Voltage-gated potassium channels elicit membrane hyperpolarization through voltage-sensor domains that regulate the conductive status of the pore domain. To better understand the inherent basis for the open-closed equilibrium in these channels, we undertook an atomistic scan using synthetic fluorinated derivatives of aromatic residues previously implicated in the gating of Shaker potassium channels. Here we show that stepwise dispersion of the negative electrostatic surface potential of only one site, Phe481, stabilizes the channel open state. Furthermore, these data suggest that this apparent stabilization is the consequence of the amelioration of an inherently repulsive open-state interaction between the partial negative charge on the face of Phe481 and a highly co-evolved acidic side chain, Glu395, and this interaction is potentially modulated through the Tyr485 hydroxyl. We propose that the intrinsic open-state destabilization via aromatic repulsion represents a new mechanism by which ion channels, and likely other proteins, fine-tune conformational equilibria.
Background: Kir6.2 potassium channels are regulated by ATP. Results: We measured Kir6.2 gating kinetics in response to rapid ATP concentration jumps. Mutations to Trp-68 and Lys-170 dramatically decelerate gating. Conclusion: Trp-68 and Lys-170 interact to form a cluster that enables rapid gating transitions. Significance: The Trp-68/Lys-170 cluster is highly conserved and may play a similar role in other Kir channels.
Objectives Antidepressants are first-line treatments for major depressive disorder (MDD), but 40–60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict treatment outcomes based on clinical symptoms and episode features. We sought to independently replicate recent machine learning methodology predicting antidepressant outcomes using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, and then externally validate these methods to train models using data from the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) dataset. Methods We replicated methodology from Nie et al (2018) using common algorithms based on linear regressions and decision trees to predict treatment-resistant depression (TRD, defined as failing to respond to 2 or more antidepressants) in the STAR*D dataset. We then trained and externally validated models using the clinical features found in both datasets to predict response (≥50% reduction on the Quick Inventory for Depressive Symptomatology, Self-Rated [QIDS-SR]) and remission (endpoint QIDS-SR score ≤5) in the CAN-BIND-1 dataset. We evaluated additional models to investigate how different outcomes and features may affect prediction performance. Results Our replicated models predicted TRD in the STAR*D dataset with slightly better balanced accuracy than Nie et al (70%-73% versus 64%-71%, respectively). Prediction performance on our external methodology validation on the CAN-BIND-1 dataset varied depending on outcome; performance was worse for response (best balanced accuracy 65%) compared to remission (77%). Using the smaller set of features found in both datasets generally improved prediction performance when evaluated on the STAR*D dataset. Conclusion We successfully replicated prior work predicting antidepressant treatment outcomes using machine learning methods and clinical data. We found similar prediction performance using these methods on an external database, although prediction of remission was better than prediction of response. Future work is needed to improve prediction performance to be clinically useful.
ImportancePredicting short- and long-term survival of patients with cancer may improve their care. Prior predictive models either use data with limited availability or predict the outcome of only 1 type of cancer.ObjectiveTo investigate whether natural language processing can predict survival of patients with general cancer from a patient’s initial oncologist consultation document.Design, Setting, and ParticipantsThis retrospective prognostic study used data from 47 625 of 59 800 patients who started cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011, and December 31, 2016. Mortality data were updated until April 6, 2022, and data were analyzed from update until September 30, 2022. All patients with a medical or radiation oncologist consultation document generated within 180 days of diagnosis were included; patients seen for multiple cancers were excluded.ExposuresInitial oncologist consultation documents were analyzed using traditional and neural language models.Main Outcomes and MeasuresThe primary outcome was the performance of the predictive models, including balanced accuracy and receiver operating characteristics area under the curve (AUC). The secondary outcome was investigating what words the models used.ResultsOf the 47 625 patients in the sample, 25 428 (53.4%) were female and 22 197 (46.6%) were male, with a mean (SD) age of 64.9 (13.7) years. A total of 41 447 patients (87.0%) survived 6 months, 31 143 (65.4%) survived 36 months, and 27 880 (58.5%) survived 60 months, calculated from their initial oncologist consultation. The best models achieved a balanced accuracy of 0.856 (AUC, 0.928) for predicting 6-month survival, 0.842 (AUC, 0.918) for 36-month survival, and 0.837 (AUC, 0.918) for 60-month survival, on a holdout test set. Differences in what words were important for predicting 6- vs 60-month survival were found.Conclusions and RelevanceThese findings suggest that models performed comparably with or better than previous models predicting cancer survival and that they may be able to predict survival using readily available data without focusing on 1 cancer type.
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