Brian P. Kelley

Conserved pathways within bacteria and yeast as revealed by global protein network alignment

We implement a strategy for aligning two protein–protein interaction networks that combines interaction topology and protein sequence similarity to identify conserved interaction pathways and complexes. Using this approach we show that the protein–protein interaction networks of two distantly related species, Saccharomyces cerevisiae and Helicobacter pylori, harbor a large complement of evolutionarily conserved pathways, and that a large number of pathways appears to have duplicated and specialized within yeast. Analysis of these findings reveals many well characterized interaction pathways as well as many unanticipated pathways, the significance of which is reinforced by their presence in the networks of both species.

PathBLAST: a tool for alignment of protein interaction networks

PathBLAST is a network alignment and search tool for comparing protein interaction networks across species to identify protein pathways and complexes that have been conserved by evolution. The basic method searches for high-scoring alignments between pairs of protein interaction paths, for which proteins of the first path are paired with putative orthologs occurring in the same order in the second path. This technique discriminates between true- and false-positive interactions and allows for functional annotation of protein interaction pathways based on similarity to the network of another, well-characterized species. PathBLAST is now available at http://www.pathblast.org/ as a web-based query. In this implementation, the user specifies a short protein interaction path for query against a target protein-protein interaction network selected from a network database. PathBLAST returns a ranked list of matching paths from the target network along with a graphical view of these paths and the overlap among them. Target protein-protein interaction networks are currently available for Helicobacter pylori, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. Just as BLAST enables rapid comparison of protein sequences between genomes, tools such as PathBLAST are enabling comparative genomics at the network level.

Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data

Mounting evidence shows that many protein complexes are conserved in evolution. Here we use conservation to find complexes that are common to yeast S. Cerevisiae and bacteria H. pylori. Our analysis combines protein interaction data, that are available for each of the two species, and orthology information based on protein sequence comparison. We develop a detailed probabilistic model for protein complexes in a single species, and a model for the conservation of complexes between two species. Using these models, one can recast the question of finding conserved complexes as a problem of searching for heavy subgraphs in an edge- and node-weighted graph, whose nodes are orthologous protein pairs.We tested this approach on the data currently available for yeast and bacteria and detected 11 significantly conserved complexes. Several of these complexes match very well with prior experimental knowledge on complexes in yeast only, and serve for validation of our methodology. The complexes suggest new functions for a variety of uncharacterized proteins. By identifying a conserved complex whose yeast proteins function predominantly in the nuclear pore complex, we propose that the corresponding bacterial proteins function as a coherent cellular membrane transport system. We also compare our results to two alternative methods for detecting complexes, and demonstrate that our methodology obtains a much higher specificity.

Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data.

Mounting evidence shows that many protein complexes are conserved in evolution. Here we use conservation to find complexes that are common to the yeast S. cerevisiae and the bacteria H. pylori. Our analysis combines protein interaction data that are available for each of the two species and orthology information based on protein sequence comparison. We develop a detailed probabilistic model for protein complexes in a single species and a model for the conservation of complexes between two species. Using these models, one can recast the question of finding conserved complexes as a problem of searching for heavy subgraphs in an edge- and node-weighted graph, whose nodes are orthologous protein pairs. We tested this approach on the data currently available for yeast and bacteria and detected 11 significantly conserved complexes. Several of these complexes match very well with prior experimental knowledge on complexes in yeast only and serve for validation of our methodology. The complexes suggest new functions for a variety of uncharacterized proteins. By identifying a conserved complex whose yeast proteins function predominantly in the nuclear pore complex, we propose that the corresponding bacterial proteins function as a coherent cellular membrane transport system. We also compare our results to two alternative methods for detecting complexes and demonstrate that our methodology obtains a much higher specificity.

Detecting Spatial Patterns in Biological Array Experiments

Chemical genetic screening and DNA and protein microarrays are among a number of increasingly important and widely used biological research tools that involve large numbers of parallel experiments arranged in a spatial array. It is often difficult to ensure that uniform experimental conditions are present throughout the entire array, and as a result, one often observes systematic spatially correlated errors, especially when array experiments are performed using robots. Here, the authors apply techniques based on the discrete Fourier transform to identify and quantify spatially correlated errors superimposed on a spatially random background. They demonstrate that these techniques are effective in identifying common spatially systematic errors in high-throughput 384-well microplate assay data. In addition, the authors employ a statistical test to allow for automatic detection of such errors. Software tools for using this approach are provided.

The Potential of a Chemical Graph Transformation System

Chemical reactions can be represented as graph transformations. Fundamental concepts that relate organic chemistry to graph rewriting, and an introduction to the SMILES chemical graph specification language are presented. The utility of both deduction and unordered finite rewriting over chemical graphs and chemical graph transformations, is suggested. The authors hope that this paper will provide inspiration for researchers involved in graph transformation who might be interested in chemoinformatic applications.