Ausnutzung neuer Informationen für grobaufgelöste Vorhersage von Protein Bewegung

Proteins are involved in almost all functions in our cells due to their ability to combine conformational motion with chemical specificity. Hence, information about the motions of a protein provides insights into its function. Proteins move on a rugged energy landscape with many local minima, which is imposed on their high-dimensional conformational space. Exhaustive sampling of this space exceeds the available computational resources for all but the smallest proteins. Computational approaches thus have to simplify the potential energy function and/or resolution of the model using information about what is relevant and what can be ignored. The accuracy of the approximation depends on the accuracy of the used information. Information that is specific to the problem domain, i.e. protein motion in our case, usually results in better models. In this thesis, I propose a novel elastic network model of learned maintained contacts, lmcENM. It expands the range of motions that can be captured by such simplified models by leveraging novel information about a protein's structure. This improves the general applicability of elastic network models. Elastic network models (ENMs) are a highly popular coarse-grained method to study protein motions. They assume that protein motions are harmonic around an equilibrium conformation and largely governed by the protein's structural connectivity. This leads to the simplified representation of a protein as elastic mass-spring-network based on residue interactions. Despite their simplicity, ENMs predict intrinsic protein motions with surprising biological relevance. Accurate ENM predictions, however, require the initial contact topology to be maintained during a protein's motion. This is naturally fulfilled for highly collective motions resulting in successful predictions. But localized functional transitions involving substantial changes in the contact topology are often poorly explained. This limits the practical relevance of ENMs because the motion type of a protein is unknown a priori and thus it is unknown whether ENMs can capture it. lmcENM overcomes this limitation by leveraging information about the dynamic behavior of contacts, i.e. whether they break or are maintained when the protein moves. The maintained contacts remain after predicted breaking contacts have been removed from the initial network. In contrast to existing ENM variants, lmcENM is able to accurately predict protein motions even for localized and uncorrelated functional transitions with changing contact topology. In the first part of my thesis, I show that the absence of observed breaking contacts enables ENMs to accurately explain localized functional transitions. The resulting network of observed maintained contacts, mcENM, can be built when start and end conformation of a functional transition are known. Of course, to apply this strategy in the standard case when only a single protein conformation is available, we need to be able to predict these breaking contacts. In the second part of my thesis, I show how the breaking contacts can be predicted. To do so, I developed a machine-learning based classifier to differentiate breaking from maintained contacts based on a graph-based encoding of their structural context. The physicochemical characteristics of a contact's structural context capture how tightly different parts of the protein are bound to each other, how this affects their movements, and ultimately their contact topology. To build lmcENM the predicted breaking contacts are removed from the initial network. Using a large set of proteins covering different motion types I demonstrate the effectiveness of lmcENM. My thesis unlocks breaking contacts, or generally dynamic contact changes, as a novel source of information that has proven valuable in coarse-grained prediction of protein motion. Because they are defined on a simplified model of the structural connectivity of a protein, they are insensitive to structural details that would otherwise make their identification and prediction more difficult. The existence and usefulness of breaking contacts demonstrated in my thesis enables future research opportunities to study the conditions under which they occur and to examine the features that contributed the most to their accurate prediction. Our framework for predicting breaking contacts can be easily extended to further advance our understanding of protein motion.Proteine sind an fast allen Funktionen in unseren Zellen beteiligt aufgrund ihrer Fähigkeit, Konformationsbewegungen mit chemischer Spezifität zu kombinieren. Informationen über die Bewegungen eines Proteins liefern somit Einblicke in seine Funktion. Proteine bewegen sich auf einer zerklüfteten Energielandschaft