Abstract
Stability and biological activity of proteins is highly dependent on their physicochemical environment. The development of realistic models of biological systems necessitates quantitative information on the response to changes of external conditions like pH, salinity and concentrations of substrates and allosteric modulators. Changes in just a few variable parameters rapidly lead to large numbers of experimental conditions, which go beyond the experimental capacity of most research groups. We implemented a computer-aided experimenting framework (“robot lab assistant”) that allows us to parameterize abstract, human-readable descriptions of micro-plate based experiments with variable parameters and execute them on a conventional 8 channel liquid handling robot fitted with a sensitive plate reader. A set of newly developed R-packages translates the instructions into machine commands, executes them, collects the data and processes it without user-interaction. By combining script-driven experimental planning, execution and data-analysis, our system can react to experimental outcomes autonomously, allowing outcome-based iterative experimental strategies. The framework was applied in a response-surface model based iterative optimization of buffer conditions and investigation of substrate, allosteric effector, pH and salt dependent activity profiles of pyruvate kinase (PYK). A diprotic model of enzyme kinetics was used to model the combined effects of changing pH and substrate concentrations. The 8 parameters of the model could be estimated from a single two-hour experiment using nonlinear least-squares regression. The model with the estimated parameters successfully predicted pH and PEP dependence of initial reaction rates, while the PEP concentration dependent shift of optimal pH could only be reproduced with a set of manually tweaked parameters. Differences between model-predictions and experimental observations at low pH suggest additional protonation-sites at the enzyme or substrates critical for enzymatic activity. The developed framework is a powerful tool to investigate enzyme reaction specifics and explore biological system behaviour in a wide range of experimental conditions.
Highlights
Lab automation systems generate large amounts of experimental data in a short amount of time and substantially reduce costs per data-point
In order to demonstrate how our framework can be used to find optimal reaction conditions for enzymatic assays in an automated iterative procedure, we performed an optimization of pH, KCl and Fructose-1,6-bisphosphate concentrations for maximum pyruvate kinase (PYK) activity in five rounds of experiments
To illustrate how our automated computer controlled framework can be used to investigate the influence of the physicochemical environment on enzyme activity, we investigated the effects of KCl, pH and Fructose 1,6-bisphosphate on PYK activity while keeping substrate (PEP) concentration constant at 1mM
Summary
Lab automation systems generate large amounts of experimental data in a short amount of time and substantially reduce costs per data-point. In order to be usable for changing experimental setups in a science lab, they have to be flexible and must provide more benefits than just a simple reduction of repetitive pipetting work. One such benefit can lie in the ability of a computer-controlled lab-automation-system to keep track of sample identities and reagent volumes. This allows the use of complicated, computer generated, experimental designs described by hundreds of decimal numbers that would be difficult or even impossible to implement manually. As the connection between sample identities, experimental parameters and measurement results is retained during the whole process, data-analysis and interpretation is greatly facilitated
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