Advances in protein topology mapping methods are urgently needed to complement the wealth of interactome data that is presently being generated at a rapid pace. Chemical crosslinking followed by mass spectrometry (MS) has evolved over the last decade as an attractive method for protein topology and interface mapping, and holds great promise as a counterpart to modern interactome studies in the field of proteomics. Furthermore, stabilization of proteins and protein complexes with crosslinking offers many advantages over high-resolution structural mapping methods, including the ability to study protein topologies in vivo. The reliance on direct detection of crosslinked peptides, however, continues to pose challenges to protein topology and interface mapping with chemical crosslinking plus MS. The present body of work aimed to develop a novel generic methodology that utilizes chemical crosslinking, cyanogen bromide (CNBr) cleavage and MS for the low-resolution mapping of protein topologies and interfaces. Through such low-resolution mapping of crosslinked regions, this novel strategy overcomes limitations associated with the direct detection of crosslinked peptides. Following optimization of various steps, the present method was validated with the bacterial DNA-directed RNA polymerase core complex and was subsequently applied to probe the tetrameric assembly of yeast Skp1p-Cdc4p heterodimers. Further improvements were made through the enrichment of crosslinked CNBr-cleaved protein fragments prior to their identification via MS. Two enrichment strategies were developed which depended upon the conjugation of tags to CNBr-cleaved peptide C-termini followed by either tandem affinity purification or tandem reversed-phase HPLC purification. These strategies were successfully applied for the efficient purification of disulfide-linked peptides from peptide mixtures. It is expected that the potential to achieve sensitive mapping of topologies and interfaces of multi-subunit protein complexes in vivo, in combination with further enhancements to permit studies on complex protein samples, will extend the utility of this method to complement large-scale interactome studies.

Advances in protein topology mapping methods are urgently needed to complement the wealth of interactome data that is presently being generated at a rapid pace. Chemical crosslinking followed by mass spectrometry (MS) has evolved over the last decade as an attractive method for protein topology and interface mapping, and holds great promise as a counterpart to modern interactome studies in the field of proteomics. Furthermore, stabilization of proteins and protein complexes with crosslinking offers many advantages over high-resolution structural mapping methods, including the ability to study protein topologies in vivo. The reliance on direct detection of crosslinked peptides, however, continues to pose challenges to protein topology and interface mapping with chemical crosslinking plus MS. The present body of work aimed to develop a novel generic methodology that utilizes chemical crosslinking, cyanogen bromide (CNBr) cleavage and MS for the low-resolution mapping of protein topologies and interfaces. Through such low-resolution mapping of crosslinked regions, this novel strategy overcomes limitations associated with the direct detection of crosslinked peptides. Following optimization of various steps, the present method was validated with the bacterial DNA-directed RNA polymerase core complex and was subsequently applied to probe the tetrameric assembly of yeast Skp1p-Cdc4p heterodimers. Further improvements were made through the enrichment of crosslinked CNBr-cleaved protein fragments prior to their identification via MS. Two enrichment strategies were developed which depended upon the conjugation of tags to CNBr-cleaved peptide C-termini followed by either tandem affinity purification or tandem reversed-phase HPLC purification. These strategies were successfully applied for the efficient purification of disulfide-linked peptides from peptide mixtures. It is expected that the potential to achieve sensitive mapping of topologies and interfaces of multi-subunit protein complexes in vivo, in combination with further enhancements to permit studies on complex protein samples, will extend the utility of this method to complement large-scale interactome studies.

