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.

A splice junction is considered differentially used between mutant and wild-type samples if the expression level of that junction differs significantly after accounting for overall expression differences of the corresponding gene locus.

We identified 1,749 junctions that were significantly differentially used between the SF3BI mutant and SF3BI wild-type samples across the three tumor types including 1,330 novel junctions, of which 1,117 are novel 3’SSs (BH-adjusted p < 0.1, 82 File).

All of the 619 proximal cryptic 3’SSs were used more often in the SF3BI mutant samples compared to the wild-type sam) and the novel junctions were enriched for novel 3’SSs ) showing that SF3BI mutations result in the usage of a large number of ples and 58% were out-of-frame relative to the nearby canonical 3’SSs, suggesting that these are not canonical 3’SSs missing from Gencode.

that allows for more accurate quantification of splicing and because the distribution of well-characterized low and high-risk CLL prognostic factors was similar between the SF3BI mutated and wild-type samples (Fig.

We observed that some cryptic 3’SSs are used exclusively in SF3BI mutants while others are also used in SF3BI wild-type samples but at a lower frequency relative to the mutants (Fig.

To investigate the potential role of NMD, we identified differentially expressed genes between the SF3BI mutant and wild-type samples in a joint analysis of all three cancers and performed a gene set enrichment analysis.

We found that while the cryptic 3’SSs are used more often in the SF3BI mutated samples compared to wild-type samples, they are used relatively infrequently (< 10%) compared to nearby canonical 3’SSs.

Prior studies have shown that mutated SF3BI CLL samples have differential exon inclusion and use some cryptic 3’ splice sites (3’SSs) relative to wild-type SF3BI CLL samples [5,6,8,10,11].

To test this, we examined splice site usage in transcriptome data from SF3BI mutant and SF3BI wild-type CLL, UM and BRCA cases.

We conducted MD simulations of GDP-bound dimers of wild-type SaFtsZ (Fig.

In the subunits of the wild-type dimer, we observed a statistically significant difference (t-test, p < 2.2e-16) in pocket scores, with the subunit with the pocket closest to the dimer interface having a better similarity score to the SaFtsZ-PC190723 co-crystal (Fig.

No such difference was evident in the PC190723-resis-tant G193D mutant dimer; both subunits had pocket scores throughout the trajectory that were similar to those of a wild-type subunit with a pocket away from the dimer interface

Similarity scores computed from the coordinates of all-atom MD simulations preserved the ranking order determined by their static crystal structures counterparts, with PC190723-resistant SaFtsZ mutants harboring pockets that were less similar to the SaFtsZ-PC190723 co-crystal than wild-type SaFtsZ pockets.

Finally, FtsZ polymerization increased the pocket similarity of wild-type SaFtsZ to the SaFtsZ-PC190723 co-crystal, but not that of a PC190723-resistant mutant.

We therefore compared the pockets of wild-type SaFtsZ with those of the PC190723-resistant mutants G193D, G196C, and N263K [12].

For example, over the simulation trajectory of the wild-type SaFtsZ monomer, an average of 17 out of the 20 residues contributed to the similarity score at any given time point during the simulation, resulting in an overall shift in the PocketFEATURE score to less negative values (decreased similarity).

All three SaFtsZ mutant monomers had significantly worse pocket scores in comparisons with the SaFtsZ-PC190723 co-crystal than a wild-type monomer, and similar scores to that of

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.

3 and were reproducible in the repeat CG-MD simulations performed with the wild-type UraA.

All the simulations with the mutated forms of UraA were initiated by replacing the wild-type protein with the mutated form of the protein after the exchange of lipids step as described in the Methods section.

In these simulations, after the exchange of the lipids, the wild-type form of UraA was replaced by the mutated form and 10 individual repeat production simulations of 1 us each were performed for each mutant.