1,420 research outputs found

    Computational Methods for the Modulation of Protein-Protein Interactions

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    During the last decades, drug discovery development has made considerable progress. However, annual numbers of released drugs for novel targets have been decreasing concomitantly. Limited success rates of combinatorial chemistry and high-throughput screening, as well as availability of feasible targets are some reasons for this problem. A strategy to overcome it is exploration of novel target classes in order to expand the druggable space. An example are protein-protein interactions (PPIs) that can be inhibited or stabilized. Inhibition aims at developing binders for one protein to prevent complex formation. However, known PPI inhibitors differ significantly from conventional drugs and current active site-biased compound libraries are probably inappropriate to discover them. The design of novel screening libraries is thus very important. PPI stabilization aims at developing molecules that bind to a protein complex to increase its stability like a molecular glue. In contrast to inhibition, it is rather unexplored but ground-breaking examples from nature inspire research efforts. This work presents novel theoretical and experimental drug discovery approaches for these challenges. In the first part, we introduce novel chemoinformatics approaches for clustering of large chemical libraries. The development of a fast algorithm for pairwise similarity calculations forms the basis for an exact and deterministic clustering method, which is able to process the available chemical space in a short time. We complement our chemoinformatics work by a novel approach for fast classification of small molecules according to the similarity of their frameworks, the so-called scaffolds. The method generates families of molecules that share geometry conserving scaffolds and we show that family members possess similar activity on identical targets. The second part introduces computational methods for PPI modulation. First, we present structure-based analysis of known stabilized PPIs, which enables the development of novel in silico approaches to screen for small molecule PPI stabilizers. We demonstrate their applicability by an experimentally tested virtual screening for 14-3-3 protein interaction stabilizers. Finally, we present a virtual screening approach dedicated to identify small molecule inhibitors of 14-3-3 protein interactions. Predicted inhibitors are experimentally verified and characterized by in vitro assays and X-ray crystallography. Structure-activity relationship studies yielded PPI inhibitors in the low micromolar range, which are also active in cell-based experiments

    The Tensor Networks Anthology: Simulation techniques for many-body quantum lattice systems

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    We present a compendium of numerical simulation techniques, based on tensor network methods, aiming to address problems of many-body quantum mechanics on a classical computer. The core setting of this anthology are lattice problems in low spatial dimension at finite size, a physical scenario where tensor network methods, both Density Matrix Renormalization Group and beyond, have long proven to be winning strategies. Here we explore in detail the numerical frameworks and methods employed to deal with low-dimension physical setups, from a computational physics perspective. We focus on symmetries and closed-system simulations in arbitrary boundary conditions, while discussing the numerical data structures and linear algebra manipulation routines involved, which form the core libraries of any tensor network code. At a higher level, we put the spotlight on loop-free network geometries, discussing their advantages, and presenting in detail algorithms to simulate low-energy equilibrium states. Accompanied by discussions of data structures, numerical techniques and performance, this anthology serves as a programmer's companion, as well as a self-contained introduction and review of the basic and selected advanced concepts in tensor networks, including examples of their applications.Comment: 115 pages, 56 figure

    Data Service Outsourcing and Privacy Protection in Mobile Internet

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    Mobile Internet data have the characteristics of large scale, variety of patterns, and complex association. On the one hand, it needs efficient data processing model to provide support for data services, and on the other hand, it needs certain computing resources to provide data security services. Due to the limited resources of mobile terminals, it is impossible to complete large-scale data computation and storage. However, outsourcing to third parties may cause some risks in user privacy protection. This monography focuses on key technologies of data service outsourcing and privacy protection, including the existing methods of data analysis and processing, the fine-grained data access control through effective user privacy protection mechanism, and the data sharing in the mobile Internet

