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An in vitro microfluidic approach to generating protein-interaction networks.

ABSTRACT: We developed an in vitro protein expression and interaction analysis platform based on a highly parallel and sensitive microfluidic affinity assay, and used it for 14,792 on-chip experiments, which exhaustively measured the protein-protein interactions of 43 Streptococcus pneumoniae proteins in quadruplicate. The resulting network of 157 interactions was denser than expected based on known networks. Analysis of the network revealed previously undescribed physical interactions among members of some biochemical pathways.

SUBMITTER: Gerber D

PROVIDER: S-EPMC4117197 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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An in vitro microfluidic approach to generating protein-interaction networks.

Gerber Doron D Maerkl Sebastian J SJ Quake Stephen R SR

Nature methods 20081221 1

We developed an in vitro protein expression and interaction analysis platform based on a highly parallel and sensitive microfluidic affinity assay, and used it for 14,792 on-chip experiments, which exhaustively measured the protein-protein interactions of 43 Streptococcus pneumoniae proteins in quadruplicate. The resulting network of 157 interactions was denser than expected based on known networks. Analysis of the network revealed previously undescribed physical interactions among members of so ...[more]

PMID: 19098921

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Project description:BACKGROUND: An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches. RESULTS: We propose a novel approach of finding correlated short sequence motifs from protein-protein interaction data to effectively circumvent the above-mentioned limitations. Correlated motifs are those motifs that consistently co-occur only in pairs of interacting protein sequences, and could possibly interact with each other directly or indirectly to mediate interactions. We adopted the (l, d)-motif model and formulate finding the correlated motifs as an (l, d)-motif pair finding problem. We present both an exact algorithm, D-MOTIF, as well as its approximation algorithm, D-STAR to solve this problem. Evaluation on extensive simulated data showed that our approach not only eliminated the need for any prior protein grouping, but is also more robust in extracting motifs from noisy interaction data. Application on two biological datasets (SH3 interaction network and TGFbeta signaling network) demonstrates that the approach can extract correlated motifs that correspond to actual interacting subsequences. CONCLUSION: The correlated motif approach outlined in this paper is able to find correlated linear motifs from sparse and noisy interaction data. This, in turn, will expedite the discovery of novel linear binding motifs, and facilitate the studies of biological pathways mediated by them.

Dataset Information

An in vitro microfluidic approach to generating protein-interaction networks.

Publications

An in vitro microfluidic approach to generating protein-interaction networks.

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