The work in this thesis has demonstrated the advantages and limitations of using MS based technologies in protein and peptide structural studies. Tandem MS, specifically electron capture dissociation (ECD) have shown the ability to provide structural insights in molecules containing the slightest of all modifications (D-AA substitution). Additionally, it can be concluded that charge localization in molecular ions is best identified with ECD and to a lesser degree using CAD. Fragment ion abundances are a quantifiable tool providing chiral recognition (RChiral). An analytical model demonstrating the detection and quantification of D-AAs within proteins and peptides has been achieved. ECD has demonstrated the ability to quantify stereoisomeric mixtures to as little as 1%. Chirality elucidation on a nano LC-MS/MS time scale has been shown. The structures of various stereoisomers of the mini protein Trp Cage were explored, each providing unique ECD fragment ion abundances suggestive of gas phase structural differences. The uniqueness of these abundances combined with MDS data have been used in proposing a new mechanism in c and z fragment ion formation in ECD. This mechanism suggests initial electron capture on a backbone amide involved in (neutral) hydrogen bonding. The wealth of solution phase (circular dichroism), transitition phase (charge state distribution, CSD) and gas phase (ECD) data for Trp Cage suggest that at low charge states (2+) the molecule has a high degree of structural similarity in solution- and gas- phases. Furthermore, quantitative information from CSD studies is garnered when using a “native” deuteriated form as part of the stereoisomeric mixture. It has also been shown that the stability of the reduced species after electron capture is indicative of the recombination energy release, which in turn is linked to the coulombic repulsion- a structural constraint that can be used for approximation of the inter-charge distance for various stereoisomers.

Experimentally derived biomolecular structures were determined by Nuclear Magnetic Resonance (NMR). The properties of selected peptides and proteins in solution and in membrane mimicking micelles were observed by circular Dichroism (CD), mass spectrometry (MS), and other spectroscopic techniques. The mDpl(1-30) peptide (30 residues) of the mouse Doppel protein was found to be positioned as an α-helix in a DHPC micelle. The same peptide can disrupt and cause leakage in small unilamellar vesicles. Single D-amino acid isomers of Trp-cage (20 residues), the smallest peptide with a protein-like fold, were analyzed by CD spectroscopy and were found to have different secondary structures and melting temperatures. They were compared against MS measurements specially designed to reveal the secondary structure of proteins. We studied a novel protein in E. coli of unknown structure that is encoded by the putative transcription factor ORF: ygiT (131 residues). This protein comprises a helix-turn-helix (HTH) domain in the C-terminus and contains two CxxC motives in the N-terminal domain, which binds Zn. This protein was named 2CxxC. We succeeded in overexpressing and purifying 2CxxC in E. coli with enough yield for a 13C, 15N uniformly labeled NMR sample. The chemical shift assignment was completed and the NMR structure was calculated in reducing, slightly acidic conditions (1mM DTT, pH 5.5). The determined HTH domain shows good similarity with structures predicted by a homology search, while the N-terminal domain has no other homologous structure in the Protein Data Bank (PDB). The structure of the paddle region (27 residues) of the HsapBK(233-260) voltage and Ca+2 activated potassium channel, in DPC micelles, was determined by NMR. It shows a helix-turn-helix loop, which agrees well with the expected structure and could help to verify the proposed models of the voltage gating mechanism. The C-repressor (dimer of 99 residues) of bacteriophage P2 was analyzed by NMR. We assigned the chemical shifts and NMR structure determination is under way.

