Bridging structural biology and genomics: assessing protein interaction data with known complexes

Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs

Proteins function mainly through interactions, especially with DNA and other proteins. While some large-scale interaction networks are now available for a number of model organisms, their experimental generation remains difficult. Consequently, interolog mapping--the transfer of interaction annotation from one organism to another using comparative genomics--is of significant value. Here we quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins. Using interaction information from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori, we find that protein-protein interactions can be transferred when a pair of proteins has a joint sequence identity >80% or a joint E-value <10(-70). (These "joint" quantities are the geometric means of the identities or E-values for the two pairs of interacting proteins.) We generalize our interolog analysis to protein-DNA binding, finding such interactions are conserved at specific thresholds between 30% and 60% sequence identity depending on the protein family. Furthermore, we introduce the concept of a "regulog"--a conserved regulatory relationship between proteins across different species. We map interologs and regulogs from yeast to a number of genomes with limited experimental annotation (e.g., Arabidopsis thaliana) and make these available through an online database at http://interolog.gersteinlab.org. Specifically, we are able to transfer approximately 90,000 potential protein-protein interactions to the worm. We test a number of these in two-hybrid experiments and are able to verify 45 overlaps, which we show to be statistically significant.

Interaction networks of the molecular machines that decode, replicate, and maintain the integrity of the human genome

The interaction of many proteins with genomic DNA is required for the expression, replication, and maintenance of the integrity of mammalian genomes. These proteins participate in processes as diverse as gene transcription and mRNA processing, as well as in DNA replication, recombination, and repair. This intricate system, where the various nuclear machineries interact with one another and bind to either common or distinct DNA regions to create an impressive network of protein-protein and protein-DNA interactions, is made even more complex by the need for a very stringent control in order to ensure normal cell growth and differentiation. A general methodology based on the in vivo pull-down of tagged components of nuclear machines and regulatory proteins was used to study genome-wide protein-protein and protein-DNA interactions in mammalian cells. In particular, this approach has been used in defining the interaction networks (or "interactome") formed by RNA polymerase II, a molecular machine that decodes the human genome. In addition, because this methodology allows for the purification of variant forms of tagged complexes having site-directed mutations in key elements, it can also be used for deciphering the relationship between the structure and the function of the molecular machines, such as RNA polymerase II, that by binding DNA play a central role in the pathway from the genome to the organism.

Predicting protein complex membership using probabilistic network reliability

Evidence for specific protein-protein interactions is increasingly available from both small- and large-scale studies, and can be viewed as a network. It has previously been noted that errors are frequent among large-scale studies, and that error frequency depends on the large-scale method used. Despite knowledge of the error-prone nature of interaction evidence, edges (connections) in this network are typically viewed as either present or absent. However, use of a probabilistic network that considers quantity and quality of supporting evidence should improve inference derived from protein networks. Here we demonstrate inference of membership in a partially known protein complex by using a probabilistic network model and an algorithm previously used to evaluate reliability in communication networks.

Recent developments in the analysis of protein complexes

The goal of this review is to analyse how recent technical developments contributed to the biochemical characterisation of protein complexes. Improvement of tags used for protein purification, including in our own laboratory, and the development of new strategies have allowed the use of generic procedures for the purification of a wide variety of protein complexes. Together with increased mass spectrometry sensitivity and automation, this made high throughput studies of protein complexes possible and allowed proteome-wide analyses of protein complexes. However, knowledge of protein complex composition, even at the cellular level, will not be sufficient to understand their function. We suggest that the next level of analysis in this area will be the definition of internal subunit arrangement in complexes as a first step toward more detailed structural analyses.

VisANT: an online visualization and analysis tool for biological interaction data

Background

New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems.

Results

We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of biological applications, including studies of pathways, gene regulation and systems biology.

Conclusion

VisANT has been developed to provide interactive visual mining of biological interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at .

TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics

Biological networks are a topic of great current interest, particularly with the publication of a number of large genome-wide interaction datasets. They are globally characterized by a variety of graph-theoretic statistics, such as the degree distribution, clustering coefficient, characteristic path length and diameter. Moreover, real protein networks are quite complex and can often be divided into many sub-networks through systematic selection of different nodes and edges. For instance, proteins can be sub-divided by expression level, length, amino-acid composition, solubility, secondary structure and function. A challenging research question is to compare the topologies of sub- networks, looking for global differences associated with different types of proteins. TopNet is an automated web tool designed to address this question, calculating and comparing topological characteristics for different sub-networks derived from any given protein network. It provides reasonable solutions to the calculation of network statistics for sub-networks embedded within a larger network and gives simplified views of a sub-network of interest, allowing one to navigate through it. After constructing TopNet, we applied it to the interaction networks and protein classes currently available for yeast. We were able to find a number of potential biological correlations. In particular, we found that soluble proteins had more interactions than membrane proteins. Moreover, amongst soluble proteins, those that were highly expressed, had many polar amino acids, and had many alpha helices, tended to have the most interaction partners. Interestingly, TopNet also turned up some systematic biases in the current yeast interaction network: on average, proteins with a known functional classification had many more interaction partners than those without. This phenomenon may reflect the incompleteness of the experimentally determined yeast interaction network.

