KKRR-ShPrP(120–232)-D178N event histogram and initial and optimal HMM models.

<p>(<b>a</b>) <b>(Left)</b> The KKRR-ShPrP(120–232)-D178N event histogram (red), and the corresponding high-state (black), mid-state (green) and low-state (blue) that make up the initial model for HMM analysis. The location of the peak (<i>q</i>) and width of the distribution (<i>b</i>) for each state are the same as for the optimal KKRR-ShPrP(120–232) model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone-0054982-g004" target="_blank">Figure 4B</a>). This choice for <i>q</i> and <i>b</i> serves to highlight how the individual states evolve and differ from that of wild-type PrP^C. <b>(Right)</b> The corresponding initial model parameters. Similar to the initial model for KKRR-ShPrP(120–232) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone-0054982-g004" target="_blank">Figure 4A</a>) we assume ignorance of the probabilities and therefore assume <i>π</i> to be uniformly distributed. Likewise, with the transition probability matrix (<i>A</i>), we assume all transitions to be equally likely. (<b>b</b>) <b>(Left)</b> After 36 iterations of the EM algorithm the optimal three-state model that best describes the data (i.e. the maximum likelihood model estimate) is converged upon. The individual states (properties and kinetics) are significantly different from wild-type PrP^C (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone-0054982-g004" target="_blank">Figure 4B</a>), highlighting the importance of amino-acid residue D178 to the dynamics and structural stability of PrP^C<b>(Right)</b> The corresponding optimal model parameters.