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A proper description of proton donor-acceptor (D-A) distance fluctuations is crucial for understanding tunneling in proton-coupled electron transport (PCET). The typical harmonic approximation for the D-A potential results in a Gaussian probability distribution, which does not appropriately reflect the electronic repulsion forces that increase the energetic cost of sampling shorter D-A distances. Because these shorter distances are the primary channel for thermally activated tunneling, the analysis of tunneling kinetics depends sensitively on the inherently anharmonic nature of the D-A interaction. Thus, we have used quantum chemical calculations to account for the D-A interaction and developed an improved model for the analysis of experimental tunneling kinetics. Strong internal electric fields are also considered and found to contribute significantly to the compressive forces when the D-A distance distribution is positioned below the van der Waals contact distance. This model is applied to recent experiments on the wild type (WT) and a double mutant (DM) of soybean lipoxygenase-1 (SLO). The compressive force necessary to prepare the tunneling-active distribution in WT SLO is found to fall in the ∼ nN range, which greatly exceeds the measured values of molecular motor and protein unfolding forces. This indicates that ∼60-100 MV/cm electric fields, aligned along the D-A bond axis, must be generated by an enzyme conformational interconversion that facilitates the PCET tunneling reaction. Based on the absolute value of the measured tunneling rate, and using previously calculated values of the electronic matrix element, the population of this tunneling-active conformation is found to lie in the range 10-10, indicating this is a rare structural fluctuation that falls well below the detection threshold of recent ENDOR experiments. Additional analysis of the DM tunneling kinetics leads to a proposal that a disordered (high entropy) conformation could be tunneling-active due to its broad range of sampled D-A distances.

Long non-coding RNA’s (lncRNA) are RNA sequences that do not encode proteins and are greater than 200 nucleotides in length. They regulate complex cellular mechanisms and have been associated with prognosis in various types of cancer. We aimed to identify lncRNA sequences that are associated with high grade serous ovarian cancer (HGSC) and assess their impact on overall survival. RNA was extracted from 112 HGSC patients and 12 normal fallopian tube samples from our Biobank tissue repository. RNA was sequenced and the Ultrafast and Comprehensive lncRNA detection and quantification pipeline (UClncR) was used for the identification of lncRNA sequences. Univariate logistic and multivariate lasso regression analyses identified lncRNA that was associated with HGSC. Univariate and multivariate Cox proportional hazard ratios were used to evaluate independent predictors of survival. 1943 of 16,325 investigated lncRNA’s were differentially expressed in HGSC as compared to controls (p < 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA’s associated with survival in HGSC. Three out of these four were found to be independently significant after accounting for all clinical covariates. Lastly, seven lncRNAs were independently associated with initial response to chemotherapy; four portended a worse response, while three were associated with improved response. More research is needed, but there is potential for these lncRNAs to be used as biomarkers of HGSC or predictors of treatment outcome in the future.

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