Ronald Jansen

Global analysis of protein activities using proteome chips

To facilitate studies of the yeast proteome, we cloned 5800 open reading frames and overexpressed and purified their corresponding proteins. The proteins were printed onto slides at high spatial density to form a yeast proteome microarray and screened for their ability to interact with proteins and phospholipids. We identified many new calmodulin- and phospholipid-interacting proteins; a common potential binding motif was identified for many of the calmodulin-binding proteins. Thus, microarrays of an entire eukaryotic proteome can be prepared and screened for diverse biochemical activities. The microarrays can also be used to screen protein-drug interactions and to detect posttranslational modifications.

Large-scale analysis of the yeast genome by transposon tagging and gene disruption

Economical methods by which gene function may be analysed on a genomic scale are relatively scarce. To fill this need, we have developed a transposon-tagging strategy for the genome-wide analysis of disruption phenotypes, gene expression and protein localization, and have applied this method to the large-scale analysis of gene function in the budding yeast Saccharomyces cerevisiae. Here we present the largest collection of defined yeast mutants ever generated within a single genetic background—a collection of over 11,000 strains, each carrying a transposon inserted within a region of the genome expressed during vegetative growth and/or sporulation. These insertions affect nearly 2,000 annotated genes, representing about one-third of the 6,200 predicted genes in the yeast genome. We have used this collection to determine disruption phenotypes for nearly 8,000 strains using 20 different growth conditions; the resulting data sets were clustered to identify groups of functionally related genes. We have also identified over 300 previously non-annotated open reading frames and analysed by indirect immunofluorescence over 1,300 transposon-tagged proteins. In total, our study encompasses over 260,000 data points, constituting the largest functional analysis of the yeast genome ever undertaken.

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

Currently, there is a major effort to map protein-protein interactions on a genome-wide scale. The utility of the resulting interaction networks will depend on the reliability of the experimental methods and the coverage of the approaches. Known macromolecular complexes provide a defined and objective set of protein interactions with which to compare biochemical and genetic data for validation. Here, we show that a significant fraction of the protein-protein interactions in genome-wide datasets, as well as many of the individual interactions reported in the literature, are inconsistent with the known 3D structures of three recent complexes (RNA polymerase II, Arp2/3 and the proteasome). Furthermore, comparison among genome-wide datasets, and between them and a larger (but less well resolved) group of 174 complexes, also shows marked inconsistencies. Finally, individual interaction datasets, being inherently noisy, are best used when integrated together, and we show how simple Bayesian approaches can combine them, significantly decreasing error rate.

Information assessment on predicting protein-protein interactions

BackgroundIdentifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.ResultsOur assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.ConclusionsIn the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.

Signal Processing in the TGF-β Superfamily Ligand-Receptor Network

The TGF-β pathway plays a central role in tissue homeostasis and morphogenesis. It transduces a variety of extracellular signals into intracellular transcriptional responses that control a plethora of cellular processes, including cell growth, apoptosis, and differentiation. We use computational modeling to show that coupling of signaling with receptor trafficking results in a highly versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand concentration, and ratios of multiple ligands. This coupling controls whether the response of the receptor module is transient or permanent and whether or not different signaling channels behave independently of each other. Our computational approach unifies seemingly disparate experimental observations and suggests specific changes in receptor trafficking patterns that can lead to phenotypes that favor tumor progression.

Integration of genomic datasets to predict protein complexes in yeast

The ultimate goal of functional genomics is to define the function of all the genes in the genome of an organism. A large body of information of the biological roles of genes has been accumulated and aggregated in the past decades of research, both from traditional experiments detailing the role of individual genes and proteins, and from newer experimental strategies that aim to characterize gene function on a genomic scale.It is clear that the goal of functional genomics can only be achieved by integrating information and data sources from the variety of these different experiments. Integration of different data is thus an important challenge for bioinformatics.The integration of different data sources often helps to uncover non-obvious relationships between genes, but there are also two further benefits. First, it is likely that whenever information from multiple independent sources agrees, it should be more valid and reliable. Secondly, by looking at the union of multiple sources, one can cover larger parts of the genome. This is obvious for integrating results from multiple single gene or protein experiments, but also necessary for many of the results from genome-wide experiments since they are often confined to certain (although sizable) subsets of the genome.In this paper, we explore an example of such a data integration procedure. We focus on the prediction of membership in protein complexes for individual genes. For this, we recruit six different data sources that include expression profiles, interaction data, essentiality and localization information. Each of these data sources individually contains some weakly predictive information with respect to protein complexes, but we show how this prediction can be improved by combining all of them. Supplementary information is available at http://bioinfo.mbb.yale.edu/integrate/interactions/.Abbreviations: TP: true possitive; TN: true negative; FP: false positive; FN: false negative; Y2H: yeast two-hybrid.

