The proper packaging of the genome in eukaryotic nuclei is essential for proper gene expression and cell function. Chromatin at the large scale is divided into two major compartments heterochromatin and euchromatin. Heterochromatin compromises the transcriptionally inactive tightly packaged regions of chromatin, while euchromatin is the transcriptionally active region of chromatin. The Heterochromatin Protein family (HP1) proteins are epigenetic hallmarks of constitutive heterochromatin. Recent evidence suggests human HP1α undergoes liquid-liquid phase separation suggesting a role for HP1 phase separation in the formation of compacted heterochromatin within HP1 droplets. Phase separation is a biophysical property of proteins with intrinsically disordered domains which are protein domains that lack a defined secondary structure and have the ability to undertake multiple conformations. In this thesis, I investigated the ability of C. elegans HP1 homologs HPL-2A and HPL-1 to phase separate utilizing directed mutations to elucidate the intermolecular interactions that govern this phenomenon and different assays to assess their phase separation. I concluded that HPL-2A is a bona fide phase separating protein that selectively condenses chromatin. HPL-2A’s phase separation depends on specific interactions, mainly dimerization and the presence of lysine and arginine residues in the hinge region. HPL-2A has a specific IDR that drives its phase separation which is the hinge region as the CTE and NTE are not essential for its phase separation.

No description available.

Serum amyloid A (SAA) is a small, evolutionarily well-conserved, acute-phase protein best known as the protein precursor for amyloid A amyloidosis. During acute injury, infection, or inflammation, SAA plasma concentration rapidly rises 1000-fold, but the benefit of this dramatic increase is unclear. SAA functions in the innate immune response, cell signaling, and lipid homeostasis. Most SAA circulates on plasma high-density lipoproteins (HDL), where it reroutes HDL for lipid recycling. The aim of this dissertation is to provide a structural basis for understanding SAA-lipid interactions and to elucidate the structure-function relationship in this ancient protein. SAA is an intrinsically disordered protein that acquires ~50% helical structure when bound to lipids, and is ~80% helical in three available atomic-resolution x-ray crystal structures. We took advantage of these crystal structures of lipid-free SAA to propose the binding site for various lipids, including lipids in HDL. We postulated that SAA, as a monomer, binds lipids via two amphipathic helices, h1 and h3, that form a concave hydrophobic surface, and that the curvature of this surface defines the binding preference of SAA for HDL versus larger lipoproteins. Next, we used murine SAA1.1 and a membrane-mimicking model phospholipid, palmitoyl-oleoyl phosphocholine (POPC), to reconstitute SAA-lipid complexes and characterize their overall structure, stability and stoichiometry using an array of spectroscopic, electron microscopic, and biochemical methods. We observed preferential formation of ~10 nm particles that mimic HDL size, accompanied by the α-helical folding. To probe the local protein conformation and dynamics in these SAA-POPC particles, we used hydrogen-deuterium exchange mass spectrometry. Analysis of the amount and the kinetics of deuterium uptake clearly established h1 and h3 as the lipid-binding site. Moreover, we determined that SAA binding to lipid follows a mixed model that combines induced fit, promoting α-folding in h3, with conformational selection, stabilizing pre-existing conformations in h1 and around the h2-h3 linker. Taken together, our results provided the structural basis necessary for understanding SAA-lipid interactions, which are central to beneficial functions of SAA as a housekeeping molecule, and to its misfolding in amyloid. This research sets the stage for understanding SAA interactions with its numerous other functional ligands.

No description available.

p53 is a transcription factor and an important tumor suppressor protein that becomes activated due to DNA damage. Because of its role as a tumor suppressor, mutations in the gene that encodes it are found in over 50% of human cancers. The N-terminal transactivation domain (NTAD) of p53 is intrinsically disordered and modulates the function and interactions of p53 in the cell. Its disordered structure allows it to be controlled closely by post-translation modifications that regulate p53’s ability to bind DNA and interact with regulatory binding partners. p53 is an attractive target for developing cancer therapeutics, but its intrinsically disordered region makes it difficult for traditional experimental techniques to resolve its heterogeneous conformational ensemble. This challenge necessitates the use of techniques that can capture the transient structural features and interactions of p53 to aid in designing effective drugs that can modulate and stabilize its activity. Hybrid-resolution (HyRes) II is a coarse-grained molecular dynamics force field that was parameterized specifically to capture the dynamics of IDPs and can give insight into secondary structure propensity and how post-translational modifications affect the structural ensemble of the protein. Nanopore experiments allow for real-time, single-molecule studies of protein dynamics and interactions with binding partners through characteristic changes in the current that passes through the nanopore. Pairing nanopore experiments with simulations can give insight into the molecular detail of IDP ensembles and interactions, revealing a fuller picture of how p53 is controlled in stressed cell conditions and how its structure is affected due to various modifications and small molecules with therapeutic implications. Herein, we show the HyRes II force field can capture the complex, long-range dynamics of the p53 tetramer and provide molecular-level detail of the p53 autoinhibition mechanism, which is enhanced by the phosphorylation of the NTAD. Secondly, we use the MspA nanopore to capture the differences in events of the wild-type NTAD and a cancer-associated NTAD mutant. Lastly, we detect a small molecule binding to the WT NTAD using nanopore sensing. This approach of integrating MD simulations and nanopore experiments can be applied to the study of other IDPs which are prevalent in biology and integral to human health and disease.

