1,720,988 research outputs found
A comparative QM/MM study of the reaction mechanism of the Hepatitis C virus NS3/NS4A protease with the three main natural substrates NS5A/5B, NS4B/5A and NS4A/4B
No full textThe reaction mechanism of the NS3/NS4A protease with the NS4B/5A and NS4A/4B natural substrates has been investigated using the QM/MM (quantum mechanics/molecular mechanics) approach, and some calculations have been performed on the reaction with the NS5A/5B natural substrate. This study widely extends a previous contribution of our group on the reaction mechanism with the NS5A/5B substrate, the main goal here being to understand the differences found between the reaction mechanism of each natural substrate and the role played by the enzymatic residues in the catalytic cycle. This knowledge will ultimately help in developing new NS3/NS4A protease inhibitors. The two first steps of the mechanism have been considered: Acylation and breaking of the peptide bond, with emphasis on the former one (rate limiting process). Energy and free energy profiles for both steps have been calculated at the AM1/MM level and corrected by means of MP2 ab initio calculations, being evident the importance of correlation energy. Acylation is the rate limiting step in all cases and occurs through a tetracoordinated intermediate, as previously suggested for other serine proteases. Specificities in the NS4B/5A mechanism can be attributed to the presence of a Proline residue in the substrate P2 position. The analysis of structures and energies confirm the importance of the oxyanion hole in the electrostatic stabilization of the tetracoordinated intermediate. Finally, the role of other residues, e.g., Arg-155 and Asp-79, has been explained, and the viability of Arg-155 mutants and its resistance to some protease inhibitors has been understood thanks to virtual mutation studies. © the Owner Societies
Topological Kolmogorov complexity and the Berezinskii-Kosterlitz-Thouless mechanism
No full textTopology plays a fundamental role in our understanding of many-body physics, from vortices and solitons in classical field theory to phases and excitations in quantum matter. Topological phenomena are intimately connected to the distribution of information content that, differently from ordinary matter, is now governed by nonlocal degrees of freedom. However, a precise characterization of how topological effects govern the complexity of a many-body state, i.e., its partition function, is presently unclear. In this paper, we show how topology and complexity are directly intertwined concepts in the context of classical statistical mechanics. We concretely present a theory that shows how the Kolmogorov complexity of a classical partition function sampling carries unique, distinctive features depending on the presence of topological excitations in the system. We confront two-dimensional Ising, Heisenberg, and XY models on several topologies and study the corresponding samplings as high-dimensional manifolds in configuration space, quantifying their complexity via the intrinsic dimension. While for the Ising and Heisenberg models the intrinsic dimension is independent of the real-space topology, for the XY model it depends crucially on temperature: across the Berezkinskii-Kosterlitz-Thouless (BKT) transition, complexity becomes topology dependent. In the BKT phase, it displays a characteristic dependence on the homology of the real-space manifold, and, for g-torii, it follows a scaling that is solely genus dependent. We argue that this behavior is intimately connected to the emergence of an order parameter in data space, the conditional connectivity, which displays scaling behavior. Our approach paves the way for an understanding of topological phenomena emergent from many-body interactions from the perspective of Kolmogorov complexity
Assessment of the performance of cluster analysis grouping using pharmacophores as molecular descriptors
No full text
A quantum mechanics/molecular mechanics study of the reaction mechanism of the hepatitis C virus NS3 protease with the NS5A/5B substrate
No full textComplexity of spin configurations dynamics due to unitary evolution and periodic projective measurements
Full text linkWe study the Hamiltonian dynamics of a many-body quantum system subjected to
periodic projective measurements which leads to probabilistic cellular automata
dynamics. Given a sequence of measured values, we characterize their dynamics
by performing a principal component analysis. The number of principal
components required for an almost complete description of the system, which is
a measure of complexity we refer to as PCA complexity, is studied as a function
of the Hamiltonian parameters and measurement intervals. We consider different
Hamiltonians that describe interacting, non-interacting, integrable, and
