A Novel Method for Characterization and Quantification of Flexibility and Mobility in Proteins

Proteins in vivo are not completely rigid molecules, and mobilities within their structure play a key role in protein function. We discuss a novel method for measuring two distinct types of protein fl exibility by comparing pairs of static protein structure coordinates. The measures focus on the mobility of a subset of atoms in the protein known as the backbone, and they quantify mobility or exibility at the level of the amino acids (or residues), which are the basic constituents of proteins. We validate our measures against a subset of proteins from the protein-protein docking benchmark, and against a number of individual proteins known to have mobility or exibility that is significant to their function. We also demonstrate the applicability of our methodology to several important biochemical topics including examples that apply to drug and enzyme design, and evaluation of computational protein structure prediction. We conclude with an analysis of protein structural and energetic terms showing which terms are associated with our exibility measures, and may therefore be useful within the context of protein modeling algorithms to predict the locality of exible regions

Exact algorithms for pairwise protein structure alignment

Klau, G.W. [Promotor

Investigation of the domain assembly in ligand-gated and voltage-gated ion channels using computational methods

Ionenkanäle sind essentiell für die Erzeugung und Weiterleitung von elektrischen Signalen im menschlichen Organismus. Sie befinden sich hauptsächling in der Zellmembran von Nerven und Muskelzellen, in denen sie spezifische Aufgaben übernehmen und daher spezielle Eigenschaften entwickelt haben, die diesen Ansprüchen entsprechen. Die Konsequenz daraus ist eine Diversifizierung der Ionenkanal Strukturen, die Aufgrund ihrer Eigenschaften wie z.B. die Ionenselektivität oder die Art der Aktivierung, in Klassen und Familien eingeteilt werden können. Die zentrale Rolle der Ionenkanäle birgt wiederum das Risiko ernsthafter Störungen im Organismus, sobald Fehlfunktionen der Kanäle auftreten. Um dem entgegenwirken zu können, wird intensiv an der strukturellen Aufklärung der Ionenkanäle geforscht. Dies ist mit sehr großen Herausforderungen verbunden und bisher sind nur wenige Ionenkanal Strukturen bekannt. In dieser Promotionsarbeit wurde die korrekte Anordnung von Ionenkanal Domänen zu einem vollständigen Kanal untersucht, und darüber hinaus die Interaktion zwischen transmembranen Helices innerhalb einer Domäne an einem entsprechenden Beispiel untersucht. Es werden Verfahren beschrieben, die zum einen computerbasierte Modelle von Ionenkanälen auf Basis verschiedenster Informationen erzeugen und zum anderen Rückschlüsse auf etwaige Eigenschaften und Funktionen aus den Modellen ableiten können. Am Beispiel des 5-HT3A Rezeptors wurde die Assoziation der 4 transmembranen Helices zu einer Domäne analysiert. Als Basis dienten Ergebnisse aus Mutagenese Experimenten, womit einzelne, für die Funktion und Stabilität des Kanals wichtige, Residuen identifiziert wurden und als Kriterien für die Erstellung eines Modells genutzt werden konnten. Mit dem Modell wurden in der Folge Molekulardynamik Simulationen und Berechnungen der Bindungsenergien einzelner Residuen durchgeführt, um den Energiebeitrag der einzelnen Aminosäuren zur Assoziation der Helices zu bestimmen und mit den experimentellen Beobachtungen in Relation zu setzen. Die in der Zwischenzeit publizierte 5-HT3A Struktur diente abschließend zur Verifizierung der Ergebnisse. Die Fragestellung beim heterotetrameren Nav1.8 Kanal war, welche Anordnung die 4 untereinander verbundenen Domänen einnehmen, um einen funktionierenden Kanal zu bilden. Neben den in der Literatur diskutierten Varianten, gegen und im Uhrzeigersinn, wurden auch Modelle mit 4 weiteren theoretisch möglichen Varianten mit kreuzweiser Domänenanordnung erzeugt und untersucht. Neben Molekulardynamik Simulationen und Energieberechnungen der Domänen Interfaces, wurde auch die sequenzbasierte Direct Coupling Analyse (DCA) verwendet, wodurch evolutionär miteinander gekoppelte Residuen identifiziert werden können. Mit der Kombination der verschiedenen Ansätze konnte eine klare Tendenz zu einer Anordnung der Domänen im Uhrzeigersinn erkannt werden, die im Einklang mit einer kürzlich veröffentlichten Kalziumkanal Struktur steht

