Coherent photodissociation reactions: Observation by a novel picosecond polarization technique

In this communication, we wish to report on a novel picosecond polarization method for measuring the degree of rotational coherence that is preserved in photodissociation reactions. The systems studied here are jet-cooled van der Waals molecules; stilbene [4-6] bound [5] to He or Ne with a 1:1 composition.[7

Ultrafast vectorial and scalar dynamics of ionic clusters: Azobenzene solvated by oxygen

The ultrafast dynamics of clusters of trans-azobenzene anion (A–) solvated by oxygen molecules was investigated using femtosecond time-resolved photoelectron spectroscopy. The time scale for stripping off all oxygen molecules from A– was determined by monitoring in real time the transient of the A– rise, following an 800 nm excitation of A– (O2)n, where n=1–4. A careful analysis of the time-dependent photoelectron spectra strongly suggests that for n>1 a quasi-O4 core is formed and that the dissociation occurs by a bond cleavage between A– and conglomerated (O2)n rather than a stepwise evaporation of O2. With time and energy resolutions, we were able to capture the photoelectron signatures of transient species which instantaneously rise (2- for A–O2 and A·O4-·(O2)n–2 for A–(O2)n, where n=2–4. Subsequent to an ultrafast electron recombination, A– rises with two distinct time scales: a subpicosecond component reflecting a direct bond rupture of the A–-(O2)n nuclear coordinate and a slower component (1.6–36 ps, increasing with n) attributed to an indirect channel exhibiting a quasistatistical behavior. The photodetachment transients exhibit a change in the transition dipole direction as a function of time delay. Rotational dephasing occurs on a time scale of 2–3 ps, with a change in the sign of the transient anisotropy between A–O2 and the larger clusters. This behavior is a key indicator of an evolving cluster structure and is successfully modeled by calculations based on the structures and inertial motion of the parent clusters

Quasiclassical negative magnetoresistance of a 2D electron gas: interplay of strong scatterers and smooth disorder

We study the quasiclassical magnetotransport of non-interacting fermions in two dimensions moving in a random array of strong scatterers (antidots, impurities or defects) on the background of a smooth random potential. We demonstrate that the combination of the two types of disorder induces a novel mechanism leading to a strong negative magnetoresistance, followed by the saturation of the magnetoresistivity $\rho_{xx}(B)$ at a value determined solely by the smooth disorder. Experimental relevance to the transport in semiconductor heterostructures is discussed.Comment: 4 pages, 2 figure

Hydrothermal sensitivities of seed populations underlie fluctuations of dormancy states in an annual plant community

Plant germination ecology involves continuous interactions between changing environmental conditions and the sensitivity of seed populations to respond to those conditions at a given time. Ecologically meaningful parameters characterizing germination capacity (or dormancy) are needed to advance our understanding of the evolution of germination strategies within plant communities. The germination traits commonly examined (e.g., maximum germination percentage under optimal conditions) may not adequately reflect the critical ecological differences in germination behavior across species, communities, and seasons. In particular, most seeds exhibit primary dormancy at dispersal that is alleviated by exposure to dry after-ripening or to hydrated chilling to enable germination in a subsequent favorable season. Population-based threshold (PBT) models of seed germination enable quantification of patterns of germination timing using parameters based on mechanistic assumptions about the underlying germination physiology. We applied the hydrothermal time (HTT) model, a type of PBT model that integrates environmental temperature and water availability, to study germination physiology in a guild of coexisting desert annual species whose seeds were after-ripened by dry storage under different conditions. We show that HTT assumptions are valid for describing germination physiology in these species, including loss of dormancy during after-ripening. Key HTT parameters, the hydrothermal time constant (θHT ) and base water potential distribution among seeds (Ψb (g)), were effective in describing changes in dormancy states and in clustering species exhibiting similar germination syndromes. θHT is an inherent species-specific trait relating to timing of germination that correlates well with long-term field germination fraction, while Ψb (g) shifts with depth of dormancy in response to after-ripening and seasonal environmental variation. Predictions based on variation among coexisting species in θHT and Ψb (g) in laboratory germination tests matched well with 25-yr observations of germination dates and fractions for the same species in natural field conditions. Seed dormancy and germination strategies, which are significant contributors to long-term species demographics under natural conditions, can be represented by readily measurable functional traits underlying variation in germination phenologies.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Energy-based Neural Networks as a Tool for Harmony-based Virtual Screening

© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. In Energy-Based Neural Networks (EBNNs), relationships between variables are captured by means of a scalar function conventionally called “energy”. In this article, we introduce a procedure of “harmony search”, which looks for compounds providing the lowest energies for the EBNNs trained on active compounds. It can be considered as a special kind of similarity search that takes into account regularities in the structures of active compounds. In this paper, we show that harmony search can be used for performing virtual screening. The performance of the harmony search based on two types of EBNNs, the Hopfield Networks (HNs) and the Restricted Boltzmann Machines (RBMs), was compared with the performance of the similarity search based on Tanimoto coefficient with “data fusion”. The AUC measure for ROC curves and 1 %-enrichment rates for 20 targets were used in the benchmarking. Five different scores were computed: the energy for HNs, the free energy and the reconstruction error for RBMs, the mean and the maximum values of Tanimoto coefficients. The performance of the harmony search was shown to be comparable or even superior (significantly for several targets) to the performance of the similarity search. Important advantages of using the harmony search for virtual screening are very high computational efficiency of prediction, the ability to reveal and take into account regularities in active structures, flexibility and interpretability of models, etc

GTM-Based QSAR Models and Their Applicability Domains

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca2+, Gd3+ and Lu3+ complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets

Stargate GTM: Bridging Descriptor and Activity Spaces

© 2015 American Chemical Society. Predicting the activity profile of a molecule or discovering structures possessing a specific activity profile are two important goals in chemoinformatics, which could be achieved by bridging activity and molecular descriptor spaces. In this paper, we introduce the "Stargate"version of the Generative Topographic Mapping approach (S-GTM) in which two different multidimensional spaces (e.g., structural descriptor space and activity space) are linked through a common 2D latent space. In the S-GTM algorithm, the manifolds are trained simultaneously in two initial spaces using the probabilities in the 2D latent space calculated as a weighted geometric mean of probability distributions in both spaces. S-GTM has the following interesting features: (1) activities are involved during the training procedure; therefore, the method is supervised, unlike conventional GTM; (2) using molecular descriptors of a given compound as input, the model predicts a whole activity profile, and (3) using an activity profile as input, areas populated by relevant chemical structures can be detected. To assess the performance of S-GTM prediction models, a descriptor space (ISIDA descriptors) of a set of 1325 GPCR ligands was related to a B-dimensional (B = 1 or 8) activity space corresponding to pKi values for eight different targets. S-GTM outperforms conventional GTM for individual activities and performs similarly to the Lasso multitask learning algorithm, although it is still slightly less accurate than the Random Forest method

GTM-Based QSAR Models and Their Applicability Domains

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca2+, Gd3+ and Lu3+ complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets

Chemical data visualization and analysis with incremental generative topographic mapping: Big data challenge

© 2014 American Chemical Society. This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the incremental version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity in the latent space was assessed by applying several metrics (Euclidean distance, Tanimoto and Bhattacharyya coefficients) to data probability distributions based on cumulated responsibility vectors. As a complementary approach, data sets were compared by considering them as individual objects on a meta-GTM map, built on cumulated responsibility vectors or property landscapes produced with iGTM. We believe that the iGTM methodology described in this article represents a fast and reliable way to analyze and visualize large chemical databases

Crosslinking Studies of Protein-Protein Interactions in Nonribosomal Peptide Biosynthesis

SummarySelective protein-protein interactions between nonribosomal peptide synthetase (NRPS) proteins, governed by communication-mediating (COM) domains, are responsible for proper translocation of biosynthetic intermediates to produce the natural product. In this study, we developed a crosslinking assay, utilizing bioorthogonal probes compatible with carrier protein modification, for probing the protein interactions between COM domains of NRPS enzymes. Employing the Huisgen 1,3-dipolar cycloaddition of azides and alkynes, we examined crosslinking of cognate NRPS modules within the tyrocidine pathway and demonstrated the sensitivity of our panel of crosslinking probes toward the selective protein interactions of compatible COM domains. These studies indicate that copper-free crosslinking substrates uniquely offer a diagnostic probe for protein-protein interactions. Likewise, these crosslinking probes serve as ideal chemical tools for structural studies between NRPS modules where functional assays are lacking