Structural Basis of Gate-DNA Breakage and Resealing by Type II Topoisomerases

Type II DNA topoisomerases are ubiquitous enzymes with essential functions in DNA replication, recombination and transcription. They change DNA topology by forming a transient covalent cleavage complex with a gate-DNA duplex that allows transport of a second duplex though the gate. Despite its biological importance and targeting by anticancer and antibacterial drugs, cleavage complex formation and reversal is not understood for any type II enzyme. To address the mechanism, we have used X-ray crystallography to study sequential states in the formation and reversal of a DNA cleavage complex by topoisomerase IV from Streptococcus pneumoniae, the bacterial type II enzyme involved in chromosome segregation. A high resolution structure of the complex captured by a novel antibacterial dione reveals two drug molecules intercalated at a cleaved B-form DNA gate and anchored by drug-specific protein contacts. Dione release generated drug-free cleaved and resealed DNA complexes in which the DNA gate instead adopts an unusual A/B-form helical conformation with a Mg2+ ion repositioned to coordinate each scissile phosphodiester group and promote reversible cleavage by active-site tyrosines. These structures, the first for putative reaction intermediates of a type II topoisomerase, suggest how a type II enzyme reseals DNA during its normal reaction cycle and illuminate aspects of drug arrest important for the development of new topoisomerase-targeting therapeutics

Exploiting bacterial DNA gyrase as a drug target: current state and perspectives

DNA gyrase is a type II topoisomerase that can introduce negative supercoils into DNA at the expense of ATP hydrolysis. It is essential in all bacteria but absent from higher eukaryotes, making it an attractive target for antibacterials. The fluoroquinolones are examples of very successful gyrase-targeted drugs, but the rise in bacterial resistance to these agents means that we not only need to seek new compounds, but also new modes of inhibition of this enzyme. We review known gyrase-specific drugs and toxins and assess the prospects for developing new antibacterials targeted to this enzyme

Assessing Algorithm Parameter Importance Using Global Sensitivity Analysis

In general, biologically-inspired multi-objective optimization algorithms comprise several parameters which values have to be selected ahead of running the algorithm. In this paper we describe a global sensitivity analysis framework that enables a better understanding of the effects of parameters on algorithm performance. For this work, we tested NSGA-III and MOEA/D on multi-objective optimization testbeds, undertaking our proposed sensitivity analysis techniques on the relevant metrics, namely Generational Distance, Inverted Generational Distance, and Hypervolume. Experimental results show that both algorithms are most sensitive to the cardinality of the population. In all analyses, two clusters of parameter usually appear: (1) the population size (Pop) and (2) the Crossover Distribution Index, Crossover Probability, Mutation Distribution Index and Mutation Probability; where the first cluster, Pop, is the most important (sensitive) parameter with respect to the others. Choosing the correct population size for the tested algorithms has a significant impact on the solution accuracy and algorithm performance. It was already known how important the population of an evolutionary algorithm was, but it was not known its importance compared to the remaining parameters. The distance between the two clusters shows how crucial the size of the population is, compared to the other parameters. Detailed analysis clearly reveals a hierarchy of parameters: on the one hand the size of the population, on the other the remaining parameters that are always grouped together (in a single cluster) without a possible significant distinction. In fact, the other parameters all have the same importance, a secondary relevance for the performance of the algorithms, something which, to date, has not been observed in the evolutionary algorithm literature. The methodology designed in this paper can be adopted to evaluate the importance of the parameters of any algorithm.</p