<p>Evolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.</p>

<p>A novel approach is proposed for modeling of structurally variable regions in proteins. In this approach, a prerequisite sequence-structure alignment is examined for regions where the target sequence is not covered by the structural template. These regions, extended with a number of residues from adjacent stem regions, are submitted to fold recognition. The alignments produced by fold recognition are integrated into the initial alignment to create a multiple alignment where gaps in the main structural template are covered by local structural templates. This multiple alignment is used to create a protein model by existing protein modeling techniques.</p><p>Several alternative parameters are evaluated using a set of ten proteins. One set of parameters is selected and evaluated using another set of 31 proteins. The most promising result is for loop regions not located at the C- or N-terminal of a protein, where the method produces an average RMSD 12% lower than the loop modeling provided with the program MODELLER. This improvement is shown to be statistically significant.</p>

Ricin is a potent cytotoxin easily purified in large quantities. It presents a significant public health concern due to its potential use as a bioterrorism agent. For this reason, extensive efforts have been underway to develop antidotes against this deadly poison. The catalytic A subunit of the heterodimeric toxin has been biochemically and structurally well characterized, and is an attractive target for structure-based drug design. Aided by computer docking simulations, several ricin toxin A chain (RTA) inhibitors have been identified; the most promising leads belonging to the pterin family. To date, the most potent RTA inhibitors developed using this approach are only modest inhibitors with apparent IC50 values in the 10-4 M range, leaving significant room for improvement. This thesis discusses the development of a subset of inhibitors belonging to the pterin family in which amino acids have been utilized as building blocks. Inhibitors in this family have achieved a significant increase in potency, and have provided valuable structural information for further development. / text

Inference for dynamic systems and conformational sampling for protein folding are two problems motivated by applied data, which pose computational challenges from a statistical perspective. Dynamic systems are often described by a set of coupled differential equations, and methods of parametric estimation for these models from noisy data can require repeatedly solving the equations numerically. Many of these models also lead to rough likelihood surfaces, which makes sampling difficult. We introduce a method for Bayesian inference on these models, using a multiple chain framework that exploits the underlying mathematical structure and interpolates the posterior to improve efficiency. In protein folding, a large conformational space must be searched for low energy states, where any energy function constructed on the states is at best approximate. We propose a method for sampling fragment conformations that accounts for geometric and energetic constraints, and explore ideas for folding entire proteins that account for uncertain energy landscapes and learning from data more effectively. These ingredients are combined into a framework for tackling the problem of generating improvements to protein structure predictions. / Statistics

Transthyretin (TTR) is one of several proteins involved in amyloid disease in humans. Unknown conformational changes of the native state of TTR result in aggregation of TTR molecules into amyloid fibrils, which accumulate in extracellular tissues. This may result in different clinical symptoms, e.g. polyneuropathy or cardiomyopathy, depending on their site of accumulation. Our long-term goal is to identify structural changes associated with amyloid formation. For this work, structural characterization of TTR from other species than human may provide valuable information. The three-dimensional X-ray crystallographic structure of TTR from sea bream (Sparus aurata) was determined at 1.75 Å resolution. Human and sea bream TTR were found to be structurally very similar. However, interesting differences were present in the area at and around -strand D, which in fish forms an extended loop region. Interestingly, this area is believed to dissociate from the structure prior to amyloid formation, to allow -strands A and B to participate in polymerization. During evolution, TTR from different species have developed differences in preference to their natural ligands, the thyroid hormones 3,5,3’-triiodo-L-thyronine (T3) and 3,5,3’,5’-tetraiodo-L-thyronine (T4). While human TTR has higher affinity for T4, the opposite is true in lower vertebrates, e.g. fish and reptiles. We have determined two separate structures of sea bream TTR in complex with T3 and T4, both at 1.9 Å resolution. A significantly wider entrance and narrower thyroid hormone binding channel provide a structural explanation to the differences in thyroid hormone preference between human and piscine TTR. In a separate work, we identified a novel protein family with structural similarity to TTR, which we named the transthyretin-related protein (TRP) family. To attain information about this protein family, we cloned, expressed, purified and characterized TRP from Escherichia coli (EcTRP). Furthermore, we solved the structure of EcTRP to 1.65 Å resolution. As predicted, EcTRP and human TTR are structurally very similar. Interesting structural differences are found in the area corresponding to the thyroid hormone binding site in TTR, which due to its amino acid conservation within the TRP family we identified as a putative ligand-binding site in TRPs. The function of the TRP is not known, however, recent studies suggest that it might be involved in purine catabolism. It has been shown that partial acid denaturation of human TTR results in amyloid-fibril formation. Interestingly, we have shown that sea bream TTR also forms amyloid-like fibrils in vitro, even though it shares only 52% sequence identity to human TTR. Corresponding studies on EcTRP did not generate amyloid-like fibrils. EcTRP has 30% sequence identity to human TTR. The fact that two of the proteins form amyloid fibrils and one does not means that they can serve as a model system for the study of amyloid formation. Further studies on these three proteins are currently performed to attain more information about the mechanism of amyloid formation.

