Proteine spielen auf der zellulären Ebene eines Organismus eine fundamentale Rolle. Sie sind quasi die „Maschinen“ der Zelle. Ihre Bedeutung wird nicht zuletzt in ihrem Namen deutlich, welcher 1838 erstmals von J. Berzelius verwendet wurde und „das Erste“, „das Wichtigste“ bedeutet. Proteine sind aus Aminosäuren aufgebaute Moleküle. Unter physiologischen Bedingungen besitzen sie eine definierte dreidimensionale Gestalt, welche für ihre biologische Funktion bestimmend ist. Es wird heutzutage davon ausgegangen, dass diese dreidimensionale, stabile Struktur von Proteinen eindeutig durch die Abfolge der einzelnen Aminosäuren, der Sequenz, bestimmt ist. Diese Abfolge ist für jedes Protein in der Desoxyribonukleinsäure (DNS) gespeichert. Es ist allerdings eines der größten ungelösten Probleme der letzten Jahrzehnte, wie die Beziehung zwischen Sequenz und 3D-Struktur tatsächlich aussieht. Die Beantwortung dieser Fragestellung erfordert interdisziplinäre Ansätze aus Biologie, Informatik und Physik. In dieser Arbeit werden mit Hilfe von Methoden der theoretischen (Bio-) Physik einige der damit verbundenen Aspekte untersucht. Das Hauptaugenmerk liegt dabei auf Wechselwirkungen der einzelnen Aminosäuren eines Proteins untereinander, wofür in dieser Arbeit ein entsprechendes Energiemodell entwickelt wurde. Es werden Grundzustände sowie Energielandschaften untersucht und mit experimentellen Daten verglichen. Die Stärke der Wechselwirkung einzelner Aminosäuren erlaubt zusätzlich Aussagen über die Stabilität von Proteinen bezüglich mechanischer Kräfte. Die vorliegende Arbeit unterteilt sich wie folgt: Kapitel 2 dient der Einleitung und stellt Proteine und ihre Funktionen dar. Kapitel 3 stellt die Modellierung der Proteinstrukturen in zwei verschiedenen Modellen vor, welche in dieser Arbeit entwickelt wurden, um 3D-Strukturen von Proteinen zu beschreiben. Anschließend wird in Kapitel 4 ein Algorithmus zum Auffinden des exakten Energieminimums dargestellt. Kapitel 5 beschäftigt sich mit der Frage, wie eine geeignete diskrete Energiefunktion aus experimentellen Daten gewonnen werden kann. In Kapitel 6 werden erste Ergebnisse dieses Modells dargestellt. Der Frage, ob der experimentell bestimmte Zustand dem energetischen Grundzustand eines Proteins entspricht, wird in Kapitel 7 nachgegangen. Die beiden Kapitel 8 und 9 zeigen die Anwendung des Modells an zwei Proteinen, dem Tryptophan cage protein als dem kleinsten, stabilen Protein und Kinesin, einem Motorprotein, für welches 2007 aufschlussreiche Experimente zur mechanischen Stabilität durchgeführt wurden. Kapitel 10 bis 12 widmen sich Membranproteinen. Dabei beschäftigt sich Kapitel 10 mit der Vorhersage von stabilen Bereichen (sog. Entfaltungsbarrieren) unter externer Krafteinwirkung. Zu Beginn wird eine kurze Einleitung zu Membranproteinen gegeben. Im folgenden Kapitel 11 wird die Entfaltung mit Hilfe des Modells und Monte-Carlo-Techniken simuliert. Mit dem an Membranproteine angepassten Wechselwirkungsmodell ist es möglich, den Einfluss von Mutationen auch ohne explizite strukturelle Informationen vorherzusagen. Dieses Thema wird in Kapitel 12 diskutiert. Die Beziehung zwischen Primär- und Tertiärstruktur eines Proteins wird in Kapitel 13 behandelt. Es wird ein Ansatz skizziert, welcher in der Lage ist, Strukturbeziehungen zwischen Proteinen zu detektieren, die mit herkömmlichen Methoden der Bioinformatik nicht gefunden werden können. Die letzten beiden Kapitel schließlich geben eine Zusammenfassung bzw. einen Ausblick auf künftige Entwicklungen und Anwendungen des Modells.

