cDNA for the human beta 2-adrenergic receptor: a protein with multiple membrane-spanning domains and encoded by a gene whose chromosomal location is shared with that of the receptor for platelet-derived growth factor.

We have isolated and sequenced a cDNA encoding the human beta 2-adrenergic receptor. The deduced amino acid sequence (413 residues) is that of a protein containing seven clusters of hydrophobic amino acids suggestive of membrane-spanning domains. While the protein is 87% identical overall with the previously cloned hamster beta 2-adrenergic receptor, the most highly conserved regions are the putative transmembrane helices (95% identical) and cytoplasmic loops (93% identical), suggesting that these regions of the molecule harbor important functional domains. Several of the transmembrane helices also share lesser degrees of identity with comparable regions of select members of the opsin family of visual pigments. We have localized the gene for the beta 2-adrenergic receptor to q31-q32 on chromosome 5. This is the same position recently determined for the gene encoding the receptor for platelet-derived growth factor and is adjacent to that for the FMS protooncogene, which encodes the receptor for the macrophage colony-stimulating factor.

Determination of Biomolecular Interdomain Motions using Nuclear Magnetic Resonance

Biological macromolecules can rearrange interdomain orientations when binding to various partners. Interdomain dynamics serve as a molecular mechanism to guide the transitions between orientations. However, our understanding of interdomain dynamics is limited because a useful description of interdomain motions requires an estimate of the probabilities of interdomain conformations, increasing complexity of the problem.

Staphylococcal protein A (SpA) has five tandem protein-binding domains and four interdomain linkers. The domains enable Staphylococcus aureus to evade the host immune system by binding to multiple host proteins including antibodies. Here, I present a study of the interdomain motions of two adjacent domains in SpA. NMR spin relaxation experiments identified a 6-residue flexible interdomain linker and interdomain motions. To quantify the anisotropy of the distribution of interdomain orientations, we measured residual dipolar couplings (RDCs) from the two domains with multiple alignments. The N-terminal domain was directly aligned by a lanthanide ion and not influenced by interdomain motions, so it acted as a reference frame to achieve motional decoupling. We also applied {\it de novo} methods to extract spatial dynamic information from RDCs and represent interdomain motions as a continuous distribution on the 3D rotational space. Significant anisotropy was observed in the distribution, indicating the motion populates some interdomain orientations more than others. Statistical thermodynamic analysis of the observed orientational distribution suggests that it is among the energetically most favorable orientational distributions for binding to antibodies. Thus, the affinity is enhanced by a pre-posed distribution of interdomain orientations while maintaining the flexibility required for function.

The protocol described above can be applied to other biological systems in general. Protein molecule calmodulin and RNA molecule trans-activation response element (TAR) also have intensive interdomain motions with relative small intradomain dynamics. Their interdomain motions were studied using our method based on published RDC data. Our results were consistent with literature results in general. The differences could be due to previous studies' use of physical models, which contain assumptions about potential energy and thus introduced non-experimental information into the interpretations.

Spontaneous Unfolding and Refolding of FNIII Domains Assayed by Thiol Exchange

Fibronectin (FN) is a large extracellular matrix (ECM) protein that is made up of

type I (FNI), type II (FNII), & type III (FNIII) domains. It assembles into an insoluble

supra-­‐‑molecular structure: the fibrillar FN matrix. FN fibrillogenesis is a cell‐‑mediated process, which is initiated when FN binds to integrins on the cell surface. The FN matrix plays an important role in cell migration, proliferation, signaling & adhesion. Despite decades of research, the FN matrix is one of the least understood supra-­‐‑molecular protein assemblies. There have been several attempts to elucidate the exact mechanism of matrix assembly resulting in significant progress in the field but it is still unclear as to what are FN-­‐‑FN interactions, the nature of these interactions and the domains of FN that

are in contact with each other. FN matrix fibrils are elastic in nature. Two models have been proposed to explain the elasticity of the fibrils. The first model: the ‘domain unfolding’ model postulates that the unraveling of FNIII domains under tension explains fibril elasticity.

The second model relies on the conformational change of FN from compact to extended to explain fibril elasticity. FN contain 15 FNIII domains, each a 7-­‐‑strand beta sandwich. Earlier work from our lab used the technique of labeling a buried Cys to study the ‘domain unfolding’ model. They used mutant FNs containing a buried Cys in a single FNIII domain and found that 6 of the 15 FNIII domains label in matrix fibrils. Domain unfolding due to tension, matrix associated conformational changes or spontaneous folding and unfolding are all possible explanation for labeling of the buried Cys. The present study also uses the technique of labeling a buried Cys to address whether it is spontaneous folding and unfolding that labels FNIII domains in cell culture. We used thiol reactive DTNB to measure the kinetics of labeling of buried Cys in eleven FN III domains over a wide range of urea concentrations (0-­‐‑9M). The kinetics data were globally fit using Mathematica. The results are equivalent to those of H-­‐‑D exchange, and

provide a comprehensive analysis of stability and unfolding/folding kinetics of each

domain. For two of the six domains spontaneous folding and unfolding is possibly the reason for labeling in cell culture. For the rest of the four domains it is probably matrix associated conformational changes or tension induced unfolding.

