Multi-­‐dimensional Protein Identification Technology

Before the rise of the Multidimentional Protein Identification Technology (MudPIT), protein and peptide mixtures were resolved using traditional proteomic technologies like the gel-­‐ based 2D chromatography that separates proteins by isoelectric point and molecular weight. This technique was tedious and limited, since the characterization of single proteins required isolation of protein gel spots, their subsequent proteolyzation and analysis using Matrix-­‐ assisted laser desorption/ionization-­‐time of flight (MALDI-­‐TOF) mass spectrometry.

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.