Protein structure and function relationships: application of computational approaches to biological and biomedical problems

In this work we have studied several cases by means of different computational approaches for the analysis of the structure and function relationships. In chapter 2 we describe a method, based on multiple neural networks, that we developed for evaluate the accuracy of predicted threedimensional protein structures. This tool has been used in different studies described in this work, in which the prediction of the 3D structure of the protein under study, has been necessary. In chapter 3, the interaction among a new class of natural sweeteners (steviol glycosides) and the human sweet taste receptor, has been analyzed by means of an insilico docking study, which allowed to identify the preferential binding site for the steviol glycosides. In chapter 4 the relationship between the dynamical properties and the function of some psychrophilic enzyme has been studied. A comparative study (psychrophile vs mesophile) of the thermodynamic properties of two different enzymes belonging to the elastases and the uracilDNAglycosylases families has been done. This study, carried out with molecular dynamic simulations, revealed that the low temperature adaptation is related to the different flexibility of the psychrophilic compared to the mesophilic enzyme. In chapter 5, we have studied the structural and functional impact of point mutations on three different proteins which are involved in serious rare diseases which cause grave metabolic disorders

Virtual screening of potential bioactive substances using the support vector machine approach

Die vorliegende Dissertation stellt eine kumulative Arbeit dar, die in insgesamt acht wissenschaftlichen Publikationen (fünf publiziert, zwei eingerichtet und eine in Vorbereitung) dargelegt ist. In diesem Forschungsprojekt wurden Anwendungen von maschinellem Lernen für das virtuelle Screening von Moleküldatenbanken durchgeführt. Das Ziel war primär die Einführung und Überprüfung des Support-Vector-Machine (SVM) Ansatzes für das virtuelle Screening nach potentiellen Wirkstoffkandidaten. In der Einleitung der Arbeit ist die Rolle des virtuellen Screenings im Wirkstoffdesign beschrieben. Methoden des virtuellen Screenings können fast in jedem Bereich der gesamten pharmazeutischen Forschung angewendet werden. Maschinelles Lernen kann einen Einsatz finden von der Auswahl der ersten Moleküle, der Optimierung der Leitstrukturen bis hin zur Vorhersage von ADMET (Absorption, Distribution, Metabolism, Toxicity) Eigenschaften. In Abschnitt 4.2 werden möglichen Verfahren dargestellt, die zur Beschreibung von chemischen Strukturen eingesetzt werden können, um diese Strukturen in ein Format zu bringen (Deskriptoren), das man als Eingabe für maschinelle Lernverfahren wie Neuronale Netze oder SVM nutzen kann. Der Fokus ist dabei auf diejenigen Verfahren gerichtet, die in der vorliegenden Arbeit verwendet wurden. Die meisten Methoden berechnen Deskriptoren, die nur auf der zweidimensionalen (2D) Struktur basieren. Standard-Beispiele hierfür sind physikochemische Eigenschaften, Atom- und Bindungsanzahl etc. (Abschnitt 4.2.1). CATS Deskriptoren, ein topologisches Pharmakophorkonzept, sind ebenfalls 2D-basiert (Abschnitt 4.2.2). Ein anderer Typ von Deskriptoren beschreibt Eigenschaften, die aus einem dreidimensionalen (3D) Molekülmodell abgeleitet werden. Der Erfolg dieser Beschreibung hangt sehr stark davon ab, wie repräsentativ die 3D-Konformation ist, die für die Berechnung des Deskriptors angewendet wurde. Eine weitere Beschreibung, die wir in unserer Arbeit eingesetzt haben, waren Fingerprints. In unserem Fall waren die verwendeten Fingerprints ungeeignet zum Trainieren von Neuronale Netzen, da der Fingerprintvektor zu viele Dimensionen (~ 10 hoch 5) hatte. Im Gegensatz dazu hat das Training von SVM mit Fingerprints funktioniert. SVM hat den Vorteil im