The next step is to capture the evolutionary nature of the folding process. We do this by identifying the evolutionary nature of SOAPs. As mentioned earlier, small proteins like BBA5 and GSGS often fold hierarchically and begin with local folded structures. As they fold, new SOAPs can be created and existing one can dissipate. To capture such evolutionary behavior, we proposed the concept of SOAP episodes, which provide an effective approach to model the evolution of interactions among spatial objects over time [5]. To reiterate, a SOAP episode E is defined as follows: E = (p, Fbeg, Fend), where p is a SOAP composed of one or more bit-patterns, p was created in frame Fbeg and persisted till frame Fend. Note that for a given p, it can be created more than once during protein folding, and thus can have more than one episode. To discover frequent (minLink = 1) SOAPs and their episodes in the trajectories of BBA5 and GSGS, we apply our SOAP mining algorithm as explained in our previous work [5].