Abstract
Our understanding of how proteins find and adopt their functional three-dimensional structure has largely arisen through experimental studies of the denaturant-and primary sequence-dependence of protein stability and the kinetics of folding. For many years, curve fitting software packages have been heavily utilized to fit simple models to these data. Although such software packages are easy to use for simple functions, they are often expensive and provide substantial impediments to applying more complex models or for the analysis of large datasets. Moreover, over the past decade, increasingly sophisticated analytical models have been generated, but without simple tools to enable routine analysis. Consequently, users have needed to generate their own tools or otherwise find willing collaborators. Here we present PyFolding, a free, open source, and extensible Python framework for the analysis and modeling of experimental protein folding and thermodynamic data. To demonstrate the utility of PyFolding, we provide examples of complex analysis: (i) multi-phase kinetic folding data fitted to linked equations and (ii) thermodynamic equilibrium data from consensus designed repeat proteins to both homo-and heteropolymer variants of the Ising model. Example scripts to perform these and other operations are supplied with the software. Further, we show that PyFolding can be used in conjunction with Jupyter notebooks as an easy way to share methods and analysis for publication and amongst research teams.
