Drugs and drug combinations have complex biological effects on cells and organisms. Little is known about how drugs affect protein dynamics that determine these effects. Here, we use a dynamic proteomics approach to accurately follow 15 protein levels in human cells in response to 13 different drugs. We find that protein dynamics in response to combinations of drugs are described accurately by a linear superposition (weighted sum) of their response to individual drugs.

The weights in this superposition describe the relative impact of each drug on each protein. Using these weights, we show that one can predict the dynamics in a three-drug or four-drug combination on the basis of the dynamics in drug pairs. Our approach might eliminate the need to increase the number of experiments exponentially with the number of drugs and suggests that it might be possible to rationally control protein dynamics with specific drug combinations. Graphical Abstract. Whereas drug effects on outcome are much studied, the effect of drug combinations on protein dynamics in the cell is much less investigated. Understanding the impact of drugs on each protein is important in the context of a vision of a future medicine that controls protein dynamics precisely, using specific combinations of a large number of drugs. Imagine that the cell state can be described in a high dimensional space in which each axis is the level (or localization or activity) of one of the cells proteins ().

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The goal is to guide the cell from a “sick state” to a “healthy state” in a rational manner (or, in the case of cancer, from a living cancer cell to a dead or quiescent cancer cell). This entails understanding the dynamical response of each protein to each drug at each dose, in the context of all possible combinations of the other drugs. To approach such rational control requires study of the degree to which protein dynamics in drug combinations can be understood.

• • • • ) to follow the dynamics of the expression level of a panel of proteins in individual living human cells at high temporal resolution and accuracy, as they respond to combinations of 13 drugs, including chemotherapeutic drugs, specific pathway inhibitors, and growth factors. We find that the dynamics of each protein in the presence of a drug combination is described accurately by a linear superposition (weighted sum) of the dynamics in the presence of each drug alone. The weights in this superposition are constant over time and depend on the drug dose. They describe the relative impact of each drug on each protein. We find that one can predict the dynamics in a multidrug combination on the basis of the dynamics in pairs of drugs. Extending this approach may allow understanding the vast space of multidrug and multidose combinations based on a relatively small number of two-drug measurements.

Results System for Measuring Protein Dynamics in Living Cells. We employed a library of cell line clones, based on the H1299 human lung cancer cell line. In each clone, a different protein is tagged with yellow fluorescent protein (YFP), inserted as an exon in the chromosomal locus of the gene. The clone expresses a full-length fusion protein from its native chromosomal location under its endogenous regulation. Immunoblots on a subset of these clones indicated that 80% of the tagged proteins serve as accurate markers for the dynamics of the endogenous protein (.

For this study, we selected 15 proteins that represent different aspects of cell biology (see, part a, available online). These include representatives from systems such as the ribosome, oxidative stress, DNA repair, metabolism, cytoskeleton, and apoptosis. The proteins localize to different cell compartments, including the nucleus, cytoplasm, and nucleoli. The dynamics of each protein in the presence of drugs was monitored by time-lapse microscopy under incubated conditions in multi-well plates as described in.

We chose 13 drugs including chemotherapy agents, pathway inhibitors, and growth factors (, part b). These drugs have diverse antagonistic or synergistic interactions when assayed for cell death using neutral red (). For example, etoposide (ETOPO) combined with camptothecin (CPT) shows synergism, in the sense of enhanced cell killing relative to Bliss independence (in which each drug kills a fraction of the cells independently of the other). In contrast, ETOPO combined with Nocodazole (NCZ) shows reduced killing (antagonism). Standard errors from six repeats are numbers in parentheses. (A) Proteins respond differently to each drug.