The dynamics and marginal stability of CopY enable allosteric activation of DNA binding by Zn(ii) and inhibition by Cu(i).

BackgroundIdentifying patients with certain clinical criteria based on manual chart review of doctors’ notes is a daunting task given the massive amounts of text notes in the electronic health records (EHR). This task can be automated using text classifiers based on Natural Language Processing (NLP) techniques along with pattern recognition machine learning (ML) algorithms. The aim of this research is to evaluate the performance of traditional classifiers for identifying patients with Systemic Lupus Erythematosus (SLE) in comparison with a newer Bayesian word vector method.MethodsWe obtained clinical notes for patients with SLE diagnosis along with controls from the Rheumatology Clinic (662 total patients). Sparse bag-of-words (BOWs) and Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) matrices were produced using NLP pipelines. These matrices were subjected to several different NLP classifiers: neural networks, random forests, naïve Bayes, support vector machines, and Word2Vec inversion, a Bayesian inversion method. Performance was measured by calculating accuracy and area under the Receiver Operating Characteristic (ROC) curve (AUC) of a cross-validated (CV) set and a separate testing set.ResultsWe calculated the accuracy of the ICD-9 billing codes as a baseline to be 90.00% with an AUC of 0.900, the shallow neural network with CUIs to be 92.10% with an AUC of 0.970, the random forest with BOWs to be 95.25% with an AUC of 0.994, the random forest with CUIs to be 95.00% with an AUC of 0.979, and the Word2Vec inversion to be 90.03% with an AUC of 0.905.ConclusionsOur results suggest that a shallow neural network with CUIs and random forests with both CUIs and BOWs are the best classifiers for this lupus phenotyping task. The Word2Vec inversion method failed to significantly beat the ICD-9 code classification, but yielded promising results. This method does not require explicit features and is more adaptable to non-binary classification tasks. The Word2Vec inversion is hypothesized to become more powerful with access to more data. Therefore, currently, the shallow neural networks and random forests are the desirable classifiers.

The cooperativity of ligand binding is central to biological regulation and new approaches are needed to quantify these allosteric relationships. Herein, we exploit a suite of mass spectrometry (MS) experiments to provide novel insights into homotropic Cu-binding cooperativity, gas-phase stabilities and conformational ensembles of the D2-symmetric, homotetrameric copper sensitive operon repressor (CsoR) as a function of Cu(I) ligation state. Cu(I) binding is overall positively cooperative, but is characterized by distinct ligation state-specific cooperativities. Structural transitions occur upon binding the first and fourth Cu(I), with the latter occurring with significantly higher cooperativity than previous steps; this results in the formation of a holo-tetramer is that markedly more resistant than apo-, and partially-ligated CsoR tetramers toward surface-induced dissociation (SID).

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