Structure based de novo design of IspD inhibitors as anti-tubercular agents

Tuberculosis is one of the leading contagious diseases, caused by Mycobacterium tuberculosis. Despite improvements in anti-tubercular agents, it remains one of the most prevalent infectious diseases worldwide, responsible for a total of 1.6 million deaths annually. The emergence of multidrug resistant strains highlighted the need of discovering novel drug targets for the development of anti-tubercular agents. 2-C-methyl-D-erythritol-4-phosphate cytidyltransferase (IspD) is an enzyme involved in MEP pathway for isoprenoid biosynthesis, which is considered an attractive target for the discovery of novel antibiotics for its essentiality in bacteria and absence in mammals. In the present study, we have employed structure based drug design approach to develop novel and potent inhibitors for IspD receptor. To explore binding affinity and hydrogen bond interaction between the ligand and active site of IspD receptor, docking studies were performed. ADMET and synthetic accessibility filters were used to screen designed molecules. Finally, ten compounds were selected and subsequently submitted for the synthesis and in vitro studies as IspD inhibitors

CCharPPI web server: computational characterization of protein–protein interactions from structure

The atomic structures of protein–protein interactions are central to understanding their role in biological systems, and a wide variety of biophysical functions and potentials have been developed for their characterization and the construction of predictive models. These tools are scattered across a multitude of stand-alone programs, and are often available only as model parameters requiring reimplementation. This acts as a significant barrier to their widespread adoption. CCharPPI integrates many of these tools into a single web server. It calculates up to 108 parameters, including models of electrostatics, desolvation and hydrogen bonding, as well as interface packing and complementarity scores, empirical potentials at various resolutions, docking potentials and composite scoring functions.The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007- 2013) under REA grant agreement PIEF-GA-2012-327899 and grant BIO2013-48213-R from Spanish Ministry of Economy and Competitiveness.Peer ReviewedPostprint (published version

A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech