ENZYMES: Catalysis, Kinetics and Mechanisms

Onemarvelsattheintricate designoflivingsystems,andwecannotbutwonderhow life originated on this planet. Whether ?rst biological structures emerged as the selfreproducing genetic templates (genetics-?rst origin of life) or the metabolic universality preceded the genome and eventually integrated it (metabolism-?rst origin of life) is still a matter of hot scienti?c debate. There is growing acceptance that the RNA world came ?rst – as RNA molecules can perform both the functions of information storage and catalysis. Regardless of which view eventually gains acceptance, emergence of catalytic phenomena is at the core of biology. The last century has seen an explosive growth in our understanding of biological systems. The progression has involved successive emphasis on taxonomy ! physiology ! biochemistry ! molecular biology ! genetic engineering and ?nally the large-scale study of genomes. The ?eld of molecular biology became largely synonymous with the study of DNA – the genetic material. Molecular biology however had its beginnings in the understanding of biomolecular structure and function. Appreciationofproteins,catalyticphenomena,andthefunctionofenzymeshadalargeroleto play in the progress of modern biology

Leaving a solitary life behind: Evolutionary processes leading to sociality in animals

Die Entwicklung stabiler Tiergruppen ist ein wichtiger Übergang in der Evolution, der aufgrund des selektiven Drucks, der mit sozialen Interaktionen verbunden ist, Veränderungen in der Populationsstruktur und in den aufkommenden Eigenschaften mit sich bringt. Die Sozialität basiert auf Kooperation, ein evolutionäres Puzzle in der darwinistischen Theorie, das auf der Konkurrenz um begrenzte Ressourcen beruht. Im ersten Kapitel stellen wir die Bedeutung der Verwandtschaftsselektion (i.e. Nepotismus) in Frage, um das Auftreten von Tiergruppen zu erklären, das das aktuelle Paradigma darstellt. Diese Theorie legt nahe, dass genetische Ähnlichkeit notwendig ist, um die Konkurrenz zwischen Individuen zu reduzieren, die eine Kooperation ermöglichen. Wir schlagen einen alternativen Rahmen vor, der die zahlreichen und unterschiedlichen Arten berücksichtigt, in denen die elterliche Fürsorge die Entwicklung des Gruppenlebens katalysiert haben könnte. Wir betonen die Bedeutung koevolutiver Prozesse zwischen Parasiten und Raubtieren mit elterlichen Investitionsstrategien, lange bevor ein Übergang zur Sozialität stattfinden kann. Aufbauend auf empirischen und theoretischen Erkenntnissen aus einem breiten Spektrum von Taxa, einschließlich Wirbeltieren und wirbellosen Tieren, schlagen wir vor, dass direkte Fitnessvorteile, die sich aus dem selektiven Druck ergeben, der mit der Evolution der elterlichen Fürsorge verbunden ist, die Kraft hinter dem Auftreten von Tiergruppen sind. In diesem Rahmen ist die Verwandtenselektion eher ein Verstärker oder sogar ein Nebenprodukt aus evolutionären Prozessen, die mit der elterlichen Fürsorge in Verbindung stehen, und nicht die Hauptvoraussetzung für die Entwicklung der Zusammenarbeit. Im zweiten Kapitel konzentrieren wir uns auf die Untersuchung fakultativ sozialer Spezies, um die Prozesse zu verstehen, die eine einsame Spezies zu einem Gruppenleben führen. In diesem Sinne beschreiben wir das Sozialsystem eines fakultativ sozialen Primaten mit gemeinschaftlicher Zucht, Microcebus murinus, anhand von Daten über mehr als 200 Individuen aus einer Wildpopulation. Durch die Untersuchung der gemeinsamen Schlafplatznutzung bei dieser einsamen Futtersuche wollen wir die soziale Flexibilität sowohl auf der Ebene der Art als auch auf der Ebene des Individuums charakterisieren. Wir finden Belege für die soziale Flexibilität bei philopatrischen Weibchen und zerstreuenden Männchen. Darüber hinaus zeigen wir, im Gegensatz zu früheren Ergebnissen, eine höhere Fähigkeit zur Sozialität und sozialen Flexibilität bei den Männchen. Unsere Ergebnisse deuten also darauf hin, dass die weibliche Gemeinschaftszucht möglicherweise nicht die einzige treibende Kraft für die Sozialität bei dieser Art ist, was den in Kapitel 1 dargelegten Rahmen kritisiert; und dass nicht verwandte Männchen genauso anfällig für die Bildung sozialer Gruppen sind wie verwandte Weibchen, was darauf hindeutet, dass die Verwandtschaftsauswahl auch nicht in der Lage ist, die Entwicklung der sozialen Systeme des Mausmakis zu erklären. Während wir in den ersten beiden Kapiteln die Übergänge zur Sozialität aus einer anpassungsorientierten Perspektive diskutiert haben, untersuchen wir in Kapitel 3.1 die Möglichkeit, dass die Sozialität bei Microcebus murinus ein passives Ergebnis der heterogenen Verteilung von Nahrungsressourcen