Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q2ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q2ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.

Modelación por homología de la proteína Luxs de Porphyromonas gingivalis cepa W83 / Modelling by homology of Luxs protein in Porphyromonas gingivalis strain W83

Antecedentes: En las proteínas no se logra siempre su cristalización, de buen tamaño y de buena calidad para someterla a difracción de rayos X. De tal manera que se abre un campo para el desarrollo de estudios teóricos moleculares y proteínicos, que permiten la representación de las moléculas en tres dimensiones, proporcionando una información espacial para estudiar la interacción entre ligandos y receptores macromoleculares. Materialesy Métodos: Estudio In silico, a partir del análisis de secuencias primarias de seis diferentes proteínas LuxS cristalizadas de diversas bacterias, se seleccionó la proteína 1J6X del Helicobacter pylori, por su similaridad con la secuencia de la proteína LuxS en Porphyromonas gingivalis (P. gingivalis) cepa W83, para producir un modelo por homología de esta proteína, utilizando los programas Sybyl y MOE. Se realizó un acoplamiento con el ligando natural para evaluar la reproducibilidad del modelo en un ambiente biológico. Resultados: Se desarrolló el modelado de la proteína LuxS de P. gingivalis cepa W83, que permite el acercamiento a una estructura que se propone, por la interacción entre la proteína y su ligando natural. El modelo generado con recursos computacionales logró una correcta estructura molecular que aceptó la realización de diversos cálculos. El acoplamiento demostró una cavidad donde se logran diversas posiciones del ligando con buenos resultados. Conclusiones: Se obtuvo un modelo 3D para la proteína LuxS en la P. gingivalis cepa W83 validado por diferentes métodos computacionales con una adecuada reproducibilidad biológica por medio del acoplamiento molecular.

Background: Crystallization is not always achieved for all proteins in a good size and a good quality for X-ray diffraction. So that condition opens a field for the development of theoretical molecular and protein studies allowing the representation of the molecules in 3D, providing spatial information to study the interaction between ligands and macromolecular receptors. Materials and Methods: In silico study from primary sequence analysis of six different proteins LuxS crystallized of several bacteria. 1J6X protein of Helicobacter pylori was selected for its similarity with the LuxS protein sequence in Porphyromonas gingivalis (P. gingivalis) strain W83 to produce a homology model of this protein, using the Sybyl and MOE software. A docking was performed to assess the reproducibility of the model in a biological environment. Results: The LuxS protein modelling of P. gingivalis strain W83 was developed, which allows the approach to a proposed structure for the interaction between the protein and its natural ligand. The model generated with computational resources achieved the correct position and biological behavior by means of developed calculations. The docking showed a cavity in which the ligand adopted several positions with good results. Conclusions: A LuxS protein model was obtained, validated by different methods. This generated a 3D model for LuxS protein in P. gingivalis strain W83 with biological reproducibility by means of molecular docking.

Hirshfeld partitioning of the electron density: atomic dipoles and their relation with functional group properties.

Atomic dipole moments, derived within the Hirshfeld partitioning of the molecular electron density, have been studied for compounds of the type H-X and Cl-X, for a series of functional groups X frequently encountered in organic molecules. In the case of the H-X compounds, the component of the atomic dipole moment on H along the axis connecting H with the central atom in X is found to be linearly correlated with the electronegativity of X, the hardness of X playing no significant role. In the case of the Cl-X compounds, the situation is less clear. However, evidence seems to point to the conclusion that for these compounds, also the group hardness plays an important role.