In this study, the first passage time(FPT) to reach the native binding state(the time required for the random walker to visit order parameter RMSD ~ 0 for the first time) is used as a typical or representative time scale for binding.

In Fig 10A—10C, we also show the one dimensional projection of binding free energy landscape to RMSD with different ligands with different intrinsic specificity characterized by ISR.

In this report, we use the RMSD as an order parameter ( RMSD represents the root mean square distance relative to the native binding structure) that represents the progress of binding towards native state.

For activation process, the order parameter or reaction coordinate RMSD is likely to be locally connected.

It is straightforward to see that the overall kinetic process involves diffusion in order parameter RMSD space.

We can use RMSD as order parameter to describe the position of an ensemble of states in the landscape.

Of the 3,021 mutations, 523 mutations (17.3%) were predicted to have a stabilizing effect according to all three metrics (energy, contacts, and RMSD ), while 421% of

(A) RMSD vs. simulation temperature.

The simulated Tm values evaluated using RMSD , total energy, and number of contacts are strongly correlated, as shown in Fig.

As mentioned, the simulated Tm values evaluated using RMSD , total energy, and number of contacts are strongly correlated in our simulations as shown in Fig.

As shown in figures 82 Fig—S4 Fig, the RMSD and total energy increased and the number of contacts decreased as each simulation proceeded, and with increasing temperature.

Plots of RMSD and contact number vs. temperature showed sigmoidal behavior, with a clearly identifiable transition temperature, and the melting temperature (Tm) could be determined by fitting to a sigmoidal function (Fig.

A 2,000,000-step simulation was then run at each of 32 temperatures, averaging over all 2,000,000 steps to obtain Energy, RMSD , and number of contacts.

Data was then plotted and fit to a sigmoid to obtain the computationally-predicted melting temperature, for each of Energy, RMSD , and number of contacts.

RMSD is averaged over 50 replications.

A high RMSD of 8.2(0.5)A beyond 25ns (Fig.

Above conformational rearrangements result in a high RMSD of ~8A at the end of the simulation (816 Fig).

Root mean square deviation ( RMSD ) calculated over 300ns simulation indicates the existence of three different ensembles (Fig.

1B): the first ensemble persists till ~16.5ns with RMSD centered around 2.8(0.7)A, the second one persists between 16.5-181ns with a RMSD of 4.7(0.7)A and the third one persists beyond ~181ns with the highest RMSD of 6.2(0.8)A.

Intriguingly, a high RMSD of 4.5(0.6)A observed between 16.5-100ns is associated with a change in glycosyl conformation of mismatched A23 and A8 from the starting anti conformation to syn conformation.

Note that While the latter attains the RMSD of ~8 A very early in the simulation, the former attains the RMSD of ~8 A only ~200ns as indicated by solid arrows.

.anti starting glycosyl conformation: one With RMSD of ~5 A during 200ns and other With RMSD of ~8 A beyond 200ns.

Importantly, the profiles calculated using the last 80-ns segments of the control and folding runs agree very well, with an overall RMSD of 0.014.

The correlation coefficient of the two contact maps is 0.91 and the RMSD is 0.016.

Clustering analysis With 5 A COL RMSD cutoff leads to numerous small clusters for all ensembles, With very feW clusters occupied over 1% (see 83—88 Figs).

The correlation efficient of two contacts is 0.91 and the RMSD is 0.016.

The resulting 4000-member ensembles were clustered using the fixed radius clustering algorithm as implemented in the MMTSB/ensclusterpl tool (with—kclust option), with a cutoff radius of 5 A Cor root-mean-square distance ( RMSD ).