A bacterial acetyltransferase targets the protein kinase ZIP1, a positive regulator of plant immunity

Pseudomonas syringae is a model bacterial pathogen that penetrates the leaf to reach the plant apoplast, where it replicates causing disease. In order to do that, the pathogen must interfere and suppress a two-tiered plant defense response: PTI (PAMP-Triggered Immunity, or basal resistance) and ETI (Effector-Triggered Immunity). P. syringae uses a type III secretion system to directly deliver effector proteins inside the plant cell cytosol, many of which are known to suppress PTI, some of which are known to trigger ETI, and a handful of which are known to suppress ETI. Bacterial infection can also trigger a systemic plant defense response that protects the plant against additional pathogen attacks known as SAR (Systemic Acquired Resistance). We are particularly interested in the molecular and cellular mechanisms involved in effector-mediated defense evasion by P. syringae, in particular those involved in the suppression of ETI and SAR, and/or mediation of hormone signaling. Here we present data describing effector-mediated interference with plant immunity, by means of acetylation of a key positive regulator of local and systemic responses. Our work identifies a novel plant target for effector function, and characterizes its function. This work illustrates how analyzing the means by which a given effector interferes with its target can provide novel information regarding eukaryotic molecular mechanisms.

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.