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.

Kinetic Analysis of AAA+ Translocases by Combined Fluorescence and Anisotropy Methods.

The multitude of varied, energy-dependent processes that exist in the cell necessitate a diverse array of macromolecular machines to maintain homeostasis, allow for growth, and facilitate reproduction. ATPases associated with various cellular activity are a set of protein assemblies that function as molecular motors to couple the energy of nucleoside triphosphate binding and hydrolysis to mechanical movement along a polymer lattice. A recent boom in structural insights into these motors has led to structural hypotheses on how these motors fulfill their function. However, in many cases, we lack direct kinetic measurements of the dynamic processes these motors undergo as they transition between observed structural states. Consequently, there is a need for improved techniques for testing the structural hypotheses in solution. Here, we apply transient-state fluorescence anisotropy and total fluorescence stopped-flow methods to the analysis of polypeptide translocation catalyzed by these ATPase motors. We specifically focus on the Hsp100-Clp protein system of ClpA, which is a well-studied, model ATPases associated with various cellular activity system that has both eukaryotic and archaea homologs. Using this system, we show that we can reproduce previously established kinetic parameters from the simultaneous analysis of fluorescence anisotropy and total fluorescence and overcome previous limitations of our previous approach. Specifically, for the first time, to our knowledge, we obtain quantitative interpretations of the translocation of polypeptide substrates longer than 100 aa.

Conformational plasticity of the ClpAP AAA+ protease couples protein unfolding and proteolysis.

The ClpAP complex is a conserved bacterial protease that unfolds and degrades proteins targeted for destruction. The ClpA double-ring hexamer powers substrate unfolding and translocation into the ClpP proteolytic chamber. Here, we determined high-resolution structures of wild-type Escherichia coli ClpAP undergoing active substrate unfolding and proteolysis. A spiral of pore loop-substrate contacts spans both ClpA AAA+ domains. Protomers at the spiral seam undergo nucleotide-specific rearrangements, supporting substrate translocation. IGL loops extend flexibly to bind the planar, heptameric ClpP surface with the empty, symmetry-mismatched IGL pocket maintained at the seam. Three different structures identify a binding-pocket switch by the IGL loop of the lowest positioned protomer, involving release and re-engagement with the clockwise pocket. This switch is coupled to a ClpA rotation and a network of conformational changes across the seam, suggesting that ClpA can rotate around the ClpP apical surface during processive steps of translocation and proteolysis.

Hsp104 and Potentiated Variants Can Operate as Distinct Nonprocessive Translocases.

Heat shock protein (Hsp) 104 is a hexameric ATPases associated with diverse cellular activities motor protein that enables cells to survive extreme stress. Hsp104 couples the energy of ATP binding and hydrolysis to solubilize proteins trapped in aggregated structures. The mechanism by which Hsp104 disaggregates proteins is not completely understood but may require Hsp104 to partially or completely translocate polypeptides across its central channel. Here, we apply transient state, single turnover kinetics to investigate the ATP-dependent translocation of soluble polypeptides by Hsp104 and Hsp104A503S, a potentiated variant developed to resolve misfolded conformers implicated in neurodegenerative disease. We establish that Hsp104 and Hsp104A503S can operate as nonprocessive translocases for soluble substrates, indicating a "partial threading" model of translocation. Remarkably, Hsp104A503S exhibits altered coupling of ATP binding to translocation and decelerated dissociation from polypeptide substrate compared to Hsp104. This altered coupling and prolonged substrate interaction likely increases entropic pulling forces, thereby enabling more effective aggregate dissolution by Hsp104A503S.