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Cystic fibrosis (CF) is a rare genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs-which increases the risk of infection and eventually causes death. CFTR is an ATP-binding cassette (ABC) transporter family protein composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (F508del), making F508del CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain F508del CFTR structure has been determined and few studies have modeled F508del using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a F508del model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and F508del CFTR structural models. These models demonstrate the destabilizing effects of F508del on NBD1 and the NBD1/TMD interface in both the inactive and active conformation of CFTR. Furthermore, we modeled F508del/R1070W and F508del bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of VX-809 on multi-domain models of F508del CFTR and pave the way for rational design of additional drugs that target F508del CFTR for treatment of CF.

Made available in DSpace on 2014-12-17T14:03:44Z (GMT). No. of bitstreams: 1 MarciaDAD_DISSERT.pdf: 4264187 bytes, checksum: 68a1d188a3ddcbd9b3e88211ae1a47e7 (MD5) Previous issue date: 2014-08-01 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Shrimp farming is one of the activities that contribute most to the growth of global aquaculture. However, this business has undergone significant economic losses due to the onset of viral diseases such as Infectious Myonecrosis (IMN). The IMN is already widespread throughout Northeastern Brazil and affects other countries such as Indonesia, Thailand and China. The main symptom of disease is myonecrosis, which consists of necrosis of striated muscles of the abdomen and cephalothorax of shrimp. The IMN is caused by infectious myonecrosis virus (IMNV), a non-enveloped virus which has protrusions along its capsid. The viral genome consists of a single molecule of double-stranded RNA and has two Open Reading Frames (ORFs). The ORF1 encodes the major capsid protein (MCP) and a potential RNA binding protein (RBP). ORF2 encodes a probable RNA-dependent RNA polymerase (RdRp) and classifies IMNV in Totiviridae family. Thus, the objective of this research was study the IMNV complete genome and encoded proteins in order to develop a system differentiate virus isolates based on polymorphisms presence. The phylogenetic relationship among some totivirus was investigated and showed a new group to IMNV within Totiviridae family. Two new genomes were sequenced, analyzed and compared to two other genomes already deposited in GenBank. The new genomes were more similar to each other than those already described. Conserved and variable regions of the genome were identified through similarity graphs and alignments using the four IMNV sequences. This analyze allowed mapping of polymorphic sites and revealed that the most variable region of the genome is in the first half of ORF1, which coincides with the regions that possibly encode the viral protrusion, while the most stable regions of the genome were found in conserved domains of proteins that interact with RNA. Moreover, secondary structures were predicted for all proteins using various softwares and protein structural models were calculated using threading and ab initio modeling approaches. From these analyses was possible to observe that the IMNV proteins have motifs and shapes similar to proteins of other totiviruses and new possible protein functions have been proposed. The genome and proteins study was essential for development of a PCR-based detection system able to discriminate the four IMNV isolates based on the presence of polymorphic sites / A carcinicultura ? uma das atividades que mais contribui para o crescimento da aquicultura mundial. Entretanto, esta atividade vem sofrendo perdas econ?micas significativas devido ao surgimento de doen?as virais como a Mionecrose Infecciosa (IMN). A IMN j? est? disseminada em toda regi?o Nordeste do Brasil e atingiu outros pa?ses como Indon?sia, Tail?ndia e China. O principal sintoma da doen?a ? a mionecrose, que consiste na necrose dos m?sculos estriados do abd?men e do cefalot?rax do camar?o. A IMN ? causada pelo v?rus da mionecrose infecciosa (IMNV), um v?rus n?o envelopado que apresenta protrus?es ao longo de seu caps?deo. O genoma viral ? formado por uma ?nica mol?cula de RNA dupla fita e possui duas Open Reading Frames (ORFs). A ORF1 codifica a prote?na principal do caps?deo (MCP) e uma poss?vel prote?na de liga??o a RNA (RBP). A ORF2 codifica uma prov?vel RNA polimerase dependente de RNA (RdRp) e classifica o IMNV dentro da fam?lia Totiviridae. Assim, o objetivo desse estudo foi estudar o genoma completo do IMNV e as prote?nas codificadas no intuito de desenvolver um sistema que identificasse diferentes isolados do v?rus com base na presen?a de polimorfismos. A rela??o filogen?tica entre alguns totiv?rus foi investigada e mostrou um novo grupo para o IMNV dentro da fam?lia Totiviridae. Dois novos genomas foram sequenciados, analisados e comparados a outros dois genomas j? depositados no GenBank. Os novos genomas foram mais semelhantes entre si do que com aqueles j? descritos. Regi?es vari?veis e conservadas do genoma foram identificadas atrav?s de gr?ficos de similaridade e alinhamentos utilizando as quatro sequ?ncias do IMNV. Esta an?lise possibilitou o mapeamento de s?tios polim?rficos e revelou que a regi?o mais vari?vel do genoma se encontra na primeira metade da ORF1 e coincide com as regi?es que possivelmente codificam a protrus?o viral, enquanto que as regi?es mais est?veis se encontraram em dom?nios conservados de prote?nas que interagem com o RNA. Al?m disso, estruturas secund?rias foram preditas para todas as prote?nas empregando diversos softwares e modelos estruturais proteicos foram calculados usando modelagens por threading e simula??es ab initio. A partir dessas an?lises foi poss?vel observar que as prote?nas do IMNV possuem motivos e formas similares ?s prote?nas de outros totiv?rus, e novas poss?veis fun??es proteicas foram propostas. O estudo do genoma e das prote?nas foi essencial para o desenvolvimento de um sistema de detec??o baseado em PCR capaz de discriminar os quatro isolados do IMNV com base na presen?a de s?tios polim?rficos

<p>Proteins are significant components of living cells. They perform a diverse range of biological functions such as cell shape and metabolism. The functions of proteins are determined by their three-dimensional structures. Cryogenic-electron microscopy (cryo-EM) is a technology known for determining the structure of large macromolecular structures including protein complexes. When individual atomic protein structures are available, a critical task in structure modeling is fitting the individual structures into the cryo-EM density map.</p> <p>In my research, I report a new computational method, MarkovFit, which is a machine learning-based method that performs simultaneous rigid fitting of the atomic structures of individual proteins into cryo-EM maps of medium to low resolution to model the three-dimensional structure of protein complexes. MarkovFit uses Markov random field (MRF), which allows probabilistic evaluation of fitted models. MarkovFit starts by searching the conformational space using FFT for potential poses of protein structures, computes scores which quantify the goodness-of-fit between each individual protein and the cryo-EM map, and the interactions between the proteins. Afterwards, proteins and their interactions are represented using a MRF graph. MRF nodes use a belief propagation algorithm to exchange information, and the best conformations are then extracted and refined using two structural refinement methods. </p> <p>The performance of MarkovFit was tested on three datasets; a dataset of simulated cryo-EM maps at resolution 10 Å, a dataset of high-resolution experimentally-determined cryo-EM maps, and a dataset of experimentally-determined cryo-EM maps of medium to low resolution. In addition to that, the performance of MarkovFit was compared to two state-of-the-art methods on their datasets. Lastly, MarkovFit modeled the protein complexes from the individual protein atomic models generated by AlphaFold, an AI-based model developed by DeepMind for predicting the 3D structure of proteins from their amino acid sequences.</p>