Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug’s activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

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Design of a regulatable multistate protein is a challenge for protein engineering. Here we design a protein with a unique topology, called uniRapR, whose conformation is controlled by the binding of a small molecule. We confirm switching and control ability of uniRapR in silico, in vitro, and in vivo. As a proof of concept, uniRapR is used as an artificial regulatory domain to control activity of kinases. By activating Src kinase using uniRapR in single cells and whole organism, we observe two unique phenotypes consistent with its role in metastasis. Activation of Src kinase leads to rapid induction of protrusion with polarized spreading in HeLa cells, and morphological changes with loss of cell-cell contacts in the epidermal tissue of zebrafish. The rational creation of uniRapR exemplifies the strength of computational protein design, and offers a powerful means for targeted activation of many pathways to study signaling in living organisms.spatiotemporal control | cell motility | endothelial-mesenchymal transition T he past two decades have seen a revolution in computational protein design, with remarkable milestones including design of a helical protein from first principles (1), redesign of zinc finger proteins (2), and de novo design of an α/β protein (3). These studies highlighted, as a proof of principle, our ability to rationally control the structure of proteins by using basic physical principles and phenomenology. These approaches are based on finding an optimal sequence for a given single structure or ensemble of related states, and do not provide a strategy to construct a protein capable of large on-demand conformational transitions (4, 5). A number of multistate protein design algorithms (4, 6) have been proposed; however, designing an experimentally confirmed, regulatable multistate protein, or a conformational switch (5), still remains as a challenging task because of the necessity of engineering and controlling multiple protein states (4,7,8).Such a conformational switch protein has great advantages in cell signaling, because it can be used as a universal regulatory domain (9) for precise, specific, and temporal control over rapidly activated signaling proteins (5, 10-15). Traditional genetically encoded methods for temporal protein control at the protein level have several drawbacks (5, 13). Recently developed protein switches, including derivatives of the light, oxygen, or voltage (LOV) domain (16, 17), can provide direct control at the protein level with light, but cannot be readily used in nontransparent animals. Our previous rapamycin regulated (RapR) kinase method (14) can potentially overcome this problem, but it requires expression and control of two proteins. The variable stoichiometry of these proteins renders the response more heterogeneous and essentially impractical in animals. Therefore, a single-chain, insertable, and transferable regulatory domain would be very valuable.Here we design a ligand-controlled conformational switch, uniRapR, a potentially broadly applicable, single-chai...

Background: Amino acid selection by tryptophanyl-tRNA synthetase (TrpRS) requires intermodular coupling. Results: Dynamic repacking of four side chains increases amino acid specificity 500-fold in contemporary TrpRS by reducing pocket size near the transition state. Conclusion: An ancient tertiary packing motif not only activates the catalytic Mg 2ϩ ion during catalysis, but also determines cognate amino acid specificity. Significance: Allosteric enforcement of specificity increases robustness to mutation.

PATH rapidly computes a path and a transition state between crystal structures by minimizing the Onsager-Machlup action. It requires input parameters whose range of values can generate different transition-state structures that cannot be uniquely compared with those generated by other methods. We outline modifications to estimate these input parameters to circumvent these difficulties and validate the PATH transition states by showing consistency between transition-states derived by different algorithms for unrelated protein systems. Although functional protein conformational change trajectories are to a degree stochastic, they nonetheless pass through a well-defined transition state whose detailed structural properties can rapidly be identified using PATH.