Nucleotide Complementarity Features in the Design of Effective Artificial miRNAs

L'importance du miARN dans la régulation des gènes a bien été établie. Cependant, le mécanisme précis du processus de reconnaissance des cibles n'est toujours pas complètement compris. Parmi les facteurs connus, la complémentarité en nucléotides, l'accessibilité des sites cibles, la concentration en espèces d'ARN et la coopérativité des sites ont été jugées importantes. En utilisant ces règles connues, nous avons précédemment conçu des miARN artificiels qui inhibent la croissance des cellules cancéreuses en réprimant l'expression de plusieurs gènes. De telles séquences guides ont été délivrées dans les cellules sous forme de shARN. Le VIH étant un virus à ARN, nous avons conçu et testé des ARN guides qui inhibent sa réplication en ciblant directement le génome viral et les facteurs cellulaires nécessaires au virus dans le cadre de mon premier projet. En utilisant une version mise à jour du programme de conception, mirBooking, nous devenons capables de prédire l'effet de concentration des espèces à ARN avec plus de précision. Les séquences guides conçues fournissaient aux cellules une résistance efficace à l'infection virale, égale ou meilleure que celles ciblant directement le génome viral par une complémentarité quasi-parfaite. Cependant, les niveaux de répression des facteurs viraux et cellulaires ne pouvaient pas être prédits avec précision. Afin de mieux comprendre les règles de reconnaissance des cibles miARN, les règles de couplage des bases au-delà du « seed » ont été approfondies dans mon deuxième projet. En concevant des séquences guides correspondant partiellement à la cible et en analysant le schéma de répression, nous avons établi un modèle unificateur de reconnaissance de cible par miARN via la protéine Ago2. Il montre qu'une fois que le « seed » est appariée avec l'ARN cible, la formation d'un duplex d'ARN est interrompue au niveau de la partie centrale du brin guide mais reprend plus loin en aval de la partie centrale en suivant un ordre distinct. L'implémentation des règles découvertes dans un programme informatique, MicroAlign, a permis d'améliorer la conception de miARN artificiels efficaces. Dans cette étude, nous avons non seulement confirmé la contribution des nucléotides non-germes à l'efficacité des miARN, mais également défini de manière quantitative la manière dont ils fonctionnent. Le point de vue actuellement répandu selon lequel les miARN peuvent cibler efficacement tous les gènes de manière égale, avec uniquement des correspondances de semences, peut nécessiter un réexamenThe importance of miRNA in gene regulation has been well established; however, the precise mechanism of its target recognition process is still not completely understood. Among the known factors, nucleotide complementarity, accessibility of the target sites, and the concentration of the RNA species, and site cooperativity were deemed important. Using these known rules, we previously designed artificial miRNAs that inhibit cancer cell growth by repressing the expression of multiple genes. Such guide sequences were delivered into the cells in the form of shRNAs. HIV is an RNA virus. We designed and tested guide RNAs that inhibit its replication by directly targeting the viral genome and cellular factors that the virus requires in my first project. Using an updated version of the design program, mirBooking, we become capable to predict the concentration effect of RNA species more accurately. Designed guide sequences provided cells with effective resistance against viral infection. The protection was equal or better than those that target the viral genome directly via near-perfect complementarity. However, the repression levels of the viral and cellular factors could not be precisely predicted. In order to gain further insights on the rules of miRNA target recognition, the rules of base pairing beyond the seed was further investigated in my second project. By designing guide sequences that partially match the target and analysing the repression pattern, we established a unifying model of miRNA target recognition via Ago2 protein. It shows that once the seed is base-paired with the target RNA, the formation of an RNA duplex is interrupted at the central portion of the guide strand but resumes further downstream of the central portion following a distinct order. The implementation of the discovered rules in a computer program, MicroAlign, enhanced the design of efficient artificial miRNAs. In this study, we not only confirmed the contribution of non-seed nucleotides to the efficiency of miRNAs, but also quantitatively defined the way through which they work. The currently popular view that miRNAs can effectively target all genes equally with only seed matches may require careful re-examination

Novel approaches for metabolic engineering of yeast at multiple scales

Living systems contain enormous potential to solve many pressing engineering problems, including the production of usable energy, the synthesis and degradation of a variety of materials, and the treatment of disease. Metabolic engineering, as one approach to harness this potential, treats the behavior of a living system as the combined product of multiple interacting modules, each of which can be tuned to maximize performance. However, the scarcity of techniques for predictive or high-throughput engineering design of these modules, especially in eukaryotes, contributes to long strain development times and high research cost. In this work, we develop several new tools to expand our capabilities for predictive design and high-throughput engineering in yeast. At the transcriptional level, we develop a method which, for the first time, enables predictive strengthening endogenous yeast promoters and also the de novo design of strong synthetic promoters. At the translational level, we show that it is possible to exploit the context resulting from the arrangement of DNA parts in order to predictably increase or decrease gene expression. We also develop a powerful new approach for directed evolution of enzymes in yeast, termed in vivo continuous evolution, which enables the creation of library sizes orders of magnitude larger than can be obtained with the current state of the art using significantly less labor. Finally, we harness the programmatic inhibitory potential of RNA interference to optimize and demonstrate a system for rapid strain engineering with minimal genomic editing. Taken together, this work provides new techniques which enable a significant reduction in the development time of new yeast strains and informs future development of new tools for metabolic engineering.Chemical Engineerin

