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Open AccessJournal ArticleDOI

Protein interaction mapping: A Drosophila case study

TLDR
This work used a high-throughput yeast two-hybrid (Y2H)-based technology to screen 102 bait proteins from Drosophila melanogaster, most of them orthologous to human cancer-related and/or signaling proteins, against high-complexity fly cDNA libraries.
Abstract
The Drosophila (fruit fly) model system has been instrumental in our current understanding of human biology, development, and diseases. Here, we used a high-throughput yeast two-hybrid (Y2H)-based technology to screen 102 bait proteins from Drosophila melanogaster, most of them orthologous to human cancer-related and/or signaling proteins, against high-complexity fly cDNA libraries. More than 2300 protein-protein interactions (PPI) were identified, of which 710 are of high confidence. The computation of a reliability score for each protein-protein interaction and the systematic identification of the interacting domain combined with a prediction of structural/functional motifs allow the elaboration of known complexes and the identification of new ones. The full data set can be visualized using a graphical Web interface, the PIMRider (http://pim.hybrigenics.com), and is also accessible in the PSI standard Molecular Interaction data format. Our fly Protein Interaction Map (PIM) is surprisingly different from the one recently proposed by Giot et al. with little overlap between the two data sets. Analysis of the differences in data sets and methods suggests alternative strategies to enhance the accuracy and comprehensiveness of the post-genomic generation of broad-scale protein interaction maps.

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Organizing the cell cortex: the role of ERM proteins

TL;DR: The ERM proteins (ezrin, radixin and moesin) have a crucial role in organizing membrane domains through their ability to interact with transmembrane proteins and the cytoskeleton and can provide structural links to strengthen the cell cortex and regulate the activities of signal transduction pathways.
Journal ArticleDOI

The model organism as a system: integrating 'omics' data sets.

TL;DR: Researchers are rising to the challenge by using omics data integration to address fundamental biological questions that would increase the understanding of systems as a whole.
Journal ArticleDOI

Estimating the size of the human interactome

TL;DR: It is found that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and ≈3 times bigger than in Caenorhabditis elegans.
References
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Journal ArticleDOI

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Journal ArticleDOI

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

TL;DR: A new membrane protein topology prediction method, TMHMM, based on a hidden Markov model is described and validated, and it is discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C-in topologies.
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Base-calling of automated sequencer traces using Phred. I. accuracy assessment

TL;DR: In this article, a base-calling program for automated sequencer traces, phred, with improved accuracy was proposed. But it was not shown to achieve a lower error rate than the ABI software, averaging 40%-50% fewer errors in the data sets examined independent of position in read, machine running conditions, or sequencing chemistry.
Journal ArticleDOI

The genome sequence of Drosophila melanogaster

Mark Raymond Adams, +194 more
- 24 Mar 2000 - 
TL;DR: The nucleotide sequence of nearly all of the approximately 120-megabase euchromatic portion of the Drosophila genome is determined using a whole-genome shotgun sequencing strategy supported by extensive clone-based sequence and a high-quality bacterial artificial chromosome physical map.
Journal ArticleDOI

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

TL;DR: The ability to estimate a probability of error for each base-call, as a function of certain parameters computed from the trace data, is developed and implemented in the base-calling program.
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