Targeting Protein Pockets with Halogen Bonds: The Role of the Halogen Environment.

Protein pockets that form a halogen bond (X-bond) with a halogenated ligand molecule simultaneously form other (mainly hydrophobic) interactions with the halogen atom that can be considered as its "X-bond environment" (XBenv). Most studies in the field have focused on the X-bond, with the properties of the XBenv usually overlooked. In this work, we derived a protocol that evaluates the XBenv strength as a measure of the propensity of a protein pocket to host an X-bond. The charge density-based topological descriptors in combination with machine learning tools were employed to predict formation and strength of the interactions that conform the XBenv as a function of their geometrical parameters. On the basis of these results, we propose that the XBenv can be used as a footprint to judge the chance of a protein pocket to form an X-bond.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy.

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.