General AMBER Force Field Parameters for Diphenyl Diselenides and Diphenyl Ditellurides.

The General AMBER Force Field (GAFF) has been extended to describe a series of selenium and tellurium diphenyl dichalcogenides. These compounds, besides being eco-friendly catalysts for numerous oxidations in organic chemistry, display peroxidase activity, i.e., can reduce hydrogen peroxide and harmful organic hydroperoxides to water/alcohols and as such are very promising antioxidant drugs. The novel GAFF parameters are tested in MD simulations in different solvents and the (77)Se NMR chemical shift of diphenyl diselenide is computed using structures extracted from MD snapshots and found in nice agreement with the measured value in CDCl3. The whole computational protocol is described in detail and integrated with in-house code to allow easy derivation of the force field parameters for analogous compounds as well as for Se/Te organocompounds in general.

Solvent accessible surface area-based hot-spot detection methods for protein-protein and protein-nucleic acid interfaces.

Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein-protein systems) and a more complex multi-objective genetic algorithm-support vector machine algorithms (protein-nucleic acid systems). The best models for these interactions have been implemented in two free Web tools.

Solvent-accessible surface area: How well can be applied to hot-spot detection?

A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as rel SASAi or rel/res SASAi , improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set.