Proteine spielen auf der zellulären Ebene eines Organismus eine fundamentale Rolle. Sie sind quasi die „Maschinen“ der Zelle. Ihre Bedeutung wird nicht zuletzt in ihrem Namen deutlich, welcher 1838 erstmals von J. Berzelius verwendet wurde und „das Erste“, „das Wichtigste“ bedeutet. Proteine sind aus Aminosäuren aufgebaute Moleküle. Unter physiologischen Bedingungen besitzen sie eine definierte dreidimensionale Gestalt, welche für ihre biologische Funktion bestimmend ist. Es wird heutzutage davon ausgegangen, dass diese dreidimensionale, stabile Struktur von Proteinen eindeutig durch die Abfolge der einzelnen Aminosäuren, der Sequenz, bestimmt ist. Diese Abfolge ist für jedes Protein in der Desoxyribonukleinsäure (DNS) gespeichert. Es ist allerdings eines der größten ungelösten Probleme der letzten Jahrzehnte, wie die Beziehung zwischen Sequenz und 3D-Struktur tatsächlich aussieht. Die Beantwortung dieser Fragestellung erfordert interdisziplinäre Ansätze aus Biologie, Informatik und Physik. In dieser Arbeit werden mit Hilfe von Methoden der theoretischen (Bio-) Physik einige der damit verbundenen Aspekte untersucht. Das Hauptaugenmerk liegt dabei auf Wechselwirkungen der einzelnen Aminosäuren eines Proteins untereinander, wofür in dieser Arbeit ein entsprechendes Energiemodell entwickelt wurde. Es werden Grundzustände sowie Energielandschaften untersucht und mit experimentellen Daten verglichen. Die Stärke der Wechselwirkung einzelner Aminosäuren erlaubt zusätzlich Aussagen über die Stabilität von Proteinen bezüglich mechanischer Kräfte. Die vorliegende Arbeit unterteilt sich wie folgt: Kapitel 2 dient der Einleitung und stellt Proteine und ihre Funktionen dar. Kapitel 3 stellt die Modellierung der Proteinstrukturen in zwei verschiedenen Modellen vor, welche in dieser Arbeit entwickelt wurden, um 3D-Strukturen von Proteinen zu beschreiben. Anschließend wird in Kapitel 4 ein Algorithmus zum Auffinden des exakten Energieminimums dargestellt. Kapitel 5 beschäftigt sich mit der Frage, wie eine geeignete diskrete Energiefunktion aus experimentellen Daten gewonnen werden kann. In Kapitel 6 werden erste Ergebnisse dieses Modells dargestellt. Der Frage, ob der experimentell bestimmte Zustand dem energetischen Grundzustand eines Proteins entspricht, wird in Kapitel 7 nachgegangen. Die beiden Kapitel 8 und 9 zeigen die Anwendung des Modells an zwei Proteinen, dem Tryptophan cage protein als dem kleinsten, stabilen Protein und Kinesin, einem Motorprotein, für welches 2007 aufschlussreiche Experimente zur mechanischen Stabilität durchgeführt wurden. Kapitel 10 bis 12 widmen sich Membranproteinen. Dabei beschäftigt sich Kapitel 10 mit der Vorhersage von stabilen Bereichen (sog. Entfaltungsbarrieren) unter externer Krafteinwirkung. Zu Beginn wird eine kurze Einleitung zu Membranproteinen gegeben. Im folgenden Kapitel 11 wird die Entfaltung mit Hilfe des Modells und Monte-Carlo-Techniken simuliert. Mit dem an Membranproteine angepassten Wechselwirkungsmodell ist es möglich, den Einfluss von Mutationen auch ohne explizite strukturelle Informationen vorherzusagen. Dieses Thema wird in Kapitel 12 diskutiert. Die Beziehung zwischen Primär- und Tertiärstruktur eines Proteins wird in Kapitel 13 behandelt. Es wird ein Ansatz skizziert, welcher in der Lage ist, Strukturbeziehungen zwischen Proteinen zu detektieren, die mit herkömmlichen Methoden der Bioinformatik nicht gefunden werden können. Die letzten beiden Kapitel schließlich geben eine Zusammenfassung bzw. einen Ausblick auf künftige Entwicklungen und Anwendungen des Modells.

Thesis (Ph.D.)--University of Alabama at Birmingham, 2008. / Title from PDF title page (viewed on Feb. 10, 2010). Includes bibliographical references.

Predicting the structure of complexes formed by two interacting proteins is an important problem in computation structural biology. Proteins perform many of their functions by binding to other proteins. The structure of protein-protein complexes provides atomic details about protein function and biochemical pathways, and can help in designing drugs that inhibit binding. Docking computationally models the structure of protein-protein complexes, given three-dimensional structures of the individual chains. Protein docking methods have two phases. In the first phase, a comprehensive, coarse search is performed for optimally docked models. In the second refinement and reranking phase, the models from the first phase are refined and reranked, with the expectation of extracting a small set of accurate models from the pool of thousands of models obtained from the first phase. In this thesis, new algorithms are developed for the refinement and reranking phase of docking. New scoring functions, or potentials, that rank models are developed. These potentials are learnt using large-scale machine learning methods based on mathematical programming. The procedure for learning these potentials involves examining hundreds of thousands of correct and incorrect models. In this thesis, hierarchical constraints were introduced into the learning algorithm. First, an atomic potential was developed using this learning procedure. A refinement procedure involving side-chain remodeling and conjugate gradient-based minimization was introduced. The refinement procedure combined with the atomic potential was shown to improve docking accuracy significantly. Second, a hydrogen bond potential, was developed. Molecular dynamics-based sampling combined with the hydrogen bond potential improved docking predictions. Third, mathematical programming compared favorably to SVMs and neural networks in terms of accuracy, training and test time for the task of designing potentials to rank docking models. The methods described in this thesis are implemented in the docking package DOCK/PIERR. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer's disease.R. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer’s disease. / text

