Fig. 5
Improved accuracy and throughput of concurrent atomic force spectroscopy. a Monte Carlo simulations show that balancing datasets obtained in concurrent experiments improves the relative standard deviation (RSD) of distributions of \(\Delta \left\langle {F_{\mathrm{u}}} \right\rangle\) at calibration uncertainties >6%. Simulations of balanced datasets (solid lines, 100 events per experiment and protein) considered two experiments per protein in traditional (blue), and two experiments in concurrent atomic force spectroscopy (black). To simulate unbalanced datasets, uneven number of unfolding events (50/150) was considered for both proteins in the first simulated experiment, and the order was reversed in the second simulated experiment (dashed blue line). The effects of balancing datasets (“trimming”) were examined by running simulations at the lowest number of events for both experiments (50, dashed black line). b Improvement in the speed of data acquisition (red) and RSD in the distribution of \(\Delta \left\langle {F_{\mathrm{u}}} \right\rangle\) (black) by concurrent measurements are estimated using Monte Carlo simulations at 10.8% calibration uncertainty (100 events per experiment and protein). Values are compared to a situation where two proteins are measured in five traditional experiments per protein