Vergleich zu anderen Methoden, dass sie in sehr hochdimensionalen Räumen gut klassifizieren kann. Dieser Zusammenhang zwischen SVM und Fingerprints war eine Neuheit, und wurde von uns erstmalig in die Chemieinformatik eingeführt. In Abschnitt 4.3 fokussiere ich mich auf die SVM-Methode. Für fast alle Klassifikationsaufgaben in dieser Arbeit wurde der SVM-Ansatz verwendet. Ein Schwerpunkt der Dissertation lag auf der SVM-Methode. Wegen Platzbeschränkungen wurde in den beigefügten Veröffentlichungen auf eine detaillierte Beschreibung der SVM verzichtet. Aus diesem Grund wird in Abschnitt 4.3 eine vollständige Einführung in SVM gegeben. Darin enthalten ist eine vollständige Diskussion der SVM Theorie: optimale Hyperfläche, Soft-Margin-Hyperfläche, quadratische Programmierung als Technik, um diese optimale Hyperfläche zu finden. Abschnitt 4.3 enthält auch eine Diskussion von Kernel-Funktionen, welche die genaue Form der optimalen Hyperfläche bestimmen. In Abschnitt 4.4 ist eine Einleitung in verschiede Methoden gegeben, die wir für die Auswahl von Deskriptoren genutzt haben. In diesem Abschnitt wird der Unterschied zwischen einer „Filter“- und der „Wrapper“-basierten Auswahl von Deskriptoren herausgearbeitet. In Veröffentlichung 3 (Abschnitt 7.3) haben wir die Vorteile und Nachteile von Filter- und Wrapper-basierten Methoden im virtuellen Screening vergleichend dargestellt. Abschnitt 7 besteht aus den Publikationen, die unsere Forschungsergebnisse enthalten. Unsere erste Publikation (Veröffentlichung 1) war ein Übersichtsartikel (Abschnitt 7.1). In diesem Artikel haben wir einen Gesamtüberblick der Anwendungen von SVM in der Bio- und Chemieinformatik gegeben. Wir diskutieren Anwendungen von SVM für die Gen-Chip-Analyse, die DNASequenzanalyse und die Vorhersage von Proteinstrukturen und Proteininteraktionen. Wir haben auch Beispiele beschrieben, wo SVM für die Vorhersage der Lokalisation von Proteinen in der Zelle genutzt wurden. Es wird dabei deutlich, dass SVM im Bereich des virtuellen Screenings noch nicht verbreitet war. Um den Einsatz von SVM als Hauptmethode unserer Forschung zu begründen, haben wir in unserer nächsten Publikation (Veröffentlichung 2) (Abschnitt 7.2) einen detaillierten Vergleich zwischen SVM und verschiedenen neuronalen Netzen, die sich als eine Standardmethode im virtuellen Screening etabliert haben, durchgeführt. Verglichen wurde die Trennung von wirstoffartigen und nicht-wirkstoffartigen Molekülen („Druglikeness“-Vorhersage). Die SVM konnte 82% aller Moleküle richtig klassifizieren. Die Klassifizierung war zudem robuster als mit dreilagigen feedforward-ANN bei der Verwendung verschiedener Anzahlen an Hidden-Neuronen. In diesem Projekt haben wir verschiedene Deskriptoren zur Beschreibung der Moleküle berechnet: Ghose-Crippen Fragmentdeskriptoren [86], physikochemische Eigenschaften [9] und topologische Pharmacophore (CATS) [10]. Die Entwicklung von weiteren Verfahren, die auf dem SVM-Konzept aufbauen, haben wir in den Publikationen in den Abschnitten 7.3 und 7.8 beschrieben. Veröffentlichung 3 stellt die Entwicklung einer neuen SVM-basierten Methode zur Auswahl von relevanten Deskriptoren für eine bestimmte Aktivität dar. Eingesetzt wurden die gleichen Deskriptoren wie in dem oben beschriebenen Projekt. Als charakteristische Molekülgruppen haben wir verschiedene Untermengen der COBRA Datenbank ausgewählt: 195 Thrombin Inhibitoren, 226 Kinase Inhibitoren und 227 Faktor Xa Inhibitoren. Es ist uns gelungen, die Anzahl der Deskriptoren von ursprünglich 407 auf ungefähr 50 zu verringern ohne signifikant an Klassifizierungsgenauigkeit zu verlieren. Unsere Methode haben wir mit einer Standardmethode für diese Anwendung verglichen, der Kolmogorov-Smirnov Statistik. Die SVM-basierte Methode erwies sich hierbei in jedem betrachteten Fall als besser als die Vergleichsmethoden hinsichtlich der Vorhersagegenauigkeit bei der gleichen Anzahl an Deskriptoren. Eine ausführliche