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.

Since the discovery of the first penicillin bacterial resistance to β-lactam antibiotics has spread and evolved promoting new resistances to pathogens. The most common mechanism of resistance is the production of β-lactamases that have spread thorough nature and evolve to complex phenotypes like CMT type enzymes. New antibiotics have been introduced in clinical practice, and therefore it becomes necessary a concise summary about their molecular targets, specific use and other properties. β-lactamases are still a major medical concern and they have been extensively studied and described in the scientific literature. Several authors agree that Glu166 should be the general base and Ser70 should perform the nucleophilic attack to the carbon of the carbonyl group of the β-lactam ring. Nevertheless there still is controversy on their catalytic mechanism. TEMs evolve at incredible pace presenting more complex phenotypes due to their tolerance to mutations. These mutations lead to an increasing need of novel, stronger and more specific and stable antibiotics. The present review summarizes key structural, molecular and functional aspects of ESBL, IRT and CMT TEM β-lactamases properties and up to date diagrams of the TEM variants with defined phenotype. The activity and structural characteristics of several available TEMs in the NCBI-PDB are presented, as well as the relation of the various mutated residues and their specific properties and some previously proposed catalytic mechanisms.

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.

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.