Predicting the structure of complexes formed by two interacting proteins is an important problem in computation structural biology. Proteins perform many of their functions by binding to other proteins. The structure of protein-protein complexes provides atomic details about protein function and biochemical pathways, and can help in designing drugs that inhibit binding. Docking computationally models the structure of protein-protein complexes, given three-dimensional structures of the individual chains. Protein docking methods have two phases. In the first phase, a comprehensive, coarse search is performed for optimally docked models. In the second refinement and reranking phase, the models from the first phase are refined and reranked, with the expectation of extracting a small set of accurate models from the pool of thousands of models obtained from the first phase. In this thesis, new algorithms are developed for the refinement and reranking phase of docking. New scoring functions, or potentials, that rank models are developed. These potentials are learnt using large-scale machine learning methods based on mathematical programming. The procedure for learning these potentials involves examining hundreds of thousands of correct and incorrect models. In this thesis, hierarchical constraints were introduced into the learning algorithm. First, an atomic potential was developed using this learning procedure. A refinement procedure involving side-chain remodeling and conjugate gradient-based minimization was introduced. The refinement procedure combined with the atomic potential was shown to improve docking accuracy significantly. Second, a hydrogen bond potential, was developed. Molecular dynamics-based sampling combined with the hydrogen bond potential improved docking predictions. Third, mathematical programming compared favorably to SVMs and neural networks in terms of accuracy, training and test time for the task of designing potentials to rank docking models. The methods described in this thesis are implemented in the docking package DOCK/PIERR. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer's disease.R. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer’s disease. / text

A molecule can crystallise in more than one crystal structure, a common phenomenon in organic compounds known as polymorphism. Different polymorphic forms may have significantly different physical properties, and a reliable prediction would be beneficial to the pharmaceutical industry. However, crystal structure prediction (CSP) based on the knowledge of the chemical structure had long been considered impossible. Previous failures of some CSP attempts led to speculation that the thermodynamic calculations in CSP methodologies failed to predict the kinetically favoured structures. Similarly, regarding the stabilities of co-crystals relative to their pure components, the results from lattice energy calculations and full CSP studies were inconclusive. In this thesis, these problems are addressed using the state-of-the-art CSP methodology implemented in the GRACE software. Firstly, it is shown that the low-energy predicted structures of four organic molecules, which have previously been considered difficult for CSP, correspond to their experimental structures. The possible outcomes of crystallisation can be reliably predicted by sufficiently accurate thermodynamic calculations. Then, the polymorphism of 5- chloroaspirin is investigated theoretically. The order of polymorph stability is predicted correctly and the isostructural relationships between a number of predicted structures and the experimental structures of other aspirin derivatives are established. Regarding the stabilities of co-crystals, 99 out of 102 co-crystals and salts of nicotinamide, isonicotinamide and picolinamide reported in the Cambridge Structural Database (CSD) are found to be more stable than their corresponding co-formers. Finally, full CSP studies of two co-crystal systems are conducted to explain why the co-crystals are not easily obtained experimentally.

Die vorliegende Dissertation befasst sich mit der theoretischen Vorhersage neuer Materialien. Ein evolutionärer Algorithmus, der zur Lösung dieses globalen Optimierungsproblems Konzepte der natürlichen Evolution imitiert, wurde entwickelt und ist als Programmpaket EVO frei verfügbar. EVO findet zuverlässig sowohl bekannte als auch neuartige Kristallstrukturen. Beispielsweise wurden die Strukturen von Germaniumnitrofluorid, einer neue Borschicht und mit dem gekreuzten Graphen einer bisher unbekannte Kohlenstoffstruktur gefunden. Ferner wurde in der Arbeit gezeigt, dass das reine Auffinden solcher Strukturen der erste Teil einer erfolgreichen Vorhersage ist. Weitere aufwendige Berechnungen sind nötig, die Aufschluss über die Stabilität der hypothetischen Struktur geben und Aussagen über zu erwartende Materialeigenschaften liefern.