Beschreibung ist in Abschnitt 4.4 gegeben. Dort sind auch verschiedene „Wrapper“ für die Deskriptoren-Auswahl beschrieben. Veröffentlichung 8 beschreibt die Anwendung von aktivem Lernen mit SVM. Die Idee des aktiven Lernens liegt in der Auswahl von Molekülen für das Lernverfahren aus dem Bereich an der Grenze der verschiedenen zu unterscheidenden Molekülklassen. Auf diese Weise kann die lokale Klassifikation verbessert werden. Die folgenden Gruppen von Moleküle wurden genutzt: ACE (Angiotensin converting enzyme), COX2 (Cyclooxygenase 2), CRF (Corticotropin releasing factor) Antagonisten, DPP (Dipeptidylpeptidase) IV, HIV (Human immunodeficiency virus) protease, Nuclear Receptors, NK (Neurokinin receptors), PPAR (peroxisome proliferator-activated receptor), Thrombin, GPCR und Matrix Metalloproteinasen. Aktives Lernen konnte die Leistungsfähigkeit des virtuellen Screenings verbessern, wie sich in dieser retrospektiven Studie zeigte. Es bleibt abzuwarten, ob sich das Verfahren durchsetzen wird, denn trotzt des Gewinns an Vorhersagegenauigkeit ist es aufgrund des mehrfachen SVMTrainings aufwändig. Die Publikationen aus den Abschnitten 7.5, 7.6 und 7.7 (Veröffentlichungen 5-7) zeigen praktische Anwendungen unserer SVM-Methoden im Wirkstoffdesign in Kombination mit anderen Verfahren, wie der Ähnlichkeitssuche und neuronalen Netzen zur Eigenschaftsvorhersage. In zwei Fällen haben wir mit dem Verfahren neuartige Liganden für COX-2 (cyclooxygenase 2) und dopamine D3/D2 Rezeptoren gefunden. Wir konnten somit klar zeigen, dass SVM-Methoden für das virtuelle Screening von Substanzdatensammlungen sinnvoll eingesetzt werden können. Es wurde im Rahmen der Arbeit auch ein schnelles Verfahren zur Erzeugung großer kombinatorischer Molekülbibliotheken entwickelt, welches auf der SMILES Notation aufbaut. Im frühen Stadium des Wirstoffdesigns ist es wichtig, eine möglichst „diverse“ Gruppe von Molekülen zu testen. Es gibt verschiedene etablierte Methoden, die eine solche Untermenge auswählen können. Wir haben eine neue Methode entwickelt, die genauer als die bekannte MaxMin-Methode sein sollte. Als erster Schritt wurde die „Probability Density Estimation“ (PDE) für die verfügbaren Moleküle berechnet. [78] Dafür haben wir jedes Molekül mit Deskriptoren beschrieben und die PDE im N-dimensionalen Deskriptorraum berechnet. Die Moleküle wurde mit dem Metropolis Algorithmus ausgewählt. [87] Die Idee liegt darin, wenige Moleküle aus den Bereichen mit hoher Dichte auszuwählen und mehr Moleküle aus den Bereichen mit niedriger Dichte. Die erhaltenen Ergebnisse wiesen jedoch auf zwei Nachteile hin. Erstens wurden Moleküle mit unrealistischen Deskriptorwerten ausgewählt und zweitens war unser Algorithmus zu langsam. Dieser Aspekt der Arbeit wurde daher nicht weiter verfolgt. In Veröffentlichung 6 (Abschnitt 7.6) haben wir in Zusammenarbeit mit der Molecular-Modeling Gruppe von Aventis-Pharma Deutschland (Frankfurt) einen SVM-basierten ADME Filter zur Früherkennung von CYP 2C9 Liganden entwickelt. Dieser nichtlineare SVM-Filter erreichte eine signifikant höhere Vorhersagegenauigkeit (q2 = 0.48) als ein auf den gleichen Daten entwickelten PLS-Modell (q2 = 0.34). Es wurden hierbei Dreipunkt-Pharmakophordeskriptoren eingesetzt, die auf einem dreidimensionalen Molekülmodell aufbauen. Eines der wichtigen Probleme im computerbasierten Wirkstoffdesign ist die Auswahl einer geeigneten Konformation für ein Molekül. Wir haben versucht, SVM auf dieses Problem anzuwenden. Der Trainingdatensatz wurde dazu mit jeweils mehreren Konformationen pro Molekül angereichert und ein SVM Modell gerechnet. Es wurden anschließend die Konformationen mit den am schlechtesten vorhergesagten IC50 Wert aussortiert. Die verbliebenen gemäß dem SVM-Modell bevorzugten Konformationen waren jedoch unrealistisch. Dieses Ergebnis zeigt Grenzen des SVM-Ansatzes auf. Wir glauben jedoch, dass weitere Forschung auf diesem Gebiet zu besseren Ergebnissen führen kann

