<p>Proteins are among the most complex entities known to science. Composed of just 20 fundamental building blocks arranged in simple linear strings, they nonetheless fold into a dizzying array of architectures that carry out the machinations of life at the molecular level.</p><p>Despite this central role in biology, we cannot reliably predict the structure of a protein from its sequence, and therefore rely on time-consuming and expensive experimental techniques to determine their structures. Although these methods can reveal equilibrium structures with great accuracy, they unfortunately mask much of the inherent molecular flexibility that enables proteins to dynamically perform biochemical tasks. As a result, much of the field of structural biology is mired in a static perspective; indeed, most attempts to naively model increased structural flexibility still end in failure.</p><p>This document details my work to validate alternative protein conformations beyond the primary or equilibrium conformation. The underlying hypothesis is that more realistic modeling of flexibility will enhance our understanding of how natural proteins function, and thereby improve our ability to design new proteins that perform desired novel functions.</p><p>During the course of my work, I used structure validation techniques to validate conformational alternatives in a variety of settings. First, I extended previous work introducing the backrub, a local, sidechain-coupled backbone motion, by demonstrating that backrubs also accompany sequence changes and therefore are useful for modeling conformational changes associated with mutations in protein design. Second, I extensively studied a new local backbone motion, helix shear, by documenting its occurrence in both crystal and NMR structures and showing its suitability for expanding conformational search space in protein design. Third, I integrated many types of local alternate conformations in an ultra-high-resolution crystal structure and discovered the combinatorial complexity that arises when adjacent flexible segments combine into networks. Fourth, I used structural bioinformatics techniques to construct smoothed, multi-dimensional torsional distributions that can be used to validate trial conformations or to propose new ones. Fifth, I participated in judging a structure prediction competition by using validation of geometrical and all-atom contact criteria to help define correctness across thousands of submitted conformations. Sixth, using similar tools plus collation of multiple comparable structures from the public database, I determined that low-energy states identified by the popular structure modeling suite Rosetta sometimes are valid conformations likely to be populated in the cell, but more often are invalid conformations attributable to artifacts in the physical/statistical hybrid energy function.</p><p>Unified by the theme of validating conformational alternatives by reference to high-quality experimental structures, my cumulative work advances our fundamental understanding of protein structural variability, and will benefit future endeavors to design useful proteins for biomedicine or industrial chemistry.</p> / Dissertation

Ribonucleic acid (RNA) is an essential molecule, which carries out a wide variety of functions within the cell, from its crucial involvement in protein synthesis to catalysing biochemical reactions and regulating gene expression. Such diverse functional repertoire is indebted to complex structures that RNA can adopt and its flexibility as an interacting molecule. It has become possible to experimentally measure these two crucial aspects of RNA regulatory role with such technological advancements as next-generation sequencing (NGS). NGS methods can rapidly obtain the nucleotide sequence of many molecules in parallel. Designing experiments, where only the desired parts of the molecule (or specific parts of the transcriptome) are sequenced, allows to study various aspects of RNA biology. Analysis of NGS data is insurmountable without computational methods. One such experimental method is RNA structure probing, which aims to infer RNA structure from sequencing chemically altered transcripts. RNA structure probing data is inherently noisy, affected both by technological biases and the stochasticity of the underlying process. Most existing methods do not adequately address the issue of noise, resorting to heuristics and limiting the informativeness of their output. In this thesis, a statistical pipeline was developed for modelling RNA structure probing data, which explicitly captures biological variability, provides automated bias-correcting strategies, and generates a probabilistic output based on experimental measurements. The output of our method agrees with known RNA structures, can be used to constrain structure prediction algorithms, and remains robust to reduced sequence coverage, thereby increasing sensitivity of the technology. Another recent experimental innovation maps RNA-protein interactions at very high temporal resolution, making it possible to study rapid binding events happening on a minute time scale. In this thesis, a non-parametric algorithm was developed for identifying significant changes in RNA-protein binding time-series between different conditions. The method was applied to novel yeast RNA-protein binding time-course data to study the role of RNA degradation in stress response. It revealed pervasive changes in the binding to the transcriptome of the yeast transcription termination factor Nab3 and the cytoplasmic exoribonuclease Xrn1 under nutrient stress. This challenged the common assumption of viewing transcriptional changes as the major driver of changes in RNA expression during stress and highlighted the importance of degradation. These findings inspired a dynamical model for RNA expression, where transcription and degradation rates are modelled using RNA-protein binding time-series data.