The goal of the following thesis research was to develop a systematic approach for the refinement of low-resolution protein models, as a part of the protein structure prediction procedure. Significant progress has been made in the field of protein structure prediction and the contemporary methods are able to assemble correct topology for a large fraction of protein domains. But such approximate models are often not detailed enough for some important applications, including studies of reaction mechanisms, functional annotation, drug design or virtual ligand screening. The development of a method that could bring those structures closer to the native is then of great importance. The minimal requirements for a potential that can refine protein structures is the existence of a correlation between the energy with native similarity and the scoring of the native structure as being lowest in energy. Extensive tests of the contemporary all-atom physics-based force fields were conducted to assess their applicability for refinement. The tests revealed flatness of such potentials and enabled the identification of the key problems in the current approaches. Guided by these results, the optimization of the AMBER (ff03) force field was performed that aimed at creating a funnel shape of the potential, with the native structure at the global minimum. Such shape should facilitate the conformational search during refinement and drive it towards the native conformation. Adjusting the relative weights of particular energy components, and adding an explicit hydrogen bond potential significantly improved the average correlation coefficient of the energy with native similarity (from 0.25 for the original ff03 potential to 0.65 for the optimized force field). The fraction of proteins for which the native structure had lowest energy increased from 0.22 to 0.90. The new, optimized potential was subsequently used to refine protein models of various native-similarity. The test employed 47 proteins and 100 decoy structures per protein. When the lowest energy structure from each trajectory was compared with the starting decoy, we observed structural improvement for 70% of the models on average. Such an unprecedented result of a systematic refinement is extremely promising in the context of high-resolution structure prediction.

A demanda criada pelos estudos biológicos resultou para predição da estrutura terciária de proteínas ser uma alternativa, uma vez que menos de 1% das sequências conhecidas possuem sua estrutura terciária determinada experimentalmente. As predições Ab initio foca nas funções baseadas da física, a qual se trata apenas das informações providas pela sequência primária. Por consequência, um espaço de busca com muitos mínimos locais ótimos deve ser pesquisado. Este cenário complexo evidencia uma carência de algoritmos eficientes para este espaço, tornando-se assim o principal obstáculo para este tipo de predição. A optimização Multi-Objetiva, principalmente os Algoritmos Evolutivos, vem sendo aplicados na predição da estrutura terciária já que na mesma se envolve um compromisso entre os objetivos. Este trabalho apresenta o framework ProtPred-PEO-GROMACS, ou simplesmente 3PG, que não somente faz predições com a mesma acurácia encontrada na literatura, mas também, permite investigar a predição por meio da manipulação de combinações de objetivos, tanto no aspecto energético quanto no estrutural. Além disso, o 3PG facilita a implementação de novas opções, métodos de análises e também novos algoritmos evolutivos. A fim de salientar a capacidade do 3PG, foi então discorrida uma comparação entre os algoritmos NSGA-II e SPEA2 aplicados na predição Ab initio da estrutura terciária de proteínas em seis combinações de objetivos. Ademais, o uso da técnica de refinamento por Dinâmica Molecular é avaliado. Os resultados foram adequados quando comparado com outras técnicas de predições: Algoritmos Evolutivo Multi-Objetivo, Replica Exchange Molecular Dynamics, PEP-FOLD e Folding@Home. / The demand created by biological studies resulted the structure prediction as an alternative, since less than 1% of the known protein primary sequences have their 3D structure experimentally determined. Ab initio predictions focus on physics-based functions, which regard only information about the primary sequence. As a consequence, a search space with several local optima must be sampled, leading to insucient sampling of this space, which is the main hindrance towards better predictions. Multi-Objective Optimization approaches, particularly the Evolutionary Algorithms, have been applied in protein structure prediction as it involves a compromise among conicting objectives. In this paper we present the ProtPred-PEO-GROMACS framework, or 3PG, which can not only make protein structure predictions with the same accuracy standards as those found in the literature, but also allows the study of protein structures by handling several energetic and structural objective combinations. Moreover, the 3PG framework facilitates the fast implementation of new objective options, method analysis and even new evolutionary algorithms. In this study, we perform a comparison between the NSGA-II and SPEA2 algorithms applied on six dierent combinations of objectives to the protein structure. Besides, the use of Molecular Dynamics simulations as a renement technique is assessed. The results were suitable when comparated with other prediction methodologies, such as: Multi-Objective Evolutionary Algorithms, Replica Exchange Molecular Dynamics, PEP-FOLD and Folding@Home.

