Supporting the identification of novel fragment-based positive allosteric modulators using a supervised molecular dynamics approach: A retrospective analysis considering the human A2A adenosine receptor as a key example

Structure-driven fragment-based (SDFB) approaches have provided efficient methods for the identification of novel drug candidates. This strategy has been largely applied in discovering several pharmacological ligand classes, including enzyme inhibitors, receptor antagonists and, more recently, also allosteric (positive and negative) modulators. Recently, Siegal and collaborators reported an interesting study, performed on a detergent-solubilized StaR adenosine A2A receptor, describing the existence of both fragment-like negative allosteric modulators (NAMs), and fragment-like positive allosteric modulators (PAMs). From this retrospective study, our results suggest that Supervised Molecular Dynamics (SuMD) simulations can support, on a reasonable time scale, the identification of fragment-like PAMs following their receptor recognition pathways and characterizing the possible allosteric binding sites

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Manifold Learning in Atomistic Simulations: A Conceptual Review

Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Such practice is needed in atomistic simulations of complex systems where even thousands of degrees of freedom are sampled. An abundance of such data makes gaining insight into a specific physical problem strenuous. Our primary aim in this review is to focus on unsupervised machine learning methods that can be used on simulation data to find a low-dimensional manifold providing a collective and informative characterization of the studied process. Such manifolds can be used for sampling long-timescale processes and free-energy estimation. We describe methods that can work on datasets from standard and enhanced sampling atomistic simulations. Unlike recent reviews on manifold learning for atomistic simulations, we consider only methods that construct low-dimensional manifolds based on Markov transition probabilities between high-dimensional samples. We discuss these techniques from a conceptual point of view, including their underlying theoretical frameworks and possible limitations

