455 research outputs found

    Markov field models of molecular kinetics

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    Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

    Efficient algorithms for simulation and analysis of many-body systems

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    This thesis introduces methods to efficiently generate and analyze time series data of many-body systems. While we have a strong focus on biomolecular processes, the presented methods can also be applied more generally. Due to limitations of microscope resolution in both space and time, biomolecular processes are especially hard to observe experimentally. Computer models offer an opportunity to work around these limitations. However, as these models are bound by computational effort, careful selection of the model as well as its efficient implementation play a fundamental role in their successful sampling and/or estimation. Especially for high levels of resolution, computer simulations can produce vast amounts of high-dimensional data and in general it is not straightforward to visualize, let alone to identify the relevant features and processes. To this end, we cover tools for projecting time series data onto important processes, finding over time geometrically stable features in observable space, and identifying governing dynamics. We introduce the novel software library deeptime with two main goals: (1) making methods which were developed in different communities (such as molecular dynamics and fluid dynamics) accessible to a broad user base by implementing them in a general-purpose way, and (2) providing an easy to install, extend, and maintain library by employing a high degree of modularity and introducing as few hard dependencies as possible. We demonstrate and compare the capabilities of the provided methods based on numerical examples. Subsequently, the particle-based reaction-diffusion simulation software package ReaDDy2 is introduced. It can simulate dynamics which are more complicated than what is usually analyzed with the methods available in deeptime. It is a significantly more efficient, feature-rich, flexible, and user-friendly version of its predecessor ReaDDy. As such, it enables---at the simulation model's resolution---the possibility to study larger systems and to cover longer timescales. In particular, ReaDDy2 is capable of modeling complex processes featuring particle crowding, space exclusion, association and dissociation events, dynamic formation and dissolution of particle geometries on a mesoscopic scale. The validity of the ReaDDy2 model is asserted by several numerical studies which are compared to analytically obtained results, simulations from other packages, or literature data. Finally, we present reactive SINDy, a method that can detect reaction networks from concentration curves of chemical species. It extends the SINDy method---contained in deeptime---by introducing coupling terms over a system of ordinary differential equations in an ansatz reaction space. As such, it transforms an ordinary linear regression problem to a linear tensor regression. The method employs a sparsity-promoting regularization which leads to especially simple and interpretable models. We show in biologically motivated example systems that the method is indeed capable of detecting the correct underlying reaction dynamics and that the sparsity regularization plays a key role in pruning otherwise spuriously detected reactions

    Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning

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    One of the main challenges in molecular dynamics is overcoming the ‘timescale barrier’: in many realistic molecular systems, biologically important rare transitions occur on timescales that are not accessible to direct numerical simulation, even on the largest or specifically dedicated supercomputers. This article discusses how to circumvent the timescale barrier by a collection of transfer operator-based techniques that have emerged from dynamical systems theory, numerical mathematics and machine learning over the last two decades. We will focus on how transfer operators can be used to approximate the dynamical behaviour on long timescales, review the introduction of this approach into molecular dynamics, and outline the respective theory, as well as the algorithmic development, from the early numerics-based methods, via variational reformulations, to modern data-based techniques utilizing and improving concepts from machine learning. Furthermore, its relation to rare event simulation techniques will be explained, revealing a broad equivalence of variational principles for long-time quantities in molecular dynamics. The article will mainly take a mathematical perspective and will leave the application to real-world molecular systems to the more than 1000 research articles already written on this subject

    Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

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    Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.Comment: 80 pages, 11 figure

    Peptide-Based Supramolecular Systems Chemistry

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    Peptide-based supramolecular systems chemistry seeks to mimic the ability of life forms to use conserved sets of building blocks and chemical reactions to achieve a bewildering array of functions. Building on the design principles for short peptide-based nanomaterials with properties, such as self-assembly, recognition, catalysis, and actuation, are increasingly available. Peptide-based supramolecular systems chemistry is starting to address the far greater challenge of systems-level design to access complex functions that emerge when multiple reactions and interactions are coordinated and integrated. We discuss key features relevant to systems-level design, including regulating supramolecular order and disorder, development of active and adaptive systems by considering kinetic and thermodynamic design aspects and combinatorial dynamic covalent and noncovalent interactions. Finally, we discuss how structural and dynamic design concepts, including preorganization and induced fit, are critical to the ability to develop adaptive materials with adaptive and tunable photonic, electronic, and catalytic properties. Finally, we highlight examples where multiple features are combined, resulting in chemical systems and materials that display adaptive properties that cannot be achieved without this level of integration

    Beyond binding change: the molecular mechanism of ATP hydrolysis by F1-ATPase and its biochemical consequences

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    F1-ATPase is a universal multisubunit enzyme and the smallest-known motor that, fueled by the process of ATP hydrolysis, rotates in 120o steps. A central question is how the elementary chemical steps occurring in the three catalytic sites are coupled to the mechanical rotation. Here, we performed cold chase promotion experiments and measured the rates and extents of hydrolysis of preloaded bound ATP and promoter ATP bound in the catalytic sites. We found that rotation was caused by the electrostatic free energy change associated with the ATP cleavage reaction followed by Pi release. The combination of these two processes occurs sequentially in two different catalytic sites on the enzyme, thereby driving the two rotational sub-steps of the 120o rotation. The mechanistic implications of this finding are discussed based on the overall energy balance of the system. General principles of free energy transduction are formulated, and their important physical and biochemical consequences are analyzed. In particular, how exactly ATP performs useful external work in biomolecular systems is discussed. A molecular mechanism of steady-state, trisite ATP hydrolysis by F1-ATPase, consistent with physical laws and principles and the consolidated body of available biochemical information, is developed. Taken together with previous results, this mechanism essentially completes the coupling scheme. Discrete snapshots seen in high-resolution X-ray structures are assigned to specific intermediate stages in the 120o hydrolysis cycle, and reasons for the necessity of these conformations are readily understood. The major roles played by the “minor” subunits of ATP synthase in enabling physiological energy coupling and catalysis, first predicted by Nath's torsional mechanism of energy transduction and ATP synthesis 25 years ago, are now revealed with great clarity. The working of nine-stepped (bMF1, hMF1), six-stepped (TF1, EF1), and three-stepped (PdF1) F1 motors and of the α3β3γ subcomplex of F1 is explained by the same unified mechanism without invoking additional assumptions or postulating different mechanochemical coupling schemes. Some novel predictions of the unified theory on the mode of action of F1 inhibitors, such as sodium azide, of great pharmaceutical importance, and on more exotic artificial or hybrid/chimera F1 motors have been made and analyzed mathematically. The detailed ATP hydrolysis cycle for the enzyme as a whole is shown to provide a biochemical basis for a theory of “unisite” and steady-state multisite catalysis by F1-ATPase that had remained elusive for a very long time. The theory is supported by a probability-based calculation of enzyme species distributions and analysis of catalytic site occupancies by Mg-nucleotides and the activity of F1-ATPase. A new concept of energy coupling in ATP synthesis/hydrolysis based on fundamental ligand substitution chemistry has been advanced, which offers a deeper understanding, elucidates enzyme activation and catalysis in a better way, and provides a unified molecular explanation of elementary chemical events occurring at enzyme catalytic sites. As such, these developments take us beyond binding change mechanisms of ATP synthesis/hydrolysis proposed for oxidative phosphorylation and photophosphorylation in bioenergetics
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