Recentemente, pesquisadores têm proposto o uso de Algoritmos Genéticos (AGs) para a determinação da estrutura tridimensional de proteínas. No entanto, este é um problema difícil para um AG tradicional, pois na maioria das vezes ocorre a convergência prematura das soluções para ótimos locais. Isto ocorre porque o uso de mecanismos de seleção no AG acarreta uma perda da diversidade das soluções. Assim, neste trabalho, são investigadas estratégias para controlar a diversidade da população do AG e evitar que a solução fique rapidamente presa em ótimos locais. São empregadas bases de dados de ângulos de torção para a cadeia principal, cadeia lateral e técnicas de controle de diversidade em AGs conhecidas como Hipermutação e Imigrantes Aleatórios. Além disso, um novo algoritmo baseado no AG com Imigrantes Aleatórios Auto-Organizáveis é proposto. Os resultados mostram que estas variações são efetivas no objetivo de não manter o conjunto de soluções preso a uma região apenas, além de melhorar o desempenho para o problema de determinação de estruturas terciárias de proteínas. / Recently, researchers have proposed the use of Genetic Algorithms (GAs) for the determination of the three-dimensional structure of proteins. However, this problem is considered a difficult problem for the standard GA, because most of the cases the convergence occurs early, into local minima instead of the global optimum. This occurs because the use of selection mechanisms in the GA leads to a loss of diversity of solutions. With this in mind, in this work, strategies to control the diversity of the population in the GA are investigated in order to avoid the solution subset to be early caught in local optima. Database sets of torsion angles for the main chain and the side chain are employed, and also modifications in the GAs, known as Hypermutation and Random Immigrants. Besides these approaches, a new algorithm based on the Self-Organizing Random Immigrants is proposed. Results show that these changes are effective in the goal of avoiding the results ensemble to be trapped in a region, and also help improve the performance for the protein structure prediction problem.

Nipah belongs to the family of paramyxoviruses that cause numerous fatal diseases in humans and farm animals. There are no FDA approved drugs for Nipah or any of the paramyxoviruses. Designing antiviral therapies that are more resistant to viral mutations require understanding of molecular details underlying infection. This dissertation focuses on obtaining molecular insights into the very first step of infection by Nipah. Such details, in fact, remain unknown for all paramyxoviruses. Infection begins with the allosteric stimulation of Nipah virus host binding protein by host cell receptors. Understanding molecular details of this stimulation process have been challenging mainly because, just as in many eukaryotic proteins, including GPCRs, PDZ domains and T-cell receptors, host receptors induce only minor structural changes (< 2 Å) and, consequently, thermal fluctuations or dynamics play a key role. This work utilizes a powerful molecular dynamics based approach, which yields information on both structure and dynamics, laying the foundation for its future applications to other paramyxoviruses. It proposes a new model for the initial phase of stimulation of Nipah’s host binding protein, and in general, highlights that (a) interfacial waters can play a crucial role in the inception and propagation of allosteric signals; (b) extensive inter-domain rearrangements can be triggered by minor changes in the structures of individual domains; and (c) mutations in dynamically stimulated proteins can induce non-local changes that spread across entire domains.