The hereditary transthyretin (TTR) amyloidoses are rare, and in severe cases, fatal disorders caused by mutations in the TTR gene. The clinical picture is diverse, involving neuropathies and myopathies, and mainly depends on the causative mutation and the sites and rates of amyloid deposition. The ultimate aim of the field of research presented in this thesis is to prevent TTR amyloid disease. To reach this ambitious goal, a thorough understanding of the normal as well as the pathological properties of the protein is essential. Here, comparisons between TTR from humans and other species may provide valuable information. The three-dimensional structure of TTR from Gilthead sea bream (Sparus aurata) was determined at 1.75 Å resolution by X-ray crystallography, and was found to be structurally similar to human TTR. However, significant differences were observed in the area at and around β-strand D, an area believed to dissociate from the structure prior to amyloid formation, thereby allowing the β-strands A and B to participate in polymerization. During evolution, the preference of TTR for the thyroid hormones, 3,5,3’-triiodo-L-thyronine (T3) and 3,5,3’,5’-tetraiodo-L-thyronine (T4), has shifted. While human TTR has higher affinity for T4, the opposite is true in lower vertebrates, e.g. fish and reptiles, where T3 is the main ligand. We have determined two separate structures of sea bream TTR in complex with T3 and T4, both at 1.9 Å resolution, as well as the complex of human TTR with T3. A significantly wider entrance and narrower thyroid hormone binding channel suggest a structural explanation to the differences in thyroid hormone preference between human and piscine TTR. The Tyr114Cys substitution in TTR is associated with severe systemic amyloidosis. The mutation introduces a second cysteinyl group in the TTR monomer, and has been shown to inhibit the formation of fibril formation in vitro, promoting the formation of disulfide-bonded amorphous aggregates. To deduce the role of intermolecular disulfide bonds in fibril formation we characterized the TTR Cys10Ala/Tyr114Cys double mutant. Our results suggest that an intermolecular disulfide bond at position 114 enhances the exposure of Cys10, which promotes the formation of additional intermolecular disulfide-linked assemblies. Also, we were able to isolate a disulfide-linked dimeric form of this mutant that formed protofibrils in vitro, suggesting the architecture of TTR amyloid may be the result of different underlying structures rather than that of a highly stringent assembly. We have also been able to successfully adapt a method of protein pre-heating to enable crystallization, thereby succeeding in a particularly problematic protein crystallization experiment. By heating the protein solution, we succeeded in separating several forms of protein micro-heterogeneities from the properly folded protein species, thereby allowing the growth of well diffracting crystals. / Ärftlig transthyretinamyloidos är en ovanlig och i allvarliga fall dödlig proteininlagringssjukdom som orsakas av mutationer i genen för transthyretin. Den kliniska bilden är huvudsakligen beroende av den bakomliggande genförändringen samt amyloidlokaliseringen och -depositionshastigheten och omfattar vanligen neuropatier och myopatier av varierande grad. Det slutgiltiga målet med forskningsfältet som presenteras i denna avhandling är att förhindra eller bota transthyretinamyloidos. En förutsättning för att lyckas med detta ambitiösa mål är en ingående förståelse för proteinets grundläggande egenskaper, såväl i normalfallet som i de patologiska processerna, bland annat genom jämförande studier av humant och icke-humant transthyretin (TTR). Den tredimensionella röntgenkristallografiska strukturen av TTR från fisken guldsparid (Sparus aurata) bestämdes till en upplösning på 1,75Å och befanns vara strukturellt snarlik humant TTR. Signifikanta skillnader observerades emellertid i och kring β-sträng D, en region som tros dissociera från huvudstrukturen innan själva bildningen av amyloid. Enligt denna hypotes leder D-strängsdissociationen till exponering av β-strängarna A och B, vilka därmed kan delta i de reaktioner som bildar amyloid. Under evolutionen har bindningspreferenserna för thyroideahormonerna T3 (3,5,3’-trijod-L-thyronin) och T4 (3,5,3’,5’-tetrajod-L-thyronin) hos TTR ändrats. Humant TTR har högre affinitet för T4 än för T3, medan det motsatta förhållandet gäller för lägre vertebrater, t ex fisk, där T3 är den huvudsakliga liganden. Strukturerna bestämdes för guldsparid i komplex med T4 och med T3 till 1,9 Å upplösning, samt för humant TTR i komplex med T3 till 1,7 Å upplösning. Jämförande analyser visade på signifikanta skillnader i thyroideahormonbindningskanalen, vilken var vidare och grundare i fisk än i människa. Dessa strukturella skillnader kan delvis förklara olikheterna i hormonbindning mellan högre och lägre vertebrater. Substitutionen Tyr114Cys i TTR är kopplad till en allvarlig form av systemisk transthyretinamyloidos. Mutationen introducerar en andra cysteinylgrupp i TTR-monomererna, vilket förhindrar fibrillbildning in vitro, men gynnar bildningen av amorfa disulfidbundna aggregat. För att närmare studera betydelsen av disulfidbindningar vid fibrillbildning av detta protein så karakteriserades dubbelmutanten TTR Cys10Ala/Tyr114Cys. Baserat på våra resultat föreslår vi att intermolekylära disulfidbindningar i position 114 ökar exponeringen av Cys10, vilket förstärker tendensen att bilda ytterligare disulfidbundna aggregat. Vi isolerade även en disulfidbunden dimerisk form av dubbelmutanten som kan bilda protofibriller in vitro. Baserat på denna observation föreslår vi att transthyretinamyloids underliggande arkitektur är sammansatt och kan nås genom sammanfogning av olika substrukturer snarare än genom en strikt ordnad uppbyggnad. Vi har också modifierat och anpassat en metod för uppvärmning av proteiner för att möjliggöra kristallisation i ett synnerligen problematiskt proteinkristallisations-experiment. Genom uppvärmning av proteinlösningen lyckades vi separera olika former av mikroheterogeniteter från det rättveckade proteinet, som sedan bildade kristaller av god röntgendiffraktiv kvalitet.

alpha-Helical membrane proteins are important for many biological functions. Due to physicochemical constraints, the structures of membrane proteins differ from the structure of soluble proteins. Historically, membrane protein structures were assumed to be more or less two-dimensional, consisting of long, straight, membrane-spanning parallel helices packed against each other. However, during the past decade, a number of the new membrane protein structures cast doubt on this notion. Today, it is evident that the structures of many membrane proteins are equally complex as for many soluble proteins. Here, we review this development and discuss the consequences for our understanding of membrane protein biogenesis, folding, evolution, and bioinformatics. / <p>authorCount :3</p>

Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers.

