MODULATION OF PROTEIN DYNAMICS BY LIGAND BINDING AND SOLVENT COMPOSITION

Many proteins undergo conformational switching in order to perform their cellular functions. A multitude of factors may shift the energy landscape and alter protein dynamics with varying effects on the conformations they explore. We apply atomistic molecular dynamics simulations to a variety of biomolecular systems in order to investigate how factors such as pressure, the chemical environment, and ligand binding at distant binding pockets affect the structure and dynamics of these protein systems. Further, we examine how such changes should be characterized. We first investigate how pressure and solvent modulate ligand access to the active site of a bacterial lipase by probing the dynamics in a variety of pressures and DMSO-water solvent mixtures. By measuring the gorge leading to the binding pocket we find small amounts of DMSO and high atmospheric pressure optimize the ability of lipids to reach the catalytic interior. Next, we examine the allosteric mechanism behind cooperative and anti-cooperative binding of nuclear hormone receptor RXR and two of its binding partners (TR and CAR). We detail why ligands of the RXR:TR (9c and t3) complex bind anti-cooperatively while ligands of RXR:CAR (9c and tcp) bind cooperatively. Finally, we describe how an intrinsically disordered protein, α-synuclein, alters its conformational dynamics in a pH-dependent manner increasing the likelihood of pathogenic aggregation and neurodegenerative disease at low pH. In each case, we apply contact analysis to uncover the collective motions underlying conformational change triggered by environmental factors or ligand binding

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book

Probing Local Atomic Environments to Model RNA Energetics and Structure

Ribonucleic acids (RNA) are critical components of living systems. Understanding RNA structure and its interaction with other molecules is an essential step in understanding RNA-driven processes within the cell. Experimental techniques like X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and chemical probing methods have provided insights into RNA structures on the atomic scale. To effectively exploit experimental data and characterize features of an RNA structure, quantitative descriptors of local atomic environments are required. Here, I investigated different ways to describe RNA local atomic environments. First, I investigated the solvent-accessible surface area (SASA) as a probe of RNA local atomic environment. SASA contains information on the level of exposure of an RNA atom to solvents and, in some cases, is highly correlated to reactivity profiles derived from chemical probing experiments. Using Bayesian/maximum entropy (BME), I was able to reweight RNA structure models based on the agreement between SASA and chemical reactivities. Next, I developed a numerical descriptor (the atomic fingerprint), that is capable of discriminating different atomic environments. Using atomic fingerprints as features enable the prediction of RNA structure and structure-related properties. Two detailed examples are discussed. Firstly, a classification model was developed to predict Mg$^{2+}$ ion binding sites. Results indicate that the model could predict Mg$^{2+}$ binding sites with reasonable accuracy, and it appears to outperform existing methods. Secondly, a set of models were developed to identify cavities in RNA that are likely to accommodate small-molecule ligands. The models were also used to identify bound-like conformations from an ensemble of RNA structures. The frameworks presented here provide paths to connect the local atomic environment to RNA structure, and I envision they will provide opportunities to develop novel RNA modeling tools.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163135/1/jingrux_1.pd

