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Peptide array X-linking (PAX): a new peptide-protein identification approach.

Okada H, Uezu A, Soderblom EJ, Moseley MA, Gertler FB, Soderling SH - PLoS ONE (2012)

Bottom Line: We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains.These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust.Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Biology, Duke University Medical School, Durham, North Carolina, United States of America.

ABSTRACT
Many protein interaction domains bind short peptides based on canonical sequence consensus motifs. Here we report the development of a peptide array-based proteomics tool to identify proteins directly interacting with ligand peptides from cell lysates. Array-formatted bait peptides containing an amino acid-derived cross-linker are photo-induced to crosslink with interacting proteins from lysates of interest. Indirect associations are removed by high stringency washes under denaturing conditions. Covalently trapped proteins are subsequently identified by LC-MS/MS and screened by cluster analysis and domain scanning. We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains. These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust. Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery.

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PAX successfully identifies interacting proteins from tissue lysates.(A) Cluster analysis of MS/MS-identified proteins. Candidate interactors with the bait peptides from SOS1, Stonin2, Epsin1 and mGluR5 were hierarchically clustered using unbiased Pearson correlation of the mean normalized spectral counts. Protein clusters of single bait interactors are indicated with blue bars. (B) Identification of selective bait interactors. Single bait interactors identified in (A) are exhibited. Proteins in brown are known to bind to the corresponding bait peptides. Bioinformatics analysis revealed that proteins in orange were previously shown to interact with the bait proteins. (C) Protein interaction domain filtering. Proteins that showed association to the control bait peptide were eliminated from the MS-identified proteins, and the rest was subjected to cluster analysis. Subsequently, the proteins that contain domains anticipated to interact with consensus binding motifs found in bait peptides were selected as high confidence interactors. (D) Identification of the interactors with anticipated peptide recognition domains. Proteins were colored as explained in (B).
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pone-0037035-g003: PAX successfully identifies interacting proteins from tissue lysates.(A) Cluster analysis of MS/MS-identified proteins. Candidate interactors with the bait peptides from SOS1, Stonin2, Epsin1 and mGluR5 were hierarchically clustered using unbiased Pearson correlation of the mean normalized spectral counts. Protein clusters of single bait interactors are indicated with blue bars. (B) Identification of selective bait interactors. Single bait interactors identified in (A) are exhibited. Proteins in brown are known to bind to the corresponding bait peptides. Bioinformatics analysis revealed that proteins in orange were previously shown to interact with the bait proteins. (C) Protein interaction domain filtering. Proteins that showed association to the control bait peptide were eliminated from the MS-identified proteins, and the rest was subjected to cluster analysis. Subsequently, the proteins that contain domains anticipated to interact with consensus binding motifs found in bait peptides were selected as high confidence interactors. (D) Identification of the interactors with anticipated peptide recognition domains. Proteins were colored as explained in (B).

Mentions: First, to decode the association pattern of the MS-identified proteins for each bait peptide, we performed cluster analysis using Pearson correlation of the mean normalized spectral counts, which are MS measurements that reflect the relative abundance of the proteins in the PAX sample (Fig. 3A). In this analysis, we focused only on the proteins that were associated with single bait peptides. These interactions are of higher confidence because the interactions with the other bait peptides function as negative controls and proteins interacting with all peptides are likely to be non-specific. The cluster analysis produced 4 protein clusters that showed a specific association to each bait peptide for mGluR5, Epsin1, Stonin2 and SOS1 (Fig. 3B). The mGluR5-specific cluster contained Homer1, a known interactor of the mGluR5 peptide (Table 1). The SOS1-specific cluster also contained GRB2, a known interactor of the SOS1 peptide. We next performed bioinformatics searches to see if there are any other known interactions among the bait proteins and the corresponding protein clusters (see the Method section). This search identified another known interactor in the mGluR5 protein cluster (Homer3) and 3 known interactors (Pacsin1, Sh3kbp1, Cd2ap) in the SOS1-specific protein cluster. Thus, single bait interaction clusters identified previously known interactions including our “positive controls” from brain lysates. The rest of the interactors in the clusters are previously-unidentified binding proteins and to verify these interactions, additional assays such as co-immunoprecipitation or co-localization experiments are required.


Peptide array X-linking (PAX): a new peptide-protein identification approach.

Okada H, Uezu A, Soderblom EJ, Moseley MA, Gertler FB, Soderling SH - PLoS ONE (2012)

PAX successfully identifies interacting proteins from tissue lysates.(A) Cluster analysis of MS/MS-identified proteins. Candidate interactors with the bait peptides from SOS1, Stonin2, Epsin1 and mGluR5 were hierarchically clustered using unbiased Pearson correlation of the mean normalized spectral counts. Protein clusters of single bait interactors are indicated with blue bars. (B) Identification of selective bait interactors. Single bait interactors identified in (A) are exhibited. Proteins in brown are known to bind to the corresponding bait peptides. Bioinformatics analysis revealed that proteins in orange were previously shown to interact with the bait proteins. (C) Protein interaction domain filtering. Proteins that showed association to the control bait peptide were eliminated from the MS-identified proteins, and the rest was subjected to cluster analysis. Subsequently, the proteins that contain domains anticipated to interact with consensus binding motifs found in bait peptides were selected as high confidence interactors. (D) Identification of the interactors with anticipated peptide recognition domains. Proteins were colored as explained in (B).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3351392&req=5

pone-0037035-g003: PAX successfully identifies interacting proteins from tissue lysates.(A) Cluster analysis of MS/MS-identified proteins. Candidate interactors with the bait peptides from SOS1, Stonin2, Epsin1 and mGluR5 were hierarchically clustered using unbiased Pearson correlation of the mean normalized spectral counts. Protein clusters of single bait interactors are indicated with blue bars. (B) Identification of selective bait interactors. Single bait interactors identified in (A) are exhibited. Proteins in brown are known to bind to the corresponding bait peptides. Bioinformatics analysis revealed that proteins in orange were previously shown to interact with the bait proteins. (C) Protein interaction domain filtering. Proteins that showed association to the control bait peptide were eliminated from the MS-identified proteins, and the rest was subjected to cluster analysis. Subsequently, the proteins that contain domains anticipated to interact with consensus binding motifs found in bait peptides were selected as high confidence interactors. (D) Identification of the interactors with anticipated peptide recognition domains. Proteins were colored as explained in (B).
Mentions: First, to decode the association pattern of the MS-identified proteins for each bait peptide, we performed cluster analysis using Pearson correlation of the mean normalized spectral counts, which are MS measurements that reflect the relative abundance of the proteins in the PAX sample (Fig. 3A). In this analysis, we focused only on the proteins that were associated with single bait peptides. These interactions are of higher confidence because the interactions with the other bait peptides function as negative controls and proteins interacting with all peptides are likely to be non-specific. The cluster analysis produced 4 protein clusters that showed a specific association to each bait peptide for mGluR5, Epsin1, Stonin2 and SOS1 (Fig. 3B). The mGluR5-specific cluster contained Homer1, a known interactor of the mGluR5 peptide (Table 1). The SOS1-specific cluster also contained GRB2, a known interactor of the SOS1 peptide. We next performed bioinformatics searches to see if there are any other known interactions among the bait proteins and the corresponding protein clusters (see the Method section). This search identified another known interactor in the mGluR5 protein cluster (Homer3) and 3 known interactors (Pacsin1, Sh3kbp1, Cd2ap) in the SOS1-specific protein cluster. Thus, single bait interaction clusters identified previously known interactions including our “positive controls” from brain lysates. The rest of the interactors in the clusters are previously-unidentified binding proteins and to verify these interactions, additional assays such as co-immunoprecipitation or co-localization experiments are required.

Bottom Line: We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains.These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust.Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Biology, Duke University Medical School, Durham, North Carolina, United States of America.

ABSTRACT
Many protein interaction domains bind short peptides based on canonical sequence consensus motifs. Here we report the development of a peptide array-based proteomics tool to identify proteins directly interacting with ligand peptides from cell lysates. Array-formatted bait peptides containing an amino acid-derived cross-linker are photo-induced to crosslink with interacting proteins from lysates of interest. Indirect associations are removed by high stringency washes under denaturing conditions. Covalently trapped proteins are subsequently identified by LC-MS/MS and screened by cluster analysis and domain scanning. We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains. These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust. Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery.

Show MeSH