Computer-Aided Elucidation of Structural Determinants for Sphingosine-1-phosphate Receptor Subtype Selectivity and Class A GPCR Activation

G-protein coupled receptors (GPCRs) represent the biggest family of membrane receptors. The physiological accessibility of drugs, their regulatory roles in a vast amount of physiological and pathophysiological processes, and their prevalence in many tissues are reasons why they are highly targeted in a therapeutic context. To exploit the modulatory capabilities of GPCRs prior knowledge of their mechanism of action on a molecular level is required. This is especially important for the successful application of rational structure-based drug design campaigns. Sphingosine-1-phosphate receptors (S1PR) has shown to be effective drug targets against multiple sclerosis (MS), but non-selective drugs suffer from serious adverse effects. Furthermore, targeting a single S1PR holds a lot of potentials to modulate different inflammatory and autoimmune diseases. In this thesis, we present in silico mechanistic models for the identification of crucial structural determinants to illuminate the molecular basis for Sphingosine-1-phosphate receptor selectivity and the general class A GPCR activation process. We employed extensive molecular dynamic (MD) simulation models of S1PR1-5 and performed a holistic comparative orthosteric protein-ligand interaction analysis of identified different binding modes of a pan-agonist to overcome the challenge of highly similar binding pockets between each subtype and rationally explained the selective behavior of marketed drugs Ozanimod and Siponimod used in multiple sclerosis therapy. Furthermore, we provide insights into the mechanism of class A GPCR activation and how dihedral angles of the protein backbone are involved in this process. By developing a data extraction and machine learning workflow we created predictive models for active and inactive state GPCR conformations and showed possible applications for GPCR homology models and MD simulation predictions in a fast and efficient way. The methods and workflow to apply the prediction models are provided as a python package on GitHub (github.com/TrungNgocNguyen/GPCRml). The unique and rational combination of state-of-the-art molecular modeling, data extraction and analysis, and machine learning methods demonstrate how explanatory in silico models can be developed to elevate the understanding of challenging problems in the field of GPCRs.G-Protein-gekoppelte Rezeptoren stellen die größte Familie von Membranrezeptoren dar. Die physiologische Zugänglichkeit für Medikamente, ihre regulatorische Rolle in einer Vielzahl von physiologischen und pathophysiologischen Prozessen und die Prävalenz in vielen Geweben sind Gründe, warum sie in einem therapeutischen Kontext gezielt genutzt werden. Um die modulierenden Fähigkeiten von GPCRs voll auszuschöpfen, ist eine vorherige Kenntnis ihres Wirkungsmechanismus auf molekularer Ebene erforderlich. Dies ist besonders wichtig für die erfolgreiche Anwendung rationaler strukturbasierter Arzneimitteldesign-Kampagnen. Sphingosin-1-Phosphat-Rezeptoren (S1PR) haben sich als wirksame Arzneimittelziele gegen Multiple Sklerose (MS) erwiesen, aber nicht-selektive Arzneimittel leiden unter schwerwiegenden Nebenwirkungen. Darüber hinaus birgt das Ansprechen einzelner S1PR ein großes Potenzial zur Modulation verschiedener Entzündungs- und Autoimmunerkrankungen. In dieser Dissertation präsentieren wir in silico mechanistische Modelle zur Identifizierung wichtiger struktureller Determinanten, um die molekularen Grundlagen für die Selektivität der Sphingosin-1-phosphat-Rezeptor Familie und den allgemeinen Aktivierungsprozess von Klasse A GPCR zu beleuchten. Wir verwenden umfangreiche molekulardynamische Modelle von S1PR1-5 und führen eine ganzheitliche vergleichende Protein-Ligand-Interaktionsanalyse der orthosterischen Bindetasche mit verschiedenen entdeckten Bindungsmodi eines Pan-agonisten durch, um das Problem der fast einheitlichen Bindetaschen zwischen den einzelnen Subtypen zu überwinden. So konnten wir das selektive Verhalten der zugelassenen Medikamente Ozanimod und Siponimod rational erklären, welche in der Multiple-Sklerose-Therapie eingesetzt werden. Darüber hinaus haben wir Einblicke in den Mechanismus der GPCR-Aktivierung der Klasse A gegeben und wie Änderungen der Torsionswinkel des Proteinrückgrats an diesem Prozess beteiligt sind. Durch die Entwicklung eines Arbeitsablaufs für die Datenextraktion mit maschinellem Lernen haben wir ein Vorhersagemodell für GPCR-Konformationen im aktiven und inaktiven Zustand erstellt und mögliche Anwendungen für die schnelle und effiziente Analyse von GPCR-Homologiemodellen und Molekular Dynamik Simulationen gezeigt. Die Methode zur Anwendung der Vorhersagemodelle ist als Python-Paket auf GitHub (https://github.com/TrungNgocNguyen/GPCRml) verfügbar. Die einzigartige und rationale Kombination aus modernster molekularer Modellierung, Datenextraktion und -analyse sowie maschinellen Lernmethoden zeigt, wie aussagekräftige In-silico-Modelle entwickelt werden können, um das Verständnis für herausfordernde Probleme auf dem Gebiet der GPCRs zu verbessern