High-nitrogen-content energetic compounds containing multiple N-N bonds are an attractive alternative towards developing new generation of environmentally friendly, and more powerful energetic materials. High-N content translates into much higher heat of formation resulting in much larger energy output, detonation pressure and velocity upon conversion to large amounts of non-toxic, strongly bonded N2 gas. This thesis describes recent advances in the computational discovery of group-I alkali and hydrogen polynitrogen materials at high pressures using powerful first-principles evolutionary crystal structure prediction methods. This is highlighted by the discovery of a new family of materials that consist of long-sought after all-nitrogen N􀀀 5 anions and metal or hydrogen cations. The work has inspired a resurgence in the efforts to synthesize the N􀀀 5 anion. After describing the methodology of first-principles crystal structure prediction, several new high-nitrogen-content energetic compounds are described. In addition to providing information on structure and chemical composition, theory/simulations also suggests specific precursors, and experimental conditions that are required for experimental synthesis of high-N pentazolate EMs. To aid in experimental detection of newly synthesized compounds, XRD patterns and corresponding Raman spectra are calculated for several candidate structures. The ultimate success was achieved in joint theoretical and experimental discovery of cesium pentazolate, which was synthesized by compressing and heating cesium azide CsN3 and N2 precursors in a diamond anvil cell. This success highlights the key role of first-principles structure prediction simulations in guiding experimental exploration of new high-N energetic materials.

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.

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. 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.

