Drug resistance is a global health threat costing society billions of dollars and impacting millions of lives each year. Current drug design strategies are inadequate because they focus on disrupting target activity and not restricting the evolutionary pathways to resistance. Improved strategies would exploit the structural and dynamic changes in the enzyme–inhibitor system integrating data from many inhibitors and variants. Using HIV-1 protease as a model system, I aimed to elucidate the underlying resistance mechanisms, characterize conserved protease-inhibitor interactions, and generate more robust inhibitors by applying these insights. For primary mechanisms of resistance, comparing interactions at the protease–inhibitor interface showed how specific modifications affected potency. For mutations distal to the active site, molecular dynamics simulations were necessary to elucidate how changes propagated to reduce inhibitor binding. These insights informed inhibitor design to improve potency against highly resistant variants by optimizing hydrogen bonding. A series of hybrid inhibitors was also designed that showed excellent potency by combining key moieties of multiple FDA-approved inhibitors. I characterized the structural basis for alterations in binding affinity in HIV-1 protease both from mutations and inhibitors. I applied these strategies to HTLV-1 protease, a potential drug target. I identified the HIV-1 inhibitor darunavir as a viable scaffold and evaluated analogues, leading to a low-nanomolar compound with potential for optimization. Hopefully, insights from this thesis will lead to the development of potent HTLV-1 protease inhibitors. More broadly, these inhibitor design strategies are applicable to other rapidly evolving targets, thereby reducing drug resistance rates in the future.

Philosophiae Doctor - PhD / Given the significant disease burden caused by human coronaviruses, the discovery of an effective antiviral strategy is paramount, however there is still no effective therapy to combat infection. This thesis details the in silica exploration of ligand libraries to identify candidate lead compounds that, based on multiple criteria, have a high probability of inhibiting the 3 chymotrypsin-like protease (3CUro) of human coronaviruses. Atomistic models of the 3CUro were obtained from the Protein Data Bank or theoretical models were successfully generated by homology modelling. These structures served the basis of both structure- and ligand-based drug design studies. Consensus molecular docking and pharmacophore modelling protocols were adapted to explore the ZINC Drugs-Now dataset in a high throughput virtual screening strategy to identify ligands which computationally bound to the active site of the 3CUro . Molecular dynamics was further utilized to confirm the binding mode and interactions observed in the static structure- and ligand-based techniques were correct via analysis of various parameters in a IOns simulation. Molecular docking and pharmacophore models identified a total of 19 ligands which displayed the potential to computationally bind to all 3CUro included in the study. Strategies employed to identify these lead compounds also indicated that a known inhibitor of the SARS-Co V 3CUro also has potential as a broad spectrum lead compound. Further analysis by molecular dynamic simulations largely confirmed the binding mode and ligand orientations identified by the former techniques. The comprehensive approach used in this study improves the probability of identifying experimental actives and represents a cost effective pipeline for the often expensive and time consuming process of lead discovery. These identified lead compounds represent an ideal starting point for assays to confirm in vitro activity, where experimentally confirmed actives will be proceeded to subsequent studies on lead optimization.

Les interactions protéine-protéine (IPPs) contrôlent de nombreux processus physiologiques importantsdans les cellules humaines. Une caractéristique des cancers est l'échappement des cellules àl'apoptose, qui est souvent associé à la surexpression de protéines anti-apoptotiques, membres de lafamille de protéines Bcl-2. Cette famille comprend des membres anti-apoptotiques (Bcl-2, Bcl-xL,Mcl-1) et pro-apoptotiques. Dans de nombreux cancers dont les cancers de l’ovaire chimiorésistants,l'équilibre entre les membres pro- et anti-apoptotiques de la famille de protéines Bcl-2 est altéré etconduit à la survie des cellules cancéreuses. Une des stratégies envisagées pour surmonter cettechimiorésistance est rétablir l’apoptose par l’inhibition concomitante des protéines Mcl-1 et Bcl-xL.L’objectif est de concevoir des inhibiteurs à dualité d’action visant les protéines Mcl-1 et Bcl-xL.Les travaux antérieurs du laboratoire ont permis la découverte d’un inhibiteur sélectif de la protéineMcl-1, appelé Pyridoclax. Par une approche combinant les méthodes de Fragment-Based Drug Designet Structure-Based Drug Design, à partir de la structure du Pyridoclax, la conception de dual inhibiteurs,leur synthèse et leur évaluation biologique, sont rapportées dans cette thèse. L’exploration de nouveauxespaces chimiques et biologiques est ainsi rendue possible par la mise en oeuvre de cette approche ausein de laquelle le développement de nouvelles méthodologies de synthèse dans le but de concevoirdes fragments tridimensionnels originaux sera présenté.Ces travaux de thèse ont permis de concevoir, synthétiser et caractériser plus de 90 molécules.Certaines ont montré une activité pro-apoptotique intéressante en inhibant les protéines Mcl-1 et Bcl-xLnotamment. / Protein-protein interactions (PPIs) control many important physiological processes within human cells.A hallmark of cancers is the escape of cells from apoptosis, which is often associated with theoverexpression of the anti-apoptotic proteins of the Bcl-2 family. This family comprises pro-survival(Bcl-2, Bcl-xL, Mcl-1) and pro-apoptotic members. In many cancers and, in particular, chemoresistantovarian cancers, the balance between the pro- and anti-apoptotic Bcl-2 family members is alteredleading to the survival of cancerous cells. One of the strategies used to overcome chemoresistance isto re-establish apoptosis by the concomitant inhibition of Mcl-1 and Bcl-xL proteins. Therefore, theobjective is to design dual Mcl-1/Bcl-xL inhibitors.Our groups previous work allowed the discovery of a selective Mcl-1 inhibitor, named Pyridoclax. UsingFragment-Based Drug Design and Structure-Based Drug Design approaches, from the structure ofPyridoclax, the design, synthesis and biological evaluation of dual inhibitors are reported in this thesis.The exploration of novel chemical and biological space is possible by the implementation of thisapproach. The development of synthetic methodologies for the design of new 3-dimensional fragmentswill be presented.In this work, around 90 molecules were synthesized using an approach which combined Fragment-Based Drug Design and Structure-Based Drug Design methods. Some showed a pro-apoptotic activityby inhibiting Mcl-1 and Bcl-xL proteins.

