To assess the similarities and the differences among protein structures, a variety of structure alignment algorithms and programs have been designed and implemented. We introduce a low-resolution approach and a high-resolution approach to evaluate the similarities among protein structures. Our results show that both the low-resolution approach and the high-resolution approach outperform state-of-the-art methods. For the low-resolution approach, we eliminate false positives through the comparison of both local similarity and remote similarity with little compromise in speed. Two kinds of contact libraries (ContactLib) are introduced to fingerprint protein structures effectively and efficiently. Each contact group from the contact library consists of one local or two remote fragments and is represented by a concise vector. These vectors are then indexed and used to calculate a new combined hit-rate score to identify similar protein structures effectively and efficiently. We tested our ContactLibs on the high-quality protein structure subset of SCOP30, which contains 3,297 protein structures. For each protein structure of the subset, we retrieved its neighbor protein structures from the rest of the subset. The best area under the ROC curve, archived by a ContactLib, is as high as 0.960. This is a significant improvement over 0.747, the best result achieved by the state-of-the-art method, FragBag. For the high-resolution approach, our PROtein STructure Alignment method (PROSTA) relies on and verifies the fact that the optimal protein structure alignment always contains a small subset of aligned residue pairs, called a seed, such that the rotation and translation (ROTRAN), which minimizes the RMSD of the seed, yields both the optimal ROTRAN and the optimal alignment score. Thus, ROTRANs minimizing the RMSDs of small subsets of residues are sampled, and global alignments are calculated directly from the sampled ROTRANs. Moreover, our method incorporates remote information and filters similar ROTRANs (or alignments) by clustering, rather than by an exhaustive method, to overcome the computational inefficiency. Our high-resolution protein structure alignment method, when applied to optimizing the TM-score and the GDT-TS score, produces a significantly better result than state-of-the-art protein structure alignment methods. Specifically, if the highest TM-score found by TM-align is lower than 0.6 and the highest TM-score found by one of the tested methods is higher than 0.5, our alignment method tends to discover better protein structure alignments with (up to 0.21) higher TM-scores. In such cases, TM-align fails to find TM-scores higher than 0.5 with a probability of 42%; however, our alignment method fails the same task with a probability of only 2%. In addition, existing protein structure alignment scoring functions focus on atom coordinate similarity alone and simply ignore other important similarities, such as sequence similarity. Our scoring function has the capacity for incorporating multiple similarities into the scoring function. Our result shows that sequence similarity aids in finding high quality protein structure alignments that are more consistent with HOMSTRAD alignments, which are protein structure alignments examined by human experts. When atom coordinate similarity itself fails to find alignments with any consistency to HOMSTRAD alignments, our scoring function remains capable of finding alignments highly similar to, or even identical to, HOMSTRAD alignments.

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As the expression of the genetic blueprint, proteins are at the heart of all biological systems. The ever increasing set of available protein structures has taught us that diversity is the hallmark of their architecture, a fundamental characteristic that enables them to perform the vast array of functionality upon which all of life depends. This diversity, however, is central to one of the most challenging problems in molecular biology: how does a folding polypeptide chain navigate its way through all of the myriad of possible conformations to find its own particular biologically active form? With few overarching structural principles to draw upon that can be applied to all protein architecture, the search for a solution to the protein folding problem has yet to produce an algorithm that can explain and duplicate this fundamental biological process. In this thesis, we take a two-pronged approach for investigating the protein folding process. Our initial statistical studies of the distributions of hydrophobic and hydrophilic residues within α-helices and β-sheets suggest (i) that hydrophobicity plays a critical role in helix and sheet formation; and (ii) that the nucleation of these motifs may result in largely unidirectional growth. Most tellingly, from an examination of the amino acids found in the smallest β-sheets, we do not find any evidence of a β-nucleating code in the primary protein sequence. Complementing these statistical analyses, we have analyzed the structural environments of several ever-widening aspects of protein topology. Our examination of the gaps between strands in the smallest β-sheets reveals a common organizational principle underlying β-formation involving strands separated by large sequential gaps: with very few exceptions, these large gaps fold into single, compact structural modules, bringing the β-strands that are otherwise far apart in the sequence close together in space. We conclude, therefore, that β-nucleation in the smallest sheets results from the co-location of two strands that are either local in sequence, or local in space following prior folding events. A second study of larger β-sheets both corroborates and extends these findings: virtually all large sequential gaps between pairs of β-strands organize themselves into an hierarchical arrangement, creating a bread-crumb model of go-and-come-back structural organization that ultimately juxtaposes two strands of a parental β-structure that are far apart in the sequence in close spatial proximity. In a final study, we have formalized this go-and-come-back notion into the concept of anti-parallel double-strandedness (DS), and measure this property across protein architecture in general. With over 90% of all residues in a large, non-redundant set of protein structures classified as DS, we conclude that DS is a unifying structural principle that underpins all globular proteins. We postulate, moreover, that this one simple principle, anti-parallel double-strandedness, unites protein structure, protein folding and protein evolution. / Thesis (Ph.D, Biochemistry) -- Queen's University, 2011-09-30 12:32:41.379

