Clustering Molecular Dynamics trajectories is a common analysis that allows grouping together similar conformations. Several algorithms have been designed and optimized to perform this routine task, and among them, Quality Threshold stands as a very attractive option. This algorithm guarantees that in retrieved clusters no pair of frames will have a similarity value greater than a specified threshold, and hence, a set of strongly correlated frames are obtained for each cluster. In this work, it is shown that various commonly used software implementations are flawed by confusing Quality Threshold with another simplistic well-known clustering algorithm published by Daura et al. (Daura, X.; van Gunsteren, W. F.; Jaun, B.; Mark, A. E.; Gademann, K.; Seebach, D. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999, 38 (1/2), 236–240). Daura’s algorithm does not impose any quality threshold for the frames contained in retrieved clusters, bringing unrelated structural configurations altogether. The advantages of using Quality Threshold whenever possible to explore Molecular Dynamic trajectories is exemplified. An in-house implementation of the original Quality Threshold algorithm has been developed in order to illustrate our comments, and its code is freely available for further use by the scientific community.

This work is about Maya Blue (MB), a pigment developed byMesoamerican civilizations between the 5th and 16th centuries from an aluminosilicate mineral (palygorskite) and an organic dye (indigo). Two different supramolecular quantum-mechanical models afford explanations for the unusual stability of MB based on the oxidation of the indigo molecule during the heating process and its interaction with palygorskite. A model considering indigo derivatives attached to several aluminates shows the principal features of the experimental visible spectrum of MB within the TD-DFT methodology. Another model of an indigo oxidized species confined within an inorganic supramolecular cavity system, that involves about 170 atoms, was calculated after a large configuration interaction of single excited determinants within the NDOL approximation (Montero-Cabrera et al., J Chem Phys, 2007, 127, 145102). It allows a correct reproduction and interpretation of the corresponding spectrum. This second methodology provides the most satisfactory results, being able to manage very big molecular systems at a QM level. Structural explanation for the unusual stability of MB is also provided.

No abstract

ABSTRACT:The interaction of two flavonoid species (resorcinolic and fluoroglucinolic) with the 20 essential amino acids was studied by the multiple minima hypersurface (MMH) procedures, through the AM1 and PM3 semiempirical methods. Remarkable thermodynamic data related to the properties of the molecular association of these compounds were obtained, which will be of great utility for future investigations concerning the interaction of flavonoids with proteins. These results are compared with experimental and classical force field results reported in the available literature, and new evidences and criteria are shown. The hydrophilic amino acids demonstrated high affinity in the interaction with flavonoid molecules; the complexes with lysine are especially extremely stable. An affinity order for the interaction of both flavonoid species with the essential amino acids is suggested. Our theoretical results are compared with experimental evidence on flavonoid interactions with proteins of biomedical interest.

Predictive Quantitative Structure-Activity Relationship (QSAR) models of anabolic and androgenic activities for the 17b-hydroxy-5a-androstane steroid family were obtained by means of multi-linear regression using quantum and physicochemical molecular descriptors and a genetic algorithm for the selection of the best set of descriptors. The model allows the identification, selection and future design of new steroid molecules with increased anabolic activity. Molecular descriptors included in reported models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity and electronic properties. The model for the anabolic/ androgenic ratio (expressed by the weight of the levator ani muscle and ventral prostate in mice) predicts that: a) 2-cyano-17-a-methyl-17-b-acetoxy-5a-androst-2-ene is the most potent anabolic steroid in the group and b) the testosterone-3-cyclopentenyl-enoleter is the less potent one. The approach described in this paper is an alternative for the discovery and optimization of leading anabolic compounds.