Intrinsically disordered proteins (IDPs) are an abundant class of proteins and protein regions which rapidly change between multiple structures without an equilibrium position. IDPs exist as a series of conformational ensembles of semi-stable conformations that can be adopted based on a hilly landscape of shallow free energy minima. Disordered sequences share characteristic features differentiating them from globular proteins, including low sequence complexity, low occurrence of hydrophobic residues, high polar and charged residue content, and high flexibility. IDPs are commonly involved in regulation in the cell, and frequently function as, or interact with, hub proteins in protein-protein interaction networks, making them an important class of macromolecules for understanding regulatory and other processes. Given their functional importance, these proteins are widely studied. Many analytical techniques are used, though rapid conformational sampling by IDPs makes it difficult to detect many states with NMR or other techniques. Computational approaches such as molecular dynamics are increasingly used to probe the binding and conformational sampling of these proteins, allowing for observation of factors that cannot be observed with traditional analytical methods such as NMR, such as differing conformational ensembles and the dipoles of individual residues. Here, we studied the role of electrostatic interactions in IDP protein-protein interaction using molecular dynamics simulations performed with the Drude-2019 force field (FF), a polarizable model that allows for more accurate representation of electrostatics, an important factor for highly charged systems like IDPs. For this project, a prototypical protein with disordered regions, p53, was simulated with two protein partners, the nuclear coactivator domain of the CREB binding protein (CBP), and the E3 ubiquitin-protein ligase mouse double minute 2 (MDM2). p53 is widely studied, and the p53 transactivation domain (TAD) is disordered and binds to many structurally diverse partners, making this protein domain a useful model for probing the role of electrostatic interactions formed by IDPs at protein-protein binding interfaces. We found that the Drude-2019 FF allows for simulation of the p53 TAD with Cα chemical shifts comparable to those observed with NMR, supporting that the Drude-2019 FF performs well in simulating IDPs. We observed large relative change in sidechain dipole moments when comparing the p53 TAD alone and when bound to either CBP or MDM2. We observed that aliphatic and aromatic amino acids experienced the largest relative change in sidechain dipole moments, and that there is sensitivity to binding shown in this dipole response. The largest percent changes in sidechain dipole moment were found to localize at and around the binding interface. Understanding the binding interactions of IDPs at a fundamental level, including the role of electrostatic interactions, may help with targeting IDPs or their partners for drug design. / Master of Science in Life Sciences / Many proteins adopt one main structure, and these proteins are called ordered proteins. Intrinsically disordered proteins (IDPs) are an abundant category of proteins which adopt multiple structures, and transition between these different structures is based on factors such as the environment around them, modifications, or interactions with other macromolecules. The flexible structures of IDPs allow them to bind to multiple different partners and to regulate processes in the cell. Since IDPs often regulate processes important to cell function, when these proteins are mutated, misfolded, or otherwise mis-regulated the resulting issues are associated with disease states. IDPs are widely studied with analytical techniques, but because IDPs frequently change shape it can be difficult to observe certain behaviors or certain factors with these techniques. Computational approaches, such as molecular dynamics (MD). MD is the study of molecular motion and interaction, and can allow observation of factors that would be difficult or impossible to observe otherwise, such as the varying structures of IDPs or the dipole moments of specific amino acids within the proteins. For this project we wanted to probe the role of dipole moments, which are charge-based interactions, in the binding of IDPs to protein partners, to better understand how IDPs bind to different partners. We used the p53 protein as an example of IDP binding and simulated it alone and bound to two other proteins, the CREB binding protein (CBP), and the E3 ubiquitin-protein ligase mouse double minute 2 (MDM2). We observed that our simulations were comparable to experiments done with nuclear magnetic resonance spectroscopy, which served to validate that our simulations were realistic. We observed that the dipole moments of the proteins change when simulating the proteins alone and in complex, and that the largest relative changes in dipole are observed for regions of the proteins involved in binding. Probing the role of charge-based interactions in protein-protein binding interactions for IDPs can help us to greater understand these interactions at a more fundamental level and could help with targeting IDPs or their partners for drug design or other problems.

