Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.

Homologous protein sequences are wonderfully diverse, indicating many possible evolutionary "solutions" to the encoding of function. Consequently, one can construct statistical models of protein sequence by analyzing amino acid frequency across a large multiple sequence alignment. A central premise is that covariance between amino acid positions reflects coevolution due to a shared functional or biophysical constraint. In this review, we describe the implementation and discuss the advantages, limitations, and recent progress on two coevolution-based modeling approaches: (1) Potts models of protein sequence (direct coupling analysis [DCA]-like), and (2) the statistical coupling analysis (SCA). Each approach detects interesting features of protein sequence and structure-the former emphasizes local physical contacts throughout the structure, while the latter identifies larger evolutionarily coupled networks of residues. Recent advances in large-scale gene synthesis and high-throughput functional selection now motivate additional work to benchmark model performance across quantitative function prediction and de novo design tasks.

Structurally distributed surface sites tune allosteric regulation.

Our ability to rationally optimize allosteric regulation is limited by incomplete knowledge of the mutations that tune allostery. Are these mutations few or abundant, structurally localized or distributed? To examine this, we conducted saturation mutagenesis of a synthetic allosteric switch in which Dihydrofolate reductase (DHFR) is regulated by a blue-light sensitive LOV2 domain. Using a high-throughput assay wherein DHFR catalytic activity is coupled to E. coli growth, we assessed the impact of 1548 viable DHFR single mutations on allostery. Despite most mutations being deleterious to activity, fewer than 5% of mutations had a statistically significant influence on allostery. Most allostery disrupting mutations were proximal to the LOV2 insertion site. In contrast, allostery enhancing mutations were structurally distributed and enriched on the protein surface. Combining several allostery enhancing mutations yielded near-additive improvements to dynamic range. Our results indicate a path toward optimizing allosteric function through variation at surface sites.

Many proteins exhibit a property called 'allostery'. In allostery, an input signal at a specific site of a protein ­ such as a molecule binding, or the protein absorbing a photon of light ­ leads to a change in output at another site far away. For example, the protein might catalyze a chemical reaction faster or bind to another molecule more tightly in the presence of the input signal. This protein 'remote control' allows cells to sense and respond to changes in their environment. An ability to rapidly engineer new allosteric mechanisms into proteins is much sought after because this would provide an approach for building biosensors and other useful tools. One common approach to engineering new allosteric regulation is to combine a 'sensor' or input region from one protein with an 'output' region or domain from another. When researchers engineer allostery using this approach of combining input and output domains from different proteins, the difference in the output when the input is 'on' versus 'off' is often small, a situation called 'modest allostery'. McCormick et al. wanted to know how to optimize this domain combination approach to increase the difference in output between the 'on' and 'off' states. More specifically, McCormick et al. wanted to find out whether swapping out or mutating specific amino acids (each of the individual building blocks that make up a protein) enhances or disrupts allostery. They also wanted to know if there are many possible mutations that change the effectiveness of allostery, or if this property is controlled by just a few amino acids. Finally, McCormick et al. questioned where in a protein most of these allostery-tuning mutations were located. To answer these questions, McCormick et al. engineered a new allosteric protein by inserting a light-sensing domain (input) into a protein involved in metabolism (a metabolic enzyme that produces a biomolecule called a tetrahydrofolate) to yield a light-controlled enzyme. Next, they introduced mutations into both the 'input' and 'output' domains to see where they had a greater effect on allostery. After filtering out mutations that destroyed the function of the output domain, McCormick et al. found that only about 5% of mutations to the 'output' domain altered the allosteric response of their engineered enzyme. In fact, most mutations that disrupted allostery were found near the site where the 'input' domain was inserted, while mutations that enhanced allostery were sprinkled throughout the enzyme, often on its protein surface. This was surprising in light of the commonly-held assumption that mutations on protein surfaces have little impact on the activity of the 'output' domain. Overall, the effect of individual mutations on allostery was small, but McCormick et al. found that these mutations can sometimes be combined to yield larger effects. McCormick et al.'s results suggest a new approach for optimizing engineered allosteric proteins: by introducing mutations on the protein surface. It also opens up new questions: mechanically, how do surface sites affect allostery? In the future, it will be important to characterize how combinations of mutations can optimize allosteric regulation, and to determine what evolutionary trajectories to high performance allosteric 'switches' look like.

Transport of Alzheimer's associated amyloid-ß catalyzed by P-glycoprotein.

P-glycoprotein (P-gp) is a critical membrane transporter in the blood brain barrier (BBB) and is implicated in Alzheimer's disease (AD). However, previous studies on the ability of P-gp to directly transport the Alzheimer's associated amyloid-ß (Aß) protein have produced contradictory results. Here we use molecular dynamics (MD) simulations, transport substrate accumulation studies in cell culture, and biochemical activity assays to show that P-gp actively transports Aß. We observed transport of Aß40 and Aß42 monomers by P-gp in explicit MD simulations of a putative catalytic cycle. In in vitro assays with P-gp overexpressing cells, we observed enhanced accumulation of fluorescently labeled Aß42 in the presence of Tariquidar, a potent P-gp inhibitor. We also showed that Aß42 stimulated the ATP hydrolysis activity of isolated P-gp in nanodiscs. Our findings expand the substrate profile of P-gp, and suggest that P-gp may contribute to the onset and progression of AD.

Strategies for Engineering and Rewiring Kinase Regulation.

Eukaryotic protein kinases (EPKs) catalyze the transfer of a phosphate group onto another protein in response to appropriate regulatory cues. In doing so, they provide a primary means for cellular information transfer. Consequently, EPKs play crucial roles in cell differentiation and cell-cycle progression, and kinase dysregulation is associated with numerous disease phenotypes including cancer. Nonnative cues for synthetically regulating kinases are thus much sought after, both for dissecting cell signaling pathways and for pharmaceutical development. In recent years advances in protein engineering and sequence analysis have led to new approaches for manipulating kinase activity, localization, and in some instances specificity. These tools have revealed fundamental principles of intracellular signaling and suggest paths forward for the design of therapeutic allosteric kinase regulators.

Multiple Drug Transport Pathways through Human P-Glycoprotein.

P-Glycoprotein (P-gp) is a plasma membrane efflux pump that is commonly associated with therapy resistances in cancers and infectious diseases. P-gp can lower the intracellular concentrations of many drugs to subtherapeutic levels by translocating them out of the cell. Because of the broad range of substrates transported by P-gp, overexpression of P-gp causes multidrug resistance. We reported previously on dynamic transitions of P-gp as it moved through conformations based on crystal structures of homologous ABCB1 proteins using in silico targeted molecular dynamics techniques. We expanded these studies here by docking transport substrates to drug binding sites of P-gp in conformations open to the cytoplasm, followed by cycling the pump through conformations that opened to the extracellular space. We observed reproducible transport of two substrates, daunorubicin and verapamil, by an average of 11-12 Å through the plane of the membrane as P-gp progressed through a catalytic cycle. Methylpyrophosphate, a ligand that should not be transported by P-gp, did not show this movement through P-gp. Drug binding to either of two subsites on P-gp appeared to determine the initial pathway used for drug movement through the membrane. The specific side-chain interactions with drugs within each pathway seemed to be, at least in part, stochastic. The docking and transport properties of a P-gp inhibitor, tariquidar, were also studied. A mechanism of inhibition by tariquidar that involves stabilization of an outward open conformation with tariquidar bound in intracellular loops or at the drug binding domain of P-gp is presented.