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Antibodies are the fundamental agents of the immune system. The CDRs, or Complementarity Determining Regions act as the functional surfaces in binding antibodies to their targets. These CDR structures, which are peptide loops, are diverse in both amino acid sequence and structure. In 2011, we surveyed a database of CDR loop structures using the affinity propagation clustering algorithm of Frey and Dueck. With the growth of the number of structures deposited in the Protein Data Bank, the number of antibody CDRs has approximately tripled. In addition, although the affinity clustering in 2011 was successful in many ways, the methods used left too much noise in the data, and the affinity clustering algorithm tended to clump diverse structures together. This work revisits the antibody CDR clustering problem and uses five different clustering algorithms to categorize the data. Three of the clustering algorithms use DBSCAN but differ in the data comparison functions used. One uses the sum of the dihedral distances, while another uses the supremum of the dihedral distances, and the third uses the Jarvis-Patrick shared nearest neighbor similarity, where the nearest neighbor lists are compiled using the sum of the dihedral distances. The other two clustering methods use the k-medoids algorithm, one of which has been modified to include the use of pairwise constraints. Overall, the DBSCAN using the sum of dihedral distances and the supremum of the dihedral distances produced the best clustering results as measured by the average silhouette coefficient, while the constrained k-medoids clustering algorithm had the worst clustering results overall. / Computer and Information Science

Doctor of Philosophy / In my research, I developed protein model quality estimation methods aimed at evaluating the reliability of computationally predicted protein models in the absence of experimentally solved ground truth structures. These methods specifically focus on estimating errors within the protein models to quantify their structural accuracy. Recognizing that even the most advanced protein structure prediction techniques may produce models with errors, I also developed a complementary protein model refinement method. This refinement method iteratively optimizes the weakly modeled regions, guided by the error estimation module of my quality estimation approach. The development of these model quality estimation methods, therefore, not only offers valuable insights into the structural reliability of protein models but also contributes to optimizing the overall reliability of protein models generated by state-of-the-art computational methods.

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Protein evolution is a complex and diverse process, yielding an incredible assortment of biological functions and pathways occurring in the cells of living organisms. The way in which a protein's structure is constrained by its functional role and its notable conservation across even distant evolutionary relationships highlight structure as an important unit when considering the evolutionary dynamics of proteins. This thesis attempts to place the structural landscape of the protein universe within an evolutionary framework. We investigate potential evolutionary histories of protein superfamilies by introducing an age, which estimates when the ancestor of that superfamily first evolved. The range of ages of known protein superfamilies goes right back to those which evolved before the diversification of life into three major superkingdoms. The structures of these proteins are varied but those which have evolved more recently tend to be shorter and have a less elaborate globular packing. Protein structures sit within a complex global landscape of three-dimensional folds and we attempt to model the dynamics of this space using networks of folds. These networks consist of a structurally diverse core of folds with older ages, and neighbouring folds tend to be of similar ages. Moreover, there are a few pivotal folds which appear repeatedly as central in the landscapes, connecting together otherwise disparate portions of the space. Sequence profiles which capture patterns of conservation and variation amongst naturally occurring proteins within a superfamily can be compared to identify distant evolutionary relationships. The power of these profiles to detect such relationships is improved by seeding them with structural alignments. A landscape of evolutionary links crossing between different protein folds is presented.