Accounting for Strong Electronic Correlation in Metalloproteins

Metalloproteins play a crucial role in many key biological processes, from oxygen transport to photosynthesis. In the case of photosynthesis, the oxygen evolving complex (OEC) --- a CaMn\textsubscript{4}O\textsubscript{5} cluster --- catalyses water-to-oxygen-gas conversion. From a computational standpoint, accurately modelling the electronic structure of the OEC and other metalloproteins \emph{ab initio} is difficult, due to two challenges. Firstly, there is that of the strong electronic correlation present due to the partially-filled $3d$-subshells of the transition metal atoms, a classic example of where semi-local density functional theory (DFT) --- a go-to method for computational physicists --- fails. The second challenge is that of size: as this thesis will demonstrate, we must consider large cluster models that are thousands of atoms in size, which takes us beyond the reach of both plane-wave DFT and quantum chemistry methods. This thesis explores the capacity of density functional theory-plus-$U$ (DFT+$U$) and dynamical mean field theory (DMFT) to meet both of these challenges. It will demonstrate how both DFT+$U$ and DMFT can be readily married with linear-scaling DFT, meaning that these theories can be applied to protein systems containing thousands of atoms. In particular, this thesis presents the unification of ONETEP (a linear-scaling DFT code) and TOSCAM (a DMFT solver). It also presents a novel approach for determining Hubbard and Hund's parameters via linear response that is compatible with linear-scaling DFT and resolves inconsistencies between the linear response method and the DFT+$U$ corrective functional. These techniques are then applied to haem, haemocyanin, and the OEC, providing insight into the role of strong correlation in their electronic structure and function. In so doing, this thesis demonstrates how one can perform large-scale simulations of metalloproteins that account for strong electronic correlation. The results of this thesis are of significant interest due to both the importance of metalloproteins in nature, and the wealth of potential applications that would spring from a thorough understanding of their catalytic and binding properties.Cambridge Rutherford Memorial Scholarshi