De nombreuses recherches ont constaté l'importance des molécules d'ARN, car ils jouent un rôle vital dans beaucoup de procédures moléculaires. Et il est accepté généralement que les structures des molécules d'ARN sont la clé de la découverte de leurs fonctions.Au cours de l'enquête de structures d'ARN, les chercheurs dépendent des méthodes bioinformatiques de plus en plus. Beaucoup de méthodes in silico de prédiction des structures secondaires d'ARN ont émergé dans cette grosse vague, y compris certains qui sont capables de prédire pseudonoeuds, un type particulier de structures secondaires d'ARN.Le but de ce travail est d'essayer de comparer les méthodes de l'état de l'art pour prédiction de pseudonoeud, et offrir aux collègues des idées sur le choix d’une méthode pratique pour la seule séquence donnée. En fait, beaucoup d'efforts ont été fait dans la prédiction des structures secondaires d'ARN parmi lesquelles le pseudonoeud les dernières décennies, contribuant à de nombreux programmes dans ce domaine. Certaines enjeux sont soulevées conséquemment. Comment est-elle la performance de chaque méthode, en particulier sur une classe de séquences d'ARN particulière? Quels sont leurs pour et contre? Que pout-on profiter des méthodes contemporaines si on veut développer de nouvelles? Cette thèse a la confiance dans l'enquête sur les réponses.Cette thèse porte sur très nombreuses comparaisons de la performance de prédire pseudonoeuds d'ARN par les méthodes disponibles. Une partie principale se concentre sur la prédiction de signaux de déphasage par deux méthodes principalement. La deuxième partie principale se concentre sur la prédiction de pseudonoeuds qui participent à des activités moléculaires beaucoup plus générale.Dans le détail, la deuxième partie du travail comprend 414 pseudonoeuds de Pseudobase et de la Protein Data Bank, ainsi que 15 méthodes dont 3 méthodes exactes et 12 heuristiques. Plus précisément, trois grandes catégories de mesures complexes sont introduites, qui divisent encore les 414 pseudonoeuds en une série de sous-classes respectivement.Les comparaisons se passent par comparer les prédictions de chaque méthode basée sur l'ensemble des 414 pseudonœuds, et les sous-ensembles qui sont classés par les deux mesures complexes et la longueur, le type de l'ARN et de l'organisme des pseudonœuds.Le résultat montre que les pseudo-noeuds portent une complexité relativement faible dans toutes les mesures. Et la performance des méthodes modernes varie de sous-classe à l’autre, mais diminue constamment lors que la complexité de pseudonoeuds augmente. Plus généralement, les méthodes heuristiques sont supérieurs globalement à celles exacts. Et les résultats de l'évaluation sensibles sont dépendants fortement de la qualité de structure de référence et le système d'évaluation. Enfin, cette partie du travail est fourni comme une référence en ligne pour la communauté bioinformatique. / Lots of researches convey the importance of the RNA molecules, as they play vital roles in many molecular procedures. And it is commonly believed that the structures of the RNA molecules hold the key to the discovery of their functions.During the investigation of RNA structures, the researchers are dependent on the bioinformatical methods increasingly. Many in silico methods of predicting RNA secondary structures have emerged in this big wave, including some ones which are capable of predicting pseudoknots, a particular type of RNA secondary structures.The purpose of this dissertation is to try to compare the state-of-the-art methods predicting pseudoknots, and offer the colleagues some insights into how to choose a practical method for the given single sequence. In fact, lots of efforts have been done into the prediction of RNA secondary structures including pseudoknots during the last decades, contributing to many programs in this field. Some challenging questions are raised consequently. How about the performance of each method, especially on a particular class of RNA sequences? What are their advantages and disadvantages? What can we benefit from the contemporary methods if we want to develop new ones? This dissertation holds the confidence in the investigation of the answers.This dissertation carries out quite many comparisons of the performance of predicting RNA pseudoknots by the available methods. One main part focuses on the prediction of frameshifting signals by two methods principally. The second main part focuses on the prediction of pseudoknots which participate in much more general molecular activities.In detail, the second part of work includes 414 pseudoknots, from both the Pseudobase and the Protein Data Bank, and 15 methods including 3 exact methods and 12 heuristic ones. Specifically, three main categories of complexity measurements are introduced, which further divide the 414 pseudoknots into a series of subclasses respectively. The comparisons are carried out by comparing the predictions of each method based on the entire 414 pseudoknots, and the subsets which are classified by both the complexity measurements and the length, RNA type and organism of the pseudoknots.The result shows that the pseudoknots in nature hold a relatively low complexity in all measurements. And the performance of contemporary methods varies from subclass to subclass, but decreases consistently as the complexity of pseudoknots increases. More generally, the heuristic methods globally outperform the exact ones. And the susceptible assessment results are dependent strongly on the quality of the reference structures and the evaluation system. Last but not least, this part of work is provided as an on-line benchmark for the bioinformatics community.

Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). These programs are able to predict model accuracy before the native structure is determined. The accuracy estimation can be divided into two parts: global (the whole model accuracy) and local (the accuracy of each residue). ProQ2 is one of the most successful MQAPs for prediction of both local and global model accuracy and is based on a Machine Learning approach. In this thesis, I present my own contribution to Model Quality Assessment (MQA) and the newest developments of ProQ program series. Firstly, I describe a new ProQ2 implementation in the protein modelling software package Rosetta. This new implementation allows use of ProQ2 as a scoring function for conformational sampling inside Rosetta, which was not possible before. Moreover, I present two new methods, ProQ3 and ProQ3D that both outperform their predecessor. ProQ3 introduces new training features that are calculated from Rosetta energy functions and ProQ3D introduces a new machine learning approach based on deep learning. ProQ3 program participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) and was one of the best methods in the MQA category. Finally, an important issue in model quality assessment is how to select a target function that the predictor is trying to learn. In the fourth manuscript, I show that MQA results can be improved by selecting a contact-based target function instead of more conventional superposition based functions. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.</p>