Conformational ensembles are models of proteins that capture variations in conformation that result from thermal fluctuations. Ensemble based models are important tools for studying Intrinsically Disordered Proteins (IDPs), which adopt a heterogeneous set of conformations in solution. In order to construct an ensemble that provides an accurate model for a protein, one must identify a set of conformations, and their relative stabilities, that agree with experimental data. Inferring the characteristics of an ensemble for an IDP is a problem plagued by degeneracy; that is, one can typically construct many different ensembles that agree with any given set of experimental measurements. In light of this problem, this thesis will introduce three tools for characterizing ensembles: (1) an algorithm for modeling ensembles that provides estimates for the uncertainty in the resulting model, (2) a fast algorithm for constructing ensembles for large or complex IDPs and (3) a measure of the degree of disorder in an ensemble. Our hypothesis is that a protein can be accurately modeled as an ensemble only when the degeneracy of the model is appropriately accounted for. We demonstrate these methods by constructing ensembles for K18 tau protein, \(\alpha\)-synuclein and amyloid beta - IDPs that are implicated in the pathogenesis of Alzheimer's and Parkinson's diseases.

Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on this, we extend the mapping of protein sequence not only to a fixed stable structure but also to an ensemble of protein conformations, which help us explain the complex interaction within a cell that was otherwise obscured. The objective of this dissertation is to develop effective ab initio methods and tools for protein un/structure prediction by developing effective statistical energy function, conformational search method, and disulfide connectivity patterns predictor. The key outcomes of this dissertation research are: i) a sequence and structure-based energy function for structured proteins that includes energetic terms extracted from hydrophobic-hydrophilic properties, accessible surface area, torsion angles, and ubiquitously computed dihedral angles uPhi and uPsi, ii) an ab initio protein structure predictor that combines optimal energy function derived from sequence and structure-based properties of proteins and an effective conformational search method which includes angular rotation and segment translation strategies, iii) an SVM with RBF kernel-based framework to predict disulfide connectivity pattern, iv) a hydrophobic-hydrophilic property based energy function for unstructured proteins, and v) an ab initio conformational ensemble generator that combines energy function and conformational search method for unstructured proteins which can help understand the biological systems involving IDPs and assist in rational drugs design to cure critical diseases such as cancer or cardiovascular diseases caused by challenging states of IDPs.

Le travail de thèse consistera à explorer et caractériser l'ensemble conformationnel de protéines intrinsèquement désordonnées (IDPs) en utilisant plusieurs techniques complémentaires, notamment des simulations avancées de dynamique moléculaire et la diffusion des rayons X aux petits angles (SAXS). Les IDPs sont des protéines possédant une ou plusieurs régions n'ayant pas de structures secondaires stables lorsqu'elles sont isolées, mais pouvant en adopter lors de leur association avec de multiples autres protéines. La question, à laquelle ce travail souhaite répondre dans le cas de trois IDPs, est de savoir si ces éléments de structures secondaires, formés à l'interfaces des complexes protéine-protéine, pré-existent de façon transitoire, ou non, à l'état non-lié des IDPs en solution. S'il est possible d'identifier et de caractériser ces éléments de reconnaissance moléculaire dans les IDPs isolées, alors les résultats de ce travail permettront de guider par la suite la détermination des structures de complexes protéiques impliquant des IDPs. / The PhD work will consist in exploring and characterizing the conformational ensemble of intrinsically disordered proteins (IDPs), by using several complementary methods, including enhanced molecular dynamics simulations and small angle X-ray scattering (SAXS). IDPs are proteins having one or several regions that lack stable secondary structures in the unbound state, but which can adopt various structured conformations to bind other proteins. In the case of three IDPs, the project aims to answer the question of whether these secondary structures formed at the protein-protein interfaces transiently pre-exist or not in the unbound state of solvated IDPs. If it is possible to identify and characterize these molecular recognition features (MoRFs) in the IDP unbound state, then the results of this work will subsequently help to determine the structures of protein complexes involving IDPs.