Engineered control of protein activity in living cells

Cell behavior results from the precise orchestration of molecular activity in time and space. The need to understand dynamics of proteins in the context of living systems has recently led to the development of a remarkable suite of protein ‘switches’, engineered domains and other approaches that cause proteins to respond to small molecules or light, enabling us to control the spatiotemporal dynamics of protein-protein interactions, posttranslational modifications, conformational change, and subcellular localization. However, existing methods suffer from many disadvantages including increased basal activity before protein activation, slow kinetics, difficulty in delivery and expression, and inefficient activation. This dissertation describes two strategies to manipulate protein activity to interrogate the role of the protein of interest in cell motility. In the first study, I developed a ligand-controlled switch to manipulate activities of various kinases dynamically. In the second study, I developed a novel and generalizable approach to control protein activity by splitting target proteins and regulating their reassembly using a ligand or light. Both methods were used to investigate the dynamics of proteins including kinases and guanine nucleotide exchange factors in cell motility.Doctor of Philosoph

Optimization Based Tumor Classification from Microarray Gene Expression Data

An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers

Gain-of-Function Mutation W493R in the Epithelial Sodium Channel Allosterically Reconfigures Intersubunit Coupling

Sodium absorption in epithelial cells is rate-limited by the epithelial sodium channel (ENaC) activity in lung, kidney, and the distal colon. Pathophysiological conditions, such as cystic fibrosis and Liddle syndrome, result from water-electrolyte imbalance partly due to malfunction of ENaC regulation. Because the quaternary structure of ENaC is yet undetermined, the bases of pathologically linked mutations in ENaC subunits α, β, and γ are largely unknown. Here, we present a structural model of heterotetrameric ENaC α1βα2γ that is consistent with previous cross-linking results and site-directed mutagenesis experiments. By using this model, we show that the disease-causing mutation αW493R rewires structural dynamics of the intersubunit interfaces α1β and α2γ. Changes in dynamics can allosterically propagate to the channel gate. We demonstrate that cleavage of the γ-subunit, which is critical for full channel activation, does not mediate activation of ENaC by αW493R. Our molecular dynamics simulations led us to identify a channel-activating electrostatic interaction between α2Arg-493 and γGlu-348 at the α2γ interface. By neutralizing a sodium-binding acidic patch at the α1β interface, we reduced ENaC activation of αW493R by more than 2-fold. By combining homology modeling, molecular dynamics, cysteine cross-linking, and voltage clamp experiments, we propose a dynamics-driven model for the gain-of-function in ENaC by αW493R. Our integrated computational and experimental approach advances our understanding of structure, dynamics, and function of ENaC in its disease-causing state

Structural and Dynamic Determinants of Protein-Peptide Recognition

Protein-peptide interactions play important roles in many cellular processes, including signal transduction, trafficking, and immune recognition. Protein conformational changes upon binding, an ill-defined peptide binding surface, and the large number of peptide degrees of freedom make the prediction of protein-peptide interactions particularly challenging. To address these challenges, we perform rapid molecular dynamics simulations in order to examine the energetic and dynamic aspects of protein-peptide binding. We find that, in most cases, we recapitulate the native binding sites and native-like poses of protein-peptide complexes. Inclusion of electrostatic interactions in simulations significantly improves the prediction accuracy. Our results also highlight the importance of protein conformational flexibility, especially side-chain movement, which allows the peptide to optimize its conformation. Our findings not only demonstrate the importance of sufficient sampling of the protein and peptide conformations, but also reveal the possible effects of electrostatics and conformational flexibility on peptide recognition

Identification of novel integrin binding partners for CIB1: structural and thermodynamic basis of CIB1 promiscuity

The short cytoplasmic tails of the α and β chains of integrin adhesion receptors regulate integrin activation and cell signaling. Significantly less is known about proteins that bind to α-integrin cytoplasmic tails (CTs) than β-CTs to regulate integrins. CIB1 was previously identified as an αIIb binding partner that inhibits agonist-induced activation of the platelet-specific integrin, αIIbβ3. A sequence alignment of all α-integrin CTs revealed that key residues in the CIB1 binding site on αIIb are well-conserved, and was used to delineate a consensus binding site (I/L-x-x-x-L/M-W/Y-K-x-G-F-F). Because the CIB1 binding site on αIIb is conserved in all α-integrins, and CIB1 expression is ubiquitous, we asked if CIB1 could interact with other α-integrin CTs. We predicted that multiple α-integrin CTs were capable of binding to the same hydrophobic binding pocket on CIB1 with docking models generated by all-atom replica exchange discrete molecular dynamics. After demonstrating novel in vivo interactions between CIB1 and other whole integrin complexes with co-immunopreceipitations, we validated the modeled predictions with solid-phase competitive binding assays showing that other α-integrin CTs compete with the αIIb CT for binding to CIB1 in vitro. Isothermal titration calorimetry measurements indicated that this binding is driven by hydrophobic interactions and depends on residues in the CIB1 consensus binding site. These new mechanistic details of CIB1-integrin binding imply that CIB1 could bind to all integrin complexes and act as a broad regulator of integrin function

