Globally Optimal Catalysts: Computerbasierte Optimierung von abstrakten katalytischen Einbettungen für beliebige chemische Reaktionen

In the context of inverse design of molecules with desired optimal properties, the long-term goal of this Thesis is to develop a general framework which tackles the design of molecular systems for an optimal catalytic effect onto arbitrary chemical reactions. For any given reaction, an arrangement of an additional molecular framework around this reaction center is sought such that the energetic reaction barrier is lowered as much as possible. As necessary abstraction layer, the so-called globally optimal catalyst (GOCAT) model is introduced, and, furthermore, evolutionary algorithms (EAs) are harnessed as implemented in our global optimization suite for chemical problems, ogolem, which was highly extended to allow for these catalysis optimizations. Starting with a maximally reductionistic approach for studying the non-bonding interactions, electrostatic GOCATs are introduced that consist of arbitrary numbers, distributions and strengths of partial point charges around reacting molecules, mostly surrounding these on a common exposed surface. In the end, two reactions are studied in detail within the general topic of electrostatic catalysis. Some of the initially present model approximations are already sufficiently lifted, still-existing ones are critically assessed and further future extensions to the framework are discussed. Moreover, many method development matters are addressed: They range from optimal shared-memory parallelization, exemplified for global parameter optimization of the reactive force field, ReaxFF, via diversity control parameters for the EAs, applied to a cluster structure optimization problem, to EA operator benchmarks and optimizations of abstract electrostatics.Im Kontext von inversem Design von Molekülen mit optimalen Eigenschaften versucht die vorliegende Arbeit als Langzeitziel eine passende Plattform zu entwickeln, welche das generelle Design molekularer Systeme für einen optimalen Katalyseeffekt auf beliebige chemische Reaktionen projektiert. Für eine gegeben Reaktion soll eine hinzukommende chemische Umgebung komponiert werden, welche die Reaktionsenergiebarriere so weit wie möglich vermindert. Als notwendige Abstraktionsschicht wird das sogenannte Modell des globally optimal catalyst (GOCAT) eingeführt und außerdem kommen Evolutionäre Algorithmen (EAs) zur Anwendung, wie sie bereits in unserem Programmpaket zur Lösung allgemeiner globaler Optimierungsprobleme der Chemie, ogolem, bereitgestellt werden, welches jedoch deutlich für diese Katalyseoptimierungen ergänzt wurde. Angefangen in einem maximal-reduktionistischen Ansatz werden elektrostatische GOCATs erarbeitet, die aus einer beliebigen Anzahl, Verteilung und Stärke von Partialladungen bestehen und rund um die reagierenden Moleküle drapiert werden, meist auf einer gemeinsamen exponierten Oberfläche. Insgesamt werden zwei Reaktionen detailliert untersucht im generellen Kontext von elektrostatischer Katalyse. Einige eingangs vorhandene Modellannahmen werden bereits systematisch verbessert, noch vorhandene kritisch beleuchtet und künftige Erweiterungen auseinandergesetzt. Weiterhin werden unterschiedliche Methodenentwicklungsaspekte angesprochen: Diese reichen von verbesserter Parallelisierung in Mehrprozessorarchitekturen, beispielhaft gezeigt anhand einer globalen Parameteroptimierung des reaktiven Kraftfeldes ReaxFF, über Diversitätskontrollparameter des EAs, illustriert mittels eines Clusterstrukturoptimierungsproblems, bis hin zu EA-Operator-Testevaluationen und allgemeinen abstrakten Elektrostatikoptimierungen

Structure and Dynamics of Viral Substrate Recognition and Drug Resistance: A Dissertation

Drug resistance is a major problem in quickly evolving diseases, including the human immunodeficiency (HIV) and hepatitis C viral (HCV) infections. The viral proteases (HIV protease and HCV NS3/4A protease) are primary drug targets. At the molecular level, drug resistance reflects a subtle change in the balance of molecular recognition; the drug resistant protease variants are no longer effectively inhibited by the competitive drug molecules but can process the natural substrates with enough efficiency for viral survival. Therefore, the inhibitors that better mimic the natural substrate binding features should result in more robust inhibitors with flat drug resistance profiles. The native substrates adopt a consensus volume when bound to the enzyme, the substrate envelope. The most severe resistance mutations occur at protease residues that are contacted by the inhibitors outside the substrate envelope. To guide the design of robust inhibitors, we investigate the shared and varied properties of substrates with the protein dynamics taken into account to define the dynamic substrate envelope of both viral proteases. The NS3/4A dynamic substrate envelope is compared with inhibitors to detect the structural and dynamic basis of resistance mutation patterns. Comparative analyses of substrates and inhibitors result in a solid list of structural and dynamic features of substrates that are not shared by inhibitors. This study can help guiding the development of novel inhibitors by paying attention to the subtle differences between the binding properties of substrates versus inhibitors