From gene networks to gene function

We propose a novel method to identify functionally related genes based on comparisons of neighborhoods in gene networks. This method does not rely on gene sequence or protein structure homologies, and it can be applied to any organism and a wide variety of experimental data sets. The character of the predicted gene relationships depends on the underlying networks;they concern biological processes rather than the molecular function. We used the method to analyze gene networks derived from genome-wide chromatin immunoprecipitation experiments, a large-scale gene deletion study, and from the genomic positions of consensus binding sites for transcription factors of the yeast Saccharomyces cerevisiae. We identified 816 functional relationships between 159 genes and show that these relationships correspond to protein-protein interactions, co-occurrence in the same protein complexes, and/or co-occurrence in abstracts of scientific articles. Our results suggest functions for seven previously uncharacterized yeast genes: KIN3 and YMR269W may be involved in biological processes related to cell growth and/or maintenance, whereas IES6, YEL008W, YEL033W, YHL029C, YMR010W, and YMR031W-A are likely to have metabolic functions.

Bridging structural biology and genetics by computational methods: An investigation into how the R774C mutation in the AR gene can result in complete androgen insensitivity syndrome

Recent structural studies of the ligand-binding domain (LBD) of the androgen receptor (AR) have raised more questions than answers, as most of the known pathogenic mutations of the AR gene causing androgen insensitivity syndrome (AIS) are not in the ligand-binding pocket. In this study, we have investigated one such pathogenic mutation, by examining details of its altered atomic structure using a computational technique of molecular dynamics (MD) simulations extended over 4 ns, effectively creating a 4D structural model. The mutation R774C, which is in the LBD of the AR gene, causes complete AIS (CAIS), producing ARs that have a unique thermolabile profile, being thermostable at 22 degrees C but thermolabile at 37 degrees C. We have therefore investigated this mutation by MD simulations at 293 K (20 degrees C), 300 K (27 degrees C), and 310 K (37 degrees C). The MD simulations indicate that: 1) the mutation causes local structural distortions, which result in changes in the shape of the ligand-binding pocket; 2) the mutation alters the dynamic nature of the protein and results in a more diverse conformational distribution of the ligand-binding pocket; and 3) the effect of the mutation on AR structure could be largely reversed by lowering the temperature at which the MD simulations were conducted. These results therefore strongly support the biochemical data, e.g., the mutants' inability to form AR-ligand complexes at 37 degrees C and its characteristic reversible thermolability, clearly indicating the value of such computational methods.

A strategy for constructing large protein interaction maps using the yeast two-hybrid system: regulated expression arrays and two-phase mating

Maps representing the binary interactions among proteins have become valuable tools for understanding how proteins work together to mediate biological processes. One of the most effective methods for detecting biologically important protein interactions has been the yeast two-hybrid system. Here we present an efficient two-hybrid strategy to facilitate construction of protein interaction maps on a genome-wide scale. The strategy begins with two arrays of yeast expressing known proteins fused to either a DNA binding domain (BD), or a transcription activation domain (AD). The fusion proteins are conditionally expressed using regulated promoters that can be repressed during construction and amplification of the yeast arrays. Interaction assays are conducted in two phases. In the first phase, small pools of AD strains are mated with the array of BD strains. In the second phase, individual BD strains are mated with appropriate subsets of the AD array corresponding to positive pools in the first phase. This strategy has several advantages over previously described approaches, including the ability to detect interactions with proteins that inhibit yeast growth or that activate transcription as BD fusions. Moreover, by minimizing the number of mating operations and sequencing reactions needed to test large sets of binary interactions, this strategy is more efficient than either matrix or library screening approaches. We also present a three-dimensional pooling scheme to further increase the efficiency of large-scale two-hybrid analyses.

A Bayesian networks approach for predicting protein-protein interactions from genomic data

We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.

Ribosomal dynamics inferred from variations in experimental measurements

The crystal structures of the ribosome reveal remarkable complexity and provide a starting set of snapshots with which to understand the dynamics of translation. To augment the static crystallographic models with dynamic information present in crosslink, footprint, and cleavage data, we examined 2691 proximity measurements and focused on the subset that was apparently incompatible with >40 published crystal structures. The measurements from this subset generally involve regions of the structure that are functionally conserved and structurally flexible. Local movements in the crystallographic states of the ribosome that would satisfy biochemical proximity measurements show coherent patterns suggesting alternative conformations of the ribosome. Three different types of data obtained for the two subunits display similar "mismatching" patterns, suggesting that the signals are robust and real. In particular, there is an indication of coherent motion in the decoding region within the 30S subunit and central protuberance and surrounding areas of the 50S subunit. Directions of rearrangements fluctuate around the proposed path of tRNA translocation and the plane parallel to the interface of the two subunits. Our results demonstrate that systematic combination and analysis of noisy, apparently incompatible data sources can provide biologically useful signals about structural dynamics.