A quantum crystallographic approach to study properties of molecules in crystals

In this dissertation, the behaviour of atoms, bonds, functional groups and molecules in vacuo but especially also in the crystal is studied using quantum crystallographic methods. The goal is to deepen the understanding of the properties of these building blocks as well as of the interactions among them, because good comprehension of the microscopic units and their interplay also enables us to explain the macroscopic properties of crystals. The first part (chapters 1-3) and second part (chapter 4) of this dissertation contain theoretical introductions about quantum crystallography. On the one hand, this expression contains the termquantum referring to quantumchemistry. Therefore, the very first chapter gives a brief overview about this field. The second chapter addresses different options to partition quantum chemical entities, such as the electron density or the bonding energy, into their components. On the other hand, quantumcrystallography consists obviously of the crystallographic part and chapter 3 covers these aspects focusing predominantly on X-ray diffraction. A more detailed introduction to quantum crystallography itself is presented in the second part (chapter 4). The third part (chapters 5-9) starts with an overview of the goals of this work followed by the results organized in four chapters. The goal is to deepen the understanding of properties of crystals by theoretically analysing their building block. It is for example studied how electrons and orbitals rearrange due to the electric field in a crystal or how high pressure leads to the formation of new bonds. Ultimately, these findings shall help to rationally design materials with desired properties such as high refractive index or semiconductivity.Mithilfe quantenkristallografischer Methoden werden Atome, Bindungen, funktionellen Gruppen und Moleküle in vacuo aber vor allem auch in Kristallen untersucht. Das Ziel ist es die Eigenschaften dieser Bestandteile zu verstehen und wie sie miteinander interagieren. Das Verständnis der Verhaltensweise der einzelnen Bausteine sowie deren Zusammenspiel auf mikroskopischer Ebene kann auch die makroskopischen Eigenschaften von Kristallen erklären. Der erste Teil dieser Doktorarbeit (Kapitel 1-3) beinhaltet eine theoretische Einleitung in die verschiedenen Bereiche der Quantenkristallografie. Wie der Name Quantenkristallografie besagt, besteht diese zum einen aus dem quantenchemischen Teil, weswegen das erste Kapitel eine kurze Einführung in die Quantenchemie gibt. Das zweite Kapitel widmet sich den verschiedenen Möglichkeiten quantenchemische Grössen wie zum Beispiel die Elektronendichte oder Bindungsenergien in Einzelteile zu zerlegen. Zum anderen trägt der kristallografische Teil zur Quantenkristallografie bei. Kapitel drei besteht daher aus einem kurzen Überblick über die Kristallografie mit Fokus auf der Röntgenbeugung. Anschliessend folgt im zweiten Teil (Kapitel 4) eine ausführlichere Einleitung in die Quantenkristallografie selbst. Der dritte Teil (Kapitel 5-9) beginnt mit einer kurzen Übersicht über die Ziele dieser Arbeit worauf die Resultate, gegliedert in vier verschiedene Kapitel, folgen. Das Ziel dieser Arbeit ist es die Eigenschaften von Kristallen besser zu verstehen, indem man ihre Einzelteile theoretisch analysiert und mit verschiedenen Methoden rationalisiert. Beispielsweise wird untersucht wie sich Elektronen und Orbitale aufgrund des elektrischen Feldes in Kristallen neu anordnen oder wie unter hohem Druck Bindungen neu geformt werden. Schlussendlich können all diese Erkenntnisse helfen, Materialien mit spezifischen gewünschten Eigenschaften herzustellen.Les atomes, les liaisons entre eux, les groupes fonctionnels et les molécules sont examinés en utilisant des méthodes de la cristallographie quantique. Le but est de comprendre les propriétés de ces composants et comment ils interagissent in vacuo mais surtout aussi dans les cristaux. En comprenant leurs caractéristiques et interactions au niveau microscopique, on peut aussi rationaliser les propriétés macroscopiques des cristaux. La première partie (chapitres 1-3) de cette thèse de doctorat contient une introduction brève à la cristallographie quantique. Comme le noml’indique, ce domaine de recherche est composé de la chimie quantique et la cristallographie. Pour cette raison le premier chapitre donne une introduction à la chimie quantique. Le deuxième chapitre présente quelques méthodes de décomposition des quantités de la chimie quantique comme la densité électronique ou l’énergie de liaison. Le troisième chapitre couvre la partie cristallographique. Ensuite dans la deuxième partie (chapitre 4) une introduction plus détaillée sur la cristallographie quantique elle-même est donnée. La troisième partie (chapitres 5-9) commence par un aperçu des objectives de cette dissertation suivis des résultats structurés en quatre chapitres. Le but est de comprendre les propriétés des cristaux en analysant leurs building blocks avec différentes méthodes théoriques. Il était par example examiné comment les électrons et les orbitales se réorganisent dans un cristal à cause du champ électrique ou comment des nouvelles liaisons sont formées sous pression. Finalement on peut utiliser ces conclusions pour modeler des matériaux avec des propriétés désirées

In silico substrate binding profiling for SARS-COV-2 main protease (mpro) using hexapeptide substrates

