Quantifying protein-protein interactions in high throughput using protein domain microarrays.
Quantifying protein-protein interactions in high throughput using protein domain microarrays.
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DOI：
10.1038/nprot.2010.36
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发表时间：
2010-04
期刊：
影响因子：
14.8
通讯作者：
MacBeath, Gavin
中科院分区：
文献类型：
--
作者：
Kaushansky, Alexis;Allen, John E.;Gordus, Andrew;Stiffler, Michael A.;Karp, Ethan S.;Chang, Bryan H.;MacBeath, Gavin
Protein microarrays provide an efficient way to identify and quantify protein–protein interactions in high throughput. One drawback of this technique is that proteins show a broad range of physicochemical properties and are often difficult to produce recombinantly. To circumvent these problems, we have focused on families of protein interaction domains. Here we provide protocols for constructing microarrays of protein interaction domains in individual wells of 96-well microtiter plates, and for quantifying domain–peptide interactions in high throughput using fluorescently labeled synthetic peptides. As specific examples, we will describe the construction of microarrays of virtually every human Src homology 2 (SH2) and phosphotyrosine binding (PTB) domain, as well as microarrays of mouse PDZ domains, all produced recombinantly in Escherichia coli. For domains that mediate high-affinity interactions, such as SH2 and PTB domains, equilibrium dissociation constants (KDs) for their peptide ligands can be measured directly on arrays by obtaining saturation binding curves. For weaker binding domains, such as PDZ domains, arrays are best used to identify candidate interactions, which are then retested and quantified by fluorescence polarization. Overall, protein domain microarrays provide the ability to rapidly identify and quantify protein–ligand interactions with minimal sample consumption. Because entire domain families can be interrogated simultaneously, they provide a powerful way to assess binding selectivity on a proteome-wide scale and provide an unbiased perspective on the connectivity of protein–protein interaction networks.
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通讯作者：
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影响因子：
64.8
作者：
Gavin, AC;Bösche, M;Superti-Furga, G
通讯作者：
Superti-Furga, G
影响因子：
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影响因子：
64.8
作者：
Ptacek, J;Devgan, G;Snyder, M
通讯作者：
Snyder, M
影响因子：
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作者：
Kaushansky, Alexis;Gordus, Andrew;MacBeath, Gavin
通讯作者：
MacBeath, Gavin