I. The 3.7 Å crystal structure of horse heart ferricytochrome C. II. The application of the Karle-Hauptman tangent formula to protein phasing

I. The 3.7 Å Crystal Structure of Horse Heart Ferricytochrome C.

The crystal structure of horse heart ferricytochrome c has been determined to a resolution of 3.7 Å using the multiple isomorphous replacement technique. Two isomorphous derivatives were used in the analysis, leading to a map with a mean figure of merit of 0.458. The quality of the resulting map was extremely high, even though the derivative data did not appear to be of high quality.

Although it was impossible to fit the known amino acid sequence to the calculated structure in an unambiguous way, many important features of the molecule could still be determined from the 3.7 Å electron density map. Among these was the fact that cytochrome c contains little or no α-helix. The polypeptide chain appears to be wound about the heme group in such a way as to form a loosely packed hydrophobic core in the molecule.

The heme group is located in a cleft on the molecule with one edge exposed to the solvent. The fifth coordinating ligand is His 18 and the sixth coordinating ligand is probably neither His 26 nor His 33.

The high resolution analysis of cytochrome c is now in progress and should be completed within the next year.

II. The Application of the Karle-Hauptman Tangent Formula to Protein Phasing.

The Karle-Hauptman tangent formula has been shown to be applicable to the refinement of previously determined protein phases. Tests were made with both the cytochrome c data from Part I and a theoretical structure based on the myoglobin molecule. The refinement process was found to be highly dependent upon the manner in which the tangent formula was applied. Iterative procedures did not work well, at least at low resolution.

The tangent formula worked very well in selecting the true phase from the two possible phase choices resulting from a single isomorphous replacement phase analysis. The only restriction on this application is that the heavy atoms form a non-centric cluster in the unit cell.

Pages 156 through 284 in this Thesis consist of previously published papers relating to the above two sections. References to these papers can be found on page 155.

Protein-protein recognition: The neonatal Fe receptor and immunoglobulin G

The neonatal Fe receptor (FeRn) binds the Fe portion of immunoglobulin G (IgG) at the acidic pH of endosomes or the gut and releases IgG at the alkaline pH of blood. FeRn is responsible for the maternofetal transfer of IgG and for rescuing endocytosed IgG from a default degradative pathway. We investigated how FeRn interacts with IgG by constructing a heterodimeric form of the Fe (hdFc) that contains one FeRn binding site. This molecule was used to characterize the interaction between one FeRn molecule and one Fe and to determine under what conditions FeRn forms a dimer. The hdFc binds one FeRn molecule at pH 6.0 with a K_d of 80 nM. In solution and with FeRn anchored to solid supports, the heterodimeric Fe does not induce a dimer of FeRn molecules. FcRnhdFc complex crystals were obtained and the complex structure was solved to 2.8 Å resolution. Analysis of this structure refined the understanding of the mechanism of the pH-dependent binding, shed light on the role played by carbohydrates in the Fe binding, and provided insights on how to design therapeutic IgG antibodies with longer serum half-lives. The FcRn-hdFc complex in the crystal did not contain the FeRn dimer. To characterize the tendency of FeRn to form a dimer in a membrane we analyzed the tendency of the hdFc to induce cross-phosphorylation of FeRn-tyrosine kinase chimeras. We also constructed FeRn-cyan and FeRn-yellow fluorescent proteins and have analyzed the tendency of these molecules to exhibit fluorescence resonance energy transfer. As of now, neither of these analyses have lead to conclusive results. In the process of acquiring the context to appreciate the structure of the FcRn-hdFc interface, we developed a study of 171 other nonobligate protein-protein interfaces that includes an original principal component analysis of the quantifiable aspects of these interfaces.

Computational design of self-assembling proteins and protein-DNA nanowires

Computational protein design (CPD) is a burgeoning field that uses a physical-chemical or knowledge-based scoring function to create protein variants with new or improved properties. This exciting approach has recently been used to generate proteins with entirely new functions, ones that are not observed in naturally occurring proteins. For example, several enzymes were designed to catalyze reactions that are not in the repertoire of any known natural enzyme. In these designs, novel catalytic activity was built de novo (from scratch) into a previously inert protein scaffold. In addition to de novo enzyme design, the computational design of protein-protein interactions can also be used to create novel functionality, such as neutralization of influenza. Our goal here was to design a protein that can self-assemble with DNA into nanowires. We used computational tools to homodimerize a transcription factor that binds a specific sequence of double-stranded DNA. We arranged the protein-protein and protein-DNA binding sites so that the self-assembly could occur in a linear fashion to generate nanowires. Upon mixing our designed protein homodimer with the double-stranded DNA, the molecules immediately self-assembled into nanowires. This nanowire topology was confirmed using atomic force microscopy. Co-crystal structure showed that the nanowire is assembled via the desired interactions. To the best of our knowledge, this is the first example of a protein-DNA self-assembly that does not rely on covalent interactions. We anticipate that this new material will stimulate further interest in the development of advanced biomaterials.

Structure Prediction of G-Protein Coupled Receptors

G-protein coupled receptors (GPCRs) form a large family of proteins and are very important drug targets. They are membrane proteins, which makes computational prediction of their structure challenging. Homology modeling is further complicated by low sequence similarly of the GPCR superfamily.