Bioinformatical approaches to ranking of anti-HIV combination therapies and planning of treatment schedules

The human immunodeficiency virus (HIV) pandemic is one of the most serious health challenges humanity is facing today. Combination therapy comprising multiple antiretroviral drugs resulted in a dramatic decline in HIV-related mortality in the developed countries. However, the emergence of drug resistant HIV variants during treatment remains a problem for permanent treatment success and seriously hampers the composition of new active regimens. In this thesis we use statistical learning for developing novel methods that rank combination therapies according to their chance of achieving treatment success. These depend on information regarding the treatment composition, the viral genotype, features of viral evolution, and the patient's therapy history. Moreover, we investigate different definitions of response to antiretroviral therapy and their impact on prediction performance of our method. We address the problem of extending purely data-driven approaches to support novel drugs with little available data. In addition, we explore the prospect of prediction systems that are centered on the patient's treatment history instead of the viral genotype. We present a framework for rapidly simulating resistance development during combination therapy that will eventually allow application of combination therapies in the best order. Finally, we analyze surface proteins of HIV regarding their susceptibility to neutralizing antibodies with the aim of supporting HIV vaccine development.Die Humane Immundefizienz-Virus (HIV) Pandemie ist eine der schwerwiegendsten gesundheitlichen Herausforderungen weltweit. Kombinationstherapien bestehend aus mehreren Medikamenten führten in entwickelten Ländern zu einem drastischen Rückgang der HIV-bedingten Sterblichkeit. Die Entstehung von Arzneimittel-resistenten Varianten während der Behandlung stellt allerdings ein Problem für den anhaltenden Behandlungserfolg dar und erschwert die Zusammenstellung von neuen aktiven Kombinationen. In dieser Arbeit verwenden wir statistisches Lernen zur Entwicklung neuer Methoden, welche Kombinationstherapien bezüglich ihres erwarteten Behandlungserfolgs sortieren. Dabei nutzen wir Informationen über die Medikamente, das virale Erbgut, die Virus Evolution und die Therapiegeschichte des Patienten. Außerdem untersuchen wir unterschiedliche Definitionen für Therapieerfolg und ihre Auswirkungen auf die Güte unserer Modelle. Wir gehen das Problem der Erweiterung von daten-getriebenen Modellen bezüglich neuer Wirkstoffen an, und untersuchen weiterhin die Therapiegeschichte des Patienten als Ersatz für das virale Genom bei der Vorhersage. Wir stellen das Rahmenwerk für die schnelle Simulation von Resistenzentwicklung vor, welches schließlich erlaubt, die bestmögliche Reihenfolge von Kombinationstherapien zu suchen. Schließlich analysieren wir das HIV Oberflächenprotein im Hinblick auf seine Anfälligkeit für neutralisierende Antikörper mit dem Ziel die Impfstoff Entwicklung zu unterstützen

Bioinformatical approaches to ranking of anti-HIV combination therapies and planning of treatment schedules

The human immunodeficiency virus (HIV) pandemic is one of the most serious health challenges humanity is facing today. Combination therapy comprising multiple antiretroviral drugs resulted in a dramatic decline in HIV-related mortality in the developed countries. However, the emergence of drug resistant HIV variants during treatment remains a problem for permanent treatment success and seriously hampers the composition of new active regimens. In this thesis we use statistical learning for developing novel methods that rank combination therapies according to their chance of achieving treatment success. These depend on information regarding the treatment composition, the viral genotype, features of viral evolution, and the patient's therapy history. Moreover, we investigate different definitions of response to antiretroviral therapy and their impact on prediction performance of our method. We address the problem of extending purely data-driven approaches to support novel drugs with little available data. In addition, we explore the prospect of prediction systems that are centered on the patient's treatment history instead of the viral genotype. We present a framework for rapidly simulating resistance development during combination therapy that will eventually allow application of combination therapies in the best order. Finally, we analyze surface proteins of HIV regarding their susceptibility to neutralizing antibodies with the aim of supporting HIV vaccine development.Die Humane Immundefizienz-Virus (HIV) Pandemie ist eine der schwerwiegendsten gesundheitlichen Herausforderungen weltweit. Kombinationstherapien bestehend aus mehreren Medikamenten führten in entwickelten Ländern zu einem drastischen Rückgang der HIV-bedingten Sterblichkeit. Die Entstehung von Arzneimittel-resistenten Varianten während der Behandlung stellt allerdings ein Problem für den anhaltenden Behandlungserfolg dar und erschwert die Zusammenstellung von neuen aktiven Kombinationen. In dieser Arbeit verwenden wir statistisches Lernen zur Entwicklung neuer Methoden, welche Kombinationstherapien bezüglich ihres erwarteten Behandlungserfolgs sortieren. Dabei nutzen wir Informationen über die Medikamente, das virale Erbgut, die Virus Evolution und die Therapiegeschichte des Patienten. Außerdem untersuchen wir unterschiedliche Definitionen für Therapieerfolg und ihre Auswirkungen auf die Güte unserer Modelle. Wir gehen das Problem der Erweiterung von daten-getriebenen Modellen bezüglich neuer Wirkstoffen an, und untersuchen weiterhin die Therapiegeschichte des Patienten als Ersatz für das virale Genom bei der Vorhersage. Wir stellen das Rahmenwerk für die schnelle Simulation von Resistenzentwicklung vor, welches schließlich erlaubt, die bestmögliche Reihenfolge von Kombinationstherapien zu suchen. Schließlich analysieren wir das HIV Oberflächenprotein im Hinblick auf seine Anfälligkeit für neutralisierende Antikörper mit dem Ziel die Impfstoff Entwicklung zu unterstützen