[pt] O problema da predição das estruturas de proteínas (Protein Structure Prediction (PSP)) é um dos desafios mais importantes na biologia molecular. Pelo fato deste problema ser muito difícil, têm sido propostos diferentes modelos simplificados para resolvê-lo. Um dos mais estudados é o modelo, Hidrofóbico-Polar (HP), o modelo HP fornece uma estimativa da energia da proteína com base na soma de interações entre pares de aminoácidos hidrofóbicos (contatos H-H). Entretanto, apesar das simplificações feitas no modelo HP, o problema permanece complexo, pertencendo à classe NP-Difícil. Muitas técnicas têm sido propostas para resolver este problema entre elas, técnicas baseadas em algoritmos genéticos. Em muitos casos, as técnicas baseadas em AG foram usadas com sucesso, mas, no entanto, abordagens utilizando AG muitas vezes não tratam adequadamente as soluções geradas, prejudicando o desempenho da busca. Além disso, mesmo que eles, em alguns casos, consigam atingir o mínimo de energia conhecido para uma conformação, estes modelos não levam em conta a forma da proteína um fator muito importante na hora de obter proteínas mais compactas. Foi desenvolvido um algoritmo genético multiobjetivo para PSP no modelo HP, de modo de avaliar de forma mais eficiente, as conformações produzidas. O modelo utiliza como avaliação uma combinação baseada no número de colisões, número de contatos hidrofóbicos, compactação dos aminoácidos hidrofóbicos e hidrofílicos, obtendo, desta forma estruturas mais naturais e de mínima energia. Os resultados obtidos demonstram a eficiência desse algoritmo na obtenção de estruturas proteicas compactas providenciando indicadores da compactação dos aminoácidos hidrofóbicos e hidrofílicos da proteína. / [en] The problem of protein structured prediction (PSP) is one of the most important challenges in molecular biology. Because this problem is very difficult, different simplified models have been proposed to solve it. One of the most studied is the Hydrophobic-Polar model HP this model provides an estimate of the protein energy based on the sum of hydrophobic contacts. However, despite the simplifications made in the HP model, the problem remains complex, belonging to the class of NP-Hard problems. Many techniques have been proposed to solve this problem as genetic algorithms. In many cases the GA techniques have been used successfully, but, however, with GA approaches often do not adequately address the generated solutions, impairing the performance of the search. Furthermore, in some cases would attain the minimum energy for a known conformation, these models do not take care the protein shape, a very important factor to obtain more compact proteins. This work developed a multiobjective genetic algorithm to PSP in HP model evaluating more efficiently, the conformations produced. This model is a combination of assessment based on the collisions numbers, hydrophobic contacts, hydrophobic and hydrophilic core compression, obtaining thus more natural structures with minimum energy. The results demonstrate the efficiency of this algorithm to obtain protein structures indicators providing compact compression of the hydrophobic and hydrophilic core protein.

The ability of mutations to change the fold of a protein provides evolutionary pathways to new structures. To study hypothetical pathways for protein fold evolution, we designed intermediate sequences between Xfaso1 and Pfl6, two homologous Cro proteins that have 40% sequence identity but adopt all–α and α+β folds, respectively. The designed hybrid sequences XPH1 and XPH2 have 70% sequence identity to each other. XPH1 is more similar in sequence to Xfaso1 (86% sequence identity) while XPH2 is more similar to Pfl6 (80% sequence identity). NMR solution ensembles show that XPH1 and XPH2 have structures intermediate between Xfaso1 and Pfl6. Specifically, XPH1 loses α-helices 5 and 6 of Xfaso1 and incorporates a small amount of β-sheet structure; XPH2 preserves most of the β-sheet of Pfl6 but gains a structure comparable to helix 6 of Xfaso1. These findings illustrate that the sequence space between two natural protein folds may encode a range of topologies, which may allow a protein to change its fold extensively through gradual, multistep mechanisms. Evolving a new fold may have consequences, such as a strained conformation. Here we show that Pfl6 represents an early, strained form of the α+β Cro fold resulting from an ancestral remnant of the all-α Cro proteins retained after the fold switch. This nascent fold can be stabilized through deletion mutations in evolution, which can relieve the strain but may also negatively affect DNA-binding function. Compensatory mutations that increase dimerization appear to offset these effects to maintain function. These findings suggest that new folds can undergo mutational editing through evolution, which may occur in parallel pathways with slightly different outcomes.

