Dataset Information

Fractionation profiling: a fast and versatile approach for mapping vesicle proteomes and protein-protein interactions.

ABSTRACT: We developed "fractionation profiling," a method for rapid proteomic analysis of membrane vesicles and protein particles. The approach combines quantitative proteomics with subcellular fractionation to generate signature protein abundance distribution profiles. Functionally associated groups of proteins are revealed through cluster analysis. To validate the method, we first profiled >3500 proteins from HeLa cells and identified known clathrin-coated vesicle proteins with >90% accuracy. We then profiled >2400 proteins from Drosophila S2 cells, and we report the first comprehensive insect clathrin-coated vesicle proteome. Of importance, the cluster analysis extends to all profiled proteins and thus identifies a diverse range of known and novel cytosolic and membrane-associated protein complexes. We show that it also allows the detailed compositional characterization of complexes, including the delineation of subcomplexes and subunit stoichiometry. Our predictions are presented in an interactive database. Fractionation profiling is a universal method for defining the clathrin-coated vesicle proteome and may be adapted for the analysis of other types of vesicles and particles. In addition, it provides a versatile tool for the rapid generation of large-scale protein interaction maps.

SUBMITTER: Borner GH

PROVIDER: S-EPMC4196868 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Fractionation profiling: a fast and versatile approach for mapping vesicle proteomes and protein-protein interactions.

Borner Georg H H GH Hein Marco Y MY Hirst Jennifer J Edgar James R JR Mann Matthias M Robinson Margaret S MS

Molecular biology of the cell 20140827 20

We developed "fractionation profiling," a method for rapid proteomic analysis of membrane vesicles and protein particles. The approach combines quantitative proteomics with subcellular fractionation to generate signature protein abundance distribution profiles. Functionally associated groups of proteins are revealed through cluster analysis. To validate the method, we first profiled >3500 proteins from HeLa cells and identified known clathrin-coated vesicle proteins with >90% accuracy. We then p ...[more]

PMID: 25165137

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Project description:The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.

Project description:MotivationProtein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods.ResultsWe extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.

Dataset Information

Fractionation profiling: a fast and versatile approach for mapping vesicle proteomes and protein-protein interactions.

Publications

Fractionation profiling: a fast and versatile approach for mapping vesicle proteomes and protein-protein interactions.

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