Development and Application of Computational Biology tools for Biomedicine

Biomolecular simulation can be considered as a virtual microscope for molecular biology, allowing to gain insights into the sub-cellular mechanisms of biological relevance at spatial and temporal scales that are difficult to observe experimentally. It provides a powerful tool to link the laws of physics with the complex behavior of biological systems. Dramatic recent advancements in achievable simulation speed and the underlying physical models will increasingly lead to molecular views of large systems. These improvements may largely affect biological sciences. In this thesis, I have applied computational molecular biology approaches to different biological systems using state of the art structural bioinformatics and computational biophysics tools (Chapter 3). My principal focus was on the computational design of molecular imprinted polymers (MIPs), which have recently attracted significant attention as cost effective substitutes for natural antibodies and receptors in chromatography, sensors and assays. I have used molecular modelling in the optimization of polymer compositions to make high affinity synthetic receptors based on Molecular Imprinting. In particular, I developed a new free of charge protocol that can be performed within just few hours that outputs a list of candidate monomers which are capable of strong binding interactions with the template. Furthermore, I have produced a new computational method for the calculation of the ideal monomer: template stoichiometric ratio to be used in the lab for the MIPs synthesis. These protocols have been implemented as a webserver that is available at http://mirate.di.univr.it/. In parallel, I have also investigated the modelling of much more complex MIPs systems by the introduction of some factors e.g. solvent and cross-linker molecules that are also essential in the polymerisation process. A novel algorithm, which mimics a radical polymerization mechanism, has been written for application in the rational design of MIPs (Chapter 4). Moreover, I have been involved in the field of computational molecular biomedicine. Indeed, in Chapters 5 and 6 I describe the work done in collaboration with two labs at the Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona. In Chapter 5, starting from unpublished experimental data I have computationally characterized the interaction of ACOT8 with HIV-1 Nef accessory protein. I have performed a detailed structural and functional characterization of these two proteins in order to infer any possible functional details about their interactions. The bioinformatics predictions were then confirmed by wet-lab experiments. I have also carried out a detailed structural and functional characterization of two pathogenic mutations of AGT-Mi (Chapter 6). In particular, I have used classical molecular dynamics (MD) simulations to study the possible interference with the dimerization process of AGT-Mi exerted by I244T-Mi and F152I-Mi mutants. Those variants are associated with Primary Hyperoxaluria type 1 disease. In Chapter 7, I present the coarse-grained MD simulations of Membrane/Human ileal bile-acid-binding protein Interactions. This study was carried out in collaboration with the NMR group at the University of Verona and it is a part of an extensive research aimed at better understanding of the main biomolecular interactions in crowded cellular environments. MD simulations results were in agreement with experimental findings

Molecular simulation studies of the prion protein: from disease-linked variants to ligand binding

Prion diseases or transmissible spongiform encephalopathies (TSEs) are fatal neu-rodegenerative disorders (198). The crucial event in the development of these diseases is the conformational change of a membrane bound protein, the cellular PrPC in Figure 3.1, into a disease associated, bril-forming isoform (199). Despite their rare incidence, TSEs have captured very large attention from the scienti c community due to the unorthodox mechanism by which prion diseases are transmitted..

Computational investigation of oxygen reduction and proton pumping in cbb3-type Cytochrome c Oxidases