Cell and molecular mechanics of biological materials

Living cells can sense mechanical forces and convert them into biological responses. Similarly, biological and biochemical signals are known to influence the abilities of cells to sense, generate and bear mechanical forces. Studies into the mechanics of single cells, subcellular components and biological molecules have rapidly evolved during the past decade with significant implications for biotechnology and human health. This progress has been facilitated by new capabilities for measuring forces and displacements with piconewton and nanometre resolutions, respectively, and by improvements in bio-imaging. Details of mechanical, chemical and biological interactions in cells remain elusive. However, the mechanical deformation of proteins and nucleic acids may provide key insights for understanding the changes in cellular structure, response and function under force, and offer new opportunities for the diagnosis and treatment of disease. This review discusses some basic features of the deformation of single cells and biomolecules, and examines opportunities for further research.

Evolution of the yeast protein interaction network

To study the evolution of the yeast protein interaction network, we first classified yeast proteins by their evolutionary histories into isotemporal categories, then analyzed the interaction tendencies within and between the categories, and finally reconstructed the main growth path. We found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life. These observations suggest synergistic selection during network evolution and provide insights into the hierarchical modularity of cellular networks.

Functional genomics and proteomics: charting a multidimensional map of the yeast cell

The challenge of large-scale functional genomics projects is to build a comprehensive map of the cell including genome sequence and gene expression data, information on protein localization, structure, function and expression, post-translational modifications, molecular and genetic interactions and phenotypic descriptions. Some of this broad set of functional genomics data has been already assembled for the budding yeast. Even though molecular cartography of the yeast cell is still far from comprehensive, functional genomics has begun to forge connections between disparate cellular events and to foster numerous hypotheses. Here we review several different genomics and proteomics technologies and describe bioinformatics methods for exploring these data to make new discoveries.

Computational methods of analysis of protein-protein interactions

Computational methods play an important role at all stages of the process of determining protein-protein interactions. They are used to predict potential interactions, to validate the results of high-throughput interaction screens and to analyze the protein networks inferred from interaction databases.

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.

Protein analysis on a proteomic scale

The long-term challenge of proteomics is enormous: to define the identities, quantities, structures and functions of complete complements of proteins, and to characterize how these properties vary in different cellular contexts. One critical step in tackling this goal is the generation of sets of clones that express a representative of each protein of a proteome in a useful format, followed by the analysis of these sets on a genome-wide basis. Such studies enable genetic, biochemical and cell biological technologies to be applied on a systematic level, leading to the assignment of biochemical activities, the construction of protein arrays, the identification of interactions, and the localization of proteins within cellular compartments.

Bioinformatics in the post-sequence era

In the past decade, bioinformatics has become an integral part of research and development in the biomedical sciences. Bioinformatics now has an essential role both in deciphering genomic, transcriptomic and proteomic data generated by high-throughput experimental technologies and in organizing information gathered from traditional biology. Sequence-based methods of analyzing individual genes or proteins have been elaborated and expanded, and methods have been developed for analyzing large numbers of genes or proteins simultaneously, such as in the identification of clusters of related genes and networks of interacting proteins. With the complete genome sequences for an increasing number of organisms at hand, bioinformatics is beginning to provide both conceptual bases and practical methods for detecting systemic functional behaviors of the cell and the organism.

Protein complexes and proteome organization from yeast to man

Protein complexes may well be the most relevant molecular units of cellular function. The activities of protein complexes have to be regulated both in time and space to integrate within the overall cell programs. The cell can be compared to a factory orchestrating individual assembly lines into integrated networks fulfilling particular and superimposed tasks. Recent proteome-wide studies provide insight into the properties of cellular protein complexes, their modular nature, their interaction with other complexes and the resulting preliminary organization chart of the proteome.

Genome-wide analysis of signaling domain function

Approximately 2.5% of human gene products contain one or more small domains that drive interactions between proteins and other cellular components in cell signaling processes. The many interactions driven by these relatively simple domains are thought to cooperate with one another to yield complex signaling networks that allow very fine control of cell function. In principle, if we can understand all domain-mediated interactions it should be possible to model these networks. Genome-wide analysis of signaling domain interactions represents a first step in this direction, and several advances of this sort in yeast have been reported over the past year. These reports suggest, for some domains at least, that the prospect of generating 'wiring diagrams' with this simple approach is feasible.