398 research outputs found
A QM/MM approach for the study of monolayer-protected gold clusters
We report the development and implementation of hybrid methods that combine
quantum mechanics (QM) with molecular mechanics (MM) to theoretically
characterize thiolated gold clusters. We use, as training systems, structures
such as Au25(SCH2-R)18 and Au38(SCH2-R)24, which can be readily compared with
recent crystallographic data. We envision that such an approach will lead to an
accurate description of key structural and electronic signatures at a fraction
of the cost of a full quantum chemical treatment. As an example, we demonstrate
that calculations of the 1H and 13C NMR shielding constants with our proposed
QM/MM model maintain the qualitative features of a full DFT calculation, with
an order-of-magnitude increase in computational efficiency.Comment: Journal of Materials Science, 201
Importance of electronic self-consistency in the TDDFT based treatment of nonadiabatic molecular dynamics
A mixed quantum-classical approach to simulate the coupled dynamics of
electrons and nuclei in nanoscale molecular systems is presented. The method
relies on a second order expansion of the Lagrangian in time-dependent density
functional theory (TDDFT) around a suitable reference density. We show that the
inclusion of the second order term renders the method a self-consistent scheme
and improves the calculated optical spectra of molecules by a proper treatment
of the coupled response. In the application to ion-fullerene collisions, the
inclusion of self-consistency is found to be crucial for a correct description
of the charge transfer between projectile and target. For a model of the
photoreceptor in retinal proteins, nonadiabatic molecular dynamics simulations
are performed and reveal problems of TDDFT in the prediction of intra-molecular
charge transfer excitations.Comment: 9 pages, 8 figures. Minor changes in content wrt older versio
Characterization of the thermal and photoinduced reactions of photochromic spiropyrans in aqueous solution
Six water-soluble spiropyran derivatives have been characterized with respect to the thermal and photoinduced reactions over a broad pH-interval. A comprehensive kinetic model was formulated including the spiro- and the merocyanine isomers, the respective protonated forms, and the hydrolysis products. The experimental studies on the hydrolysis reaction mechanism were supplemented by calculations using quantum mechanical (QM) models employing density functional theory. The results show that (1) the substitution pattern dramatically influences the pKa-values of the protonated forms as well as the rates of the thermal isomerization reactions, (2) water is the nucleophile in the hydrolysis reaction around neutral pH, (3) the phenolate oxygen of the merocyanine form plays a key role in the hydrolysis reaction. Hence, the nonprotonated merocyanine isomer is susceptible to hydrolysis, whereas the corresponding protonated form is stable toward hydrolytic degradation
Scoring docking conformations using predicted protein interfaces
BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations
Searching image in blue jays: Facilitation and interference in sequential priming
Repeated exposure to a single target type (sequential priming) during visual search for multiple cryptic targets commonly improves performance on subsequent presentations of that target. It appears to be an attentional phenomenon, a component of the searching image effect. It has been argued, however, that if searching image is an attentional process, sequential priming should also interfere with performance on subsequent nonprimed targets, and such interference has never been unequivocally demonstrated. In blue jays (Cyanocitta cristata) searching in an operant apparatus for targets derived from images of cryptic moths, detection performance was strongly facilitated in the course of a sequential prime but was relatively unaffected by sequences of mixed target types. Detection accuracy in subsequent probe trials was enhanced by priming with targets of the same type, whereas accuracy on cryptic probes following priming with a more conspicuous target was significantly degraded. The results support an attentional interpretation of searching image
Exploring the potential of 3D Zernike descriptors and SVM for protein–protein interface prediction
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