mit vielen lokalen Minima über ihrem hochdimensionalen Konformationsraum. Eine erschöpfende Abtastung dieses Raums übersteigt die verfügbaren Rechenressourcen für alle bis auf die kleinsten Proteine. Computergestützte Ansätze müssen daher die Energiefunktion und/oder die Auflösung des Modells vereinfachen aufgrund von Informationen darüber, was relevant ist und was ignoriert werden kann. Die Genauigkeit der Approximation hängt von der Genauigkeit der verwendeten Information ab. Informationen, die spezifisch für die Problemdomäne sind, d. h. Proteinbewegung in unserem Fall, führen normalerweise zu besseren Modellen. In dieser Arbeit stelle ich ein neuartiges elastisches Netzwerkmodell von erlernten erhaltenen Kontakten, genannt lmcENM, vor. Es erweitert die Bewegungsreichweite, die durch diese Netzwerke erfasst werden können, durch das Ausnutzen neuer Informationen über die Struktur eines Proteins. Dies verbessert die allgemeine Anwendbarkeit von elastischen Netzwerkmodellen. Elastische Netzwerkmodelle (ENMs) sind eine sehr populäre grobkörnige Methode zur Untersuchung von Proteinbewegungen. Sie nehmen an, dass Proteinbewegungen harmonisch um eine Gleichgewichtskonformation verlaufen und weitgehend von der strukturellen Konnektivität des Proteins bestimmt werden. Dies führt zur vereinfachten Darstellung eines Proteins als elastisches Masse-Feder-Netzwerk auf der Basis von Residue-Interaktionen. Trotz ihrer Einfachheit sagen ENMs intrinsische Proteinbewegungen mit überraschender biologischer Relevanz voraus. Genaue ENM-Vorhersagen erfordern jedoch, dass die anfängliche Kontakttopologie während der Bewegung eines Proteins aufrechterhalten wird. Dies ist natürlicherweise für hoch kollektive Bewegungen erfüllt, was zu ihrer erfolgreichen Vorhersagen führt. Lokalisierte Funktionsbewegungen, die wesentliche Änderungen in der Kontakttopologie beinhalten, werden jedoch oft nur unzureichend erklärt. Dies begrenzt die praktische Relevanz von ENMs, da der Bewegungstyp eines Proteins a priori unbekannt ist und daher unbekannt ist, ob ENMs es erfassen können. lmcENM überwindet diese Einschränkung, indem Informationen über das dynamische Verhalten von Kontakten genutzt werden, d. h. ob sie brechen oder erhalten bleiben, wenn sich das Protein bewegt. Die erhaltenen Kontakte bleiben übrig, nachdem die brechenden Kontakte aus dem ursprünglichen Netzwerk entfernt wurden. Im Gegensatz zu existierenden ENM-Varianten ist lmcENM in der Lage, Proteinbewegungen auch für lokalisierte und unkorrelierte Funktionstransitionen mit sich ändernder Kontakttopologie genau vorherzusagen. Im ersten Teil meiner Arbeit zeige ich, dass die Abwesenheit von beobachteten brechenden Kontakten ENMs in die Lage versetzt, lokalisierte Funktionstransitionen genau zu erklären. Das resultierende Netzwerk von beobachteten bleibenden Kontakten, mcENM, kann erstellt werden, wenn die Anfangs- und Endkonformation eines Funktionsübergangs bekannt ist. Um diese Strategie im Standardfall anzuwenden, wenn nur eine einzige Proteinkonformation zur Verfügung steht, müssen wir diese brechenden Kontakte natürlich vorhersagen können. Im zweiten Teil meiner Arbeit zeige ich, wie die brechenden Kontakte vorhergesagt werden können. Um dies zu erreichen, entwickelte ich einen maschinell lernenden Klassifikator, der die brechenden von den bleibenden Kontakten unterscheidet auf Grundlage einer graph-basierten Kodierung ihres strukturellen Kontexts. Die physikalisch-chemischen Eigenschaften des strukturellen Kontexts eines Kontakts erfassen, wie stark verschiedene Teile des Proteins miteinander verbunden sind, wie sich dies auf ihre Bewegungen und letztendlich auf ihre Kontakttopologie auswirkt. Zum Erstellen von lmcENM werden die vorhergesagten brechenden Kontakte aus dem ursprünglichen Netzwerk entfernt. Anhand eines großen Datensatzes von Proteinen, die verschiedene Bewegungstypen abdecken, demonstriere ich die Effektivität von lmcENM. Meine Dissertation erschließt brechende Kontakte oder allgemein dynamische Kontaktänderungen  als eine neue Informationsquelle, die sich bei der grobkörnigen Vorhersage von Proteinbewegung als wertvoll erwiesen hat. Da diese dynamische Kontaktänderungen auf einem vereinfachten Modell der strukturellen Konnektivität eines Proteins definiert sind, sind sie unempfindlich gegenüber strukturellen Details, die ansonsten ihre Identifizierung und Vorhersage erschweren würden. Die Existenz und Nützlichkeit von brechenden Kontakten, die in meiner Dissertation gezeigt wurden, ermöglicht zukünftige Forschung, um die Bedingungen, unter denen sie auftreten, zu untersuchen sowie die Merkmale, die am meisten zu ihrer genauen Vorhersage beigetragen haben. Unser Framework für die Vorhersage von brechenden Kontakten kann leicht erweitert werden, um unser Verständnis der Proteinbewegung weiter voranzutreiben