An infinitesimal fraction of the practically infinite sequence space has achieved enormous functional diversity of proteins during evolution. Intrinsically disordered proteins (IDPs) which lack a fully defined three-dimensional structure are the most likely precursors to today's proteins because of their flexible conformation and functional diversity. But how have these proteins evolved into often rigid and highly specialized protein structures? This evolutionary trajectory has the greatest support in the theory of induced fold whereby the development of the structure was mediated by the interaction and coevolution of primordial unstructured proteins with different cofactors or RNA molecules. Although some random sequences from the sequence space which is not used by nature are also able to form folded proteins the more suitable candidates for evolution of structure and function appear to be random sequences with a high content of disordered which have low aggregation propensity. The selected random protein sequences with high disorder content have been structurally characterized in this work for their further use in evolutionary studies. Three artificial proteins were selected from a random-sequence library based on previous study in our laboratory. In the present work they were purified and...

O câncer cervical afeta milhões de mulheres em todo o mundo a cada ano. A maioria dos casos de câncer cervical é causada pelo vírus do papiloma humano (HPV) que é sexualmente transmissível. Cerca de 40 tipos de HPV infectam o colo do útero e estes são designados como sendo de alto ou de baixo risco com base no seu potencial para provocar lesões de alto grau e câncer. A oncoproteína E7 do HPV está diretamente envolvida no aparecimento de câncer de colo do útero. Esta se associada com a proteína pRb e outros alvos celulares que promovem a imortalização celular e carcinogênese. Apesar de muito progresso nos estudos sobre os HPVs de alto risco, ainda não existe uma terapêutica adequada para o tratamento das lesões e câncer causados por este vírus. Este trabalho teve como objetivo entender as diferenças estruturais entre E7 de alto e baixo risco e sugerir, através de análises de bioinformática, possíveis sítios de ligação e inibidores para a E7. Esta é a primeira descrição da modelagem e análise de dinâmica molecular de quatro estruturas tridimensionais completas da E7 dos tipos de alto risco (HPV tipos 16 e 18), de baixo risco (HPV tipo 11) e não relacionadas ao câncer cervical (HPV tipo 1A). Os modelos foram construídos por uma abordagem híbrida usando modelagem por homologia e ab initio. Os modelos foram usados em simulações de dinâmica molecular por 50 ns, sob condições normais de temperatura e pressão. A desordem intrínseca da sequência da proteína E7 foi avaliada com o uso de ferramentas in silico. Os domínios N-terminal de todas as E7 estudadas, mesmo as de alto risco, exibiram estruturas secundárias depois da modelagem. Nas análises da trajetória da dinâmica molecular, as E7s dos HPVs dos tipos 16 e 18 apresentaram maior instabilidade nos seus domínios N-terminais em relação aos do HPV dos tipos 11 e 01. No entanto, esta variação não afetou a conformação das estruturas secundárias durante a simulação. A análise com ANCHOR indicou que as regiões CR1 e CR2 regiões dos tipos de HPV 16 e 18 contêm possíveis alvos para a descoberta da droga. Já a região CR3 do domínio C-terminal indicou estabilidade nas análises in silico e, por isso, foi usada como alvo de busca de modelos farmacofóricos e docking macromolecular. A proteína usada como modelo foi a E7 do HPV tipo 45 resultante de análises de ressonância magnética nuclear (RMN) e depositada no banco de dados de proteína (ID: 2F8B). Foram selecionados por análises sequenciais de busca farmacofórica, docking e re-docking, 19 compostos (extraídos de amplas bibliotecas de pequenos ligantes) com potencial para candidatos a inibidores da E7. Eles foram avaliados quanto a sua função de pontuação, mapas de interação receptor-ligante e toxicidade e os melhores foram indicados para estudos futuros. / Cervical cancer affects millions of women around the world each year. Most cases of cervical cancer are caused by human papilloma virus (HPV) which is sexually transmitted. About 40 types of HPV infect the cervix and these are designated as being at high or low risk based on their potential to cause high-grade lesions and cancer. The E7 oncoprotein from HPV is directly involved in the onset of cervical cancer. It associates with the pRb protein and other cellular targets that promote cell immortalization and carcinogenesis. Although the progress in studies with high-risk HPVs there is still no adequate therapy for the treatment of lesions and cancers caused by this virus. This study aimed to understand the structural differences between E7 of high and low risk and suggest, with the aid of bioinformatics analyzes, possible binding sites and inhibitors for the E7. This is the first description of the modeling and molecular dynamics analysis of four complete three-dimensional structures of E7 from high-risk types (HPV types 16 and 18), low risk (HPV type 11) and that not related to cervical cancer (HPV 01). The models were constructed by a hybrid approach using homology modeling and ab initio. The models were used in molecular dynamics simulations for 50 ns, under normal temperature and pressure. The intrinsic disorder of the E7 protein sequence was assessed using in silico tools. The N-terminal domains of all E7s, even the high-risks, showed secondary structures after modeling. In the trajectory analyzes of molecular dynamics, the E7s of HPV types 16 and 18 showed high instability in their N-terminal domains than those of HPV types 11 and 01, however, this variation did not affect the conformation of secondary structures during the simulation. The analysis with ANCHOR indicated that regions CR1 and CR2 regions of types of HPV 16 and 18 contain possible targets for drug discovery. The CR3 region of the C-terminal domain indicated stability by in silico analyzes and was therefore used as target to search for pharmacophoric models and \"docking\". The protein used as a model was the E7, from HPV type 45, constructed by analysis of nuclear magnetic resonance (NMR) and deposited in the protein data bank (ID: 2F8B). It was selected 19 compounds as potential candidates for E7 inhibitors (extracted from large libraries of small ligands) using sequential pharmacophore search, docking and re-docking analyzes. They were evaluated for their scoring function, maps of receptor-ligand interactions and toxicity and the best suited were indicated for future studies.