Dans cette thèse nous utilisons des méthodes globaux de prédiction des structures cristallographiques combinés à des techniques de grande capacité de traitement de données afin de prédire la structure cristalline de différents systèmes et dans des conditions thermodynamiques variées. Nous avons réalisé des prédictions structurales utilisant l'analyse cristalline par optimisation par essaims particuliers (CALYPSO) combinés avec la Théorie Fonctionnel de la Densité (DFT) ce qui a permis de mettre en évidence la stabilité de plusieurs composés jusqu'à la inconnus dans le digramme de phases du système Ba-Si et dans le système N-H-O. Nous avons également réalisé une étude à haute capacité de traitement de données sur un système ternaire de composition ABX2. Nous avons utilisé la Théorie Fonctionnel de la Densité combinant calculs de prototypes structuraux à partir des prédictions structurelles avec la méthode. Dans les paragraphes suivants nous résumons le contenu de différents chapitres de cette thèse. Le premier chapitre qui constitue une brève introduction au travail de cette thèse est suivi du chapitre 2 présentant les aspects théoriques utilisés dans ce travail. D'abord il est fait une brève introduction à la Théorie Fonctionnel de la Densité. A continuation nous décrivons quelques fonctions d'échange-corrélation choisies qui constituent des approximations rendant l'utilisation de la DFT efficace. Ensuite nous présentons différents procédés de prédiction structurale, et en particulier les algorithmes d'optimisation par essaims particuliers et de « Minima Hopping » qeu nous avons utilisés dans cette thèse. Finalement il est discuté comment doit-on se prendre pour évaluer la stabilité thermodynamique des nouvelles phases identifiées. Dans le chapitre 3, nous considérons le système Ba-Si. A travers l'utilisation d'une recherche structurale non-biaisée basée sur l'algorithme d'optimisation par essaims particuliers combinée avec des calculs DFT, nous faisons une étude systématique de la stabilité des phases et de la diversité structurale du système binaire Ba-Si sous haute pression. Le diagramme de phases résultant est assez complexe avec plusieurs compositions se stabilisant et se déstabilisant en fonction de la pression. En particulier, nous avons identifié des nouvelles phases de stœchiométrie BaSi, BaSi2, BaSi3 et BaSi5 qui devraient pouvoir être synthétisées expérimentalement dans un domaine de pressions étendu. Dans le chapitre 4 est présentée notre étude du diagramme de phases du système N-H-O. S'appuyant sur une recherche structural «évolutive » de type ab initio, nous prédisons deux nouvelles phases du système ternaire N-H-O qui sont NOH4 et HNO3 à de pressions allant jusqu'à 150 GPa. La nouvelle phase de NOH4 est stable entre 71 et 150 GPa, tandis que HNO3 est stable entre 39 et 150 GPa (la pression maximum de cette étude). Ces deux nouvelles phases sont lamellaires. Nous confirmons également que la composition NOH5 perd son stabilité pour des pressions supérieures à 122 GPa se décomposant en NH3 et H2O à cette pression. Le chapitre 5 se focalise sur les électrodes transparentes de type-p à base des chalcogénures ternaires. Nous utilisons une approche à grande capacité de traitement de données basée sur la DFT pour obtenir la delafossite et d'autres phases voisines de composition ABX2. Nous trouvons 79 systèmes qui sont absents de la base de données « Materials project database », qui sont stables du point de vue thermodynamique et qui cristallisent soit dans la structure delafossite, soit dans des structures très proches. Cette caractérisation révèle une grande diversité de propriétés allant depuis les métaux ordinaires aux métaux magnétiques et permettant d'identifier quelques candidats pour des électrodes transparents de type-p. Nous présentons enfin à la fin du manuscrit nos conclusions générales et les perspectives de ce travail / In this thesis we use global structural prediction methods (Particle Swarm Optimization and Minima Hopping Method) and high-throughput techniques to predict crystal structures of different systems under different conditions. We performed structural prediction by using the Crystal structure Analysis by Particle Swarm Optimization (CALYPSO) combined with Density Functional Theory (DFT) that made possible to unveil several stable compounds, so far unknown, on the phase diagrams of Ba-Si systerm and N-H-O system. Afterwards, we performed a high-throughput investigation on ternary compounds of composition ABX2, where A and B are elements of the periodic table up to Bi, and X is a chalcogen (O, S, Se, and Te) by using density functional theory and combining calculations of crystal prototypes with structural prediction (Minima Hopping Method). The following paragraphs summarize the content by chapter of this document. Chapter 1 is a short introduction of this thesis. Chapter 2 consists of the basic theory used in this thesis. Firstly, a short introduction of Density Function Theory (DFT) is presented. Then, we describe some approximate exchange- correlation functions that make DFT practical. Next, we introduce different structural prediction algorithms, especially Particle Swarm Optimization and Minima Hopping Method which we used in this thesis. Finally, we discuss the thermodynamic stablility criteria for a new a new structure. In Chapter 3, we first consider Ba–Si system. Using an unbiased structural search based on a particle-swarm optimization algorithm combined with DFT calculations, we investigate systematically the ground-state phase stability and structural diversity of Ba–Si binaries under high pressure. The phase diagram turns out to be quite intricate, with several compositions stabilizing/destabilizing as a function of pressure. In particular, we identify novel phases of BaSi, BaSi2, BaSi3, and BaSi5 that might be synthesizable experimentally over a wide range of pressures. Chapter 4 contains the investigation of the phases diagram of the N–H–O system. By using ab initio evolutionary structural search, we report the prediction of two novel phases of the N–H–O ternary system, namely NOH4 and HNO3 (nitric acid) at pressure up to 150 GPa. Our calculations show that the new C2/m phase of NOH4 is stable under a large range of pressure from 71 GPa to 150 GPa while the P21/m phase of HNO3 (nitric acid) is stable from 39 GPa to 150 GPa (the maximum pressure which we have studied). We also confirmed that the composition NOH5 (NH3H2O) becomes unstable for pressures above 122 GPa. It decomposes into NH3 and H2O at this pressure. Chapter 5 focuses on p-type transparent electrodes of ternary chalcogenides. We use a high-throughput approach based on DFT to find delafossite and related layered phases of composition ABX2, where A and B are elements of the periodic table, and X is a chalcogen (O, S, Se, and Te). From the 15 624 compounds studied in the trigonal delafossite prototype structure, 285 are within 50 meV/atom from the convex hull of stability. These compounds are further investigated using global structural prediction methods to obtain their lowest- energy crystal structure. We find 79 systems not present in the "Materials project database" that are thermodynamically stable and crystallize in the delafossite or in closely related structures. These novel phases are then characterized by calculating their band gaps and hole effective masses. This characterization unveils a large diversity of properties, ranging from normal metals, magnetic metals, and some candidate compounds for p-type transparent electrodes. At the end of the thesis, we give our general conclusion and an outlook

