As an alternative to conventional, target-oriented drug discovery, we report a strategy that identifies compounds on the basis of the state that they induce in a signaling network. Immortalized human cells are grown in microtiter plates and treated with compounds from a small-molecule library. The target network is then activated and lysates derived from each sample are arrayed onto glasssupported nitrocellulose pads. By probing these microarrays with antibodies that report on the abundance or phosphorylation state of selected proteins, a global picture of the target network is obtained. As proof of concept, we screened 84 kinase and phosphatase inhibitors for their ability to induce different states in the ErbB signaling network. We observed functional connections between proteins that match our understanding of ErbB signaling, indicating that state-based screens can be used to define the topology of signaling networks. Additionally, compounds sort according to the multidimensional phenotypes they induce, suggesting that state-based screens may inform efforts to identify the targets of biologically active small molecules.The signal transduction networks that control human cellular physiology typically comprise tens to hundreds of proteins that interrelate in a complex, nonlinear fashion. Defects in these systems underlie many human pathologies, including cancer 1 , autoimmunity 2 and developmental abnormalities 3 . One of the principal challenges of systems biology is to understand how information flows through these networks and how we can best intervene to halt or to redirect the flow of aberrant signaling. Small molecules that modulate the activity of signaling proteins are useful both as tools to dissect protein function and as potential therapeutics. Currently, most efforts to discover such compounds are target-based: active compounds are identified by their ability to modulate the function of a specific protein of interest. Altering the flow of information through a network, however, may require nonintuitive solutions; it may even require molecules that target more than one protein.To address this need, we developed a method that identifies compounds by their ability to induce different states in a network. Here we define the 'state' of a network as the quantitative levels of its components and assume that an informative picture can be obtained by measuring a subset of these components in cell lysates. We refer to this strategy as 'state-based discovery'. Having the ability to push networks into different states will provide us with tools to dissect how information is directed, and redirected, through these systems in real time.

Receptor tyrosine kinases (RTKs) process extracellular cues by activating a broad array of signaling proteins. Paradoxically, they often use the same proteins to elicit diverse and even opposing phenotypic responses. Binary, 'on-off' wiring diagrams are therefore inadequate to explain their differences. Here, we show that when six diverse RTKs are placed in the same cellular background, they activate many of the same proteins, but to different quantitative degrees. Additionally, we find that the relative phosphorylation levels of upstream signaling proteins can be accurately predicted using linear models that rely on combinations of receptor-docking affinities and that the docking sites for phosphoinositide 3-kinase (PI3K) and Shc1 provide much of the predictive information. In contrast, we find that the phosphorylation levels of downstream proteins cannot be predicted using linear models. Taken together, these results show that information processing by RTKs can be segmented into discrete upstream and downstream steps, suggesting that the challenging task of constructing mathematical models of RTK signaling can be parsed into separate and more manageable layers.

One of the principal challenges in systems biology is to uncover the networks of protein-protein interactions that underlie most biological processes. To date, experimental efforts directed at this problem have largely produced only qualitative networks that are replete with false positives and false negatives. Here, we describe a domain-centered approach--compatible with genome-wide investigations--that enables us to measure the equilibrium dissociation constant (K(D)) of recombinant PDZ domains for fluorescently labeled peptides that represent physiologically relevant binding partners. Using a pilot set of 22 PDZ domains, 4 PDZ domain clusters, and 20 peptides, we define a gold standard dataset by determining the K(D) for all 520 PDZ-peptide combinations using fluorescence polarization. We then show that microarrays of PDZ domains identify interactions of moderate to high affinity (K(D) < or = 10 microM) in a high-throughput format with a false positive rate of 14% and a false negative rate of 14%. By combining the throughput of protein microarrays with the fidelity of fluorescence polarization, our domain/peptide-based strategy yields a quantitative network that faithfully recapitulates 85% of previously reported interactions and uncovers new biophysical interactions, many of which occur between proteins that are co-expressed. From a broader perspective, the selectivity data produced by this effort reveal a strong concordance between protein sequence and protein function, supporting a model in which interaction networks evolve through small steps that do not involve dramatic rewiring of the network.

Although many anticancer drugs that target receptor tyrosine kinases (RTKs) provide clinical benefit, their long-term use is limited by resistance that is often attributed to increased abundance or activation of another RTK that compensates for the inhibited receptor. To uncover common and unique features in the signaling networks of RTKs, we measured time-dependent signaling in six isogenic cell lines, each expressing a different RTK as downstream proteins were systematically perturbed by RNA interference. Network models inferred from the data revealed a conserved set of signaling pathways and RTK-specific features that grouped the RTKs into three distinct classes: (i) an EGFR/FGFR1/c-Met class constituting epidermal growth factor receptor, fibroblast growth factor receptor 1, and the hepatocyte growth factor receptor c-Met; (ii) an IGF-1R/NTRK2 class constituting insulin-like growth factor 1 receptor and neurotrophic tyrosine receptor kinase 2; and (iii) a PDGFRβ class constituting platelet-derived growth factor receptor β. Analysis of cancer cell line data showed that many RTKs of the same class were coexpressed and that increased abundance of an RTK or its cognate ligand frequently correlated with resistance to a drug targeting another RTK of the same class. In contrast, abundance of an RTK or ligand of one class generally did not affect sensitivity to a drug targeting an RTK of a different class. Thus, classifying RTKs by their inferred networks and then therapeutically targeting multiple receptors within a class may delay or prevent the onset of resistance.

Summary Although many methods exist to study the recognition and signaling properties of proteins in isolation, it remains a challenge to perform these investigations on a system-wide or proteome-wide scale and within the context of biological complexity. Protein microarray technology provides a powerful tool to assess the selectivity of protein–protein interactions in high-throughput and to quantify the abundances and post-translational modification states of many different proteins in complex mixtures. Here, we provide an overview of the various applications of protein microarray technology and compare the strengths and technical challenges associated with each approach. Overall, we conclude that if this technology is to have a substantial impact on our understanding of cell biology and physiology, increased emphasis must be placed on obtaining rigorously controlled, quantitative data from protein function microarrays and on assessing the selectivity of reagents used in conjunction with protein-detecting microarrays.