In this paper, we present MolFind, a highly multi-threaded pipeline type software package for use as an aid in identifying chemical structures in complex biofluids and mixtures. MolFind is specifically designed for high performance liquid chromatography/mass spectrometry (HPLC/MS) data inputs typical of metabolomics studies where structure identification is the ultimate goal. MolFind enables compound identification by matching HPLC/MS based experimental data obtained for an unknown compound with computationally derived HPLC/MS values for candidate compounds downloaded from chemical databases such as PubChem. The downloaded “bins” consist of all compounds matching the monoisotopic molecular weight of the unknown. The computational HPLC/MS values predicted include retention index (RI), ECOM50 (energy required to fragment 50% of a selected precursor ion), drift time and collision induced dissociation (CID) spectrum. RI, ECOM50, and drift time models are used for filtering compounds downloaded from PubChem. The remaining candidates are then ranked based on CID spectra matching. Current RI and ECOM50 models allow for the removal of about 28% of compounds from PubChem bins. Our estimates suggest that this could be improved to as much as 87% with additional chemical structures included in the computational models. Quantitative structure property relationship based modeling of drift times showed a better correlation with experimentally determined drift times than did Mobcal cross sectional areas. In 23/35 example cases, filtering PubChem bins with RI and ECOM50 predictive models resulted in improved ranking of the unknown compound compared to previous studies using CID spectra matching alone. In 19/35 examples, the correct candidate was ranked within the top 20 compounds in bins containing an average of 1635 compounds.

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Recent advances in the fields of chromatography, mass spectrometry, and chemical analysis have greatly improved the efficiency with which carotenoids can be extracted and analyzed from avian plumage. Prior to these technological developments, Brush (1968) [1] concluded that the burgundy-colored plumage of the male pompadour Cotinga Xipholena punicea is produced by a combination of blue structural color and red carotenoids, including astaxanthin, canthaxanthin, isozeaxanthin, and a fourth unidentified, polar carotenoid. However, X. punicea does not in fact exhibit any structural coloration. This work aims to elucidate the carotenoid pigments of the burgundy color of X. punicea plumage using advanced analytical methodology. Feathers were collected from two burgundy male specimens and from a third aberrant orange-colored specimen. Pigments were extracted using a previously published technique (McGraw et al. (2005) [2]), separated by high-performance liquid chromatography (HPLC), and analyzed by UV/Vis absorption spectroscopy, chemical analysis, mass spectrometry, nuclear magnetic resonance (NMR), and comparison with direct synthetic products. Our investigation revealed the presence of eight ketocarotenoids, including astaxanthin and canthaxanthin as reported previously by Brush (1968) [1]. Six of the ketocarotenoids contained methoxyl groups, which is rare for naturally-occurring carotenoids and a novel finding in birds. Interestingly, the carotenoid composition was the same in both the burgundy and orange feathers, indicating that feather coloration in X. punicea is determined not only by the presence of carotenoids, but also by interactions between the bound carotenoid pigments and their protein environment in the barb rami and barbules. This paper presents the first evidence of metabolically-derived methoxy-carotenoids in birds.

The MolFind application has been developed as a nontargeted metabolomics chemometric tool to facilitate structure identification when HPLC biofluids analysis reveals a feature of interest. Here synthetic compounds are selected and measured to form the basis of a new, more accurate, HPLC retention index model for use with MolFind. We show that relatively inexpensive synthetic screening compounds with simple structures can be used to develop an artificial neural network model that is successful in making quality predictions for human metabolites. A total of 1955 compounds were obtained and measured for the model. A separate set of 202 human metabolites was used for independent validation. The new ANN model showed improved accuracy over previous models. The model, based on relatively simple compounds, was able to make quality predictions for complex compounds not similar to training data. Independent validation metabolites with feature combinations found in three or more training compounds were predicted with 97% sensitivity while metabolites with feature combinations found in less than three training compounds were predicted with >90% sensitivity. The study describes the method used to select synthetic compounds and new descriptors developed to encode the relationship between lipophilic molecular subgraphs and HPLC retention. Finally, we introduce the QRI (qualitative range of interest) modification of neural network backpropagation learning to generate models simultaneously based on quantitative and qualitative data.

Azobenzene (AB) undergoes (E) → (Z) isomerization upon exposure to light. Whereas the light‐induced structural change can be exploited for numerous applications, a second, light‐orthogonal switch would facilitate the development of new uses for AB derivatives. Electron‐donating groups on the AB ring system change not only the absorption wavelengths, but also the isomerization properties. Experimental observations and computational studies suggest that the inclusion of multiple electron‐donating groups can short‐circuit the concerted inversion isomerization mechanism of AB by providing new conical intersections between excited states. This phenomenon has been exploited in a unique AB derivative where the conversion of a phenol into an ester restores the isomerization activity.