Hopfield lagrange network based method for economic emission dispatch problem of fixed-head hydro thermal systems

This paper proposes a Hopfield Lagrange Network (HLN) based method (HLNM) for economic emission dispatch of fixed head hydrothermal systems. HLN is a combination of Lagrange function and continuous Hopfield neural network where the Lagrange function is directly used as the energy function for the continuous Hopfield neural network. In the proposed method, HLN is used to find a set of non-dominated solutions and a fuzzy based mechanism is then exploited to determine the best compromise solution among the obtained ones. The proposed method has been tested on four hydrothermal systems and the obtained results in terms of total fuel cost, emission, and computational time have been compared to those other methods in the literature. The result comparisons have indicated that the proposed method is favorable for solving the economic emission dispatch problem of fixed-head hydrothermal systems

DEVELOPING EFL LEARNERS' AUTONOMY IN SPEAKING ENGLISH: AN INVESTIGATION INTO STUDENTS' PRACTICE AT A UNIVERSITY IN THE MEKONG DELTA, VIETNAM

Learner autonomy (LA) is currently concentrated in the Vietnamese educational context as it equips learners with the capacity for lifelong learning. For the EFL context, tertiary students encounter numerous challenges practicing language skills, especially English speaking because of lacking the language environment. This study aims at exploring what Vietnamese EFL students practice to develop LA in speaking English. The study using a mix-method approach was conducted at a university in the Mekong Delta, Vietnam with the participation of 102 English-majored students ranging from freshmen to seniors. A questionnaire and a semi-structured interview were used to explore students’ practice to develop LA for their English speaking development. The result reveals that the level in practicing LA among groups of students reaches the average, and it also figures out the components of LA in English speaking skills that students have performed as well as activities that students still encounter many obstacles in practicing.  Article visualizations

Understanding Interaction Capacity of CO2 with Organic Compounds at Molecular Level: A Theoretical Approach

In this chapter, interactions of CO2 with a number of organic compounds at molecular level are discussed in detail. The naked and substituted hydrocarbons along with compounds functionalized by hydroxyl, carbonyl, thiocarbonyl, carboxyl, sulfonyl, and amide groups have attracted much attention as CO2-philic agents. In general, interaction capacity between the functionalized organic compounds with CO2 is stronger than the hydrocarbon and its derivatives. An addition of more CO2 molecules into the interaction system formed by the functionalized organic compounds and CO2 leads to an increase in the stability of the complexes. The obtained results indicate that π…π linkages between CO2 and aromatic rings can significantly contribute to the interactions between CO2 and MOF/ZIF materials. Formic acid (HCOOH) is likely to be the most soluble compound as compared to the remaining host molecules (CH3OH, CH3NH2, HCHO, HCOOCH3, and CH3COCH3) when dissolved in CO2. The carbonyl (>C═O, >C═S) and sulfonyl (>S═O, >S═S) compounds have presented a higher stability, as compared to other functionalized groups, when they interact with CO2. Therefore, they can be valuable candidates in the design of CO2-philic materials and in the search of materials to adsorb CO2

Trichinellosis in Vietnam

Trichinellosis is a zoonotic parasitic disease with a worldwide distribution. The aim of this work was to describe the epidemiological and clinical data of five outbreaks of trichinellosis, which affected ethnic minorities living in remote mountainous areas of northwestern Vietnam from 1970 to 2012. Trichinellosis was diagnosed in 126 patients, of which 11 (8.7%) were hospitalized and 8 (6.3%) died. All infected people had consumed raw pork from backyard and roaming pigs or wild boar at wedding, funeral, or New Year parties. The short incubation period (average of 9.5 days), the severity of the symptoms, which were characterized by diarrhea, abdominal pain, fever, myalgia, edema, weight loss, itch, and lisping, and the high mortality, suggest that patients had ingested a high number of larvae. The larval burden in pigs examined in one of the outbreaks ranged from 70 to 879 larvae/g. These larvae and those collected from a muscle biopsy taken from a patient from the 2012 outbreak were identified as Trichinella spiralis. Data presented in this work show that the northern regions of Vietnam are endemic areas for Trichinella infections in domestic pigs and humans