Low-Dose Vertical Inhibition of the RAF-MEK-ERK Cascade Causes Apoptotic Death of KRAS Mutant Cancers

We address whether combinations with a pan-RAF inhibitor (RAFi) would be effective in KRAS mutant pancreatic ductal adenocarcinoma (PDAC). Chemical library and CRISPR genetic screens identify combinations causing apoptotic anti-tumor activity. The most potent combination, concurrent inhibition of RAF (RAFi) and ERK (ERKi), is highly synergistic at low doses in cell line, organoid, and rat models of PDAC, whereas each inhibitor alone is only cytostatic. Comprehensive mechanistic signaling studies using reverse phase protein array (RPPA) pathway mapping and RNA sequencing (RNA-seq) show that RAFi/ERKi induced insensitivity to loss of negative feedback and system failures including loss of ERK signaling, FOSL1, and MYC; shutdown of the MYC transcriptome; and induction of mesenchymal-to-epithelial transition. We conclude that low-dose vertical inhibition of the RAF-MEK-ERK cascade is an effective therapeutic strategy for KRAS mutant PDAC.Peer reviewe

FRET binding antenna reports spatiotemporal dynamics of GDI-Cdc42 GTPase interactions

Guanine-nucleotide dissociation inhibitors (GDI) are negative regulators of Rho family GTPases that sequester the GTPases away from the membrane. Here we ask how GDI-Cdc42 interaction regulates localized Cdc42 activation for cell motility. The sensitivity of cells to overexpression of Rho family pathway components led us to a new biosensor design (GDI.Cdc42 FLARE), in which Cdc42 was modified with a FRET ‘binding antenna’ that selectively reported Cdc42 binding to endogenous GDI. Similar antennae could also report GDI-Rac1 and GDI-RhoA interaction. Through computational multiplexing and simultaneous imaging, we determined the spatiotemporal dynamics of GDI-Cdc42 interaction and Cdc42 activation during cell protrusion and retraction. This revealed a remarkably tight coordination of GTPase release and activation on a time scale of 10 seconds, suggesting that GDI-Cdc42 interactions are a critical component in the spatiotemporal regulation of Cdc42 activity, and not merely a mechanism for global sequestration of an inactivated pool of signaling molecules

Profiling cellular morphodynamics by spatiotemporal spectrum decomposition

<div><p>Cellular morphology and associated morphodynamics are widely used for qualitative and quantitative assessments of cell state. Here we implement a framework to profile cellular morphodynamics based on an adaptive decomposition of local cell boundary motion into instantaneous frequency spectra defined by the Hilbert-Huang transform (HHT). Our approach revealed that spontaneously migrating cells with approximately homogeneous molecular makeup show remarkably consistent instantaneous frequency distributions, though they have markedly heterogeneous mobility. Distinctions in cell edge motion between these cells are captured predominantly by differences in the magnitude of the frequencies. We found that acute photo-inhibition of Vav2 guanine exchange factor, an activator of the Rho family of signaling proteins coordinating cell motility, produces significant shifts in the frequency distribution, but does not affect frequency magnitude. We therefore concluded that the frequency spectrum encodes the wiring of the molecular circuitry that regulates cell boundary movements, whereas the magnitude captures the activation level of the circuitry. We also used HHT spectra as multi-scale spatiotemporal features in statistical region merging to identify subcellular regions of distinct motion behavior. In line with our conclusion that different HHT spectra relate to different signaling regimes, we found that subcellular regions with different morphodynamics indeed exhibit distinct Rac1 activities. This algorithm thus can serve as an accurate and sensitive classifier of cellular morphodynamics to pinpoint spatial and temporal boundaries between signaling regimes.</p></div