Leveraging Novel Information for Coarse-Grained Prediction of Protein Motion

Proteins are involved in almost all functions in our cells due to their ability to combine conformational motion with chemical specificity. Hence, information about the motions of a protein provides insights into its function. Proteins move on a rugged energy landscape with many local minima, which is imposed on their high-dimensional conformational space. Exhaustive sampling of this space exceeds the available computational resources for all but the smallest proteins. Computational approaches thus have to simplify the potential energy function and/or resolution of the model using information about what is relevant and what can be ignored. The accuracy of the approximation depends on the accuracy of the used information. Information that is specific to the problem domain, i.e. protein motion in our case, usually results in better models. In this thesis, I propose a novel elastic network model of learned maintained contacts, lmcENM. It expands the range of motions that can be captured by such simplified models by leveraging novel information about a protein's structure. This improves the general applicability of elastic network models. Elastic network models (ENMs) are a highly popular coarse-grained method to study protein motions. They assume that protein motions are harmonic around an equilibrium conformation and largely governed by the protein's structural connectivity. This leads to the simplified representation of a protein as elastic mass-spring-network based on residue interactions. Despite their simplicity, ENMs predict intrinsic protein motions with surprising biological relevance. Accurate ENM predictions, however, require the initial contact topology to be maintained during a protein's motion. This is naturally fulfilled for highly collective motions resulting in successful predictions. But localized functional transitions involving substantial changes in the contact topology are often poorly explained. This limits the practical relevance of ENMs because the motion type of a protein is unknown a priori and thus it is unknown whether ENMs can capture it. lmcENM overcomes this limitation by leveraging information about the dynamic behavior of contacts, i.e. whether they break or are maintained when the protein moves. The maintained contacts remain after predicted breaking contacts have been removed from the initial network. In contrast to existing ENM variants, lmcENM is able to accurately predict protein motions even for localized and uncorrelated functional transitions with changing contact topology. In the first part of my thesis, I show that the absence of observed breaking contacts enables ENMs to accurately explain localized functional transitions. The resulting network of observed maintained contacts, mcENM, can be built when start and end conformation of a functional transition are known. Of course, to apply this strategy in the standard case when only a single protein conformation is available, we need to be able to predict these breaking contacts. In the second part of my thesis, I show how the breaking contacts can be predicted. To do so, I developed a machine-learning based classifier to differentiate breaking from maintained contacts based on a graph-based encoding of their structural context. The physicochemical characteristics of a contact's structural context capture how tightly different parts of the protein are bound to each other, how this affects their movements, and ultimately their contact topology. To build lmcENM the predicted breaking contacts are removed from the initial network. Using a large set of proteins covering different motion types I demonstrate the effectiveness of lmcENM. My thesis unlocks breaking contacts, or generally dynamic contact changes, as a novel source of information that has proven valuable in coarse-grained prediction of protein motion. Because they are defined on a simplified model of the structural connectivity of a protein, they are insensitive to structural details that would otherwise make their identification and prediction more difficult. The existence and usefulness of breaking contacts demonstrated in my thesis enables future research opportunities to study the conditions under which they occur and to examine the features that contributed the most to their accurate prediction. Our framework for predicting breaking contacts can be easily extended to further advance our understanding of protein motion

