to literature in structural proteomics

Technical advances on several frontiers have expanded the applicability of existing methods in structural biology and helped close the resolution gaps between them. As a result, we are now poised to integrate structural information gathered at multiple levels of the biological hierarchy - from atoms to cells - into a common framework. The goal is a comprehensive description of the multitude of interactions between molecular entities, which in turn is a prerequisite for the discovery of general structural principles that underlie all cellular processes.