One approach to protein structure prediction is to first predict from sequence, a thresholded and binary 2D representation of a protein's topology known as a contact map. Then, the predicted contact map can be used as distance constraints to construct a 3D structure. We focus on the latter half of the process and aim to obtain a set of non-binary distance constraints from contacts maps. This thesis proposes an approach to extend the traditional binary definition of “in contact” by incorporating fuzzy logic to construct fuzzy contact maps from a set of contact maps at different thresholds, providing a vehicle for error handling. Then, a novel template-based similarity search and distance geometry methods were applied to predict distance constraints in the form of a distance map. The three-dimensional coordinates were then calculated from the predicted distance constraints. Experiments were conducted to test our approach for various levels of noise. As well, we compare the performance of fuzzy contact maps to binary contact maps in the framework of our methodology. Our results showed that fuzzy contact map similarity was indicative of distance map similarity. Thus, we were able to retrieved similar distance map regions using fuzzy contact map similarity. The retrieved distance map regions provided a good starting point for adaptation and allowed for the extrapolation of missing distance values. We were thus able to predict distance maps from which, the three-dimensional coordinates were able to be calculated. Testing of this framework on binary contact maps revealed that fuzzy contact maps had better performance with or without noise due to a stronger correlation between fuzzy contact map similarity and distance map similarity. Thus, the methodology described in this thesis is able to predict good distance maps from fuzzy contact maps in the presence of noise and the resulting coordinates were highly correlated to the performance of the predicted distance maps. / Thesis (Ph.D, Computing) -- Queen's University, 2012-05-23 13:59:28.12

Les protéines jouent un rôle important dans les cellules, via leur activité enzymatique et les interactions qu’elles mettent en jeu. Ces fonctions sont principalement basées sur la structure des protéines. Afin de détecter leur présence, et de caractériser leur structure, nous nous sommes appuyés sur les propriétés optiques des nanostructures. La résonance des plasmons de surface localisés (RPSL), ainsi que la diffusion Raman exaltée de surface(DRES), nous ont permis de détecter différentes protéines. Une optimisation des nanostructures nous a également permis de concevoir un biocapteur basé sur la DRES, qui soit sensible, reproductible et spécifique. En effet, la détection spécifique d’un biomarqueur pathologique, la protéine Manganèse Super Oxide Dismutase (MnSOD), a été réalisée grâce à l’utilisation de nanostructures optimisées et fonctionnalisées avec un aptamère (séquence ADN). Avec ce système, nous avons démontré la détection de la MnSOD à des concentrations physiologiques dans des fluides corporels comme le sérum et la salive. Enfin, l’étude de la structure de la protéine Spleen Tyrosine kinase (Syk), par DRES, nous a permis de mettre en évidence un réarrangement structurale de Syk lors de sa phosphorylation. Une étude complémentaire par Western Blot montre que son activité kinase est dépendante de son état de phosphorylation indiquant que la structure et l’activité de Syk sont liées. L’ensemble de ces travaux contribue à une meilleure connaissance de l’interface entre la physique et la biologie. / Proteins play an important role in cells via their enzymatic activity and the irinteractions. Their functions are mainly based on the protein structure. In order to detect their presence and to characterize their structure, we used optical properties of nanostructures. The localized surface plasmon resonance (LSPR), as well as the surface enhanced Raman scattering (SERS), allowed us to detect various proteins. We also optimized nanostructures to build a sensitive, reproducible and specific biosensor based on SERS. Indeed, specific detection of one pathological biomarker, the Manganese Super Oxide Dismutase (MnSOD) protein, was investigated by using optically optimized and aptamer-functionalized nanostructures. Using this system, we were able to detect the MnSOD at physiological concentration in body fluids, such as serum and saliva. Finally, the structural study of the Spleen Tyrosine kinase (Syk) protein by SERS, allowed us to demonstrate that its structure varied with its phosphorylation levels. A complementary Western Blot analysis showed that the Syk kinase activity depended also on its phosphorylation state, meaning that the structure and the activity of Syk were linked. Altogether, these data contributed to a better understanding of the interface between physics and biology.

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