2,757 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Specificity Determining Features at the Interface of Biomolecular Complexes as Regulators of Biological Functions

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    Amino acid residues at the biomolecular interface play essential roles in many biological and cellular processes; relevant to this thesis, protein-protein interactions regulate signaling pathways and enzymatic activity, whereas protein-DNA interactions control gene expression, and protein-peptide interactions are central to the immune system. Biomolecular recognition and binding stability are largely determined by residues at the molecular interface. In this thesis, we focused on three biological datasets that are related to humans and human health: 1) dysregulated citrullination in the inflamed joints of rheumatoid arthritis patients, 2) a novel family of PRD-like transcription factors critical to the first few cell divisions in human life, and 3) epitopes that likely activate a cytotoxic T cell-mediated immune response against SARS-CoV-2 infection. For each dataset, in order to study the structural and functional consequences of molecular interactions, we applied a wide range of bioinformatics techniques to analyze sequences, structures and biological data retrieved from various databases, as well as taking into account experimental results from collaborators and from the literature. In rheumatoid arthritis, normally cytoplasmic peptidylarginine deiminase (PAD) enzymes citrullinate arginine residues in extracellular matrix (ECM) proteins. To examine specificity determining features that regulate the citrullination activity, we analyzed the sequence and structure data of the ECM proteins that were found citrullinated in chronically inflamed human joints. For citrullination, we found that an arginine side chain needs to be exposed to solvent but can arise from ÎČ-strands, α-helices, loops and ÎČ-turns. Moreover, there is no sequence motif linked to enzymatic activity. In addition, we studied the effect of citrullination on proteins important for a normal ECM, focusing on integrin binding to fibronectin and transforming growth factor-ÎČ (TGF-ÎČ). Citrullination of these proteins was found to inhibit cell attachment and spreading since PAD-treatment of the isoDGR motif in fibronectin and the RGD motif in TGF-ÎČ significantly reduced their binding with integrin αVÎČ3 and αVÎČ6, respectively. The expression of the human paired (PRD)-like transcription factors (TFs) are limited to the period of embryonic genome activation up to the 8-cell stage. We identified that one of these PRD-like TFs, LEUTX, binds to a TAATCC sequence motif. Sequence comparisons revealed that LEUTX protein is comprised of two domains: the DNA-binding homeodomain and a Leutx domain containing a transactivation domain. We identified specificity determining residues in the LEUTX homeodomain that are important for recognition of the TAATCC-containing 36 bp DNA motif enriched in genes involved in embryonic genome activation. We demonstrated using molecular models why a heterozygotic missense mutation A54V at the DNA-specificity determining position of LEUTX has significantly reduced overall transcriptional activity, as well as why the double mutant – I47T and A54V – form of LEUTX restores binding to the DNA motif similarly to that seen in the I47T mutation alone. At the onset of the COVID-19 pandemic we sought to understand the molecular factors that trigger the cytotoxic T cell-mediated immune response against the SARS-CoV-2 virus, taking advantage of binding data and 3D structures for related viruses and other pathogenic organisms. We first predicted the MHC class I (MHC-I)-specific immunogenic epitopes of length 8- to 11 amino acids from the SARS-CoV-2 proteins. Next, we predicted that the 9-mer epitopes would have the highest potential to elicit a strong immune response. For experimental validation, the predicted 9-mer epitopes were matched with the SARS-CoV-derived epitopes that are known to elicit an effective T cell response in vitro. Furthermore, our observations provide a structural explanation for the binding of SARS-CoV-2 epitopes to MHC-I molecules, identifying conserved immunogenic epitopes essential for understanding the pathogenesis of COVID-19. The three investigated datasets were made in concert with collaborative experimental studies and/or considering publicly available experimental data. The experimental studies generally provided the starting point for the in silico studies, which in turn had the objective of providing a detailed explanation of the experimental results. Furthermore, the in silico results could be used to devise novel and focused experiments, suggesting that bioinformatics predictions and wet-laboratory experimental investigations optimally take place with multiple advantages. Overall, this thesis demonstrates the synergy that is possible by applying this interdisciplinary approach to understanding the consequences of molecular interactions.Aminosyror i kontaktytan mellan olika biomolekyler spelar en viktig roll i mĂ„nga biologiska och cellulĂ€ra processer; relevanta