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

The Endogenous and Exogenous Substrate Spectrum of the Human Organic Cation Transporter OCT1 – a Comprehensive Characterization

The organic cation transporter 1 (OCT1) is most extensively expressed in the human liver. OCT1 is involved in the hepatic uptake of several drugs and endogenous compounds, which then undergo bioactivation, recycling, metabolism, or elimination. The aim of this work was to extend the knowledge about OCT1 substrates and non-substrates of endogenous and exogenous origin. Establishing an in vitro model for studying uptake and subsequent metabolism was an additional goal, as well as finding an endogenous biomarker for OCT1 activity. These studies should contribute to our basic understanding of the biological role of this transporter and to the understanding of the role of OCT1 in pharmacology and toxicology. Influx transport of 18 psychostimulant or hallucinogenic compounds, which all meet the conventional physicochemical criteria of OCT1 substrates, was investigated for OCT1 and related transporters. Mescaline was newly identified as a substrate of OCT1. To more systematically and comprehensively search for additional substrates, a machine learning-based model was used. This approach exploited existing knowledge about OCT1 substrates. Machine learning-aided prediction of new substrates was highly reliable as subsequent in vitro validation showed. The in silico prediction, which was based on two-dimensional structures, did not include three-dimensional information, which is important for enantiomers. Therefore, stereoselectivity of OCT1 transport was investigated in vitro. It revealed a surprisingly stereoselective cell uptake for some substrates, e.g. fenoterol, but not all, e.g. salbutamol. For the purpose of in vitro-to-in vivo translation in more complex biological systems, a cell model was developed, which allowed the chromosomal integration of two genes of interest in a targeted manner, to investigate uptake and metabolism in a more holistic fashion. Both genes can be transfected simultaneously and are expressed with equal strength. As a first proof of the value, the sequential uptake of proguanil and subsequent CYP-dependent activation to cycloguanil resembled indeed the uptake and metabolism in primary hepatocytes. Several European and American investigators have already expressed their interest to adopt the system for their research. Thiamine (Vitamin B1) had been proposed as a biomarker for in vivo OCT1 activity. Although thiamine was a substrate of OCT1 in vitro, thiamine was rejected as a suitable biomarker for OCT1 activity through a human clinical trial. After the intake of a large thiamine dose, neither thiamine trough concentrations nor maximum concentrations showed any correlation with individual OCT1 activity according to genotype. Results suggested that other transporters play the central role in thiamine uptake into the liver and other organs. In total, I could add more than 20 previously unknown substrates to the list of well characterized OCT1 substrates. I could show that OCT1 transport can be quite different depending on subtle structural differences between enantiomers. Moreover, the developed cell model may serve as an interesting tool to mimic the complex interplay between hepatic uptake and metabolism, and human pharmacokinetics of thiamine is not dependent on OCT1 - at least not in a quantitatively relevant fashion. Together, these findings may contribute to a gradually improving understanding of OCT1 functionality and biomedical relevance.2021-07-2

The Rise of Mitochondria in Medicine

Mitochondria are critical bioenergetic and biosynthetic machines that are essential for normal cell function. Traditionally, mitochondria have been considered the powerhouse of the cell, as they supply most of the cellular energy through oxidative phosphorylation. In addition, they supply the building blocks needed for the synthesis of cellular biomass. More recently, mitochondria have been recognized as signaling hubs that receive and transmit signals throughout the cell, thereby affecting cell functionality and fate. The signals generated by mitochondria include changes in metabolites, the NAD+/NADH ratio, ATP/ADP ratio, Ca2+, and reactive oxygen species (ROS), but our understanding of their nature, dynamics, targets, and roles in different physiopathological contexts is still under development. Mitochondrial dysfunction, which may originate from primary defects within the organelles or from stress conditions in the microenvironment, is a hallmark of many common diseases, including ischaemia–reperfusion injury, cancer, metabolic disease, and neurodegenerative disorders, and has become a major research focus in medicine. Understanding the biology of mitochondrial signaling and the role of mitochondrial dysfunction in the pathogenesis of many metabolic, degenerative, cardiovascular, and neoplastic diseases is crucial for the development of strategies aimed at therapeutically restoring mitochondrial functionality. This Special Issue presents current knowledge in the field of mitochondrial signaling in health and disease, and recent advances in mitochondrial pharmacology