Pathogenic bacteria use an arsenal of toxic protein virulence factors to cause disease in host cells. The mono-ADP-ribosyltransferase (mART) toxins are a family of exotoxins produced by pathogens which contribute to a wide range of diseases including cholera, diphtheria and whooping cough. Specifically, mART toxins act by transferring ADP-ribose from NAD+ to target proteins in host cells, altering or inhibiting target activity with deleterious downstream effects. Recently, in silico analyses have revealed two novel mARTs, Photox and Certhrax, from pathogenic organisms. Photox, from Photorhabdus luminescens was successfully expressed and purified from E. coli and was shown to target actin, specifically at Arg177. This covalent modification inhibits actin polymerization and leads to observed cytotoxicity in yeast cells. Photox has 35% identity with SpvB from Salmonella enterica, which allowed for a structural model to be built, showing the location of all characteristic mART active site components, and the binding site for potential inhibitors. Certhrax originates from Bacillus cereus G9241, implicated in a number of severe pneumonia cases. Certhrax shares 31% sequence identity with anthrax lethal factor from Bacillus anthracis; however, we demonstrated that the toxicity of Certhrax resides in the mART domain, whereas anthrax uses a metalloprotease mechanism. In vivo tests employing toxin gene expression in yeast, and receptor-mediated infection of mammalian, cells showed the extreme cytotoxicity of Certhrax (LD50 = 100 pg/mL against mouse macrophage cells), making it 60 times more toxic than its infamous counterpart, anthrax lethal factor. In vitro analysis indicated that Certhrax possesses NAD+ glycohydrolase activity, characteristic of many mART toxins, but we continue to search for the natural host protein target of this toxic enzyme. We determined the crystal structure of Certhrax to 2.2 Å, which illustrates a close structural similarity with anthrax lethal factor. Furthermore, we identified several small molecule inhibitors which show protection against Certhrax both in vitro and in vivo. We determined a 1.9 Å crystal structure of one inhibitor in complex with Certhrax. Through identification and characterization of novel mART enzymes, we seek to better understand this family of toxic enzymes to aid in the discovery and development of more potent therapeutics. / National Sciences and Engineering Research Council, Canadian Institutes of Health Research

The homologous p10 fusion-associated small transmembrane (FAST) proteins of the fusogenic avian (ARV) and Nelson Bay (NBV) reoviruses are the smallest known proteins capable of mediating syncytiogenesis. Their extremely small size precludes them from following the paradigmatic membrane fusion pathway proposed for enveloped viral fusion proteins. I exploited the sequence conservation/divergence and differential syncytiogenic rates between ARV and NBV to define functional motifs in the p10 ectodomains. Using chimeric p10 constructs, I determined the 40-residue ectodomain (sizes refer to ARV) comprises two distinct functional motifs essential for syncytiogenesis. Cellular syncytiogenic and surface biotinylation assays identified an indivisible, 25- residue, N-terminal ectodomain motif required for cystine loop fusion peptide formation. I further determined the roles of this cystine loop in promoting lipid binding and cholesterol-dependent lipid destabilization. Immunofluorescence staining, FRET analysis and cholesterol depletion/repletion studies identified a second motif comprising the 13 membrane-proximal ectodomain residues (MPER). This motif governs the reversible, cholesterol-dependent assembly of p10 multimers in the plasma membrane. I demonstrate that ARV and NBV homomultimers segregate to separate foci in the plasma membrane, and the four juxtamembrane residues present in the multimerization motif dictate species- specific homomultimerization. I also discovered the novel codependency of p10 multimerization and cholesterol-dependent microdomain localization. The majority of enveloped virus membrane fusion proteins function as stable multimers, which nonetheless must undergo dramatic, irreversible, tertiary structure rearrangements to mediate membrane fusion. Cholesterol-rich membrane microdomains have also been implicated in the function of several enveloped virus fusion proteins, and a limited number of studies have investigated the role of cholesterol in multimerization. My results reveal cholesterol-dependent p10 homomultimerization is an essential aspect of p10- mediated syncytium formation, and I identify the motifs responsible for this process. The reversible nature of p10 cholesterol-dependent multimerization at the plasma membrane is in line with several other studies suggesting that the dynamic clustering and dispersion of cholesterol microdomains, as well as protein transitioning from multimeric to monomeric intermediates, are essential phenomena of protein mediated membrane fusion.

Comparing protein structures based on their contact maps is an important problem in structural proteomics. Building a system for reconstructing protein tertiary structures from their contact maps is one of the motivations for devising novel contact map comparison algorithms. Several methods that address the contact map comparison problem have been designed which are briefly discussed in this thesis. However, they suggest scoring schemes that do not satisfy the two characteristics of “metricity” and “universality”. In this research we investigate the applicability of the Universal Similarity Metric (USM) to the contact map comparison problem. The USM is an information theoretical measure which is based on the concept of Kolmogorov complexity. The ultimate goal of this research is to use the USM in case-based reasoning system to predict protein structures from their predicted contact maps. The fact that the contact maps that will be used in such a system are the ones which are predicted from the protein sequences and are not noise-free, implies that we should investigate the noise-sensitivity of the USM. This is the first attempt to study the noise-tolerance of the USM. In this research, as the first implementation of the USM we converted the two-dimensional data structures (contact maps) to one-dimensional data structures (strings). The results of this implementation motivated us to circumvent the dimension reduction in our second attempt to implement the USM. Our suggested method in this thesis has the advantage of obtaining a measure which is noise tolerant. We assess the effectiveness of this noise tolerance by testing different USM implementation schemes against noise-contaminated versions of distinguished data-sets. / Thesis (Master, Computing) -- Queen's University, 2008-09-27 05:53:31.988

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.

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