A long-­‐‑standing debate in the protein-­‐‑folding field is whether unfolding rate

constants or folding rate constants correlate to the stability of a protein. FNIII domains all have the same ß sandwich structure but very different stabilities and amino acid sequences. Our study analyzed the kinetics of unfolding and folding and stabilities of eleven FNIII domains and our results show that folding rate constants for FNIII domains are relatively similar and the unfolding rates vary widely and correlate to stability. FN forms a fibrillar matrix and the FN-­‐‑FN interactions during matrix fibril formation are not known. FNI 1-­‐‑9 or the N-­‐‑ terminal region is indispensible for matrix formation and its major binding partner has been shown to be FNIII 2. Earlier work from our lab, using FRET analysis showed that the interaction of FNI 1-­‐‑9 with a destabilized FNIII 2 (missing the G strand, FNIII 2ΔG) reduces the FRET efficiency. This efficiency is restored in the presence of FUD (bacterial adhesion from S. pyogenes) that has been known to interact with FNI 1-­‐‑9 via a tandem ß zipper. In the present study we

use FRET analysis and a series of deletion mutants of FNIII 2ΔG to study the shortest fragment of FNIII 2ΔG that is required to bind FNI 1-­‐‑9. Our results presented here are qualitative and show that FNIII 2ΔC’EFG is the shortest fragment required to bind FNI 1-­‐‑9. Deletion of one more strand abolishes the interaction with FNI 1-­‐‑9.

Identification of the endophilins (SH3p4/p8/p13) as novel binding partners for the beta1-adrenergic receptor.

Several G-protein coupled receptors, such as the beta1-adrenergic receptor (beta1-AR), contain polyproline motifs within their intracellular domains. Such motifs in other proteins are known to mediate protein-protein interactions such as with Src homology (SH)3 domains. Accordingly, we used the proline-rich third intracellular loop of the beta1-AR either as a glutathione S-transferase fusion protein in biochemical "pull-down" assays or as bait in the yeast two-hybrid system to search for interacting proteins. Both approaches identified SH3p4/p8/p13 (also referred to as endophilin 1/2/3), a SH3 domain-containing protein family, as binding partners for the beta1-AR. In vitro and in human embryonic kidney (HEK) 293 cells, SH3p4 specifically binds to the third intracellular loop of the beta1-AR but not to that of the beta2-AR. Moreover, this interaction is mediated by the C-terminal SH3 domain of SH3p4. Functionally, overexpression of SH3p4 promotes agonist-induced internalization and modestly decreases the Gs coupling efficacy of beta1-ARs in HEK293 cells while having no effect on beta2-ARs. Thus, our studies demonstrate a role of the SH3p4/p8/p13 protein family in beta1-AR signaling and suggest that interaction between proline-rich motifs and SH3-containing proteins may represent a previously underappreciated aspect of G-protein coupled receptor signaling.

From Polarity to Morphogenesis PAK Behaviors and Mechanism for Bud Sensing in Morphogenesis Checkpoint

Bud formation by Saccharomyces cerevisiae is a fundamental process for yeast proliferation. Bud emergence is initiated by the polarization of the cytoskeleton, leading to local secretory vesicle delivery and gulcan synthase activity. The master regulator of polarity establishment is a small Rho-family GTPase – Cdc42. Cdc42 forms a clustered patch at the incipient budding site in late G1 and mediates downstream events which lead to bud emergence. Cdc42 promotes morphogenesis via its various effectors. PAKs (p21-activated kinases) are important Cdc42 effectors which mediate actin cytoskeleton polarization and septin filament assembly. The PAKs Cla4 and Ste20 share common binding domains for GTP-Cdc42 and they are partially redundant in function. However, we found that Cla4 and Ste20 behaved differently during the polarization and this depended on their different membrane interaction domains. Also, Cla4 and Ste20 compete for a limited number of binding sites at the polarity patch during bud emergence. These results suggest that PAKs may be differentially regulated during polarity establishment.

Morphogenesis of yeast must be coordinated with the nuclear cycle to enable successful proliferation. Many environmental stresses temporarily disrupt bud formation, and in such circumstances, the morphogenesis checkpoint halts nuclear division until bud formation can resume. Bud emergence is essential for degradation of the mitotic inhibitor, Swe1. Swe1 is localized to the septin cytoskeleton at the bud neck by the Swe1-binding protein Hsl7. Neck localization of Swe1 is required for Swe1 degradation. Although septins form a ring at the presumptive bud site prior to bud emergence, Hsl7 is not recruited to the septins until after bud emergence, suggesting that septins and/or Hsl7 respond to a “bud sensor”. Here we show that recruitment of Hsl7 to the septin ring depends on a combination of two septin-binding kinases: Hsl1 and Elm1. We elucidate which domains of these kinases are needed, and show that artificial targeting of those domains suffices to recruit Hsl7 to septin rings even in unbudded cells. Moreover, recruitment of Elm1 is responsive to bud emergence. Our findings suggest that Elm1 plays a key role in sensing bud emergence.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.