Indiana University-Purdue University Indianapolis (IUPUI) / Intrinsically disordered proteins (IDPs) are flexible proteins without defined 3D structures. Studies show that IDPs are abundant in nature and actively involved in numerous biological processes. Two crucial subjects in the study of IDPs lie in analyzing IDPs’ functions and identifying them. We thus carried out three projects to better understand IDPs. In the 1st project, we propose a method that separates IDPs into different function groups. We used the approach of CH-CDF plot, which is based the combined use of two predictors and subclassifies proteins into 4 groups: structured, mixed, disordered, and rare. Studies show different structural biases for each group. The mixed class has more order-promoting residues and more ordered regions than the disordered class. In addition, the disordered class is highly active in mitosis-related processes among others. Meanwhile, the mixed class is highly associated with signaling pathways, where having both ordered and disordered regions could possibly be important. The 2nd project is about identifying if an unknown protein is entirely disordered. One of the earliest predictors for this purpose, the charge-hydropathy plot (C-H plot), exploited the charge and hydropathy features of the protein. Not only is this algorithm simple yet powerful, its input parameters, charge and hydropathy, are informative and readily interpretable. We found that using different hydropathy scales significantly affects the prediction accuracy. Therefore, we sought to identify a new hydropathy scale that optimizes the prediction. This new scale achieves an accuracy of 91%, a significant improvement over the original 79%. In our 3rd project, we developed a per-residue C-H IDP predictor, in which three hydropathy scales are optimized individually. This is to account for the amino acid composition differences in three regions of a protein sequence (N, C terminus and internal). We then combined them into a single per-residue predictor that achieves an accuracy of 74% for per-residue predictions for proteins containing long IDP regions.

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.

Morphogenesis of the intricate patterns of diatom silica cell walls is a protein-guided process, yet to date only very few such silica biomineralization proteins have been identified. Therefore, it is currently unknown whether all diatoms share conserved proteins of a basal silica forming machinery, and whether unique proteins are responsible for the morphogenesis of species-specific silica patterns. To answer these questions, we extracted proteins from the silica of three diatom species (Thalassiosira pseudonana, Thalassiosira oceanica, and Cyclotella cryptica) by complete demineralization of the cell walls. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) analysis of the extracts identified 92 proteins that we name ‘soluble silicome proteins’ (SSPs). Surprisingly, no SSPs are common to all three species, and most SSPs showed very low similarity to one another in sequence alignments. In-depth bioinformatics analyses revealed that SSPs could be grouped into distinct classes based on short unconventional sequence motifs whose functions are yet unknown. The results from the in vivo localization of selected SSPs indicates that proteins, which lack sequence homology but share unconventional sequence motifs may exert similar functions in the morphogenesis of the diatom silica cell wall.