Mechanism of the AppA_BLUF Photocycle Probed by Site-Specific Incorporation of Fluorotyrosine Residues: Effect of the Y21 p<i>K</i>_a on the Forward and Reverse Ground-State Reactions

The transcriptional antirepressor AppA is a blue light using flavin (BLUF) photoreceptor that releases the transcriptional repressor PpsR upon photoexcitation. Light activation of AppA involves changes in a hydrogen-bonding network that surrounds the flavin chromophore on the nanosecond time scale, while the dark state of AppA is then recovered in a light-independent reaction with a dramatically longer half-life of 15 min. Residue Y21, a component of the hydrogen-bonding network, is known to be essential for photoactivity. Here, we directly explore the effect of the Y21 p<i>K</i>_a on dark state recovery by replacing Y21 with fluorotyrosine analogues that increase the acidity of Y21 by 3.5 pH units. Ultrafast transient infrared measurements confirm that the structure of AppA is unperturbed by fluorotyrosine substitution, and that there is a small (3-fold) change in the photokinetics of the forward reaction over the fluorotyrosine series. However, reduction of 3.5 pH units in the p<i>K</i>_a of Y21 increases the rate of dark state recovery by 4000-fold with a Brønsted coefficient of ∼1, indicating that the Y21 proton is completely transferred in the transition state leading from light to dark adapted AppA. A large solvent isotope effect of ∼6–8 is also observed on the rate of dark state recovery. These data establish that the acidity of Y21 is a crucial factor for stabilizing the light activated form of the protein, and have been used to propose a model for dark state recovery that will ultimately prove useful for tuning the properties of BLUF photosensors for optogenetic applications

Additional file 1 of A tumor focused approach to resolving the etiology of DNA mismatch repair deficient tumors classified as suspected Lynch syndrome

Additional file 1: Table S1. Table displaying optimal cut-offs for the six tumor features determined previously (Walker et al. 2023) in the additive feature combination approach. Table S2. SLS tumors (n=13) that showed discordant MMR IHC findings between clinical diagnostic testing before study entry and testing completed internally during this study and the change in their MMR status and/or pattern of MMR protein loss. Table S3. The concordance between the final MMR IHC result and the predicted dMMR status from the additive feature combination approach overall and by tumor type. Table S4. The tumor MLH1 methylation testing completed for SLS tumors prior to entering the study showing either negative, inconclusive, or not tested results and the subsequent MLH1 methylation testing results from internal testing using MethyLight and MS-HRM assays highlighting the positive MLH1 methylation results found by this study. Table S5. Presentation of germline pathogenic variants and variants of uncertain clinical significance (VUS) identified in the MMR, MUTYH and POLE genes. Table S6. Summary of the clinicopathological features for the double somatic MMR mutation (dMMR-DS) tumors overall and by tumor type. Figure S1. Bar plots presenting the results from the additive tumor feature combination approach to assess the MMR status in the double somatic mutation cohort for A) all tumors combined and separated by B) CRC, C) EC and D) SST tissue types. Figure S2. Bar plot presenting the prevalence of pathogenic/likely pathogenic somatic mutations (including loss of heterozygosity, LOH) by subtype for the study cohort. Figure S3. Pie graphs displaying the frequency of the mutation combination type (two single somatic mutations versus a single somatic mutation with loss of heterozygosity (LOH)) as well as the type of mutation A) overall and B) separated by tissue type. Figure S4. Bar graphs presenting the site distribution in the double somatic mutation cohort across all CRCs and SSTs. Figure S5. Boxplots presenting the site distribution in the double somatic mutation cohort across all A) CRCs and B) SSTs. Significant (< 0.05) p-values are indicated for pairwise (t-test) and multigroup comparisons (Anova). Figure S6. Scatter plots presenting the PREMM5 score distribution in the test cohort for A) all tumors combined and separated by B) CRC, C) EC and D) SST tissue types. Figure S7. The distribution of tumor values for each of the six features that are included in the additive feature combination approach for determining tumor dMMR status grouped by molecular subtype and by combining sporadic dMMR groups dMMR-DS and dMMR-MLH1me into a “sporadic combined” group