Modeling biomolecules: interactions, forces and free energies

La biología ha sido tradicionalmente una ciencia cualitativa. El principal problema que presenta es que trata con sistemas muy complejos, mucho más que las moléculas de las que se ocupa la química, o que muchos sistemas físicos. Sin embargo, en los últimos años, hemos sido testigos de un desarrollo enorme hacia planteamientos cuantitativos para resolver problemas biológicos, impulsado principalmente por el desarrollo de diversas técnicas avanzadas en biofísica, o por la emergencia de las herramientas computacionales. En particular, en biofísica computacional, dado un determinado problema a estudiar, la estrategia es proponer un modelo que describa el comportamiento de nuestro sistema y realizar simulaciones numéricas sobre este modelo. Este planteamiento presenta una dificultad principal que es la elección de la escala a la cual realizamos nuestro modelo. Es necesario llegar a un compromiso entre el nivel de detalle y la capacidad computacional de que disponemos. Así, modelos muy detallados son capaces de proporcionar información de gran resolución, sin embargo sólo para sistemas moleculares de tamaño limitado, con propiedades que se manifiesten a escalas temporales cortas. Si necesitamos tratar con sistemas de mayor tamaño, o nos interesan propiedades que se manifiestan en escalas temporales mayores, es necesario identificar cuáles son los grados de libertad relevantes para nuestro sistema y despreciar el resto. Aparte de este problema, el siguiente reto que se nos plantea es transformar todos los datos numéricos producidos en información relevante que pueda responder de manera objetiva a las preguntas que nos planteamos. Para ello, debemos disponer de métodos de análisis lo bastante robustos como para transformar la información en bruto producida en nuestras simulaciones, en conocimiento directo de una manera no sesgada. La presente Tesis Doctoral se enmarca en este ámbito, ya que estudiaremos tres problemas biológicos diferentes haciendo énfasis en la fase de modelización de nuestro sistema, así como en el empleo de técnicas de análisis avanzadas para comprenderlo. En la primera parte, nos centramos en el análisis de la dinámica de proteínas, enfatizando las distintas descripciones que pueden usarse para comprender su paisaje de energía libre. Para ello escogemos un sistema relativamente simple, una proteína modelo coarse-grained a la cual aplicamos una fuerza constante para promover su desplegamiento. Realizaremos simulaciones numéricas en este sistema y nos plantearemos cuál es la mejor manera de obtener una descripción fiel de su espacio configuracional así como de su mecanismo de desplegamiento. Para ello emplearemos dos métodos distintos. Primero, proyectaremos su paisaje de energía libre –de gran dimensión- sobre distintos parámetros de orden, obteniendo representaciones unidimensionales. Éstas proporcionarán una visión globalmente correcta del sistema, sin embargo fallarán en la descripción adecuada de su mecanismo de desnaturalización. Por otra parte, emplearemos modelos de Markov para representar el paisaje de energía libre. Estos revelarán un espacio configuracional más complejo que el previsto anteriormente, con varios intermediarios que tendrán un papel relevante, especialmente para comprender el mecanismo de desplegamiento. En la segunda parte de la Tesis Doctoral, mostramos el estudio de un modelo de DNA al nivel del par de bases, el modelo de Peyrard-Bishop-Dauxois. En particular, extenderemos este modelo para introducir la interacción proteína-DNA. Proponiendo un método de análisis adecuado basado en modelos de Markov, podremos emplear este modelo para analizar secuencias de promotores, relacionando los estados que encontramos en la dinámica del sistema con sitios de unión proteína-DNA. Este modelo lo emplearemos para el análisis de nueve secuencias de promotores de una cianobacteria en particular. Nos centraremos en la identificación del sitio de inicio de la transcripción (TSS), región donde se une la RNA polimerasa para iniciar este proceso. En cada uno de los promotores, gracias al modelo somos capaces de identificar esta región como un estado de relevancia en la dinámica, con tendencia a que la partícula se una, formando una burbuja. Asimismo, gracias al método de análisis, cuantificamos estos estados, proporcionando magnitudes estadísticas que podemos relacionar con el conocimiento biológica acerca de estos promotores. La tercera parte está dedicada a los experimentos de molécula individual. Presentamos una colaboración experimental en la cual analizamos experimentos de disociación mecánica de dos complejos proteína:proteína. Nuestro objetivo es proporcionar una visión adecuada del paisaje de energía libre que gobierna este proceso. Para ello proponemos un método que permite recuperar la barrera de energía libre así como la energía libre de disociación para complejos biológicos. En particular, emplearemos este método para analizar experimentos de espetroscopía de fuerza, permitiendo obtener estas magnitudes y discutirlas en el contexto de la biología del sistema. Asimismo, proponemos un modelo físico para este tipo de experimentos, sobre el cual realizamos simulaciones numéricas que analizamos con el mismo método, con objeto de validarlo y respaldar su empleo

All-scale structural analysis of biomolecules through dynamical graph partitioning

From femtosecond bond vibrations to millisecond domain motions, the dynamics of biomolecules spans a wide range of time and length scales. This hierarchy of overlapping scales links the molecular and biophysical details to key aspects of their functionality. However, the span of scales combined with their intricate coupling rapidly drives atomic simulation methods to their limits, thereby often resulting in the need for coarse-graining techniques which cannot take full account of the biochemical details. To overcome this tradeoff, a graph-theoretical framework inspired by multiscale community detection methods and stochastic processes is here introduced for the analysis of protein and DNA structures. Using biophysical force fields, we propose a general mapping of the 3D atomic coordinates onto an energy-weighted network that includes the physico-chemical details of interatomic bonds and interactions.Making use of a dynamics-based approach for community detection on networks, optimal partitionings of the structure are identified which are biochemically relevant over different scales. The structural organisation of the biomolecule is shown to be recovered bottom-up over the entire range of chemical, biochemical and biologically meaningful scales, directly from the atomic information of the structure, and without any reparameterisation. This methodology is applied and discussed in five proteins and an ensemble of DNA quadruplexes. In each case, multiple conformations associated with different states of the biomolecule or stages of the underlying catalytic reaction are analysed. Experimental observations are shown to be correctly captured, including the functional domains, regions of the protein with coherent dynamics such as rigid clusters, and the spontaneous closure of some enzymes in the absence of substrate. A computational mutational analysis tool is also derived which identifies both known and new residues with a significant impact on ligand binding. In large multimeric structures, the methodology highlights patterns of long range communication taking place between subunits. In the highly dynamic and polymorphic DNA quadruplexes, key structural features for their physical stability and signatures of their unfolding pathway are identified in the static structure.Open Acces

Molecular Dynamics simulations of amyloidogenic proteins. Unfolding, misfolding and aggregation.