From existing data to novel hypotheses : design and application of structure-based Molecular Class Specific Information Systems

As the active component of many biological systems, proteins are of great interest to life scientists. Proteins are used in a large number of different applications such as the production of precursors and compounds, for bioremediation, as drug targets, to diagnose patients suffering from genetic disorders, etc. Many research projects have therefore focused on the characterization of proteins and on improving the understanding of the functional and mechanistic properties of proteins. Studies have examined folding mechanisms, reaction mechanisms, stability under stress, effects of mutations, etc. All these research projects have resulted in an enormous amount of available data in lots of different formats that are difficult to retrieve, combine, and use efficiently. The main topic of this thesis is the 3DM platform that was developed to generate Molecular Class Specific Information Systems (3DM systems) for protein superfamilies. These superfamily systems can be used to collect and interlink heterogeneous data sets based on structure based multiple sequence alignments. 3DM systems can be used to integrate protein, structure, mutation, reaction, conservation, correlation, contact, and many other types of data. Data is visualized using websites, directly in protein structures using YASARA, and in literature using Utopia Documents. 3DM systems contain a number of modules that can be used to analyze superfamily characteristics namely Comulator for correlated mutation analyses, Mutator for mutation retrieval, and Validator for mutant pathogenicity prediction. To be able to determine the characteristics of subsets of proteins and to be able to compare the characteristics of different subsets a powerful filtering mechanism is available. 3DM systems can be used as a central knowledge base for projects in protein engineering, DNA diagnostics, and drug design. The scientific and technical background of the 3DM platform is described in the first two chapters. Chapter 1 describes the scientific background, starting with an overview of the foundations of the 3DM platform. Alignment methods and tools for both structure and sequence alignments, and the techniques used in the 3DM modules are described in detail. Alternative methods are also described with the advantages and disadvantages of the various strategies. Chapter 2 contains a technical description of the implementation of the 3DM platform and the 3DM modules. A schematic overview of the database used to store the data is provided together with a description of the various tables and the steps required to create new 3DM systems. The techniques used in the Comulator, Mutator and Validator modules of the 3DM platforms are discussed in more detail. Chapter 3 contains a concise overview of the 3DM platform, its capabilities, and the results of protein engineering projects using 3DM systems. Thirteen 3DM systems were generated for superfamilies such as the PEPM/ICL and Nuclear Receptors. These systems are available online for further examination. Protein engineering studies aimed at optimizing substrate specificity, enzyme activity, or thermostability were designed targeting proteins from these superfamilies. Preliminary results of drug design and DNA diagnostics projects are also included to highlight the diversity of projects 3DM systems can be applied to. Project HOPE: a biomedical tool to predict the effect of a mutation on the structure of a protein is described in chapter 4. Project HOPE is developed at the Radboud University Nijmegen Medical Center under supervision of H. Venselaar. Project HOPE employs webservices to optimally reuse existing databases and computing facilities. After selection of a mutant in a protein, data is collected from various sources such as UniProt and PISA. A homology model is created to determine features such as contacts and side-chain accessibility directly in the structure. Using a decision tree, the available data is evaluated to predict the effects of the mutation on the protein. Chapter 5 describes Comulator: the 3DM module for correlated mutation analyses. Two positions in an alignment correlate when they co-evolve, that is they mutate simultaneously or not at all. Comulator uses a statistical coupling algorithm to calculate correlated mutation analyses. Correlated mutations are visualized using heatmaps, or directly in protein structures using YASARA. Analyses of correlated mutations in various superfamilies showed that positions that correlate are often found in networks and that the positions in these networks often share a common function. Using these networks, mutants were predicted to increase the specificity or activity of proteins. Mutational studies confirmed that correlated mutation analyses are a valuable tool for rational design of proteins. Mutator, the text mining tool used to incorporate mutations into 3DM systems is described in chapter 6. Mutator was designed to automatically retrieve mutations from literature and store these mutations in a 3DM system. A PubMed search using keywords from the 3DM system is used to preselect articles of interest. These articles are retrieved from the internet, converted to text, and parsed for mutations. Mutations are then grounded to proteins and stored in a 3DM database. Mutation retrieval was tested on the alpha-amylase superfamily as this superfamily contains the enzyme involved in Fabry’s disease: an x linked lysosomal storage disease. Compared to existing mutant databases, such as the HGMD and SwissProt, Mutator retrieved 30% more mutations from literature. A major problem in DNA diagnostics is the differentiation between natural variants and pathogenic mutations. To distinguish between pathogenic mutations and natural variation in proteins the Validator modules was added to 3DM. Validator uses the data available in a 3DM system to predict the pathogenicity of a mutant using, for example, the residue conservation of the mutants alignment position, side-chain accessibility of the mutant in the structure, and the number of mutations found in literature for the alignment position. Mutator and Validator can be used to study mutants found in disorder related genes. Although these tools are not the definitive solution for DNA diagnostics they can hopefully be used to increase our understanding of the molecular basis of disorders. Chapter 7 and 8 describe applied research projects using 3DM systems containg proteins of potential commercial interest. A 3DM system for the a/b-beta hydrolases superfamily is described in chapter 7. This superfamily consists of almost 20,000 proteins with a diverse range of functions. Superfamily alignments were generated for the common beta-barrel fold shared by all superfamily members, and for five distinct subtypes within the superfamily. Due to the size and functional diversity of the superfamily, there is a lot of potential for industrial application of superfamily members. Chapter 8 describes a study focusing on a sucrose phosphorylase enzyme from the a-amylase superfamily. This enzyme can be potentially used in an industrial setting for the transfer of glucose to a wide variety of molecules. The aim of the study was to increase the stability of the protein at higher temperatures. A combination of rational design using a 3DM system, and in-depth study of the protein structure, led to a series of mutations that resulted in more than doubling the half-life of the protein at 60°C. 3DM systems have been successfully applied in a wide range of protein engineering and DNA diagnostics studies. Currently, 3DM systems are applied most successfully in project studying a single protein family or monogenetic disorder. In the future, we hope to be able to apply 3DM to more complex scenarios such as enzyme factories and polygenetic disorders by combining multiple 3DM systems for interacting proteins.</p