Mechanism of action of non-synonymous single nucleotide variations associated with α-carbonic anhydrases II, IV and VIII

The carbonic anhydrase (CA) group of enzymes are Zinc (Zn2+) metalloproteins responsible for the reversible hydration of CO2 to bicarbonate (BCT or HCO− 3 ) and protons (H+) for the facilitation of acid-base balance and homeostasis within the body. Across all organisms, a minimum of six CA families exist, including, α (alpha), β (beta), γ (gamma), δ (delta), η (eta) and ζ (zeta). Some organisms can have more than one family, with exception to humans that contain the α family solely. The α-CA family comprises of 16 isoforms (CA-I to CA-XV) including the CA-VIII, CA-X and CA-XI acatalytic isoforms. Of the catalytic isoforms, CA-II and CA-IV possess one of the fastest rates of reaction, and any disturbances to the function of these enzymes results in CA deficiencies and undesirable phenotypes. CA-II deficiencies result in osteopetrosis with renal tubular acidosis and cerebral calcification, whereas CA-IV deficiencies result in retinitis pigmentosa 17 (RP17). Phenotypic effects generally manifest as a result of poor protein folding and function due to the presence of non-synonymous single nucleotide variations (nsSNVs). Even within the acatalytic isoforms such as CA-VIII that llosterically regulates the affinity of inositol triphosphate (IP3) for the IP3 receptor type 1 (ITPR1) and regulates calcium (Ca2+) signalling, the presence of SNVs also causes phenotypes cerebellar ataxia, mental retardation, and dysequilibrium syndrome 3 (CAMRQ3). Currently the majority of research into the CAs is focused on the inhibition of these proteins to achieve therapeutic effects in patients via the control of HCO− production or reabsorption as observed in glaucoma and diuretic medications. Little research has therefore been devoted into the identification of stabilising or activating compound that could rescue protein function in the case of deficiencies. The main aim of this research was to identify and characterise the effects of nsSNVs on the structure and function of CA-II, CA-IV and CA-VIII to set a foundation for rare disease studies into the CA group of proteins. Combined bioinformatics approaches divided into four main objectives were implemented. These included variant identification, sequence analysis and protein characterisation, force field (FF) parameter generation, molecular dynamics (MD) simulation and dynamic residue network analysis (DRN). Six variants for each of the CA-II, CA-IV and CA-VIII proteins with pathogenic annotations were identified from the HUMA and Ensembl databases. These included the pathogenic variants K18E, K18Q, H107Y, P236H, P236R and N252D for CA-II. CA-IV included the pathogenic R69H, R219C and R219S, and benign N86K, N177K and V234I variants. CA-VIII included pathogenic S100A, S100P, G162R and R237Q, and benign S100L and E109D variants. CA-II has been more extensively studied than CA-IV and CA-VIII, therefore residues essential to its function and stability are known. To discover important residues and regions within the CA-IV and CA-VIII proteins sequence and motif analysis was performed across the α-CA family, using CA-II as a reference. Sequence analysis identified multiple conserved residues between the two acatalytic CA-II and CA-IV, and the acatalytic CA-VIII isoforms that were proposed to be essential for protein stability. With exception to the benign N86K CA-IV variant, none of the other pathogenic or benign CA-II, CA-IV and CA-VIII SNVs were located at functionally or structurally important residues. Motif analysis identified 11 conserved and important motifs within the α-CA family. Several of the identified variants were located on these motifs including K18E, K18Q, H107Y and N252D (CA-II); N86K, R219C, R219S and V234I (CA-IV); and E109D, G162R and R237Q (CA-VIII). As there were no x-ray crystal structures of the variant proteins, homology modelling was performed to calculate the protein structures for characterisation. In CA-VIII, the substitution of Ser for Pro at position 100 (variant S100P) resulted in destruction of the β-sheet that the SNV was located on. Little is known about the mechanism of interaction between CA-VIII and ITPR1, and residues