An optimized energy potential can predict SH2 domain-peptide interactions

Peptide recognition modules (PRMs) are used throughout biology to mediate protein-protein interactions, and many PRMs are members of large protein domain families. Members of these families are often quite similar to each other, but each domain recognizes a distinct set of peptides, raising the question of how peptide recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2-peptide interactions to study the physical origin of domain-peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein-DNA interactions

Bayesian Modeling of the Yeast SH3 Domain Interactome Predicts Spatiotemporal Dynamics of Endocytosis Proteins

A genome-scale specificity and interaction map for yeast SH3 domain-containing proteins reveal how family members show selective binding to target proteins and predicts the dynamic localization of new candidate endocytosis proteins

A yeast-based assay for protein tyrosine kinase substrate specificity and inhibitor resistance

Phosphorylation of tyrosines by protein kinases is a fundamental mode of signal transduction in all eukaryotic cells, leading to a wide variety of cellular outcomes, including proliferation, differentiation, transcriptional activation, and programmed cell death. Perturbations to tyrosine kinase signaling networks by activation, overexpression, or mutation is the driving factor in many diseases, most notably cancers. The development of tyrosine kinase inhibitors, 37 of which are currently FDA-approved, has led to a revolution in cancer treatment. Imatinib, the first FDA-approved kinase inhibitor, has drastically improved prognosis for patients with Bcr-abl-positive leukemias. Despite this unprecedented success, however, up to one-third of patients lose response to imatinib due to mutations within the tyrosine kinase domain of Bcr-abl. Subsequent generations of Bcr-abl inhibitors, including dasatinib and ponatinib, have been developed to overcome these resistance mutations, but in each case, novel resistance mutations have arisen. We present a high-throughput yeast-based assay for the prediction of dasatinib- and ponatinib-resistant mutations in the ABL1 kinase domain. Our results not only recapitulate all known dasatinib-resistant mutations, but confirm recent patient data emphasizing the importance of compound mutations in ponatinib resistance. Furthermore, with hundreds of kinase inhibitors in development for the treatment of a wide range of diseases, understanding the cellular pathway of each kinase is critically important to the selection of ideal drug targets and avoiding potentially toxic side effects. Discovery of novel tyrosine kinase substrates is hindered by the presence of 90 human tyrosine kinases, which are often active in the same pathways. Phosphoproteomics, chemical genetics, and in vitro assays have been used to great success, yet only 30% of phosphorylated tyrosines in the human proteome have been assigned to a specific kinase. Recent advances in predicting tyrosine kinase substrates have been made by combining large data sets on kinase domain specificity, cellular localization, and protein-protein interactions in probabilistic algorithms. However, the high-quality data sets required for accurate predictions are often lacking. In chapter 2, we present a high-throughput yeastbased assay for screening millions of putative kinase substrates, which we then use to build a probabilistic model to accurately predict the in vitro phosphorylation of candidate substratesBiochemistr

Systematic discovery of linear binding motifs targeting an ancient protein interaction surface on MAP kinases

Mitogen-activated protein kinases (MAPK) are broadly used regulators of cellular signaling. However, how these enzymes can be involved in such a broad spectrum of physiological functions is not understood. Systematic discovery of MAPK networks both experimentally and in silico has been hindered because MAPKs bind to other proteins with low affinity and mostly in less-characterized disordered regions. We used a structurally consistent model on kinase-docking motif interactions to facilitate the discovery of short functional sites in the structurally flexible and functionally under-explored part of the human proteome and applied experimental tools specifically tailored to detect low-affinity protein-protein interactions for their validation in vitro and in cell-based assays. The combined computational and experimental approach enabled the identification of many novel MAPK-docking motifs that were elusive for other large-scale protein-protein interaction screens. The analysis produced an extensive list of independently evolved linear binding motifs from a functionally diverse set of proteins. These all target, with characteristic binding specificity, an ancient protein interaction surface on evolutionarily related but physiologically clearly distinct three MAPKs (JNK, ERK, and p38). This inventory of human protein kinase binding sites was compared with that of other organisms to examine how kinase-mediated partnerships evolved over time. The analysis suggests that most human MAPK-binding motifs are surprisingly new evolutionarily inventions and newly found links highlight (previously hidden) roles of MAPKs. We propose that short MAPK-binding stretches are created in disordered protein segments through a variety of ways and they represent a major resource for ancient signaling enzymes to acquire new regulatory roles

Comparative analysis of Saccharomyces cerevisiae WW domains and their interacting proteins

BACKGROUND: The WW domain is found in a large number of eukaryotic proteins implicated in a variety of cellular processes. WW domains bind proline-rich protein and peptide ligands, but the protein interaction partners of many WW domain-containing proteins in Saccharomyces cerevisiae are largely unknown. RESULTS: We used protein microarray technology to generate a protein interaction map for 12 of the 13 WW domains present in proteins of the yeast S. cerevisiae. We observed 587 interactions between these 12 domains and 207 proteins, most of which have not previously been described. We analyzed the representation of functional annotations within the network, identifying enrichments for proteins with peroxisomal localization, as well as for proteins involved in protein turnover and cofactor biosynthesis. We compared orthologs of the interacting proteins to identify conserved motifs known to mediate WW domain interactions, and found substantial evidence for the structural conservation of such binding motifs throughout the yeast lineages. The comparative approach also revealed that several of the WW domain-containing proteins themselves have evolutionarily conserved WW domain binding sites, suggesting a functional role for inter- or intramolecular association between proteins that harbor WW domains. On the basis of these results, we propose a model for the tuning of interactions between WW domains and their protein interaction partners. CONCLUSION: Protein microarrays provide an appealing alternative to existing techniques for the construction of protein interaction networks. Here we built a network composed of WW domain-protein interactions that illuminates novel features of WW domain-containing proteins and their protein interaction partners