<div> <div> <div> <p>Proteins are drivers of almost all biological processes in the cell. The functions of a protein are dependent on their three-dimensional structure and elucidating the structure and function of proteins is key to understanding how a biological system operates. In this research, we developed computational methods using machine learning techniques to predicts the structure and function of proteins. Protein 3D structure prediction has advanced significantly in recent years, largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). The performance of these models depends on the number of similar protein sequences to the query protein, wherein some cases similar sequences are few but dissimilar sequences with local similarities are more and can be helpful. We have developed a novel deep learning-based approach AttentiveDist which further improves over the previous state of art. We added an attention mechanism where dis-similar sequences are also used (increasing number of sequences) and the model itself determines which information from such sequences it should attend to. We showed that the improvement of distance predictions was successfully transferred to achieve better protein tertiary structure modeling. We also show that structure prediction from a predicted distance map can be further enhanced by using predicted inter-residue sidechain center distances and main-chain hydrogen-bonds. Protein function prediction is another avenue we explored where we want to predict the function that a protein will perform. The crux of the approach is to predict the function of protein based on the function of similar sequences. Here, we developed a method where we use dissimilar sequences to extract additional information and improve performance over the previous approaches. We used phylogenetic analysis to determine if a dissimilar sequence can be close to the query sequence and thus can provide functional information. Our method was ranked highly in worldwide protein function prediction competition CAFA3 (2016-2019). Further, we expanded the method with a neural network to predict protein toxicity that can be used as a safety check for human-designed protein sequences.</p></div></div></div>

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Indiana University-Purdue University Indianapolis (IUPUI) / Non-coding RNAs across the genome have been associated with various biological processes, ranging from regulation of splicing to remodeling of chromatin. Amongst the repertoire of non-coding sequences lies a critical species of RNAs called long non-coding RNAs (lncRNAs). LncRNAs significantly contribute to a large spectrum of human phenotypes, including cancers, Heart failure, Diabetes, and Alzheimer’s disease. This dissertation emphasizes the need to characterize the functional role of lncRNAs to improve our understanding of human diseases. This work consolidates a resource from multiple computational genomics and natural language processing-based approaches to advance our ability to functionally annotate hundreds of lncRNAs and their interactions, providing a one-stop lncRNA functional annotation and dynamic interaction network and multi-facet omics data visualization platform. RNA interactions are vital in various cellular processes, from transcription to RNA processing. These interactions dictate the functional scope of the RNA. However, the multifaceted functional nature of RNA stems from its ability to form secondary structures. Therefore, this work establishes a computational method to characterize RNA secondary structure by integrating SHAPE-seq and long-read sequencing to enhance further our understanding of RNA structure in modulating the post-transcriptional regulatory processes and deciphering the influence at several layers of biological features, ranging from structure composition to consequent protein occupancy. This study will potentially impact the research community by providing methods, web interfaces, and computational pipelines, improving our functional understanding of long non-coding RNAs. This work also provides novel integration methods of technologies like Oxford Nanopore-based long-read sequencing, RNA structure-probing methods, and machine learning. The approaches developed in this dissertation are scalable and adaptable to investigate further the functional and regulatory role of RNA and its structure. Overall, this study accelerates the development of RNA-based diagnostics and the identification of therapeutic targets in human disease.