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.

Combining Charge Density Analysis with Machine Learning Tools To Investigate the Cruzain Inhibition Mechanism.

Trypanosoma cruzi, a flagellate protozoan parasite, is responsible for Chagas disease. The parasite major cysteine protease, cruzain (Cz), plays a vital role at every stage of its life cycle and the active-site region of the enzyme, similar to those of other members of the papain superfamily, is well characterized. Taking advantage of structural information available in public databases about Cz bound to known covalent inhibitors, along with their corresponding activity annotations, in this work, we performed a deep analysis of the molecular interactions at the Cz binding cleft, in order to investigate the enzyme inhibition mechanism. Our toolbox for performing this study consisted of the charge density topological analysis of the complexes to extract the molecular interactions and machine learning classification models to relate the interactions with biological activity. More precisely, such a combination was useful for the classification of molecular interactions as "active-like" or "inactive-like" according to whether they are prevalent in the most active or less active complexes, respectively. Further analysis of interactions with the help of unsupervised learning tools also allowed the understanding of how these interactions come into play together to trigger the enzyme into a particular conformational state. Most active inhibitors induce some conformational changes within the enzyme that lead to an overall better fit of the inhibitor into the binding cleft. Curiously, some of these conformational changes can be considered as a hallmark of the substrate recognition event, which means that most active inhibitors are likely recognized by the enzyme as if they were its own substrate so that the catalytic machinery is arranged as if it is about to break the substrate scissile bond. Overall, these results contribute to a better understanding of the enzyme inhibition mechanism. Moreover, the information about main interactions extracted through this work is already being used in our lab to guide docking solutions in ongoing prospective virtual screening campaigns to search for novel noncovalent cruzain inhibitors.

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.

Non-essential element concentrations in brown grain rice: Assessment by advanced data mining techniques.

The concentrations of 17 non-essential elements (Al, As, Ba, Be, Cd, Ce, Cr, Hg, La, Li, Pb, Sb, Sn, Sr, Th, Ti, and Tl) were determined in brown grain rice samples of two varieties: Fortuna and Largo Fino. The samples were collected from the four main producing regions of Corrientes province (Argentina). Quantitative determinations were performed by inductively coupled plasma mass spectrometry (ICP-MS), using a validated method. The contents of As, Be, Cd, Ce, Cr, Hg, Pb, Sb, Sn, Th, and Tl were very low or not detected in most samples. The non-essential element levels detected were in line with studies conducted in rice from different parts of the world. In order to characterize the influence of geographical origin in the samples, the following classification methods were carried out: linear discriminant analysis (LDA), k-nearest neighbors (k-NN), partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and random forests (RF). The best performance was obtained by using RF (96%) and SVM (96%). The results reported here showed the variation in the non-essential element profiles in rice grain depending on the geographical origin.