Comparative Genomics of Microbial Chemoreceptor Sequence, Structure, and Function

Microbial chemotaxis receptors (chemoreceptors) are complex proteins that sense the external environment and signal for flagella-mediated motility, serving as the GPS of the cell. In order to sense a myriad of physicochemical signals and adapt to diverse environmental niches, sensory regions of chemoreceptors are frenetically duplicated, mutated, or lost. Conversely, the chemoreceptor signaling region is a highly conserved protein domain. Extreme conservation of this domain is necessary because it determines very specific helical secondary, tertiary, and quaternary structures of the protein while simultaneously choreographing a network of interactions with the adaptor protein CheW and the histidine kinase CheA. This dichotomous nature has split the chemoreceptor community into two major camps, studying either an organism’s sensory capabilities and physiology or the molecular signal transduction mechanism. Fortunately, the current vast wealth of sequencing data has enabled comparative study of chemoreceptors. Comparative genomics can serve as a bridge between these communities, connecting sequence, structure, and function through comprehensive studies on scales ranging from minute and molecular to global and ecological. Herein are four works in which comparative genomics illuminates unanswered questions across the broad chemoreceptor landscape. First, we used evolutionary histories to refine chemoreceptor interactions in Thermotoga maritima, pairing phylogenetics with x-ray crystallography. Next, we uncovered the origin of a unique chemoreceptor, isolated only from hypervirulent strains of Campylobacter jejuni, by comparing chemoreceptor signaling and sensory regions from Campylobacter and Helicobacter. We then selected the opportunistic human pathogen Pseudomonas aeruginosa to address the question of assigning multiple chemoreceptors to multiple chemotaxis pathways within the same organism. We assigned all P. aeruginosa receptors to pathways using a novel in silico approach by incorporating sequence information spanning the entire taxonomic order Pseudomonadales and beyond. Finally, we surveyed the chemotaxis systems of all environmental, commensal, laboratory, and pathogenic strains of the ubiquitous Escherichia coli, where we discovered an ancestral chemoreceptor gene loss event that may have predisposed a well-studied subpopulation to adopt extra-intestinal pathogenic lifestyles. Overall, comparative genomics is a cutting edge method for comprehensive chemoreceptor study that is poised to promote synergy within and expand the significance of the chemoreceptor field