In this dissertation, we analyze the conserved inter-helical contacts of recently solved crystal structures, and we develop a unified sequence-structural alignment of the GPCR superfamily. We use this method to align 817 human GPCRs, 399 of which are nonolfactory. This alignment can be used to generate high quality homology models for the 817 GPCRs.

To refine the provided GPCR homology models we developed the Trihelix sampling method. We use a multi-scale approach to simplify the problem by treating the transmembrane helices as rigid bodies. In contrast to Monte Carlo structure prediction methods, the Trihelix method does a complete local sampling using discretized coordinates for the transmembrane helices. We validate the method on existing structures and apply it to predict the structure of the lactate receptor, HCAR1. For this receptor, we also build extracellular loops by taking into account constraints from three disulfide bonds. Docking of lactate and 3,5-dihydroxybenzoic acid shows likely involvement of three Arg residues on different transmembrane helices in binding a single ligand molecule.

Protein structure prediction relies on accurate force fields. We next present an effort to improve the quality of charge assignment for large atomic models. In particular, we introduce the formalism of the polarizable charge equilibration scheme (PQEQ) and we describe its implementation in the molecular simulation package Lammps. PQEQ allows fast on the fly charge assignment even for reactive force fields.

Computational Predictions of G Protein-Coupled Receptor Structures and Binding Sites

G protein-coupled receptors (GPCRs) are the largest family of proteins within the human genome. They consist of seven transmembrane (TM) helices, with a N-terminal region of varying length and structure on the extracellular side, and a C-terminus on the intracellular side. GPCRs are involved in transmitting extracellular signals to cells, and as such are crucial drug targets. Designing pharmaceuticals to target GPCRs is greatly aided by full-atom structural information of the proteins. In particular, the TM region of GPCRs is where small molecule ligands (much more bioavailable than peptide ligands) typically bind to the receptors. In recent years nearly thirty distinct GPCR TM regions have been crystallized. However, there are more than 1,000 GPCRs, leaving the vast majority of GPCRs with limited structural information. Additionally, GPCRs are known to exist in a myriad of conformational states in the body, rendering the static x-ray crystal structures an incomplete reflection of GPCR structures. In order to obtain an ensemble of GPCR structures, we have developed the GEnSeMBLE procedure to rapidly sample a large number of variations of GPCR helix rotations and tilts. The lowest energy GEnSeMBLE structures are then docked to small molecule ligands and optimized. The GPCR family consists of five subfamilies with little to no sequence homology between them: class A, B1, B2, C, and Frizzled/Taste2. Almost all of the GPCR crystal structures have been of class A GPCRs, and much is known about their conserved interactions and binding sites. In this work we particularly focus on class B1 GPCRs, and aim to understand that family’s interactions and binding sites both to small molecules and their native peptide ligands. Specifically, we predict the full atom structure and peptide binding site of the glucagon-like peptide receptor and the TM region and small molecule binding sites for eight other class B1 GPCRs: CALRL, CRFR1, GIPR, GLR, PACR, PTH1R, VIPR1, and VIPR2. Our class B1 work reveals multiple conserved interactions across the B1 subfamily as well as a consistent small molecule binding site centrally located in the TM bundle. Both the interactions and the binding sites are distinct from those seen in the more well-characterized class A GPCRs, and as such our work provides a strong starting point for drug design targeting class B1 proteins. We also predict the full structure of CXCR4 bound to a small molecule, a class A GPCR that was not closely related to any of the class A GPCRs at the time of the work.

Investigating the Role of the GET3-GET4/GET5 Interaction During Tail-Anchor Protein Targeting

The proper targeting of membrane proteins is essential to the viability of all cells. Tail-anchored (TA) proteins, defined as having a single transmembrane helix at their C-terminus, are post-translationally targeted to the endoplasmic reticulum (ER) membrane by the GET pathway (Guided Entry of TA proteins). In the yeast pathway, the handover of TA substrates is mediated by the heterotetrameric Get4/Get5 (Get4/5) complex, which tethers the co-chaperone Sgt2 to the central targeting factor, the Get3 ATPase. Although binding of Get4/5 to Get3 is critical for efficient TA targeting, the mechanisms by which Get4 regulates Get3 are unknown. To understand the molecular basis of Get4 function, we used a combination of structural biology, biochemistry, and cell biology. Get4/5 binds across the Get3 dimer interface, in an orientation only compatible with a closed Get3, providing insight into the role of nucleotide in complex formation. Additionally, this structure reveals two functionally distinct binding interfaces for anchoring and ATPase regulation, and loss of the regulatory interface leads to strong defects in vitro and in vivo. Additional crystal structures of the Get3-Get4/5 complex give rise to an alternate conformation, which represents an initial binding interaction mediated by electrostatics that facilitates the rate of subsequent inhibited complex formation. This interface is supported by an in-depth kinetic analysis of the Get3-Get4/5 interaction confirming the two-step complex formation. These results allow us to generate a refined model for Get4/5 function in TA targeting.

Microfluidics-based single-cell functional proteomics microchip for portraying protein signal transduction networks within the framework of physicochemical principles, with applications in fundamental and translational cancer research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research.

The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow.

The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM).

The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model.

The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out.

In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.