Background: Identifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable. Results: The novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known “simultaneous alignment and folding” of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P’s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (≈400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P webcite. Conclusions: ExpaRNA-P’s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.

Bulge-helix-bulge (BHB) elements guide the enzymatic splicing machinery that in Archaea excises introns from tRNAs, rRNAs from their primary precursor, and accounts for the assembly of piece-wise encoded tRNAs. This processing pathway renders the intronic sequences as circularized RNA species. Although archaeal transcriptomes harbor a large number of circular small RNAs, it remains unknown whether most or all of them are produced through BHB-dependent splicing. We therefore conduct a genome-wide survey of BHB elements of a phylogenetically diverse set of archaeal species and complement this approach by searching for BHB-like structures in the vicinity of circularized transcripts. We find that besides tRNA introns, the majority of box C/D snoRNAs is associated with BHB elements. Not all circularized sRNAs, however, can be explained by BHB elements, suggesting that there is at least one other mechanism of RNA circularization at work in Archaea. Pattern search methods were unable, however, to identify common sequence and/or secondary structure features that could be characteristic for such a mechanism.

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.

A esquistossomose é uma das principais causas de morbidade em países Tropicais e Subtropicais, gerando graves consequências socioeconômicas. Atualmente, os fármacos disponíveis para o tratamento da desta doença são praziquantel e oxamniquina, porém relatos de baixa susceptibilidade do parasita a esses medicamentos sugerem a necessidade de novas estratégias terapêuticas para o tratamento da doença. Todavia, existe pouco interesse da indústria farmacêutica no desenvolvimento de fármacos contra doenças tropicais e negligenciadas, entre as quais se encontra a esquistossomose. Devido a estes fatores, o presente trabalho teve por objetivo geral utilizar ferramentas computacionais para identificar inibidores da SmDHFR candidatos a novos fármacos. Avaliou-se as características exclusivas para a proteína de S. mansoni por meio de uma análise das sequências FASTA em comparação com a DHFR de outros organismos. A fim de garantir a ação seletiva dessas moléculas frente a enzima do parasita, os campos moleculares de interação seletivos para SmDHFR foram calculados e empregados na construção do modelo farmacofórico, o qual foi utilizado na triagem virtual de inibidores de SmDHFR. Os estudos computacionais realizados nos permitiram a seleção de 20 moléculas com uma boa complementariedade com o modelo farmacofórico gerado e com potencial para serem inibidores de SmDHFR. / Schistosomiasis is one of morbidity\'s main causes in tropical and subtropical countries, which leads to serious socioeconomic consequences. Praziquantel and oxamniquina are the drugs currently available for treating this disease, but reports points that the parasite has been resistant to both drugs, which suggests the need for new therapeutic strategies for the treatment of this disease. However, there is little interest in the pharmaceutical industry in developing drugs against neglected tropical diseases, including schistosomiasis. Due to these factors, the present work has the general objective to use computational tools to identify SmDHFR inhibitors which could be good candidates for developing new drugs. Evaluation of the exclusive characteristics of the S. mansoni protein were performed by FASTA sequence analyses in comparison to DHFR from other organisms. In order to guarantee the selective action of these molecules against the parasite enzyme, the molecular interaction fields selective for SmDHFR were calculated and used in the construction of the pharmacophoric model, which was further used in the virtual screening of SmDHFR inhibitors. Computational studies were performed and those led us to 20 molecules with a good complementarity with the pharmacophoric model that was previously generated and with potential to be SmDHFR inhibitors.

<p>The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented.</p><p>For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions.</p><p>A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands.</p><p>A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding.</p><p>The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.</p>

The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented. For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions. A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands. A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding. The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.

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