Augu valsts uztura riski grūtniecības un zīdīšanas laikā - literatūras apskats

MedicīnaVeselības aprūpeMedicineHealth CareGrūtniecības laikā sievietes piedzīvo vielmaiņas izmaiņas un fizioloģiskas izmaiņas, un viņu vajadzības ir lielākas, lai uzturētu embrija augšanu un attīstību. Dažādu iemeslu dēļ arvien vairāk sieviešu nolemj no uztura izslēgt dažādus dzīvnieku izcelsmes produktus. Par spīti daudziem ieguvumiem veselībai, šīs īpašās diētas var izraisīt arī dažādas patoloģijas. Lai analizētu uztura vadlīnijas un to izmaiņas gadu gaitā, aprakstīšanas procesā tika iekļauti 53 dažādi starptautiski publicēti pētījumi angļu valodā. Veiktie pētījumi liecina, ka uzturs ar nepietiekamu vitamīnu, mikroelementu un makroelementu daudzumu var izraisīt nopietnas patoloģijas mātēm un pēcnācējiem, ja tas netiek novērsts vai koriģets. Galvenās veselības sekas ir osteoporoze, pavājināta imūnsistēma, eklampsija un preeklampsija, anēmija, dislipidēmija un izmaiņas vairogdziedzera funkcijā. Sekas bērniem, nepietiekama uztura dēļ grūtniecības laikā vai mātes piena sastāva izmaiņu dēļ, bija mazs dzimšanas svars pret gestācijas vecumu (SGA), nervu caurules defekti (NTD), uzturvielu nepietiekamība jaundzimušo anēmija, priekšlaicīgas dzemdības un attīstības traucējumi. Šī pētījuma mērķis bija pārskatīt augu izcelsmes uztura sekas, tā ietekmi uz grūtniecēm un sievietēm, kas baro bērnu ar krūti, kā tas ietekmē pēcnācējus, kā arī to, kā novērst uztura radītās sekas veselībai. Šī literatūras apskata mērķis bija salīdzināt dažādu augu izcelsmes diētu līdzības un riskus ar visēdāju diētu. Augu izcelsmes uzturs ietver sevī dažādus uztura veidus, kuriem kopīga ir dzīvnieku izcelsmes produktu izslēgšana. Pastāv dažādas apakšgrupas, taču galvenais mērķis bija analizēt vegāna un veģetāra uztura modeļus salīdzinājumā ar visēdājiem, bez īpašiem izņēmumiem. Kopumā starptautiski veikti pētījumi ļāva iegūt plašu datu ieguvi par veģetāru diētu grūtniecēm un sievietēm, kas baro bērnu ar krūti, par kaitīgajiem riskiem, viņu uztura vajadzībām un dzīvesveidu, kā arī iespējamiem rezultātiem pēcnācējiem. Veģetārais uzturs ir plaši pētīts, savukārt vegānu diētas, kā arī dažādas to apakšgrupas vēl nav pietiekami izpētītas. Uz doto brīdi nav pietiekami daudz informācijas par vegāna uztura ilgtermiņa riskiem un iespējamiem veselības riskiem topošajām mātēm un viņu pēcnācējiem. Tomēr zinātne apstiprina to, ka no vienas puses, barības vielu deficīta risks ir lielāks veģetāru diētu gadījumā, no otras puses, šāds uzturs ir drošs, ja tas tiek pārdomāti lietots un papildus tiek uzņemti uztura bagātinātāji. Vegāna diēta rada vēl lielāku risku iespējamību, taču profesionāļa uzraudzībā tā var būt droša gan mātei, gan bērnam.During pregnancy, women undergo metabolic and physiologic changes, and their needs are higher, to maintain the growth and development of the fetus. Due to different reasons, an increasing number of women decide to exclude different types of animal products from their diet. Besides many health benefits, these special diet forms may also lead to various minor and major pathologies. In order, to analyze nutritional guidelines and their changes over the years, 53 different internationally published studies in the English language were included in the descriptive process. Different internationally conducted studies exemplified that minor malnutrition of vitamins, micronutrients, and macronutrients may lead to major pathologies in mothers and offspring if not prevented or treated. The main health consequences in women were osteoporosis, decreased immune system, eclampsia and pre-eclampsia, anemia, dyslipidemia, and alternate function in the thyroid gland. The outcomes in offspring either due to malnutrition during pregnancy or due to changes in breast milk composition were small for gestational age (SGA), neural tube defects (NTD), fetal depletion, neonatal anemia, preterm birth, and developmental disorders. This study sought to review the consequences of a plant-based diet, its impact on pregnant and breastfeeding women, sequentially affecting preterm and postnatal offspring, and how to prevent health consequences caused by nutrition. This literature review aimed to compare the similarities and risks of different plant-based diets with omnivore diets. A plant-based diet comprises different diet types, in specific diet types excluding animal products. Different subgroups exist, however focal point was to analyze vegan and vegetarian diet patterns versus omnivores, without any specific exceptions. Overall, internationally conducted studies gave rise to a broad data acquisition regarding the vegetarian diet in pregnant and breastfeeding women regarding harmful risks, their nutritional needs, and lifestyle, as well as possible outcomes in offspring. The vegetarian diet is a well-studied lifestyle, whereas vegan diets as well as different subgroups are not well-researched yet. To embrace, the evidence about vegan diet type long-term risks and possible outcomes on expecting mothers and offspring is not enough. Nevertheless, science confirmed that, on the one hand, risks for nutrient deficits are higher in vegetarian diet types, on the other hand, it is safe if conscious choices about food and supplements are made. A vegan diet type bears even more risks but is also safe under professional supervision