Heme-copper oxidases terminate the respiratory chain in many eukaryotes and prokaryotes as the final electron acceptors. They catalyze the reduction of molecular oxygen to water, and conserve the free-energy by proton pumping across the inner mitochondrial membrane or plasma membrane of bacteria. This leads to the generation of an electrochemical gradient across the membrane, which is utilized in the synthesis of ATP. The catalytic mechanism of oxidase is a complex coupling of electrons and protons, which has been studied with the help of numerous biophysical and biochemical methods. The superfamily of oxidases is classified into three different subfamilies; A-, B- and C-type. The A- and B-type oxidases have been studied in great depth, whereas relatively less is known about the molecular mechanism of distinct C-type (or cbb3-type) oxidases. The latter enzymes, which are known to possess unusually high oxygen affinity relative to the former class of enzymes, also share little sequence or structural similarity with the A- and B-type oxidases. In the work presented in this thesis, C-type oxidases have been studied using a variety of computational procedures, such as homology modeling, molecular dynamics simulations, density functional theory calculations and continuum electrostatics. Homology models of the C-type oxidase correctly predicts the side-chain orientation of the cross-linked tyrosine and a proton-channel. The active-site region is also modelled with high accuracy in the models, which are subsequently used in the DFT calculations. With the help of these calculations it is proposed that the different orientation of the cross-linked tyrosine, and a strong hydrogen bond in the proximal side of the high-spin heme are responsible for the higher apparent oxygen affinity and a more rhombic EPR signal in the C-type oxidases. Furthermore, the pKa profiles of two amino acid residues, which are located close to the active-site, suggest a strong electron-proton coupling and a unique proton pumping route. Molecular dynamics simulations on the two-subunit C-type oxidase allowed for the first time to observe redox state dependent water-chain formation in the protein interior, which can be utilized for the redox coupled proton transfer.Soluhengityksessä hapen pelkistystä vedeksi katalysoi niin kutsutut hemi-kuparioksidaasientsyymit, joita esiintyy aitotumallisten solujen mitokondrioiden sisäkalvossa ja bakteerien solukalvossa. Nämä entsyymit pystyvät käyttämään hapen pelkistymisreaktiosta vapautuvan energian vetyionien (protonien) kuljetukseen kalvon ylitse, mikä johtaa elektrokemiallisen protonigradientin syntymiseen. Tätä gradienttia käytetään sittemmin hyväksi adenosiinitrifosfaatin (ATP:n) muodostamisessa adenosiinidifosfaatista (ADP:sta) ja epäorgaanisesta fosfaatista (Pi:sta). ATP:n hydrolyysi takaisin ADP:ksi ja Pi:ksi taas on yleinen energianlähde lähes kaikissa solun energiaa vaativissa reaktioissa. Hemi-kuparioksidaasit muodostavat suuren entsyymiperheen, joka voidaan jakaa kolmeksi alaryhmäksi, A, B ja C. Näistä C-ryhmän entsyymit eli cbb3-tyypin sytokromioksidaasit, joita esiintyy muun muassa joissakin patogeenisissa bakteereissa, ovat vähiten tutkittuja. Näillä entsyymeillä on erityisen korkea affiniteetti hapelle ja ne ovat rakenteellisestikin kauimpina esim. mitokondrioiden A-tyyppisistä oksidaaseista. Tässä väitöstyössä C-tyypin oksidaaseja tutkittiin erilaisia laskennallisia menetelmiä, kuten homologiamallinnusta, molekyylidynamiikkaa, kvanttimekaanista tiheysfunktioteoriaa (DFT), sekä sähköstaattisia laskelmia hyväksikäyttäen. C-tyypin oksidaasien homologiamallit ennustivat oikein keskeisiä yksityiskohtia tämän ryhmän entsyymien rakenteesta, kuten aktiivisen keskuksen tyrosiinitähteen orientaation ja protoninkuljetuskanavan sijainnin. DFT-laskujen perusteella oli mahdollista selittää korkea happiaffiniteetti sekä hemi b3:n rombinen EPR-signaali johtuviksi tyrosiinitähteen sivuketjun orientaatiosta ja hemi b3:n proksimaalisen histidiiniligandin vetysidoksesta glutamaattitähteeseen. Molekyylidynamiikkasimulaatioissa havaittiin entsyymin hapetus-pelkistystilasta riippuvaista vesimolekyyliketjujen muodostumista proteiinin sisällä, mikä voi osoittautua tärkeäksi entsyymin protoninkuljetuksen mekanismin ymmärtämiselle