Genome-wide analysis relating expression level with protein subcellular localization

e investigate the relationship between protein subcellularlocalization and gene expression for a variety of whole-genome expression datasets. We find high expression levels forcytoplasmic proteins and low ones for nuclear and membraneproteins. Excreted proteins have large fluctuations in expres-sion level over various time courses. Our results can be inter-preted in terms of protein structure and function. Detailed sta-tistics are at http://bioinfo.mbb.yale.edu/genome/expression.

Tools and databases to analyze protein flexibility; Approaches to mapping implied features onto sequences

Publisher Summary This chapter describes the way protein flexibility can be analyzed statistically in a database. The database of macromolecular movements, which is accessible over the Internet, organizes a few hundred well-characterized motions on the basis of size and then packing, with the involvement of a well-packed interface in the motion being a key classifying feature. The chapter describes the computational tools employed in the database analysis—namely, (1) structure comparison, which is useful to align and superpose different conformations, (2) adiabatic mapping interpolation, which is implemented on a large scale by the morph server, provides movie-like pathways between two superposed conformations, and in the process, generates many standardized statistics, (3) normal mode analysis, which provides readily interpretable information about the flexibility of a single conformation, and (4) Voronoi volume calculations, which provide a rigorous basis for characterizing packing. The chapter also explains the way structural features in the motions database can be related to sequence, an important part of the overall process of transferring annotation to uncharacterized genomic data. This allows determination of a sequence-propensity scale for amino acids to be in linkers in general or flexible hinges in particular.

Studying Macromolecular Motions in a Database Framework: From Structure to Sequence

We describe database approaches taken in our lab to the study of protein and nucleic acid motions. We have developed a database of macromolecular motions, which is accessible on the World Wide Web with an entry point at http://bioinfo.mbb.yale.edu/MolMovDB. This attempts to systematize all instances of macromolecular movement for which there is at least some structural information. At present it contains detailed descriptions of more than 100 motions, most of which are of proteins. Protein motions are further classified hierarchically into a limited number of categories, first on the basis of size (distinguishing between fragment, domain, and subunit motions) and then on the basis of packing. Our packing classification divides motions into various categories (shear, hinge, other) depending on whether or not they involve sliding over a continuously maintained and tightly packed interface. We quantitatively systematize the description of packing through the use of Voronoi polyhedra and Delaunay triangulation. In addition to the packing classification, the database provides some indication about the evidence behind each motion (i.e. the type of experimental information or whether the motion is inferred based on structural similarity) and attempts to describe many aspects of a motion in terms of a standardized nomenclature (e.g. the maximum rotation, the residue selection of a fixed core, etc). Currently, we use a standard relational design to implement the database. However, the complexity and heterogeneity of the information kept in the database makes it an ideal application for an object-relational approach, and we are moving it in this direction. The database, moreover, incorporates innovative Internet cooperatively features that allow authorized remote experts to serve as database editors. The database also contains plausible representations for motion pathways, derived from restrained 3D interpolation between known endpoint conformations. These pathways can be viewed in a variety of movie formats, and the database is associated with a server that can automatically generate these movies from submitted coordinates. Based on the structures in the database we have developed sequence patterns for linkers and flexible hinges and are currently using these for the annotation of genome sequence data.

Toward a systematic definition of protein function that scales to the genome level: defining function in terms of interactions

The ultimate goal of functional genomics is to elucidate the function of all the genes in the genome. However the current notions of function are crafted for individual proteins. The degree to which they can scale to the genomic level is not clear In this paper we review the diverse approaches to functional classification, focusing on their ability to meet this challenge of scale. Our review emphasizes a number of key parameters of the systems: their accuracy, comprehensiveness, level of standardization, flexibility, and support for data mining. We then propose an approach that synthesizes a number of the promising features of the existing systems. Our approach, which we call a function grid, is based on the notion of defining a proteins function through molecular interactions-specifically, in terms of its probability of interaction with various ligands, the list of which can be expanded infinitely. To illustrate how our function grid can be used in genome-wide prediction of function, we construct a grid of yeast genes; combine it with other genomic information, including sequence features, structure, subcellular localization, and messenger ribonucleic acid expression; and then use decision trees and support vector machines to predict deoxyribonucleic acid binding.