involved. SiteMap and CPORT were used to identify binding site amino for CA-VIII and results identified 38 potential residues. Traditional FFs are incapable of performing MD simulations of metalloproteins. The AMBER ff14SB FF was extended and Zn2+ FF parameters calculated to add support for metalloprotein MD simulations. In the protein, Zn2+ was noted to have a charge less than +1. Variant effects on protein structure were then investigated using MD simulations. Root mean square deviation (RMSD) and radius of gyration (Rg) results indicated subtle SNV effects to the variant global structure in CA-II and CA-IV. However, with regards to CA-VIII RMSD analysis highlighted that variant presence was associated with increases to the structural rigidity of the protein. Principal component analysis (PCA) in conjunction with free energy analysis was performed to observe variant effects on protein conformational sampling in 3D space. The binding of BCT to CA-II induced greater protein conformational sampling and was associated with higher free energy. In CA-IV and CA-VIII PCA analysis revealed key differences in the mechanism of action of pathogenic and benign SNVs. In CA-IV, wild-type (WT) and benign variant protein structures clustered into single low energy well hinting at the presence of more stable structures. Pathogenic variants were associated with higher free energy and proteins sampled more conformations without settling into a low energy well. PCA analysis of CA-VIII indicated the opposite to CA-IV. Pathogenic variants were clustered into low energy wells, while the WT and benign variants showed greater conformational sampling. Dynamic cross correlation (DCC) analysis was performed using the MD-TASK suite to determine variant effects on residue movement. CA-II WT protein revealed that BCT and CO2 were associated with anti-correlated and correlated residue movement, highlighting at opposite mechanisms. In CA-IV and CA-VIII variant presence resulted in a change to residue correlation compared to the WT proteins. DRN analysis was performed to investigate SNV effects of residue accessibility and communication. Results demonstrated that SNVs are associated with allosteric effects on the CA protein structures, and effects are located on the stability assisting residues of the aromatic clusters and the active site of the proteins. CA-II studies discovered that Glu117 is the most important residue for communication, and variant presence results in a decrease to the usage of the residue. This effect was greatest in the CA-II H107Y SNV, and suggests that variants could have an effect on Zn2+ dissociation from the active site. Decreases to the usage of Zn2+ coordinating residues were also noted. Where this occurred, compensatory increases to the usage of other primary and secondary coordination residues were observed, that could possibly assist with the maintenance of Zn2+ within the active site. The CA-IV variants R69H and R219C highlighted potentially similar pathogenic mechanisms, whereas N86K and N177K hinted at potentially similar benign mechanisms. Within CA-VIII, variant presence was associated with changes to the accessibility of the N-terminal binding site residues. The benign CA-VIII variants highlighted possible compensatory mechanisms, whereby as one group of N-terminal residues loses accessibility, there was an increase to the accessibility of other binding site residues to possibly balance the effect. Catalytically, the proton shuttle residue His64 in CA-II was found to occupy a novel conformation named the “faux in” that brought the imidazole group even closer to the Zn2+ compared to the “in” conformation. Overall, compared to traditional MD simulations the incorporation of DRN allowed more detailed investigations into the variant mechanisms of action. This highlights the importance of network analysis in the study of the effects of missense mutations on the structure and function of proteins. Investigations of diseases at the molecular level is essential in the identification of disease pathogenesis and assists with the development of specifically tailored and better treatment options especially in the cases of genetically associated rare diseases

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field