The main goal of this thesis is to design intrinsically safe line analyzer. Thesis describes a device that consists of three measuring circuits and a communication circuit. The device is required to measure posistor resistance, isolation state of power cable and line continuity in potentially explosive areas. Document describes basic requirements of ATEX directive matching standards that are related to device. The function of each logical part of the device is described with help of block diagram. After the basic function description an electrical schematic design and component selection is discussed. After this a printed circuit board design follows. Another part of the thesis describes testing software development. At the end of the document the device is assembler and its basic function is verified.

A great challenge in bioinformatics is to accurately predict protein structure and function from its amino acid sequence, including annotation of protein domains, identification of protein disordered regions and detecting protein stability changes resulting from amino acid mutations. The combination of bioinformatics, genomics and proteomics becomes essential for the investigation of biological, cellular and molecular aspects of disease, and therefore can greatly contribute to the understanding of protein structures and facilitating drug discovery. In this thesis, a PREDICTOR, which consists of three machine learning methods applied to three different but related structure bioinformatics tasks, is presented: using profile Hidden Markov Models (HMMs) to identify remote sequence homologues, on the basis of protein domains; predicting order and disorder in proteins using Conditional Random Fields (CRFs); applying Support Vector Machines (SVMs) to detect protein stability changes due to single mutation. To facilitate structural instability and disease studies, these methods are implemented in three web servers: FISH, OnD-CRF and ProSMS, respectively. For FISH, most of the work presented in the thesis focuses on the design and construction of the web-server. The server is based on a collection of structure-anchored hidden Markov models (saHMM), which are used to identify structural similarity on the protein domain level. For the order and disorder prediction server, OnD-CRF, I implemented two schemes to alleviate the imbalance problem between ordered and disordered amino acids in the training dataset. One uses pruning of the protein sequence in order to obtain a balanced training dataset. The other tries to find the optimal p-value cut-off for discriminating between ordered and disordered amino acids.  Both these schemes enhance the sensitivity of detecting disordered amino acids in proteins. In addition, the output from the OnD-CRF web server can also be used to identify flexible regions, as well as predicting the effect of mutations on protein stability. For ProSMS, we propose, after careful evaluation with different methods, a clustered by homology and a non-clustered model for a three-state classification of protein stability changes due to single amino acid mutations. Results for the non-clustered model reveal that the sequence-only based prediction accuracy is comparable to the accuracy based on protein 3D structure information. In the case of the clustered model, however, the prediction accuracy is significantly improved when protein tertiary structure information, in form of local environmental conditions, is included. Comparing the prediction accuracies for the two models indicates that the prediction of mutation stability of proteins that are not homologous is still a challenging task. Benchmarking results show that, as stand-alone programs, these predictors can be comparable or superior to previously established predictors. Combined into a program package, these mutually complementary predictors will facilitate the understanding of structural instability and disease from protein sequence.