und Schlafplätzen sein könnte. Wir finden keine Belege für einen Effekt der Verfügbarkeit von Nahrungsressourcen oder der Einschränkung der Nistplätze auf individuelle Sozialstrategien. Daher könnten die intrinsischen Vorteile, die mit dem gemeinsamen Schlafen und der gemeinsamen Nutzung eines Heimbereichs mit anderen verbunden sind, bei dieser Art im Spiel sein. In Kapitel 3.2 entwickeln wir die in Kapitel 3.1 angewandte Methode zur Beurteilung der Zuverlässigkeit der für jedes Individuum gesammelten Informationen, um mit Hilfe der Michaelis-Menten-Modellierung Heimatorte zu bauen. Wir glauben, dass dies ein potenziell nützliches Instrument für Studien in der freien Natur sein könnte, wo sowohl die Knappheit der Daten als auch die individuellen Unterschiede in der Menge der gesammelten Daten bewegungsökologische Analysen erschweren können. Abschließend betonen wir, dass die soziale Evolution ein vielfältiger Prozess ist, der mehrere Ebenen der Lebenskomplexität in sich birgt und miteinander verflochten ist und sich den Versuchen einer einheitlichen Erklärung ihrer Ursprünge widersetzt.The evolution of stable animal groups is a major transition in evolution entailing changes in population structure and emerging properties due to the selective pressures associated with social interactions. Sociality is based on cooperation, an evolutionary puzzle in Darwinian theory that is grounded on competition for limited resources. In the first chapter, we challenge the importance of kin selection (i.e. nepotism) to explain the appearance of animal groups, which is the current paradigm. This theory suggests that genetic similarity is needed to reduce competition between individuals allowing cooperation to be selected. We propose an alternative framework that takes into account the numerous and diverse ways in which parental care may have catalyzed the evolution of group living. We emphasize the importance of coevolutionary processes between parasites and predators with parental investment strategies long before transitions to sociality may occur. Building on empirical and theoretical evidence from a wide range of taxa, including vertebrates and invertebrates, we suggest that direct fitness benefits arising from selective pressures associated with parental care evolution are the force behind the appearance of animal groups. Under this framework, kin-selection is rather an enhancer or even a by-product derived from evolutionary processes related to parental care and not the main prerequisite for cooperation to evolve. In the second chapter, we focus on studying facultatively social species to understand the processes that lead a solitary species to become group-living. In this sense, we describe the social system of a facultatively social primate with communal breeding, Microcebus murinus, using data on more than 200 individuals from a wild population. By studying sleeping site sharing in this solitary foraging species, we aim to characterize the social flexibility both at the species as well as at the individual levels. We find evidence for social flexibility in philopatric females and dispersing males. Moreover, contrary to previous findings, we show a higher capacity for sociality and social flexibility in males. Thus, our results suggest that female communal breeding may not be the only force driving sociality in this species, criticizing the framework exposed in chapter 1; and that unrelated males may be as prone as related females to form social groups, which suggests that kin-selection is also unable to explain the evolution of mouse lemurs’ social systems. While in the first two chapters, we discussed transitions to sociality from an adaptationist perspective, in Chapter 3.1, we examine the possibility that sociality in Microcebus murinus may be a passive result of heterogeneous distribution of food resources and sleeping sites. We find no evidence for an effect of food resource availability or nesting limitation on individual social strategies. Thus, intrinsic benefits associated with sleeping together and sharing a home range with others may be at play in this species. In chapter 3.2, we develop the method used in chapter 3.1 to assess the reliability of information gathered per individual to construct home ranges using Michaelis-Menten modeling. We believe this might be a potentially useful tool for studies in the wild where scarcity of data as well as between-individual variation in the amount of data collected may hamper movement ecology analyses. We end by emphasizing that social evolution is a manifold process that embeds and intertwines several layers of life complexity, resisting attempts for unitary explanations of its origins