A topic of research that is frequently studied in Structural Biology is the problem of determining the degree of similarity between two protein structures. The most common solution is to perform a three dimensional structural alignment on the two structures. Rigid structural alignment algorithms have been developed in the past to accomplish this but treat the protein molecules as immutable structures. Since protein structures can bend and flex, rigid algorithms do not yield accurate results and as a result, flexible structural alignment algorithms have been developed. The problem with these algorithms is that the protein structures are represented using thousands of atomic coordinate variables. This results in a great computational burden due to the large number of degrees of freedom required to account for the flexibility. Past research in dimensionality reduction techniques has shown that a linear dimensionality reduction technique called Principal Component Analysis (PCA) is well suited for high dimensionality reduction. This thesis introduces a new flexible structural alignment algorithm called FlexSADRA, which uses PCA to perform flexible structural alignments. Test results show that FlexSADRA determines better alignments than rigid structural alignment algorithms. Unlike existing rigid and flexible algorithms, FlexSADRA addresses the problem in a significantly lower dimensionality problem space and assesses not only the structural fit but the structural feasibility of the final alignment.

A topic of research that is frequently studied in Structural Biology is the problem of determining the degree of similarity between two protein structures. The most common solution is to perform a three dimensional structural alignment on the two structures. Rigid structural alignment algorithms have been developed in the past to accomplish this but treat the protein molecules as immutable structures. Since protein structures can bend and flex, rigid algorithms do not yield accurate results and as a result, flexible structural alignment algorithms have been developed. The problem with these algorithms is that the protein structures are represented using thousands of atomic coordinate variables. This results in a great computational burden due to the large number of degrees of freedom required to account for the flexibility. Past research in dimensionality reduction techniques has shown that a linear dimensionality reduction technique called Principal Component Analysis (PCA) is well suited for high dimensionality reduction. This thesis introduces a new flexible structural alignment algorithm called FlexSADRA, which uses PCA to perform flexible structural alignments. Test results show that FlexSADRA determines better alignments than rigid structural alignment algorithms. Unlike existing rigid and flexible algorithms, FlexSADRA addresses the problem in a significantly lower dimensionality problem space and assesses not only the structural fit but the structural feasibility of the final alignment.

Conformational selection is the idea that proteins traverse positions on the conformational space represented by their potential energy landscape, and in particular positions considered as local energy minima. Conformational selection a useful concept in ligand binding studies and in exploring the behavior of protein structures within that energy landscape. Often, research that explores protein function requires the generation of conformational ensembles, or collections of protein conformations from a single structure. We describe a method of conformational ensemble generation that uses joint-constrained rigid-body dynamics (an approach that allows for explicit consideration of rigidity) and the elastic network model (providing structurally derived directional guides for the rigid-body model). We test our model on a selection of unbound proteins and examine the structural validity of the resulting ensembles, as well as the ability of such an approach to generate conformations with structural overlaps close to the ligand-bound versions of the proteins.

This thesis is composed of two parts. The first part pertains to tree-based models. The second part deals with multiple testing in large-scale contingency tables. Tree-based models have gained enormous popularity in statistical modeling and data mining. We propose a novel tree-pruning algorithm called frontier-based tree-pruning algorithm (FBP). The new method has an order of computational complexity comparable to cost-complexity pruning (CCP). Regarding tree pruning, it provides a full spectrum of information. Numerical study on real data sets reveals a surprise: in the complexity-penalization approach, most of the tree sizes are inadmissible. FBP facilitates a more faithful implementation of cross validation, which is favored by simulations. One of the most common test procedures using two-way contingency tables is the test of independence between two categorizations. Current test procedures such as chi-square or likelihood ratio tests provide overall independency but bring limited information about the nature of the association in contingency tables. We propose an approach of testing independence of categories in individual cells of contingency tables based on a multiple testing framework. We then employ the proposed method to identify the patterns of pair-wise associations between amino acids involved in beta-sheet bridges of proteins. We identify a number of amino acid pairs that exhibit either strong or weak association. These patterns provide useful information for algorithms that predict secondary and tertiary structures of proteins.

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