Cereals contain naturally occurring biopolymers (for example proteins and starches) that can be used as renewable raw materials in a variety of speciality chemical applications. The separation of protein and starch biopolymers from wheat is well established and relies on a group of proteins called glutens that have a unique network-forming functionality. Oat and other cereals do not naturally contain these gluten proteins and typically rely on chemical-based separation techniques which alter the chemical and physical structures and damage the inherent natural functionality of the biopolymers. This research study investigated the separation of the protein and starch fractions from cereals using the Al-Hakkak Process, a new aqueous process. This process involves adding water and wheat gluten protein to cereals that do not contain gluten. The wheat gluten interacts with the cereal proteins, facilitating the separation of the starch and protein fractions whilst retaining their inherent natural functionality. The aim of this research project was to investigate and optimise the pilot scale separation performance of the Al-Hakkak Process using oat flour. As very little prior research had been carried out, the focus was to characterise the oat starch and protein separation performance and gain an understanding of the mechanisms involved. A variety of techniques were employed. Large scale deformation rheology was used to gain an understanding of the oat-gluten dough rheology and establish the relationship between the rheology and the separation performance. Confocal scanning laser microscopy was used to investigate the structure of the oat-gluten protein network. The molecular interactions between the oat and gluten proteins were studied using gel electrophoresis. The network-forming functionality of the new oat-gluten protein was explored. The influence of various processing parameters on the pilot scale separation performance was investigated and the results compared with other data collected through the study to identify key processing parameters. This research programme has resulted in interesting, encouraging and some unexpected outcomes and these are discussed in detail in the thesis. It was concluded that an insoluble protein network formed in the oat-gluten dough and both kneading and extraction processes were found to contribute to the formation of this. A key conclusion was that the changes that took place in the oat-gluten dough were similar to, but not identical to, the changes that occur in wheat dough. It was proposed that the mechanism for the development of a protein network in oat-gluten dough differed from wheat dough for two main reasons: a) the presence of the oat flour disrupted the normal wheat gluten behaviour, and b) components in the oat flour altered the activity of the gluten proteins. The research identified key processing parameters for the Al-Hakkak Process including kneading time, gluten content, and sodium chloride content of the oat-gluten dough as well as sodium chloride concentration, pH, and temperature of the extract liquor. An important discovery was that the oat and gluten proteins interacted at a molecular level through reducible, covalent, bonding (most likely disulphide linkages) to form the insoluble protein network in the oat-gluten dough. It was concluded that these reducible bonds coupled the individual protein subunits to form new hybrid oat-gluten protein molecules (a combination of oat proteins and gluten proteins). Both insoluble and soluble proteins in the oat and gluten flour were involved in the formation of the insoluble protein network in the oat-gluten dough. This outcome has applications beyond the Al-Hakkak Process, as this new knowledge can be applied to the wider dough processing industry. It was concluded that the wheat gluten was the source of the protein network-forming functionality of the hybrid oat-gluten protein and that the oat proteins had a diluting effect. It was proposed that oat-gluten protein flour from the Al-Hakkak Process could be reused to replace the commercial wheat gluten flour in subsequent production batches. During spray drying of the starch stream, the soluble biopolymers in the extract liquor were found to act as an adhesive and glued individual starch granules together to form spherical agglomerates. Acidification of the extract liquor was found to enhance this agglomeration. It was proposed the acidified starch granules were sticker during spray drying due to the partial acid hydrolysis of the starch granule suface which enhanced the agglomeration.

Despite the fundamental role of experimental protein structure determination, computational methods are of essential importance to bridge the ever growing gap between available protein sequence and structure data. Common structure prediction methods rely on experimental data, which is not available for about half of the known protein families. Recent advancements in amino acid contact prediction have revolutionized the field of protein structure prediction. Contacts can be used to guide template-free structure predictions that do not rely on experimentally solved structures of homologous proteins. Such methods are now able to produce accurate models for a wide range of protein families. We developed PconsC2, an approach that improved existing contact prediction methods by recognizing intra-molecular contact patterns and noise reduction. An inherent problem of contact prediction based on maximum entropy models is that large alignments with over 1000 effective sequences are needed to infer contacts accurately. These are however not available for more than 80% of all protein families that do not have a representative structure in PDB. With PconsC3, we could extend the applicability of contact prediction to families as small as 100 effective sequences by combining global inference methods with machine learning based on local pairwise measures. By introducing PconsFold, a pipeline for contact-based structure prediction, we could show that improvements in contact prediction accuracy translate to more accurate models. Finally, we applied a similar technique to Pfam, a comprehensive database of known protein families. In addition to using a faster folding protocol we employed model quality assessment methods, crucial for estimating the confidence in the accuracy of predicted models. We propose models tobe accurate for 558 families that do not have a representative known structure. Out of those, over 75% have not been reported before. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 4: In press.</p><p> </p>

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