To allow a quantitative comparison, we calculated the minimum Cor root mean square deviation ( RMSD ) distance between members of the cluster of dimeric complexes that formed during the simulations and each crystal structure listed in 82 Table.

The calculated RMSD values of S3 and S4 Tables suggest that the dimer interface from simulations that is closest to one inferred from crystal structures is the TM1,2,H8/TM1,2,H8 interface.

The relatively small RMSD values listed in S3 Table, indicate that the simulations of the 5-OR system also reproduced both symmetric and asymmetric dimer interfaces inferred from CXCR4 crystal structures [24] (see S2 Table for details) with reasonable accuracy.

Comparisons with available crystal structures of parallel interacting GPCRs (see 82 Table for a current list) were evaluated by calculating the overall COL RMSD .

For the evaluation of the identified hetero-dimeric interfaces, both pair combinations (e.g., u-OR/S-OR and 5-OR/u-OR) were considered for the superposition onto crystal structures, and the RMSD was defined as the minimum between the two individual RMSD values.

Minimum RMSD distances (below 10 A) between homo-dimeric complexes formed during simulation and selected crystal structures.

For each cluster of dimeric complexes that formed during simulation, the configuration corresponding to the lowest RMSD (highlighted in bold) is depicted in Figs.

Notably, the root mean square deviation ( RMSD ) of this loop conformation from the resolved loop of the newest high-resolution crystal structure of 5-OR [40] is 0.46 A overall.

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.

To test whether the chemical and physical properties evaluated by PocketFEATURE are captured by traditional structural-only metrics such as RMSD, we calculated the RMSD of the 20 pocket residues aligned to the same 20 residues of the 4DXD SaFtsZ-PC190723 co-crystal structure for each MD trajectory (Fig.

Large-magnitude pocket scores did not correspond with low RMSD from the SaFtsZ-PC190723 co-crystal, with SaFtsZ and SeFtsZ having values intermediate between the low values of APO SaFtsZ and BsFtsZ and the high values of SaFtsZG193D.

Our results establish that PocketFEATURE is able to not only distinguish aspects of the drug-binding pocket in FtsZ structures from different species that are not evident from methods such as RMSD , which only considers structural information; the algorithm also can evaluate how protein pockets in molecules from resistant mutants may differ from the optimal binding structure.

Root mean squared deviation ( RMSD ) of the pockets of the crystal structures to the pocket of the SaFtsZ-PC190723 co-crystal (Fig.

2B) clearly identified that Staphylococcus species have pocket structures that are more similar to that of the SaFtsZ-PC190723 co-crystal, but in contrast to PocketFEATURE, RMSD is unable to distinguish among non-Staphylococcus species.

(B) RMSD of the 3D coordinates of FtsZ crystal structures from the P0190723 co-crystal coarsely separate the proteins into close and distant relations of GDP-bound Staphylococcus FtsZ, but this measurement does not capture additional features of the drug pockets that distinguish between structures of medium and low similarity to the SaFtsZ-PC1 90723 co-crystal.

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 ).

Calculation of the root mean square displacement (RMSD) of the protein during the simulation revealed a sharp increase of the protein RMSD at the beginning of the simulations to ~ 0.35 nm.

The RMSD for the longer AT-MD simulations subsequently increased to final values of ~ 0.5 nm (89A Fig).

Root mean square deviation ( RMSD ) and the closed state of UraA.

Root mean square deviation ( RMSD ) of UraA as a function of the simulation time for the atomistic systems (UraA-AT in PE, PG, CL, UraA-pc-AT in PC and UraA-pe-AT in PE in Table 1 and 81 Table).