The results show that the calculated ensembles reproduce local structural features of wild-type p53-TAD and the effects of K24N mutation quantitatively.

The calculated ensembles are in quantitative agreement With several types of existing NMR data on the Wild-type protein and the K24N mutant.

As shown in Fig 4A, the simulated helicity profile for the wild-type p53-TAD is highly consistent with NMR secondary chemical shift and NOE analysis[38] , predicting three partial helices in the same regions that show significant negative secondary chemical shifts, namely residues 18—27, 40—44 and 48—52.

A) Comparison of the average residue helicity profile with the secondary Hd chemical shifts for the wild-type p53-TAD[38].

Fig 5 compares the theoretical PRE profiles calculated from the last 80-ns of the folding RE-GA simulation of wild-type p53-TAD with experimental results previously measured for four site-specific spin labels[43].

As shown in Fig 2 for the wild-type p53-TAD, the residue helicity profiles calculated using various 80-ns segments quickly reach stationary states, showing very small differences between proflles calculated using data from 40—120 ns or 120—200 ns of the simulations (Fig 2A).

82 Fig illustrates that helical substate distributions largely stabilize by the end of 200-ns RE-GA simulations for both the wild-type p53-TAD and its cancer mutants and that the final distributions from the control and folding runs are largely consistent.

Furthermore, as shown in Fig 3, the structural ensembles derived from the control and folding simulations of the wild-type protein contain essentially identical sets of long-range contacts and with largely similar probabilities.

The quality of simulated ensembles Will be critically assessed by direct comparison With a Wide range of existing data that provide structural information on both the secondary and tertiary levels for the Wild-type protein and one of its mutants [37,39,40,43].

Residue helicity profiles for the wild-type p53-TAD and five cancer mutants, derived from the last 80-ns segments of the RE-GA simulations.

Estimated uncertainties are similar for all profiles and only shown for the wild-type for clarity.

Escherichia coli wild-type K12 strain NCM3722 [55,56] was used in our experiment.

In the main text, we used wild-type K12 strain NCM3722 and characterize its survival kinetics (solid symbols).

Note that NCFU of starving wild-type E. coli cells reported previously in the literature can be well approximated by a single-phase exponential decay [17—19].

We use these simulations to predict which mutants will be more thermodynamically stable (i.e., reside more often in the native folded state vs. the unfolded state) than the wild-type protein, and we confirm our predictions experimentally, creating several highly stable and catalytically active mutants.

We plan to use an approach developed in our lab [48] to endogenously introduce stabilized DHFR mutants into the bacterial chromosome and we will evaluate mutant fitness relative to wild-type using growth rates and competition experiments.

the mutational shift in observed unfolding temperature, normalized to the observed unfolding temperature of the wild-type at the same simulation condition does not depend on the simulation length, provided that the simulation is sufficiently equilibrated in the native basin so that the rules of transition state theory apply.