Development of a pseudoreceptor model for virtual screening

Im Rahmen dieser Arbeit wurde die Eignung von Pseudorezeptoren im virtuellen Screening untersucht. Hierzu wurde nach intensiver Auseinandersetzung mit bisher bekannten Konzepten ein neues Computerprogramm zur automatischen Konstruktion von Pseudorezeptormodellen entwickelt. Das Ziel von Pseudorezeptoren ist die Konstruktion eines alternativen, artifiziellen Wirtssystems aus bekannten Liganden eines Zielproteins, dessen dreidimensionale Struktur unbekannt ist. Der generierte Pseudorezeptor ist zu verstehen als die Menge aller Pseudoatome, die um die Ausgangssubstanz(en) projiziert werden. Bei multiplen Referenzliganden wird eine Gewichtung der Pseudoatome durchgeführt. Zudem wird ausschließlich von Distanz- und Winkelparametern Gebrauch gemacht, die aus Untersuchungen von Kokristall-strukturen gewonnenen wurden. Eine abschließende Kodierung generierter Pseudorezeptoren als 90-dimensionalen Korrelationsvektor wurde zum virtuellen Screening eingesetzt. In zwei retrospektiven Fallbeispielen wird gezeigt, dass die generierten Pseudorezeptoren für COX-2 und PPARα mit den realen Zuständen ihrer kokristallisierten Bindetaschen in den PDB Einträge 6cox und 2p54 kompatibel sind. Im retrospektiven virtuellen Screening in der Wirkstoffdatenbank COBRA (8.311 Moleküle) nach COX-2 Inhibitoren (136 Aktive) konnte eine Anreicherung der aktiven Strukturen in den ersten zwei Perzentilen gezeigt werden (54% der Aktiven). Zudem konnten 80% der aktiven Moleküle bereits nach Vorhersage von 10% Falsch-Positiven gefunden werden. Im Falle des retrospektiven Screenings nach 94 PPAR Liganden konnten 30% der aktiven Moleküle nach der Vorhersage von 10% Falsch-Positiven entdeckt. Nach 20% Falsch-Positiver wurden 46% der PPAR Liganden wieder gefunden. Weiterhin konnte mit den ligandenbasierten Informationen eines H4 Pseudorezeptors eine Justierung einer potentiellen Bindetasche des Histamin H4 Rezeptors aus einer molekularen Dynamiksimulation vorgenommen werden. Schließlich wurde in einem prospektiven virtuellen Screening nach Histamin H4 Liganden mit einem Pseudorezeptor zwei Strukturen mit unterschiedlichem Grundgerüst und einem Ki ~ 30 µM identifiziert.In this thesis, the suitability of pseudoreceptors for virtual screening applications was analyzed. An automated pseudoreceptor construction program was developed after known design principles had been thoroughly studied and compared. The aim of pseudoreceptor modelling is the construction of an alternative host system for known ligands of a given target protein in the absence of three-dimensional structure information. The constructed pseudoreceptor is represented as the sum of all pseudoatoms, which are projected around reference ligand(s). A weighting scheme is introduced, when pseudoreceptors are generated from multiple reference ligands. For pseudoatom placing distance and angle parameters from a survey of known co-crystal structures were used. For virtual screening pseudoreceptors were encoded as correlation vectors. It is demonstrated that the generated pseudoreceptors match with their respective co- crystallized binding pockets, taking COX-2 and PPAR-alpha as an example (PDB entries 6cox and 2p54). In a retrospective virtual screening in the drug collection COBRA (8,311 molecules) for COX-2 inhibitors (136 actives) high enrichment of ligands in the first two percentiles was yielded (54% of the actives). 80% of all active compounds were found after the prediction of only 10% false-positives. In a retrospective screening study for 94 PPAR ligands, 30% of the actives were found together with 10% false-positives. After the prediction of 20% false-positives, 46% of all PPAR ligands could be found. In addition, a putative binding pocket of the histamine H4 receptor from a molecular dynamics simulation could be adjusted using ligand-based information of a H4 pseudoreceptor. Finally, two micromolar ligands with different scaffolds were identified with a Ki ~ 30 µM by a pseudoreceptor-based prospective virtual screening for novel H4 ligands

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Periodic Space Partitioners (PSP) and their relations to Crystal Chemistry

We are looking back on 30 years development of periodic space partitioners (PSP) and their relations to their periodic relatives, i.e. minimal surfaces (PMS), zero potential surfaces (P0PS), nodal surfaces (PNS), and exponential scale surfaces. Hans-Georg von Schnering and Sten Andersson have pioneered this field especially in terms of applications to crystal chemistry. This review relates the early attempts to approximate periodic minimal surfaces which established a systematic classification of all PSP in terms space group symmetry and consecutive applications in a variety of different fields. A consistent nomenclature is outlined and different methods for deriving PSP are described. Characteristic structure factor sets which solely define PNS by can be used to discriminate structure types of a given symmetry or even to determine complicated crystal structures. The concept of PSP relates space group symmetry, topology, and chemical bonding in an intriguing way and tessellations on PSP which can be generated in a straight forward way allow to predict new framework types. Through transformation of such continuous topological forms a new entry has been found for understanding and interpreting reconstructive phase transitions. Finally we indicate the importance of PSP models for soft matter scienc

