CYP19A1, also known as aromatase or estrogen synthetase, is the rate-limiting enzyme in the biosynthesis of estrogens from their corresponding androgens. Several clinically used breast cancer therapies target aromatase. In this work, explicitly solvated all-atom molecular dynamics simulations of aromatase with a model of the lipid bilayer and the transmembrane helix are performed. The dynamics of aromatase and the role of titration of an important amino acid residue involved in aromatization of androgens are investigated via two 250-ns long simulations. One simulation treats the protonated form of the catalytic aspartate 309, which appears more consistent with crystallographic data for the active site, while the simulation of the deprotonated form shows some notable conformational shifts. Ensemble-based computational solvent mapping experiments indicate possible novel druggable binding sites that could be utilized by next-generation inhibitors. In addition, the effects of protonation on the ligand positioning and channel dynamics are investigated using geometrical models that estimate the opening width of critical channels. Significant differences in channel dynamics between the protonated and deprotonated trajectories are exhibited, suggesting that the mechanism for substrate and product entry and the aromatization process may be coupled to a “locking” mechanism and channel opening. Our results may be particularly relevant in the design of novel drugs, which may be useful therapeutic treatments of cancers such as those of the breast and prostate.

actual: 152 words)The recent explosion of biomedical knowledge presents both a major opportunity and challenge for scientists tackling complex problems in healthcare. Here we present an approach for synthesizing biomedical knowledge based on a combination of word-embeddings and select cooccurrences. We evaluated our ability to recapitulate and retrospectively predict disease-gene associations from the Online Mendelian Inheritance in Man (OMIM) resource. Our metrics achieved an area under the curve (AUC) value of 0.981 at the recapitulation task for 2,400 disease-gene associations. At the most stringent cutoff, our metrics predicted 13.89% of these associations before their first cooccurrence in the literature, with a median time of 4 years between prediction and first cooccurrence. Finally, our literature metrics can be combined with human genetics data to retrospectively predict disease-gene associations, IL-6 and Giant Cell Arteritis provided as an example. We believe this framework can provide robust biomedical hypotheses at a much faster pace than current standard practices.

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Tumor mutation burden (TMB) is used to select patients to receive immune checkpoint inhibitors (ICIs) but has mixed predictive capabilities. We hypothesized that inactivation of antigen presenting genes (APGs) that result from increased TMBs would result in inherent resistance to ICIs. We observed that somatic mutations in APGs were associated with increasing TMBs across 9,418 tumor samples of 33 different histological subtypes. In adenocarcinomas of the lung, ITGAX and CD1B were some of the most commonly mutated APGs. In 62 patients with non-small cell lung cancers treated with a PD-1 inhibitor in second or later lines of therapy, there was an association of increased TMB with mutations in APGs; however, mutations in one or more APGs were associated with improved progression-free survival. Contrary to our hypothesis, mutations in APGs were associated with improved progression-free survival with nivolumab, possibly due to the involvement of single alleles rather than complete loss.

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