Elastic network model of learned maintained contacts to predict protein motion

<div><p>We present a novel elastic network model, <i>lmc</i>ENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein’s contact topology. Existing elastic network models assume that the contact topology remains unchanged throughout the motion and are thus most appropriate to simulate highly collective function-related movements. <i>lmc</i>ENM uses machine learning to differentiate breaking from maintained contacts. We show that <i>lmc</i>ENM accurately captures functional transitions unexplained by the classical ENM and three reference ENM variants, while preserving the simplicity of classical ENM. We demonstrate the effectiveness of our approach on a large set of proteins covering different motion types. Our results suggest that accurately predicting a “deformation-invariant” contact topology offers a promising route to increase the general applicability of ENMs. We also find that to correctly predict this contact topology a combination of several features seems to be relevant which may vary slightly depending on the protein. Additionally, we present case studies of two biologically interesting systems, Ferric Citrate membrane transporter FecA and Arachidonate 15-Lipoxygenase.</p></div

Accuracy improvement of <i>mc</i>ENM over ENM in relation to percent of observed breaking contacts on whole data set (90 proteins).

<p>The blue bars depict the absolute accuracy improvement of <i>mc</i>ENM over ENM averaged over each group, whereas the green bars show the average amount of removed breaking contacts. The accuracy improvement is calculated by the difference between cumulative mode overlap of the first ten low-frequency modes of <i>mc</i>ENM and ENM. (A) Proteins grouped by motion types. (B) Proteins grouped by SCOP fold class.</p

Cumulative mode overlaps and fluctuation profiles of <i>lmc</i>ENM, <i>mc</i>ENM, and the reference ENM variants for 15S-LOX1.

<p>(A) Reached cumulative overlap (curves) of the first 50 normal modes with the conformational transition. The bars depict how much of the movement individual modes capture. <i>lmc</i>ENM largely outperforms the baseline ENM and the reference ENM variants (color coding is the same as in panel B). The vertical dotted line marks the cumulative mode overlaps reached with the first ten low-frequency modes. (B) Residue fluctuations along the first ten low-frequency modes scaled to fit the observed displacement magnitudes (filled gray curve) between the two conformations. The Pearson correlation coefficient is given in brackets behind the ENM labels.</p

Accuracy of <i>mc</i>ENM compared to ENM measured by cumulative mode overlap (A) and dimensionality of deformation subspaces(B) of <i>mc</i>ENM on subset of local and domain motions (80 proteins).

<p>(A) The distribution of cumulative mode overlap is evaluated for the first ten low-frequency normal modes (<i>CO</i>(10)). <i>mc</i>ENM consistently improves over ENM in each motion category. <i>mc</i>ENM is particularly effective for proteins with localized functional transitions yielding an improvement between 15% and 21% for independent and coupled local motions. (B) The panels show the median number of normal modes (spanning the deformation subspace) required to explain between 70% and 90% of the functional transition (measured in cumulative mode overlap (%)). <i>mc</i>ENM consistently requires fewer modes to capture the same amount of conformational change as ENM.</p

Dependence of accuracy (A) and dimensionality of deformation subspaces (B) of ENM (baseline), <i>lmc</i>ENM (our method), <i>mc</i>ENM (theoretical upper bound), and ENM on motion type of protein, subset of local and domain motions (80 proteins).

<p>(A) Accuracy is measured by the cumulative mode overlap of the first ten low-frequency normal modes (<i>CO</i>(10)). <i>lmc</i>ENM consistently improves over ENM in each motion category, being particularly effective for proteins with coupled localized functional transitions. (B) The panels show the median number of normal modes (spanning the deformation subspace) required to explain between 70% and 90% of the functional transition (measured in cumulative mode overlap (%)). <i>lmc</i>ENM consistently requires fewer modes to capture the same amount of conformational change as ENM.</p

Conformational transition of outer membrane transporter FecA compared to observed and learned changes in its contact topology.

<p>(A,D): Function-related movement from unbound to bound conformation. The highlighted loops 7 (red) and 8 (blue) move the most to cover the ligand (green spheres) in the binding pocket. (B,E) <i>Observed</i> contact network of the unbound conformation mostly residing around the two highlighted loops. (C,F) <i>Learned</i> contact network. True positive (TP) predicted breaking contacts accurately match the observed ones around loop 7 and 8. The top view (C) reveals a cluster of false positive (FP, violet) predictions around loops 3, 4, and 5. Between loop 4 and 5 a single breaking contact is observed, which is not predicted. Some more FP breaking contacts are predicted around the plug domain within the <i>β</i>-barrel and turn 4 at the bottom of the barrel (F). For clarity, we omit drawing short-range contacts (sequence separation < 4 residues).</p