interaktioner för den hĂ€r avhandlingen Ă€r protein-protein interaktioner som reglerar signaleringsrutter och enzymatisk aktivitet, protein-DNA interaktioner som kontrollerar genexpression, samt protein-peptid interaktioner som har en central roll i immunförsvaret. BiomolekylĂ€r igenkĂ€nning och bindningsstabilitet beror till stor del pĂ„ de aminosyror som finns i den molekylĂ€ra kontaktytan. I den hĂ€r avhandlingen fokuserade vi pĂ„ tre biologiska dataset som Ă€r relaterade till mĂ€nniskor och mĂ€nniskors hĂ€lsa: 1) felreglerad citrullinering i inflammerade leder hos patienter med reumatoid artrit, 2) en nyupptĂ€ckt familj av PRD (human paired)-lika transkriptionsfaktorer som Ă€r nödvĂ€ndiga för de första celldelningarna i mĂ€nniskolivet, och 3) epitoper som troligen aktiverar en cytotoxisk T-cell-förmedlad immunrespons mot SARS-CoV-2 infektioner. För att studera de strukturella och funktionella konsekvenserna av de molekylĂ€ra interaktionerna i varje dataset, anvĂ€ndes en mĂ€ngd olika bioinformatiska tekniker för att analysera sekvenser, strukturer och biologiska data frĂ„n olika databaser och dessutom beaktades experimentella resultat frĂ„n samarbetspartners och frĂ„n litteraturen. I reumatoid artrit citrullinerar vanligen PAD (cytoplasmatiska peptidyl arginin deiminas)-enzymer arginin-aminosyror i proteiner i det extracellulĂ€ra matrixet (ECM). För att undersöka egenskaper som avgör specificiteten hos citrullineringsaktiviteten analyserade vi sekvens- och strukturdata för ECM-proteiner som blir citrullinerade i kroniskt inflammerade leder hos mĂ€nniskor. Vi upptĂ€ckte att en argininsidokedja mĂ„ste vara i kontakt med det omgivande lösningsmedlet för att kunna citrullineras, att de kan finnas i beta-strĂ€ngar, alfa-helixar och beta-svĂ€ngar, samt att det inte finns nĂ„gra sekvensmotiv som Ă€r kopplade till enzymatisk aktivitet. Utöver detta studerade vi effekten av citrullinering pĂ„ proteiner som Ă€r viktiga för normal extracellulĂ€r matrix, med fokus pĂ„ integrinbinding till fibronektin och TGF-ÎČ (transforming growth factor-ÎČ). Citrullinering av dessa proteiner upptĂ€cktes inhibera cellvidhĂ€ftning och spridning eftersom PAD-behandling av isoDGR-motivet i fibronektin och RGD-motivet i TGF-ÎČ ordentligt reducerar deras bindning till integrin αVÎČ3 och αVÎČ6, respektive. ExpressionsnivĂ„erna av PRD-lika transkriptionsfaktorer (TF) Ă€r begrĂ€nsade till perioden av zygotens genomaktivering upp till 8-cells stadiet. Vi identifierade att en av dessa PRD-lika transkriptionsfaktorer, LEUTX, binder till ett TAATCC sekvensmotiv. SekvensjĂ€mförelser avslöjade att LEUTX proteinet bestĂ„r av tvĂ„ domĂ€ner, det DNA-bindande homeodomĂ€net och en leutx-domĂ€n som innehĂ„ller en transaktiveringsdomĂ€n. Vi identifierade specificitetsbestĂ€mmande aminosyror i LEUTX homeodomĂ€nen som Ă€r viktiga för igenkĂ€nning av TAATCC-innehĂ„llande 36 baspars DNA-motivet som Ă€r berikad med gener involverade i zygotens genomaktivering. Vi anvĂ€nde molekylĂ€ra modeller för att visa varför en heterozygotisk missense-mutation, A54V, i DNA-specificitetsbestĂ€mmande positionen i LEUTX har ordentligt minskad generell transkriptionsaktivitet, och varför dubbelmutanten I47T och A54V Ă„terstĂ€ller bindning till DNA-motivet pĂ„ samma sĂ€tt som observerats i enbart I47T mutationen. NĂ€r COVID-19 pandemin inleddes försökte vi förstĂ„ de molekylĂ€ra faktorer som startar den cytotoxiska T-cell-förmedlade immunresponsen mot SARS-CoV-2 viruset, genom att utnyttja bindningsdata och 3D strukturer för relaterade virus och andra patogena organismer. Vi förutspĂ„dde först MHC klass I (MHC-I)-specifika immunogena epitoper av lĂ€ngden 8 till 11 aminosyror frĂ„n SARS-CoV-2 proteiner. DĂ€refter förutspĂ„dde vi att epitoper bestĂ„ende av 9 aminosyror hade den högsta potentialen att orsaka en stark immunrespons. För experimentell validering matchades de 9 aminosyror lĂ„nga epitoperna med epitoper frĂ„n SARS-CoV som man vet att orsakar en effektiv T-cell respons in vitro. VĂ„ra observationer bidrar ocksĂ„ med en strukturell förklaring för bindningen av SARS-CoV-2 epitoper till MHC-I molekyler, vilket identifierar konserverade immunogena epitoper som Ă€r nödvĂ€ndiga för att förstĂ„r patogenesen hos COVID-19. De tre undersökta dataseten gjordes i samarbete med experimentella studier och/eller genom att ta allmĂ€nt tillgĂ€ngliga experimentella data i beaktande. De experimentella studierna gav en startpunkt för in silico-studierna, vilka i sin tur hade som mĂ„l att ge en detaljerad förklaring till de experimentella resultaten. In silico-resultaten kan ocksĂ„ anvĂ€ndas för att utveckla nya och fokuserade experiment, vilket indikerar att bioinformatiska förutspĂ„elser och experimentella studier optimalt sker med mĂ„nga fördelar. Över lag visar denna avhandling synergin som Ă€r möjlig genom att anvĂ€nda detta interdisciplinĂ€ra arbetssĂ€tt för att förstĂ„ konsekvenserna av molekylĂ€ra interaktioner

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    Undergraduate Catalog of Studies, 2022-2023

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    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023
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