Multi-start Evolutionary Nonlinear OpTimizeR (MENOTR): A hybrid parameter optimization toolbox.

Parameter optimization or "data fitting" is a computational process that identifies a set of parameter values that best describe an experimental data set. Parameter optimization is commonly carried out using a computer program utilizing a non-linear least squares (NLLS) algorithm. These algorithms work by continuously refining a user supplied initial guess resulting in a systematic increase in the goodness of fit. A well-understood problem with this class of algorithms is that in the case of models with correlated parameters the optimized output parameters are initial guess dependent. This dependency can potentially introduce user bias into the resultant analysis. While many optimization programs exist, few address this dilemma. Here we present a data analysis tool, MENOTR, that is capable of overcoming the initial guess dependence in parameter optimization. Several case studies with published experimental data are presented to demonstrate the capabilities of this tool. The results presented here demonstrate how to effectively overcome the initial guess dependence of NLLS leading to greater confidence that the resultant optimized parameters are the best possible set of parameters to describe an experimental data set. While the optimization strategies implemented within MENOTR are not entirely novel, the application of these strategies to optimize parameters in kinetic and thermodynamic biochemical models is uncommon. MENOTR was designed to require minimal modification to accommodate a new model making it immediately accessible to researchers with a limited programming background. We anticipate that this toolbox can be used in a wide variety of data analysis applications. Prototype versions of this toolbox have been used in a number of published investigations already, as well as ongoing work with chemical-quenched flow, stopped-flow, and molecular tweezers data sets. STATEMENT OF SIGNIFICANCE: Non-linear least squares (NLLS) is a common form of parameter optimization in biochemistry kinetic and thermodynamic investigations These algorithms are used to fit experimental data sets and report corresponding parameter values. The algorithms are fast and able to provide good quality solutions for models involving few parameters. However, initial guess dependence is a well-known drawback of this optimization strategy that can introduce user bias. An alternative method of parameter optimization are genetic algorithms (GA). Genetic algorithms do not have an initial guess dependence but are slow at arriving at the best set of fit parameters. Here, we present MENOTR, a parameter optimization toolbox utilizing a hybrid GA/NLLS algorithm. The toolbox maximizes the strength of each strategy while minimizing the inherent drawbacks.

Transient-state kinetic analysis of multi-nucleotide addition catalyzed by RNA polymerase I.

RNA polymerases execute the first step in gene expression: transcription of DNA into RNA. Eukaryotes, unlike prokaryotes, express at least three specialized nuclear multisubunit RNA polymerases (Pol I, Pol II, and Pol III). RNA polymerase I (Pol I) synthesizes the most abundant RNA, ribosomal RNA. Nearly 60% of total transcription is devoted to ribosomal RNA synthesis, making it one of the cell's most energy consuming tasks. While a kinetic mechanism for nucleotide addition catalyzed by Pol I has been reported, it remains unclear to what degree different nucleotide sequences impact the incorporation rate constants. Furthermore, it is currently unknown if the previous investigation of a single-nucleotide incorporation was sensitive to the translocation step. Here, we show that Pol I exhibits considerable variability in both kmax and K1/2values using an in vitro multi-NTP incorporation assay measuring AMP and GMP incorporations. We found the first two observed nucleotide incorporations exhibited faster kmax-values (∼200 s-1) compared with the remaining seven positions (∼60 s-1). Additionally, the average K1/2 for ATP incorporation was found to be approximately threefold higher compared with GTP, suggesting Pol I has a tighter affinity for GTP compared with ATP. Our results demonstrate that Pol I exhibits significant variability in the observed rate constant describing each nucleotide incorporation. Understanding of the differences between the Pol enzymes will provide insight on the evolutionary pressures that led to their specialized roles. Therefore, the findings resulting from this work are critically important for comparisons with other polymerases across all domains of life.

Defining the divergent enzymatic properties of RNA polymerases I and II.

Eukaryotes express at least three nuclear DNA-dependent RNA polymerases (Pols) responsible for synthesizing all RNA required by the cell. Despite sharing structural homology, they have functionally diverged to suit their distinct cellular roles. Although the Pols have been studied extensively, direct comparison of their enzymatic properties is difficult because studies are often conducted under disparate experimental conditions and techniques. Here, we directly compare and reveal functional differences between Saccharomyces cerevisiae Pols I and II using a series of quantitative in vitro transcription assays. We find that Pol I single-nucleotide and multinucleotide addition rate constants are faster than those of Pol II. Pol I elongation complexes are less stable than Pol II elongation complexes, and Pol I is more error prone than Pol II. Collectively, these data show that the enzymatic properties of the Pols have diverged over the course of evolution, optimizing these enzymes for their unique cellular responsibilities.

A Novel Assay for RNA Polymerase I Transcription Elongation Sheds Light on the Evolutionary Divergence of Eukaryotic RNA Polymerases.

Eukaryotic cells express at least three nuclear RNA polymerases (Pols), each with a unique set of gene targets. Though these enzymes are homologous, there are many differences among the Pols. In this study, a novel assay for Pol I transcription elongation was developed to probe enzymatic differences among the Pols. In Saccharomyces cerevisiae, a mutation in the universally conserved hinge region of the trigger loop, E1103G, induces a gain of function in the Pol II elongation rate, whereas the corresponding mutation in Pol I, E1224G, results in a loss of function. The E1103G Pol II mutation stabilizes the closed conformation of the trigger loop, promoting the catalytic step, the putative rate-limiting step for Pol II. In single-nucleotide and multinucleotide addition assays, we observe a decrease in the rate of nucleotide addition and dinucleotide cleavage activity by E1224G Pol I and an increase in the rate of misincorporation. Collectively, these data suggest that Pol I is at least in part rate-limited by the same step as Pol II, the catalytic step.