Fig. 5: Biological implications of molecular binding memory.

Source data are provided as a Source Data file. a Fluctuation autocorrelation function of the number of binding pairs formed between stickers in a sticker-spacer polymer model with three different sequence types. While all sequences feature power-law behavior, they exhibit significantly different scaling exponents. The sequence patterns and corresponding typical conformations are shown on the right. b BAF of molecules adsorbed on the surface of a liquid droplet, in comparison with binding dynamics in bulk. Molecules at the droplet interface exhibit a stronger binding memory effect. Typical simulation snapshots of the two different systems are shown on the right. c BAF of CTCF, modeled as a ring polymer undergoing sliding adsorption on chromatin. A simulation snapshot, shown below, illustrating this process, with the CTCF molecule depicted as a red ring polymer and specific binding sites marked as blue dots on the gray chromatin polymer. d BAF characterization of interactions between two lipid components in mixed DPPC/DPPS and DOPC/DHPC lipid bilayers. Side and top views of the simulated mixed lipid bilayer (DOPC/DHPC) are presented below, while a corresponding snapshot of the DPPC/DPPS bilayer is provided in Supplementary Fig. 14. Data points in panels (a–d) represent averages over 40, 200, 200, and 1 independent simulation runs, respectively. Error bars indicate the standard deviation across independent trajectories. Power-law fitting were performed for each BAF curve over the following time ranges: (a) 7500-200000 steps; (b) 4000 to 35000 steps (droplet interface), 3000–100000 steps (bulk case); (c) 60000–1000000 steps; (d) 100–2000 ns. Scaling exponents and fitted errors are shown in the respective panels.