How organelles acquire their unique shapes is a fundamental question of cell biology. The peripheral endoplasmic reticulum (ER) consists of a vast network of membrane sheets and tubules, the formation of which requires homotypic membrane fusion. Previous studies suggest that the dynamin-like GTPase, atlastin (ATL), mediates ER fusion, but the mechanism by which this occurs is unclear. In this study, I investigate 1) the role of dimerization and conformational changes in the N-terminal domain of ATL, 2) how the C-terminal amphipathic helix and the transmembrane domain of ATL cooperate with the N-terminal domain, and 3) the formation of cis and trans ATL dimers in the fusion mechanism. ATL has a cytosolic N-terminal domain, consisting of a GTPase domain and three-helix bundle (3HB), followed by two transmembrane segments (TMs) and a cytosolic C-terminal tail (CT). Crystal structures of ATL and biochemical experiments suggest that nucleotide-dependent dimerization between ATL molecules sitting in different membranes can tether the membranes together. A subsequent conformational change triggered by GTP hydrolysis could pull the membranes toward one another for fusion. This mechanism is supported by in vitro membrane tethering and fusion assays using vesicles containing full-length Drosophila ATL. The CT and TMs of ATL are also required for efficient membrane fusion. A synthetic peptide corresponding to a conserved amphipathic helix in the CT can act in trans to restore the fusion activity of a tailless ATL mutant. We characterize CT mutants to show that the C-terminal helix promotes fusion by perturbing the lipid bilayer. The TMs of ATL also mediate nucleotide-independent oligomerization, which may allow ATL molecules in the same membrane to synchronously undergo the conformational change leading to fusion. Lastly, we show that continuous GTP hydrolysis is required for membrane tethering, occasionally resulting in fusion. The N-terminal cytosolic domain mediates trans dimer formation between ATL molecules on different membranes. GTP binding induces dimerization through the GTPase domains and 3HBs. We propose that GTP hydrolysis and phosphate release are required not just to drive fusion, but also to dissociate cis dimers that form on the same membrane, thus allowing ATL molecules to form trans dimers.

Anion exchanger 1 (AE1) is a red cell membrane glycoprotein that associates with cytoskeletal protein 4.2 in a complex bridging the cell membrane to the cytoskeleton. Disruption of this linkage results in unstable erythrocytes and hereditary spherocytosis (HS). Three HS mutations (E40K, G130R and P327R) in the cytoplasmic domain of AE1 (cdAE1) result in a decreased level of protein 4.2 in the red cell yet maintain normal amounts of AE1. Biophysical analyses showed the HS mutations had little effect on the structure and conformational stability of the isolated domain. However, the conformation of the cytoplasmic domain of the kidney anion exchanger, lacking the first 65 amino acids including a central -strand, was thermally destabilized relative to cdAE1 and had a more open structure. In transfected human embryonic kidney (HEK)-293 cells the HS mutants had similar expression levels as wild-type AE1, and protein 4.2 expression level was not dependent on the presence of AE1. Protein 4.2 localized to the plasma membrane with wild-type AE1, the HS mutants of AE1, the membrane domain of AE1 and kidney AE1, and to the ER with Southeast Asian ovalocytosis AE1. A fatty acylation mutant of protein 4.2, G2A/C173A, could not localize to the plasma membrane in the absence of AE1. Subcellular fractionation showed wild-type and G2A/C173A protein 4.2 were mostly associated with the cytoskeleton. Co-immunoprecipitation and Ni-NTA pull-down assays revealed impaired binding of protein 4.2 to HS mutants compared to AE1, while the membrane domain of AE1 was unable to bind protein 4.2. These studies show that HS mutations in cdAE1 cause impaired binding of protein 4.2, without causing gross structural changes in the domain. The mutations change the binding surface on cdAE1 by the introduction of positive charges into an otherwise acidic domain. This binding impairment may render protein 4.2 more susceptible to degradation or loss during red cell development.