non-integrable systems, including random local Hamiltonians and translational
invariant random local Hamiltonians. In all these scenarios, we find that the
PCA complexity grows rapidly in time before approaching a plateau. The dynamics
of the PCA complexity can vary quantitatively and qualitatively as a function
of the Hamiltonian parameters and measurement protocol. Importantly, the
dynamics of PCA complexity present behavior that is considerably less sensitive
to the specific system parameters for models which lack simple local dynamics,
as is often the case in non-integrable models. In particular, we point out a
figure of merit that considers the local dynamics and the measurement direction
to predict the sensitivity of the PCA complexity dynamics to the system
parameters.Comment: 9 pages, 8 figure
Beyond Local Structures in Critical Supercooled Water through Unsupervised Learning
No full textThe presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often requires designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the liquid-liquid critical point (LLCP). Encoding the information on the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy nonlocal descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality
High-Throughput Screening of Promising Redox-Active Molecules with MolGAT
Full text linkRedox flow batteries (RFBs) have emerged as a promisingoptionfor large-scale energy storage, owing to their high energy density,low cost, and environmental benefits. However, the identificationof organic compounds with high redox activity, aqueous solubility,stability, and fast redox kinetics is a crucial and challenging stepin developing an RFB technology. Density functional theory-based computationalmaterials prediction and screening is a time-consuming and computationallyexpensive technique, yet it has a high success rate. To speed up thediscovery of new materials with desired properties, machine-learning-basedmodels can be trained on large data sets. Graph neural networks (GNNs)are particularly well-suited for non-Euclidean data and can modelcomplex relationships, making them ideal for accelerating the discoveryof novel materials. In this study, a GNN-based model called MolGATwas developed to predict the redox potential of organic moleculesusing molecular structures, atomic properties, and bond attributes.The model was trained on a data set of over 15,000 compounds withredox potentials ranging from -4.11 to 2.56. MolGAT outperformedother GNN variants, such as the Graph Attention Network, Graph ConvolutionNetwork, and AttentiveFP models. The trained model was used to screena vast chemical data set comprising 581,014 molecules, namely OMDB,QM9, ZINC, CHEMBL, and DELANEY, and identified 23,467 potential redox-activecompounds for use in redox flow batteries. Of those, 20,716 moleculeswere identified as potential catholytes with predicted redox potentialsup to 2.87 V, while 2,751 molecules were deemed potential anolyteswith predicted redox potentials as low as -2.88 V. This workdemonstrates the capabilities of graph neural networks in condensedmatter physics and materials science to screen promising redox-activespecies for further electronic structure calculations and experimentaltesting
Increasing the quantum tunneling probability through a learned ancilla-assisted protocol
No full textIncreasing the probability of quantum tunneling between two states, while keeping constant the resources of the underlying physical system, is a task of key importance in several physical contexts and platforms, including ultracold atoms confined by double-well potentials and superconducting qubits. We propose a novel ancillary assisted protocol showing that when a quantum system—such as a qubit—is coupled to an ancilla, one can learn the optimal ancillary component and its coupling, to increase the tunneling probability. As a case study, we consider a quantum system that, due to the presence of an energy detuning between two modes, cannot transfer by tunneling the particles from one mode to the other. However, it does it through a learned coupling with an ancillary system characterized by a detuning not smaller than the one of the primary system. We provide several illustrative examples for the paradigmatic case of a two-mode system and a two-mode ancilla in the presence of interacting particles. This reduces to a qubit coupled to an ancillary qubit in the case of one particle in the system and one in the ancilla. Our proposal provides an effective method to increase the tunneling probability in all those physical situations where no direct improvement of the system parameters, such as tunneling coefficient or energy detuning, is either possible or resource efficient. Finally, we also argue that the proposed strategy is not hampered by weak coupling to noisy environments
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