    Connectable Components for Protein Design

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    Protein design requires reusable, trustworthy, and connectable parts in order to scale to complex challenges. The recent explosion of protein structures stored within the Protein Data Bank provides a wealth of small motifs we can harvest, but we still lack tools to combine them into larger proteins. Here I explore two approaches for connecting reusable protein components on two different length scales. On the atomic scale, I build an interactive search engine for connecting chemical fragments together. Protein fragments built using this search engine recapitulate native-like protein assemblies that can be integrated into existing protein scaffolds using backbone search engines such as MaDCaT. On the protein domain scale, I quantitatively dissect structural variations in two-component systems in order to extract general principles for engineering interfacial flexibility between modular four-helix bundles. These bundles exhibit large scissoring motions where helices move towards or away from the bundle axis and these motions propagate across domain boundaries. Together, these two approaches form the beginnings of a multiscale methodology for connecting reusable protein fragments where there is a constant interplay and feedback between design of atomic structure, secondary structure, and tertiary structure. Rapid iteration, visualization, and search glue these diverse length scales together into a cohesive whole

    Algorithms for Constructing Exact Nearest Neighbor Graphs

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    University of Minnesota Ph.D. dissertation.June 2016. Major: Computer Science. Advisor: George Karypis. 1 computer file (PDF); xi, 151 pages.Nearest neighbor graphs (NNGs) contain the set of closest neighbors, and their similarities, for each of the objects in a set of objects. They are widely used in many real-world applications, such as clustering, online advertising, recommender systems, data cleaning, and query refinement. A brute-force method for constructing the graph requires O(n^2) similarity comparisons for a set of n objects. One way to reduce the number of comparisons is to ignore object pairs with low similarity, which are unimportant in many domains. Current methods for construction of the graph tackle the problem by either pruning the similarity search space, avoiding comparisons of objects that can be determined to not meet the similarity bounding conditions, or they solve the problem approximately, which can miss some of the neighbors. This thesis addresses the problem of efficiently constructing the exact nearest neighbor graph for a large set of objects, i.e., the graph that would be found by comparing each object against all other objects in the set. In this context, we address two specific problems. The epsilon-nearest neighbor graph (epsilon-NNG) construction problem, also known as all-pairs similarity search (APSS), seeks to find, for each object, all other objects with a similarity of at least some threshold epsilon. On the other hand, the k-nearest neighbor graph (k-NNG) construction problem seeks to find the k closest other objects to each object in the set. For both problems, we propose filtering techniques that are more effective than previous ones, and efficient serial and parallel algorithms to construct the graph. Our methods are ideally suited for sparse high dimensional data

    Adaptation of the Retina to Stimulus Correlations

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    Visual scenes in the natural world are highly correlated. To efficiently encode such an environment with a limited dynamic range, the retina ought to reduce correlations to maximize information. On the other hand, some redundancy is needed to combat the effects of noise. Here we ask how the degree of redundancy in retinal output depends on the stimulus ensemble. We find that retinal output preserves correlations in a spatially correlated stimulus but adaptively reduces changes in spatio-temporal input correlations. The latter effect can be explained by stimulus-dependent changes in receptive fields. We also find evidence that horizontal cells in the outer retina enhance changes in output correlations. GABAergic amacrine cells in the inner retina also enhance differences in correlation, albeit to a lesser degree, while gylcinergic amacrine cells have little effect on output correlation. These results suggest that the early visual system is capable of adapting to stimulus correlations to balance the challenges of redundancy and noise

    Dynamics of clusters and fragments in heavy-ion collisions

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    A review is given on the studies of formation of light clusters and heavier fragments in heavy-ion collisions at incident energies from several tens of MeV/nucleon to several hundred MeV/nucleon, focusing on dynamical aspects and on microscopic theoretical descriptions. Existing experimental data already clarify basic characteristics of expanding and fragmenting systems typically in central collisions, where cluster correlations cannot be ignored. Cluster correlations appear almost everywhere in excited low-density nuclear many-body systems and nuclear matter in statistical equilibrium where the properties of a cluster may be influenced by the medium. On the other hand, transport models to solve the time evolution have been developed based on the single-nucleon distribution function. Different types of transport models are reviewed putting emphasis both on theoretical features and practical performances in the description of fragmentation. A key concept to distinguish different models is how to consistently handle single-nucleon motions in the mean field, fluctuation or branching induced by two-nucleon collisions, and localization of nucleons to form fragments and clusters. Some transport codes have been extended to treat light clusters explicitly. Results indicate that cluster correlations can have strong impacts on global collision dynamics and correlations between light clusters should also be taken into account.Comment: review article, 64 pages, 27 figure

    Quantum Information Methods in Many-Body Physics

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