COVID-19, as a disease resulting from SARS-CoV-2 infection, and a pandemic has had a devastating effect on the world. There are limited effective measures that control the spread and treatment of COVID-19 illness. The homodimeric cysteine main protease (Mpro) is crucial to the life cycle of the virus, as it cleaves the large polyproteins 1a and 1ab into matured, functional non-structural proteins. The Mpro exhibits high degrees of conservation in sequence, structure and specificity across coronavirus species, making it an ideal drug target. The Mpro substrate-binding profiles remain, despite the resolution of its recognition sequence and cleavage points (Leu-Gln↓(Ser/Ala/Gly)). In this study, a series of hexapeptide sequences containing the appropriate recognition sequence and cleavage points were generated and screened against the Mpro to study these binding profiles, and to further be the basis for efficiency-driven drug design. A multi-conformer hexapeptide substrate library comprising optimised 81000 models of 810 unique sequences was generated using RDKit within the context of python. Terminal capping with ACE and NMe was effected using SMILES and SMARTS matching. Multiple hexapeptides were complexed with chain B of crystallographic Mpro (PDS ID: 6XHM), following the validation of chain B for this purpose using AutoDock Vina at high levels of exhaustiveness (480). The resulting Vina scores ranged between -8.7 and -7.0 kcal.mol-1, and the reproducibility of best poses was validated through redocking. Ligand efficiency indices were calculated to identify substrate residues with high binding efficiency at their respective positions, revealing Val (P3), Ala (P1′); and Gly and Ala (P2′ and P3′) as leading efficient binders. Binding efficiencies were lowered by molecular weight. Substrate recognition was assessed by mapping of binding subsites, and Mpro specificity was evaluated through the resolution of intermolecular interaction at the binding interface. Molecular dynamics simulations for 20 ns were performed to assess the stability and behaviour of 132 Mpro systems complexed with KLQ*** substrates. Principal component analysis (PCA), was performed to assess II protein motions and conformational changes during the simulations. A strategy was formulated to classify and evaluate relations in the Mpro PCA motions, revealing four main clades of similarity. Similarity within a clade (Group 2) and dissimilarity between clades were confirmed. Trajectory visualisation revealed complex stability, substrate unbinding and dimer dissociation for various Mpro systems.Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 202

Essential dynamics of proteins using geometrical simulations and subspace analysis

Essential dynamics is the application of principal component analysis to a dynamic trajectory derived from a simulation protocol in order to extract biologically relevant information contained in the high dimensional data. In this work, we apply the methodology of essential dynamics to protein trajectories derived from geometrical simulations, which are based on the perturbation of geometrical constraints inherent in a protein. Specifically, we show that the geometrical simulation model is highly efficient for the determination of native state dynamics. Furthermore, by the application of subspace analysis to the essential subspaces of multiple sets of proteins that were simulated under multiple modeling paradigms, we show that the geometrical modeling paradigm is internally consistent and provides results that are qualitatively and quantitatively similar to results obtained from the more commonly employed methods of elastic network models and molecular dynamics. The geometrical paradigm is therefore established as a viable alternative or co-model for the investigation of native state protein dynamics with application to both small, single domain proteins as well as large, multi domain systems

Spectral approaches for identifying kinetic features in molecular dynamics simulations of globular proteins

Proteins live in an environment of random thermal vibrations yet they convert this constant disorder into selective biological function. As data acquisition methods for resolving protein motions improve more of the randomness is also captured; there is thus a parallel need for analysis methods that filter out the disorder and clarify functionally-relevant protein behavior. Few behaviors are more relevant than folding in the first place, and this thesis opens by addressing which conformational states are kinetically relevant for promoting or inhibiting attainment of the folded native state. Our modeling approach discretizes simulation data into a network of nodes and edges representing, respectively, different protein conformations and observed conformational transitions. A perturbative strategy is then invoked to quantify the importance of each node, i.e. conformational substate, with regard to theoretical folding rates. On a test of 10 proteins this framework identifies unique ‘kinetic traps’ and ‘facilitator substates’ that sometimes evade detection with traditional RMSD-based analysis. We then apply spectral approaches and auto-regressive models to (1) address efficiency concerns for more general networks and (2) mimic protein flexibility with compact linear models