Molecular Mechanisms of Resistance and Structure-Based Drug Design in Homodimeric Viral Proteases

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

In silico investigation of the mechanism of ricin-catalysed depurination reaction and design of novel ricin inhibitors

Includes abstract.Includes bibliographical references.Ricin is a dimeric enzyme found in the castor bean plant. It is extremely toxic with a fatal dose for humans ranging from 0.1-1.0 ug/kg. This has lead to its use as a biological weapon. Cell death is caused when ricin ceases the protein synthesis by removing a specific adenine (A-4324) of the GAGA tetra loop of 28S ribosomal RNA. Despite this destructive feature, ricin has been touted as a potential therapeutic agent where applications such as immunotoxins to treat cancer, AIDS and other diseases are actively being pursued. However, the prime challenge in such applications is the non specific cytotoxicity of ricin, which cannot currently be treated due to the absence of an effective antidote. The primary objective of this thesis is to describe the catalytic mechanism of ricin using computational reaction dynamics. For an accurate simulation of the ricin-catalysed reaction, a reasonable model of the target natural substrate is required

Dynamic modelling of the processing of peptides for presentation on major histocompatibility complex class I proteins

Antigen presentation is broadly implicated in disease and represents an important target for prophylactic and therapeutic treatments. A better understanding of the components of this system is fundamental to our understanding of disease path- ways and to treatment design. This thesis focuses on modelling the processing of peptides by enzymes in the cytosol and in the endoplasmic reticulum (ER) in the context of major histocompatibility complex class I (MHC) antigen presentation, and expounds upon current knowledge of the mechanistic details and specificity of both the proteasome and the endoplasmic reticulum aminopeptidase-1 (ERAP1). We use nonlinear ordinary differential equations to model the biochemical reaction pathways of amino-terminal peptide trimming by ERAP1 and distinguish parameter dependencies of two prevailing theories for the mechanism of ERAP1 trimming us- ing algebraic and numerical analysis. Importantly, we show that ERAP1 has a role in peptide optimisation when MHC acts as a template, but not when it trims free peptide using an internal molecular ruler. We present testable hypotheses that may elucidate the dominant trimming mechanism used by ERAP1 in vivo, which has been the subject of debate for more than 25 years. We show that all ERAP1 trimming mechanism hypotheses are able to predict the qualitative distribution of cell surface presentation of SIINFEKL derived from amino-terminally extended precursors. Notably, we find that the molecular ruler trimming mechanism is more robust than the MHC-as-template mechanism. Finally, we use neural networks to predict carboxyl-terminal cleavage by the proteasome, and demonstrate that we are able to distinguish between cleavage and non-cleavage sites on an unseen set of known peptide epitopes. Overall, this thesis contributes a more thorough quantitative and mechanistic understanding of the generation of peptides presented on MHC class I molecules

