Protein domain interactions with short linear peptides, such as Src homology 2 (SH2) domain interactions with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell - both in their central importance to cell physiology, and to the sheer scale of possible interactions. The desire to map and quantify these interactions has resulted in the development of increased throughput quantitative measurement techniques, such as microarray or plate-based fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to having covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published datasets led us to re-evaluate the analysis pipelines of published SH2-pTyr datasets. We identified several opportunities for improving the identification of positive and negative interactions, and the accuracy of affinity measurements. These methods account for protein aggregation and degradation, and use model fitting and evaluation that are more appropriate for the non-linear behavior of binding interaction data. In addition to improve affinity accuracy, and increased certainty in negative interactions, we find the reanalyzed data results in significantly improved classification of binding vs non-binding when using machine learning techniques, suggesting improved coherence in the reanalyzed datasets. In addition to providing the revised dataset, we propose this new analysis pipeline and necessary protein activity controls should be part of the design process of many such high-throughput biochemical measurements.

New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data