PDZ domains have long been thought to cluster into discrete functional classes defined by their peptide-binding preferences. We used protein microarrays and quantitative fluorescence polarization to characterize the binding selectivity of 157 mouse PDZ domains with respect to 217 genome-encoded peptides. We then trained a multidomain selectivity model to predict PDZ domain-peptide interactions across the mouse proteome with an accuracy that exceeds many large-scale, experimental investigations of protein-protein interactions. Contrary to the current paradigm, PDZ domains do not fall into discrete classes; instead, they are evenly distributed throughout selectivity space, which suggests that they have been optimized across the proteome to minimize cross-reactivity. We predict that focusing on families of interaction domains, which facilitates the integration of experimentation and modeling, will play an increasingly important role in future investigations of protein function.

PDZ domains constitute one of the largest families of interaction domains and function by binding the C termini of their target proteins 1,2 . Using Bayesian estimation, we constructed a threedimensional extension of a position-specific scoring matrix that predicts to which peptides a PDZ domain will bind, given the primary sequences of the PDZ domain and the peptides. The model, which was trained using interaction data from 82 PDZ domains and 93 peptides encoded in the mouse genome 3 , successfully predicts interactions involving other mouse PDZ domains, as well as PDZ domains from Drosophila melanogaster and, to a lesser extent, PDZ domains from Caenorhabditis elegans. The model also predicts the differential effects of point mutations in peptide ligands on their PDZ domain-binding affinities. Overall, we show that our approach captures, in a single model, the binding selectivity of the PDZ domain family.Most efforts to define the binding selectivity of an interaction domain report either a consensus sequence for the domain's peptide ligands 4-6 or a position-specific scoring matrix that captures the domain's binding preferences 7-9 . Although these approaches are clearly useful, they are based on experimental data that are specific to the domain being studied and so are silent with respect to other members of the domain family. A truly general model-one that could be used to predict interactions involving PDZ domains for which no data are available -would take into account the sequence not only of the peptide, but also of the PDZ domain. We reasoned that, if the amino acid identity at specific positions in the PDZ domain's threedimensional structure determines that domain's preferences for amino acids at specific positions in the peptide ligand, it might be possible to capture this information for the entire PDZ domain family in a single model by integrating sequence information, structural information and protein interaction data (Fig. 1a).

Over the past several years, multivariate approaches have been developed that address the problem of disease diagnosis. Here, we report an integrated approach to the problem of prognosis that uses protein microarrays to measure a focused set of molecular markers and non-parametric methods to reveal non-linear relationships among these markers, clinical variables, and patient outcome. As proof-of-concept, we applied our approach to the prediction of early mortality in patients initiating kidney dialysis. We found that molecular markers are not uniformly prognostic, but instead vary in their value depending on a combination of clinical variables. This may explain why reports in this area aiming to identify prognostic markers, without taking into account clinical variables, are either conflicting or show that markers have marginal prognostic value. Just as treatments are now being tailored to specific subsets of patients, our results show that prognosis can also benefit from a 'personalized' approach.

Determinants of the switching speed of the genetic toggle switch remain unknown. Analysis shows that the decay rate of proteins predominantly sets the speed limit, but its modification introduces a trade-off between increased speed and decreased bistability. Incorporating protein-modifying enzymes into the switch gives extra degrees of freedom to address this trade-off. The condition for bistability when such enzymes are incorporated is derived. Under this condition, speed increases with the maximal rate of gene expression.