Discovering and exploiting hidden pockets at protein interfaces

The number of three-dimensional structures of potential protein targets available in several platforms such as the Protein Data Bank is subjected to a constant increase over the last decades. This observation should be an additional motivation to use structure-based methodologies in drug discovery. In the recent years, different success stories of Structure Based Drug Design approach have been reported. However, it has also been shown that a lack of druggability is one of the major causes of failure in the development of a new compound.The concept of druggability can be used to describe proteins with the capability to bind drug-like compounds. A general consensus suggests that around 10% of the human genome codes for molecular targets that can be considered as druggable. Over the years, the protein druggability was studied with a particular interest to capture structural descriptors in order to develop computational methodologies for druggability assessment. Different computational methods have been published to detect and evaluate potential binding sites at protein surfaces. The majority of methods currently available are designed to assess druggability of a static structure. However it is well known that sometimes a few local rearrangements around the binding site can profoundly influence the affinity of a small molecule to its target. The use of techniques such as molecular dynamics (MD) or Metadynamics could be an interesting way to simulate those variations. The goal of this thesis was to design a new computational approach, called JEDI, for druggability assessment using a combination of empirical descriptors that can be collected ‘on-the-fly’ during MD simulations. JEDI is a grid-based approach able to perform the druggability assessment of a binding site in only a few seconds making it one of the fastest methodologies in the field. Agreement between computed and experimental druggability estimates is comparable to literature alternatives. In addition, the estimator is less sensitive than existing methodologies to small structural rearrangements and gives consistent druggability predictions for similar structures of the same protein. Since the JEDI function is continuous and differentiable, the druggability potential can be used as collective variable to rapidly detect cryptic druggable binding sites in proteins with a variety of MD free energy methods

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Multivariat analyse som verktøy til forståelse og reduksjon av kompleksitet av matematiske modeller i systembiologi

In the area of systems biology, technologies develop very fast, which allows us to collect massive amounts of various data. The main interest of scientists is to receive an insight into the obtained data sets and discover their inherent properties. Since the data often are rather complex and intimidating equations may be required for modelling, data analysis can be quite challenging for the majority of bio-scientists who do not master advanced mathematics. In this thesis it is proposed to use multivariate statistical methods as a tool for understanding the properties of complex models used for describing biological systems. The methods of multivariate analysis employed in this thesis search for latent variables that form a basis of all processes in a system. This often reduces dimensions of the system and makes it easier to get the whole picture of what is going on. Thus, in this work, methods of multivariate analysis were used with a descriptive purpose in Papers I and IV to discover effects of input variables on a response. Often it is necessary to know a functional form that could have generated the collected data in order to study the behaviour of the system when one or another parameter is tuned. For this purpose, we propose the Direct Look-Up (DLU) approach that is claimed here to be a worthy alternative to the already existing fitting methods due to its high computational speed and ability to avoid many problems such as subjectivity, choice of initial values, local optima and so on (Papers II and III). Another aspect covered in this thesis is an interpretation of function parameters by the custom human language with the use of multivariate analysis. This would enable mathematicians and bio-scientists to understand each other when describing the same object. It was accomplished here by using the concept of a metamodel and sensory analysis in Paper IV. In Paper I, a similar approach was used even though the main focus of the paper was slightly different. The original aim of the article was to show the advantages of the multi-way GEMANOVA analysis over the traditional ANOVA analysis for certain types of data. However, in addition, the relationship between human profiling of data samples and function parameters was discovered. In situations when funds for conducting experiments are limited and it is unrealizable to study all possible parameter combinations, it is necessary to have a smart way of choosing a few but most representative conditions for a particular system. In Paper V Multi-level Binary Replacement design (MBR) was developed as such, which can also be used for searching for a relevant parameter range. This new design method was applied here in Papers II and IV for selection of samples for further analyses.Teknologiutviklingen innenfor systembiologien er nå så rask at det gir mulighet til å samle svært store datamengder på kort tid og til relativ lav pris. Hovedinteressen til forskerne er typisk å få innsikt i dataene og deres iboende egenskaper. Siden data kan være ganske komplekse og ofte beskrives ved kompliserte, gjerne ikke-lineære, funksjoner, kan dataanalyse være ganske utfordrende for mange bioforskere som ikke behersker avansert matematikk. I dette arbeidet er det foreslått å bruke multivariat statistisk analyse for å komme nærmere en forståelse av egenskapene av kompliserte modeller som blir brukt for å beskrive biologiske systemer. De multivariate metodene som er benyttet i denne avhandlingen søker etter latente variabler som utgjør en lineær basis og tilnærming til de komplekse prosessene i et system. Dermed kan man oppnå en forenkling av systemet som er lettere å tolke. I dette arbeidet ble multivariate analysemetoder brukt i denne beskrivende hensikten i Artikler (Papers) I og IV til å oppdage effekter av funksjonsparametre på egenskapene til komplekse matematiske modeller. Ofte er det nødvendig å finne en matematisk funksjon som kunne ha generert de innsamlede dataene for å studere oppførselen av systemet. Med den hensikt foreslår vi en metode for modelltilpasning ved DLU-metoden (the Direct Look-Up) som her påstås å være et verdifullt alternativ til de eksisterende estimeringsmetodene på grunn av høy fart og evne til å unngå typiske problemer som for eksempel subjektivitet, valg av initialverdier, lokale optima, m.m (Artikler II og III). Et annet aspekt dekket i denne avhandlingen er bruken av multivariat analyse til å gi tolking av matematiske funksjonsparametre ved hjelp av et dagligdags vokabular. Dette kan gjøre det enklere for matematikere og bioforskere å forstå hverandre når de beskriver det samme objektet. Det var utført her ved å benytte ideen om en metamodell og sensorisk analyse i Artikkel IV. I Artikkel I var en lignende metode også brukt for å få sensoriske beskrivelser av bilder generert fra differensiallikninger. Hovedfokuset i Artikkel I var imidlertid et annet, nemlig å vise fordelen ved multi-way GEMANOVA-analyse fremfor den tradisjonelle ANOVA-analysen for visse datatyper. I denne artikkelen ble GEMANOVA brukt til å avdekke sammenhengen mellom kompliserte kombinasjoner av funksjonsparametrene og bildedeskriptorer. I situasjoner der ressurser til å utføre eksperimenter er begrenset og det er umulig å prøve ut alle kombinasjoner av parametre, er det behov for metoder som kan bestemme et fåtall av parameterinnstillinger som er mest mulig representative for et bestemt system. I Artikkel V ble derfor Multi-level Binary Replacement (MBR) design utviklet som en sådan, og den kan også brukes for å søke etter et relevant parameterrom for datasimuleringer. Den nye designmetoden ble anvendt i Artikler II og IV for utvelgelse av parameterverdier for videre analyser

