Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors.

In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

Discovering key residues of dengue virus NS2b-NS3-protease: New binding sites for antiviral inhibitors design.

The NS2B-NS3 protease is essential for the Dengue Virus (DENV) replication process. This complex constitutes a target for efficient antiviral discovery because a drug could inhibit the viral polyprotein processing. Furthermore, since the protease is highly conserved between the four Dengue virus serotypes, it is probable that a drug would be equally effective against all of them. In this article, a strategy is reported that allowed us to identify influential residues on the function of the Dengue NS2b-NS3 Protease. Moreover, this is a strategy that could be applied to virtually any protein for the search of alternative influential residues, and for non-competitive inhibitor development. First, we incorporated several features derived from computational alanine scanning mutagenesis, sequence, structure conservation, and other structure-based characteristics. Second, these features were used as variables to obtain a multilayer perceptron model to identify defined groups (clusters) of key residues as possible candidate pockets for binding sites of new leads on the DENV protease. The identified residues included: i) amino acids close to the beta sheet-loop-beta sheet known to be important in its closed conformation for NS2b ii) residues close to the active site, iii) several residues evenly spread on the NS2b-NS3 contact surface, and iv) some inner residues most likely related to the overall stability of the protease. In addition, we found concordance on our list of residues with previously identified amino acids part of a highly conserved peptide studied for vaccine development.