Molecular Dynamics for Synthetic Biology

Synthetic biology is the field concerned with the design, engineering, and construction of organisms and biomolecules. Biomolecules such as proteins are nature's nano-bots, and provide both a shortcut to the construction of nano-scale tools and insight into the design of abiotic nanotechnology. A fundamental technique in protein engineering is protein fusion, the concatenation of two proteins so that they form domains of a new protein. The resulting fusion protein generally retains both functions, especially when a linker sequence is introduced between the two domains to allow them to fold independently. Fusion proteins can have features absent from all of their components; for example, FRET biosensors are fusion proteins of two fluorescent proteins with a binding domain. When the binding domain forms a complex with a ligand, its dynamics translate the concentration of the ligand to the ratio of fluorescence intensities via FRET. Despite these successes, protein engineering remains laborious and expensive. Computer modelling has the potential to improve the situation by enabling some design work to occur virtually. Synthetic biologists commonly use fast, heuristic structure prediction tools like ROSETTA, I-TASSER and FoldX, despite their inaccuracy. By contrast, molecular dynamics with modern force fields has proven itself accurate, but sampling sufficiently to solve problems accurately and quickly enough to be relevant to experimenters remains challenging. In this thesis, I introduce molecular dynamics to a structural biology audience, and discuss the challenges and theory behind the technique. With this knowledge, I introduce synthetic biology through a review of fluorescent sensors. I then develop a simple computational tool, Rangefinder, for the design of one variety of these sensors, and demonstrate its ability to predict sensor performance experimentally. I demonstrate the importance of the choice of linker with yet another sensor whose performance depends critically thereon. In chapter 6, I investigate the structure of a conserved, repeating linker sequence connecting two domains of the malaria circumsporozoite protein. Finally, I develop a multi-scale enhanced sampling molecular dynamics approach to predicting the structure and dynamics of fusion proteins. It is my hope that this work contributes to the structural biology community's understanding of molecular dynamics and inspires new techniques developed for protein engineering

Studying protein-ligand interactions using a Monte Carlo procedure

[eng] Biomolecular simulations have been widely used in the study of protein-ligand interactions; comprehending the mechanisms involved in the prediction of binding affinities would have a significant repercussion in the pharmaceutical industry. Notwithstanding the intrinsic difficulty of sampling the phase space, hardware and methodological developments make computer simulations a promising candidate in the resolution of biophysically relevant problems. In this context, the objective of the thesis is the development of a protocol that permits studying protein-ligand interactions, in view to be applied in drug discovery pipelines. The author contributed to the rewriting PELE, our Monte Carlo sampling procedure, using good practices of software development. These involved testing, improving the readability, modularity, encapsulation, maintenance and version control, just to name a few. Importantly, the recoding resulted in a competitive cutting-edge software that is able to integrate new algorithms and platforms, such as new force fields or a graphical user interface, while being reliable and efficient. The rest of the thesis is built upon this development. At this point, we established a protocol of unbiased all-atom simulations using PELE, often combined with Markov (state) Models (MSM) to characterize the energy landscape exploration. In the thesis, we have shown that PELE is a suitable tool to map complex mechanisms in an accurate and efficient manner. For example, we successfully conducted studies of ligand migration in prolyl oligopeptidases and nuclear hormone receptors (NHRs). Using PELE, we could map the ligand migration and binding pathway in such complex systems in less than 48 hours. On the other hand, with this technique we often run batches of 100s of simulations to reduce the wall-clock time. MSM is a useful technique to join these independent simulations in a unique statistical model, as individual trajectories only need to characterize the energy landscape locally, and the global characterization can be extracted from the model. We successfully applied the combination of these two methodologies to quantify binding mechanisms and estimate the binding free energy in systems involving NHRs and tyorsinases. However, this technique represents a significant computational effort. To reduce the computational load, we developed a new methodology to overcome the sampling limitations caused by the ruggedness of the energy landscape. In particular, we used a procedure of iterative simulations with adaptive spawning points based on reinforcement learning ideas. This permits sampling binding mechanisms at a fraction of the cost, and represents a speedup of an order of magnitude in complex systems. Importantly, we show in a proof-of-concept that it can be used to estimate absolute binding free energies. Overall, we hope that the methodologies presented herein help streamline the drug design process.[spa] Las simulaciones biomoleculares se han usado ampliamente en el estudio de interacciones proteína-ligando. Comprender los mecanismos involucrados en la predicción de afinidades de unión tiene una gran repercusión en la industria farmacéutica. A pesar de las dificultades intrínsecas en el muestreo del espacio de fases, mejoras de hardware y metodológicas hacen de las simulaciones por ordenador un candidato prometedor en la resolución de problemas biofísicos con alta relevancia. En este contexto, el objetivo de la tesis es el desarrollo de un protocolo que introduce un estudio más eficiente de las interacciones proteína-ligando, con vistas a diseminar PELE, un procedimiento de muestreo de Monte Carlo, en el diseño de fármacos. Nuestro principal foco ha sido sobrepasar las limitaciones de muestreo causadas por la rugosidad del paisaje de energías, aplicando nuestro protocolo para hacer analsis detallados a nivel atomístico en receptores nucleares de hormonas, receptores acoplados a proteínas G, tirosinasas y prolil oligopeptidasas, en colaboración con una compañía farmacéutica y de varios laboratorios experimentales. Con todo ello, esperamos que las metodologías presentadas en esta tesis ayuden a mejorar el diseño de fármacos