Crystallographic studies of the Crimean-Congo haemorrhagic fever virus and tomato spotted wilt virus N proteins

The Bunyaviridae family is a group of segmented negative sense RNA viruses (sNSV) that cause severe disease in both plants and humans. This group of viruses is further classified into the Orthobunyavirus, Tospovirus, Nairovirus, Phlebovirus and Hantavirus genera. Crimean-Congo haemorrhagic fever (CCHF) is a potentially fatal tick borne viral meta-zoonotic haemorrhagic disease, which is caused by Crimean-Congo haemorrhagic fever virus (CCHFV), a member of the Nairovirus genus. Despite the medical importance and severity of disease, the molecular biology of CCHFV remains poorly understood, primarily because outbreaks are sporadic, and its handling requires the highest containment facilities, bio-safety level 4 (BSL-4). Tomato spotted wilt virus (TSWV) is a member of the Tospovirus genus, which are the only plant infecting members of the bunyaviruses. TSWV causes serious disease in over 80 plant families, including peppers, potatoes, lettuce and tomatos, causing approximately 80 million dollars worth of loss to the food industry per year. The pathogenesis of CCHFV and TSWV depends on the formation of the ribonucleoprotein (RNP) complex, in which their genomes and antigenome are entirely encapsidated by the virus-encoded nucleocapsid (N) protein. Only in the form of the RNP is the genome replicated, transcribed and packaged into new progeny particles. This work has implemented X-ray crystallography to better understand the structural and functional roles of the CCHFV and TSWV N proteins. This work presents the 2.1 Å crystal structure of the CCHFV N protein, which shows a high degree of structural homology with the N protein from another sNSV member Lassa virus (LASV), and reveals important insight into sNSV phylogenetics, and the interaction of CCHFV with the host cell. This work also includes the expression, purification, and crystallization of the TSWV N protein. Crystals generated from TSWV N protein diffracted to 2.6 Å and structure determination is ongoing

Predictions of Backbone Dynamics in Intrinsically Disordered Proteins Using De Novo Fragment-Based Protein Structure Predictions

Intrinsically disordaered proteins (IDPs) are a prevalent phenomenon with over 30% of human proteins estimated to have long disordered regions. Computational methods are widely used to study IDPs, however, nearly all treat disorder in a binary fashion, not accounting for the structural heterogeneity present in disordered regions. Here, we present a new de novo method, FRAGFOLD-IDP, which addresses this problem. Using 200 protein structural ensembles derived from NMR, we show that FRAGFOLD-IDP achieves superior results compared to methods which can predict related data (NMR order parameter, or crystallographic B-factor). FRAGFOLD-IDP produces very good predictions for 33.5% of cases and helps to get a better insight into the dynamics of the disordered ensembles. The results also show it is not necessary to predict the correct fold of the protein to reliably predict per-residue fluctuations. It implies that disorder is a local property and it does not depend on the fold. Our results are orthogonal to DynaMine, the only other method significantly better than the naïve prediction. We therefore combine these two using a neural network. FRAGFOLD-IDP enables better insight into backbone dynamics in IDPs and opens exciting possibilities for the design of disordered ensembles, disorder-to-order transitions, or design for protein dynamics