X-ray Computed Tomography and image-based modelling of plant, root and soil systems, for better understanding of phosphate uptake

A major constraint to crop growth is the poor bioavailability of edaphic nutrients, especially phosphate (P). Improving the nutrient acquisition efficiency of crops is crucial in addressing pressing global food-security issues arising from increasing world population, reduced fertile land and changes in the climate. Despite the undoubted importance of root architecture and root/soil interactions to nutrient uptake, there is a lack of approaches for quantifying plant roots non-invasively at all scales. Mathematical models have allowed our understanding of root and soil interactions to be improved, but are almost invariably reliant on idealised geometries or virtual root growth models. In order to improve phenotyping of advantageous traits for low-P conditions and improve the accuracy of root growth and uptake models, more sophisticated and robust approaches to in vivo root and soil characterisation are needed. Microfocus X-ray Computed Tomography (?-CT) is a methodology that has shown promise for noninvasive imaging of roots and soil at various scales. However, this potential has not been extended to consideration of either very small (rhizosphere scale) or large (mature root system scale) samples. This thesis combines discovery experiments and method development in order to achieve two primary objectives:• The development of more robust, well-described approaches to root and soil ?-CT imaging. Chapters 2 and 3 explore the potential of clinical contrasting methods in root investigation, and show how careful consideration of imaging parameters combined with development of user invariant image-processing protocol can improve measurement of macro-porous volume fraction, a key soil parameter. • Chapter 4 develops an assay for first-time 3D imaging of root hairs in situ within the rhizosphere. The resulting data is used to parameterise an explicit P uptake model at the hair scale, suggesting a different contribution of hairs to uptake than was predicted using idealised geometries. Chapter 5 then extends the paradigm for root hair imaging and model generation, building a robust, modular workflow for investigating P dynamics in the rhizosphere that can accommodate non-optimal soil-water states