Combining computer simulations and deep learning to understand and predict protein structural dynamics

Molecular dynamics simulations provide a means to characterize the ensemble of structures that a protein adopts in solution. These structural ensembles provide crucial information about how proteins function, and these ensembles also reveal potential drug binding sites that are not observable from static protein structures (i.e. cryptic pockets). However, analyzing these high- dimensional datasets to understand protein function remains challenging. Additionally, finding cryptic pockets using simulation data is slow and expensive, which makes the appeal of computationally screening for cryptic pockets limited to a narrow set of circumstances. In this thesis, I develop deep learning based methods to overcome these challenges. First, I develop a deep learning algorithm, called DiffNets, to deal with the high-dimensionality of structural ensembles. DiffNets takes structural ensembles from similar systems with different biochemical properties and learns to highlight structural features that distinguish the systems, ultimately connecting structural signatures to their associated biochemical properties. Using DiffNets, I provide structural insights that explain how naturally occurring genetic variants of the oxytocin receptor alter signaling. Additionally, DiffNets help reveal how a SARS-CoV-2 protein involved in immune evasion becomes activated. Next, I use MD simulations to hunt for cryptic pockets across the SARS-CoV-2 proteome, which led to the discovery of more than 50 new potential druggable sites. Because this effort required an extraordinary amount of resources, I developed a deep learning approach to predict sites of cryptic pockets from single protein structures. This approach reduces the time to identify if a protein has a cryptic pocket by ~10,000-fold compared to the next best method

Understanding and Exploiting Protein Allostery and Dynamics Using Molecular Simulations

Protein conformational landscapes contain much of the functionally relevant information that is useful for understanding biological processes at the chemical scale. Understanding and mapping out these conformational landscapescan provide valuable insight into protein behaviors and biological phenomena, and has relevance to the process of therapeutic design. While structural biology methods have been transformative in studying protein dynamics, they are limited by technicallimitations and have inherent resolution limits. Molecular dynamics (MD) simulations are a powerful tool for exploring conformational landscapes, and provide atomic-scale information that is useful in understanding protein behaviors. With recent advances in generating datasets of large timescale simulations (using Folding@home) and powerful methods to interpret conformational landscapes such as Markov State Models (MSMs), it is now possible to study complex biological phenomena and long-timescale processes. However, inferring communication between residues across long distances, referred to as allosteric communication, remains a challenge. Allostery is a ubiquitious biological phenomena by which two distant regions of a protein are coupled to one anotherover large distances. Allosteric coupling is the mechanism through which events in one region (such as ligand binding) alter the conformation or dynamics of another region (ie. large conformational domain motions). For example, allostery plays a critical role in cellular signaling, such as in the transfer of a signal from outside the cell to cytosolic proteins for generating a cellular response. While many methods have made tremendous progress in inferring and measuring allosteric communication usingstructures or molecular simulations, they rely on a structural view of allostery and do not account for the role of conformational entropy. Furthermore, it remains a challenge to interpret allosteric coupling in large, complex biomolecules relevant to physiology and disease. In this thesis, I present a method to measure the Correlation of All Rotameric and Dynamical States (CARDS) whichis used to construct and interpret allosteric networks in biological systems. CARDS allows us to infer allostery both via concerted changes in protein structure and in correlated changes in conformational entropy (dynamic allostery). CARDS does so by parsing trajectories into dynamical states which reflect whether a residue is locally ordered (ie. stable in a single rotameric basin) or disordered (ie. rapidly hopping between rotamers). Here I explain the CARDS methodology (chapter 2) and demonstrate applications to a variety of disease-relevantsystems. In particular, I apply CARDS and other sophisticated computational methods to understand the process of G protein activation (chapter 3), a protein whose mutations are linked to cancers such as uveal melanoma. I further demonstrate the utility of CARDS in the study a potentially druggable pocket in the ebolavirus protein VP35 (chapter 4). The analyses and models constructed in this work are supported by experimental testing. Lastly, I demonstrate how integrating MD with experiments, sometimes with the help of citizen-scientists around the world, can provide unique insight into biological systems and identify potentially useful targets. In particular, I highlight our recent effort converting Folding@home into an exascale computer platform to hunt for potentially druggable pockets in the proteome of SARS-CoV-2 (chapter 7) (the cause of the COVID19 pandemic)

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book