A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N˙ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening

Solution Structures of \u3cem\u3eMycobacterium tuberculosis\u3c/em\u3e Thioredoxin C and Models of Intact Thioredoxin System Suggest New Approaches to Inhibitor and Drug Design

Here, we report the NMR solution structures of Mycobacterium tuberculosis (M. tuberculosis) thioredoxin C in both oxidized and reduced states, with discussion of structural changes that occur in going between redox states. The NMR solution structure of the oxidized TrxC corresponds closely to that of the crystal structure, except in the C-terminal region. It appears that crystal packing effects have caused an artifactual shift in the α4 helix in the previously reported crystal structure, compared with the solution structure. On the basis of these TrxC structures, chemical shift mapping, a previously reported crystal structure of the M. tuberculosis thioredoxin reductase (not bound to a Trx) and structures for intermediates in the E. coli thioredoxin catalytic cycle, we have modeled the complete M. tuberculosis thioredoxin system for the various steps in the catalytic cycle. These structures and models reveal pockets at the TrxR/TrxC interface in various steps in the catalytic cycle, which can be targeted in the design of uncompetitive inhibitors as potential anti-mycobacterial agents, or as chemical genetic probes of function

Bound States in the Continuum Realized in the One-Dimensional Two-Particle Hubbard Model with an Impurity

We report a bound state of the one-dimensional two-particle (bosonic or fermionic) Hubbard model with an impurity potential. This state has the Bethe-ansatz form, although the model is nonintegrable. Moreover, for a wide region in parameter space, its energy is located in the continuum band. A remarkable advantage of this state with respect to similar states in other systems is the simple analytical form of the wave function and eigenvalue. This state can be tuned in and out of the continuum continuously.Comment: A semi-exactly solvable model (half of the eigenstates are in the Bethe form

A Dynamical Model of Binding in Visual Cortex During Incremental Grouping and Search

Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the cortical module extracts stimulus features across a hierarchy of spatial scales, the thalamic module provides a purely spatial relevance map. Both modules interact bidirectionally to enter locations of task-relevance into thalamus, while allowing integration of context with local features within cortical maps. This integration realizes the model\u27s binding mechanism. It is implemented by pyramidal neurons with dynamical basal and apical compartments performing coincidence detection (Larkum, TINS, 2013). The basal compartment is driven by bottom-up feature information and produces the neuron\u27s output, the apical compartment computes top-down contextual information akin to an interaction skeleton (Roelfsema&Singer, Cereb Cortex, 1998). Apical-basal integration yields an up-modulation in neuron activity binding it to the attended configuration. Gating information from thalamus restricts this integration to task-relevant locations (Saalman&Kastner, Curr Op Neurobiol, 2009). By model simulations, we show how altering the apical compartment\u27s operation regime steers binding to either perform search or incremental grouping

Spontaneous charge carrier localization in extended one-dimensional systems

Charge carrier localization in extended atomic systems has been described previously as being driven by disorder, point defects or distortions of the ionic lattice. Here we show for the first time by means of first-principles computations that charge carriers can spontaneously localize due to a purely electronic effect in otherwise perfectly ordered structures. Optimally-tuned range-separated density functional theory and many-body perturbation calculations within the GW approximation reveal that in trans-polyacetylene and polythiophene the hole density localizes on a length scale of several nanometers. This is due to exchange-induced translational symmetry breaking of the charge density. Ionization potentials, optical absorption peaks, excitonic binding energies and the optimally-tuned range parameter itself all become independent of polymer length as it exceeds the critical localization scale. Moreover, lattice disorder and the formation of a polaron result from the charge localization in contrast to the traditional view that lattice distortions precede charge localization. Our results can explain experimental findings that polarons in conjugated polymers form instantaneously after exposure to ultrafast light pulses.Comment: 5 pages, 4 figure

Theoretical and Computational Basis for CATNETS - Annual Report Year 3

In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined. --Grid Computing

Theoretical and Computational Basis for CATNETS - Annual Report Year 2

In this work the self-organising potential of the CATNETS allocation mechanism is described to provide a more comprehensive view on the research done in this project. The formal description of either the centralised and decentralised approach is presented. Furthermore the agents' bidding model is described and a comprehensive overview on how the catallactic mechanism is incorporated into the middleware and simulator environments is given. --Decentralized Market Mechanisms,Centralized Market Mechanisms,Catallaxy,Market Engineering,Simulator Integration,Prototype Integration

Hierarchical relative entropy policy search

Many real-world problems are inherently hierarchically structured. The use of this structure in an agent’s policy may well be the key to improved scalability and higher performance. However, such hierarchical structures cannot be exploited by current policy search algorithms. We will concentrate on a basic, but highly relevant hierarchy — the ‘mixed option’ policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. In this paper, we reformulate learning a hierarchical policy as a latent variable estimation problem and subsequently extend the Relative Entropy Policy Search (REPS) to the latent variable case. We show that our Hierarchical REPS can learn versatile solutions while also showing an increased performance in terms of learning speed and quality of the found policy in comparison to the nonhierarchical approach