Drug repositioning applies existing drugs to new disease indications. A prerequisite for drug repurposing is drug promiscuity - a drug's ability to bind to several targets, possibly leading to side effects on the other hand. One reason for drug promiscuity is binding site similarity between (otherwise unrelated) proteins. In this thesis, a new algorithm for remote binding site similarity assessment and its application to the whole of the Protein Data Bank (PDB) is presented, forming the base for off-target identification and drug repositioning. The present thesis contributes to a long-standing debate on the reasons for drug promiscuity, being one of the pioneer studies investigating these from a protein structural point of view. Except for a small influence of flexibility, the analysis of all promiscuous drugs in the PDB revealed that drug properties are of minor importance. However, a strong correlation between promiscuity and binding site similarity of protein targets is found (r = 0.81), suggesting binding site similarity as the main reason for drug promiscuity. For 71 % of the promiscuous drugs at least one pair of their targets' binding sites is similar and for 18 % all are similar. In order to overcome issues in detection of remotely similar binding sites, a score for binding site similarity is developed: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary binding site alignments and also works on distinct ligands on a structural proteome scale. To answer the question on which other targets might be hit when targeting a particular protein, an all-to-all binding site alignment of 32,202 protein structures is analyzed. Of the hundreds of million possible protein pairs, 0.27 % were found to have similar binding sites. Extrapolating to the human proteome, for one human protein are 54 proteins with a similar binding site expected on average. Clearly, this is in contrast to the one drug-one target paradigm in drug development. Based on these data, disadvantageous off-targets can be uncovered and drug-repositioning candidates inferred. The enormous potential is demonstrated with the example of Viagra, proposing it for repositioning to Alzheimer's disease and prostate cancer. The findings in this thesis question the established single-target dogma in drug discovery. Drugs are triggered to modulate multiple targets simultaneously by the widespread binding site similarity. With the presented pipeline, drug targets can be reliably predicted: Starting from a target protein, additional targets are predicted based on binding site similarity and prioritized according to the resulting ligand structural overlap. Identifying drug targets helps to understand severe side effects and opens the door for drug repositioning.

Pentameric ligand-gated ion channels (pLGICs) are membrane bound receptors found in the nervous system. They are responsible for detecting neurotransmitters released from neurons and subsequently mediating responses of the cells on which they are found. Thus, pLGICs play an invaluable role in communication between cells of the nervous system and understanding their function is pivotal to understanding how the nervous system works in general. One factor which is known to mediate pLGIC function is lipids found in the membrane environment in which pLGICs are embedded. This dissertation explores the various ways in which lipids interact with and modulate the function of pLGIC. Potential mechanisms and biological consequences of this modulation will be presented and discussed within the context of our current state of knowledge of pLGIC and nervous system function.

<p>Nuclear magnetic resonance (NMR) spectroscopy is an established technique for macromolecular structure determination at atomic resolution. However, the majority of the current structure determination approaches require a large set of experiments and use large amount of data to elucidate the three dimensional protein structure. While current structure determination protocols may perform well in data-rich settings, protein structure determination still remains to be a difficult task in a sparse-data setting. Sparse data arises in high-throughput settings, for larger proteins, membrane proteins, and symmetric protein complexes; thereby requiring novel algorithms that can compute structures with provable guarantees on solution quality and running time.</p><p>In this dissertation project we made an effort to address the key computational bottlenecks in NMR structural biology. Specifically, we improved and extended the recently-developed techniques by our laboratory, and developed novel algorithms and computational tools that will enable protein structure determination from sparse NMR data. An underlying goal of our project was to minimize the number of NMR experiments, hence the amount of time and cost to perform them, and still be able to determine protein structures accurately from a limited set of experimental data. The algorithms developed in this dissertation use the global orientational restraints from residual dipolar coupling (RDC) and residual chemical shift anisotropy (RCSA) data from solution NMR, in addition to a sparse set of distance restraints from nuclear Overhauser effect (NOE) and paramagnetic relaxation enhancement (PRE) measurements. We have used tools from algebraic geometry to derive analytic expressions for the bond vector and peptide plane orientations, by exploiting the mathematical interplay between RDC- or RCSA-derived sphero-conics and protein kinematics, which in addition to improving our understanding of the geometry of the restraints from these experimental data, have been used by our algorithms to compute the protein structures provably accurately. Our algorithms, which determine protein backbone global fold from sparse NMR data, were used in the high-resolution structure determination protocol developed in our laboratory to solve the solution NMR structures of the FF Domain 2 of human transcription elongation factor CA150 (RNA polymerase II C-terminal domain interacting protein), which have been deposited into the Protein Data Bank. We have developed a novel, sparse data, RDC-based algorithm to compute ensembles of protein loop conformations in the presence of a moderate level of dynamics in the loop regions. All the algorithms developed in this dissertation have been tested on experimental NMR data. The promising results obtained by our algorithms suggest that our algorithms can be successfully applied to determine high-quality protein backbone structures from a limited amount of experimental NMR data, and hence will be useful in automated NOE assignments and high-resolution protein backbone structure determination from sparse NMR data. The algorithms and the software tools developed during this project are made available as free open-source to the scientific community.</p> / Dissertation