Dynamic Atomic Scale Sintering of Nanoparticle Catalysts

The classical view of a chemical reaction involves the formation and breaking of bonds, facilitated by the transfer of electrons between atoms. To fundamentally understand the mechanisms occurring, atomic resolution of the catalysts enabling the reaction is required. Using the recently developed York JEOL Nanocentre Environmental Scanning Transmission Electron Microscope, single atoms and nanoparticles can be observed in representative industrial reaction conditions, allowing for atomic scale quantification of catalyst deactivation mechanisms, such as Ostwald Ripening. This is initially understood through the industrially important process of methanol synthesis. Copper nanoparticles, one component of a methanol synthesis catalyst, are imaged under both Hydrogen and high vacuum conditions. This is the first use of ESTEM to study sintering, which is shown to be governed by the Ostwald Ripening mechanism and significantly enhanced by the presence of Hydrogen. Further understanding is developed by comparison with kinetic models to deconvolute the variables affecting Ostwald Ripening. In order to study Ostwald Ripening at the single atom scale in Hydrogen, Platinum nanoparticles are used as a model system. Particle decay is found to be initiated by a lack of local single atoms, and a subsequent increase in single atom density suggests anchoring of atoms onto sites on the Carbon substrate. These observations build an atomic level understanding of Ostwald Ripening, informing both traditional nanoparticle, and the emerging field of single atom, catalysis

Development and Application of Pseudoreceptor Modeling

Quantitative Structure-Activity Relationship (QSAR) methods are a commonly used tool in the drug discovery process. These methods attempt to form a statistical model that relates descriptor properties of a ligand to the activity of that ligand compound towards a specific desired physiological response. QSAR methods are known as a ligand-based method, as they specifically use information from ligands and not protein structural data. However, a derivation of QSAR methods are pseudoreceptor methods. Pseudoreceptor methods go beyond standard QSAR by building a model representation of the protein pocket. However, the ability of pseudoreceptors to accurately replicate natural protein surfaces has not been studied. The goal of this thesis work is to investigate the necessary descriptors to map a protein binding pocket and a method to accurately recreate the 3-D spatial structure of the binding pocket. In addition, additional applications of existing pseudoreceptor methods are explored

Data mining for important amino acid residues in multiple sequence alignments and protein structures

Enzymes are highly efficient bio-catalysts interesting for industries and medicine. Therefore, a goal of utmost importance in biochemical research is to understand how an enzyme catalyzes a chemical reaction. Here, the computational identification of functionally or structurally important residue positions can be of tremendous help. The datasets that are most informative for the algorithms are the 3D structure of a protein and a multiple sequence alignment (MSA) composed of homologous sequences. For example, an MSA allows for the quantification of residue conservation. Residue conservation at a given position indicates that only one type of amino acid fulfills all constraints imposed by protein structure or function. Furthermore, a detailed analysis of less strictly conserved residue positions may identify pairs, whose orchestration is mutually dependent and induces correlated mutations. Both of these conservation signals are indicative of functionally or structurally important positions. In the first part of this thesis, methods of machine learning were used to identify and classify these residue positions. It was the aim to predict in a mutually exclusively manner a role in catalysis, ligand-binding or protein stability for each residue position of a protein. Unfortunately, for many proteins the 3D structure is unknown. For other proteins, the number of known homologs is not sufficient to compile a meaningful MSA. Therefore, three variants of a classifier were designed and implemented, named CLIPS-1D, CLIPS-3D, and CLIPS-4D. These multi-class support vector machines allow for a classification based on an MSA (CLIPS-1D), a 3D structure (CLIPS-3D), and a combination of both (CLIPS-4D). CLIPS-1D exploits seven sequence-based features, whereas CLIPS-3D utilizes seven structure-based features. CLIPS-4D combines the seven sequence-based features of CLIPS-1D with those two structure-based features that increased its classification performance. A comparison with existing methods and a detailed analysis on a well-studied enzyme confirmed state-of-the-art prediction quality for CLIPS-1D and CLIPS-4D. In the second part of this thesis an algorithm for the identification of correlated mutations was improved. A common method for the identification of correlated mutations is to deduce the mutual information (MI) of a pair of residue positions from an MSA. The classical MI is based on Shannon’s information theory that utilizes probabilities only. Consequently, these approaches do not consider the similarity of residue pairs, which is a severe limitation. In order to improve these algorithms, H2rs was developed for this thesis. Thus, the MIvalues originate from the von Neumann entropy (vNE), which takes into account amino acid similarities modeled by means of a substitution matrix. To further improve the specificity of H2rs, the significance of MIvNE-values was assessed with a bootstrapping approach. The analysis of a large in silico testbed and the detailed assessment of five well-studied enzymes demonstrated state-of-the-art performance