Proteins are the main bulding blocks of biological systems. Their structure and function have been extensively studied so far both by experiments (Nuclear Magnetic Resonance, X-ray crystallography, Mass Spectrometry, etc.) and modeling strategies (Molecular Dynamics and Monte Carlo simulations, Density Functional Theory. etc.). In vivo in general and in solution in particular, they mostly adopt different and unique secondary and tertiary configurations, owing to their conformational freedom. The route and mechanism by which a specific shape is formed, i.e. the folding, which is not reversible in many cases, is not fully understood for several protein models, nothwithstanding the fulgurant advances achieved in experimental and in silico techniques in the last decades. Under specific conditions (pH, temperature, concentration, etc.), such three-dimensional arrangement as well as the intra/inter-chains interactions can be lost, and species such as disordered or fibrilar aggregates involved in several known human pathologies may develop. In this thesis we probe the atomistic scale conformational dynamics of two amyloidogenic proteins, transthyretin and \u3b22-microglobulin, using molecular dynamics simulations. We aim at understanding the major factors driving the misfolding and/or (un)folding of the latter specified proteins, which play a precursor and prominent role in neurodegrative deseases. To this end the dynamics and dissociation of wild-type and mutant transthyretin is simulated. In particular the behaviour of a triple mutant (designed by Prof. R. Berni and coworkers to be monomeric) is studied. It comes out that the mutation considerably shifts the tetramer-folded monomer equilibrium towards the monomer, making this triple mutant a useful tool for structural and dynamical studies. The interaction of \u3b22-microglobulin with hydrophobic surfaces is studied by molecular dynamics and the thermodynamics of the process is addressed using end-point free energy calculations. The results rationalize experimental observation reported in the literature. Protein conformational dynamics and thermodynamics are currently experimentally probed by the backbone amide hydrogen exchange experiment (HDX). The Bluu-Tramp experiment developed by prof. Esposito and coworkers allows the measurement of free energy, enthalpy and entropy of exchange in a single experiment. A proper comparison between experimental and simulation data require modeling of the process at atomic detail. Hence, we analyze also this aspect and try to relate the amide hydrogen protection observed in NMR experiments to various microscopic properties of the protein structure computed in the simulations. Using free energy calculations we aim at reproducing also the temperature dependence of the process. Given the predominant role of protein association in most biological functions, we introduce a modeling approach to estimate the entropy loss upon complex formation, a contribution which is almost always neglected in many free energy calculation methodologies due to the high dimensionality of the degrees of freedom, and adequate theoretical methods. The approach is applied to the case proteins considered in this thesis and an exact and approximate estimation of the full rotational-translational entropy are obtained in the context of nearest neighbor-based entropy formulation. Overall, this thesis explores various aspects favouring the formation of misfolded and/or (un)folded protein species, ranging from dissociation of an homotetramer of transthyretin engineered in silico, through the interaction of \u3b22-microglobulin with an hydrophobic surface model, to the backbone amide hydrogen exchange pattern of protection of the latter. Lastly and not the least, the thesis presents a computational methodology to address the roto-translational entropy loss upon complex formation of biomolecules

Prediction of protein allosteric signalling pathways and functional residues through paths of optimised propensity

Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few computational methods are available to effectively predict allosteric sites, identify signalling pathways involved in allostery, or to aid with the design of suitable molecules targeting such sites. Recently, bond-to-bond propensity analysis has been shown successful at identifying allosteric sites for a large and diverse group of proteins from knowledge of the orthosteric sites and its ligands alone by using network analysis applied to energy-weighted atomistic protein graphs. To address the identification of signalling pathways, we propose here a method to compute and score paths of optimised propensity that link the orthosteric site with the identified allosteric sites, and identifies crucial residues that contribute to those paths. We showcase the approach with three well-studied allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design. By using the computed path scores, we were also able to differentiate the activity of different allosteric modulators