Micromechanical Properties and Structure of the Pericellular Coat of Living Cells Modulated by Nanopatterned Substrates

Das mechanisch stark beanspruchte Knorpelgewebe in Gelenken besteht zum überwiegenden Teil aus einer komplexen extrazellulären Matrix (ECM). Chondrozyten, spezialisierte in der Matrix eingebettete Zellen, erneuern diese fortwährend, um deren Abrieb und Verschleißzu verhindern. Die Zellen werden durch eine mikrometerdicke perizelluläre Matrix (PCC) geschützt, die ein Überleben und ein Teilen der Zellen trotz der hohen mechanischen Belastung ermöglicht. Die PCC ist von entscheidender Bedeutung für eine Vielzahl weiterer biologischer Prozesse, wie der Motilität, der Zellalterung und der Ostheoarthrose. Auf molekularer Ebene ist die Zusammensetzung und Wechselwirkung der verschiedenen PCC-Komponenten gut verstanden: Der überwiegende Teil der PCC besteht aus Wasser und ist damit mit lichtmikroskopischen Methoden nicht detektierbar. Das Rückgrat der PCC wird aus stark hydratisierten Hyaluronsäurepolymeren und daran angebundenen HA-Bindungsproteinen gebildet. Informationen über die mesoskopische Struktur der PCC sind allerdings kaum vorhanden. Diese ist jedoch von fundamentaler Bedeutung für das Verständnis der Kraftübertragung aus dem Knorpelgewebe auf die Zellen sowie zur Aufklärung des Mechanismus, der den Zellen eine aktive Anpassung der PCC ermöglicht Im Rahmen dieser Arbeit wurden daher neue Methoden zur Visualisierung der PCC etabliert, die eine dreidimensionale Darstellung, sowie die mikromechanische Charakterisierung der PCC lebender Zellen ermöglichen. Diese Methoden erlaubten die Untersuchung der dynamischen Anpassung der PCC bei Zellteilung, Motilität und Phagozytose. Die mesoskopische Struktur der PCC konnte von den erhaltenen Messdaten abgeleitet und durch entsprechende Modellsysteme aus endständig angebundenen HA Molekülen unterstützt werden. Darüber hinaus konnte das Wechselspiel von PCC und ECM mit Hilfe von Adhäsionsstudien auf homogenen sowie nanostrukturierten Oberflächen, welche die ECM-Wechselwirkungen kontrollieren, untersucht werden

Computational Approaches to Simulation and Analysis of Large Conformational Transitions in Proteins

abstract: In a typical living cell, millions to billions of proteins—nanomachines that fluctuate and cycle among many conformational states—convert available free energy into mechanochemical work. A fundamental goal of biophysics is to ascertain how 3D protein structures encode specific functions, such as catalyzing chemical reactions or transporting nutrients into a cell. Protein dynamics span femtosecond timescales (i.e., covalent bond oscillations) to large conformational transition timescales in, and beyond, the millisecond regime (e.g., glucose transport across a phospholipid bilayer). Actual transition events are fast but rare, occurring orders of magnitude faster than typical metastable equilibrium waiting times. Equilibrium molecular dynamics (EqMD) can capture atomistic detail and solute-solvent interactions, but even microseconds of sampling attainable nowadays still falls orders of magnitude short of transition timescales, especially for large systems, rendering observations of such "rare events" difficult or effectively impossible. Advanced path-sampling methods exploit reduced physical models or biasing to produce plausible transitions while balancing accuracy and efficiency, but quantifying their accuracy relative to other numerical and experimental data has been challenging. Indeed, new horizons in elucidating protein function necessitate that present methodologies be revised to more seamlessly and quantitatively integrate a spectrum of methods, both numerical and experimental. In this dissertation, experimental and computational methods are put into perspective using the enzyme adenylate kinase (AdK) as an illustrative example. We introduce Path Similarity Analysis (PSA)—an integrative computational framework developed to quantify transition path similarity. PSA not only reliably distinguished AdK transitions by the originating method, but also traced pathway differences between two methods back to charge-charge interactions (neglected by the stereochemical model, but not the all-atom force field) in several conserved salt bridges. Cryo-electron microscopy maps of the transporter Bor1p are directly incorporated into EqMD simulations using MD flexible fitting to produce viable structural models and infer a plausible transport mechanism. Conforming to the theme of integration, a short compendium of an exploratory project—developing a hybrid atomistic-continuum method—is presented, including initial results and a novel fluctuating hydrodynamics model and corresponding numerical code.Dissertation/ThesisDoctoral Dissertation Physics 201