Person: Stultz, Collin
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Stultz
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Collin
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Stultz, Collin
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Publication ECG Morphological Variability in Beat Space for Risk Stratification After Acute Coronary Syndrome(Blackwell Publishing Ltd, 2014) Liu, Yun; Syed, Zeeshan; Scirica, Benjamin; Morrow, David; Guttag, John V.; Stultz, CollinBackground: Identification of patients who are at high risk of adverse cardiovascular events after an acute coronary syndrome (ACS) remains a major challenge in clinical cardiology. We hypothesized that quantifying variability in electrocardiogram (ECG) morphology may improve risk stratification post‐ACS. Methods and Results: We developed a new metric to quantify beat‐to‐beat morphologic changes in the ECG: morphologic variability in beat space (MVB), and compared our metric to published ECG metrics (heart rate variability [HRV], deceleration capacity [DC], T‐wave alternans, heart rate turbulence, and severe autonomic failure). We tested the ability of these metrics to identify patients at high risk of cardiovascular death (CVD) using 1082 patients (1‐year CVD rate, 4.5%) from the MERLIN‐TIMI 36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non‐ST‐Elevation Acute Coronary Syndrome—Thrombolysis in Myocardial Infarction 36) clinical trial. DC, HRV/low frequency–high frequency, and MVB were all associated with CVD (hazard ratios [HRs] from 2.1 to 2.3 [P<0.05 for all] after adjusting for the TIMI risk score [TRS], left ventricular ejection fraction [LVEF], and B‐type natriuretic peptide [BNP]). In a cohort with low‐to‐moderate TRS (N=864; 1‐year CVD rate, 2.7%), only MVB was significantly associated with CVD (HR, 3.0; P=0.01, after adjusting for LVEF and BNP). Conclusions: ECG morphological variability in beat space contains prognostic information complementary to the clinical variables, LVEF and BNP, in patients with low‐to‐moderate TRS. ECG metrics could help to risk stratify patients who might not otherwise be considered at high risk of CVD post‐ACS.Publication Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome(Nature Publishing Group, 2016) Liu, Yun; Scirica, Benjamin; Stultz, Collin; Guttag, John V.Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phenomena at different heart rates. An alternative is to consider frequency with respect to heartbeats, or beatquency. We compared the use of frequency and beatquency domains to predict patient risk after an acute coronary syndrome. We then determined whether machine learning could further improve the predictive performance. We first evaluated the use of pre-defined frequency and beatquency bands in a clinical trial dataset (N = 2302) for the HRV risk measure LF/HF (the ratio of low frequency to high frequency power). Relative to frequency, beatquency improved the ability of LF/HF to predict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.001). Next, we used machine learning to learn frequency and beatquency bands with optimal predictive power, which further improved the AUC for beatquency to 0.753 (p < 0.001), but not for frequency. Results in additional validation datasets (N = 2255 and N = 765) were similar. Our results suggest that beatquency and machine learning provide valuable tools in physiological studies of HRV.Publication Structural basis of low-affinity nickel binding to the nickel-responsive transcription factor NikR from Escherichia coli(American Chemical Society, 2010) Phillips, Christine M.; Schreiter, Eric R.; Stultz, Collin; Drennan, Catherine L.Escherichia coli NikR regulates cellular nickel uptake by binding to the nik operon in the presence of nickel and blocking transcription of genes encoding the nickel uptake transporter. NikR has two binding affinities for the nik operon: a nanomolar dissociation constant with stoichiometric nickel and a picomolar dissociation constant with excess nickel [Bloom, S. L., and Zamble, D. B. (2004) Biochemistry 43, 10029−10038; Chivers, P. T., and Sauer, R. T. (2002) Chem. Biol. 9, 1141−1148]. While it is known that the stoichiometric nickel ions bind at the NikR tetrameric interface [Schreiter, E. R., et al. (2003) Nat. Struct. Biol. 10, 794−799; Schreiter, E. R., et al. (2006) Proc. Natl. Acad. Sci. U.S.A. 103, 13676−13681], the binding sites for excess nickel ions have not been fully described. Here we have determined the crystal structure of NikR in the presence of excess nickel to 2.6 Å resolution and have obtained nickel anomalous data (1.4845 Å) in the presence of excess nickel for both NikR alone and NikR cocrystallized with a 30-nucleotide piece of double-stranded DNA containing the nik operon. These anomalous data show that excess nickel ions do not bind to a single location on NikR but instead reveal a total of 22 possible low-affinity nickel sites on the NikR tetramer. These sites, for which there are six different types, are all on the surface of NikR, and most are found in both the NikR alone and NikR−DNA structures. Using a combination of crystallographic data and molecular dynamics simulations, the nickel sites can be described as preferring octahedral geometry, utilizing one to three protein ligands (typically histidine) and at least two water molecules.Publication Modeling Intrinsically Disordered Proteins with Bayesian Statistics(American Chemical Society, 2010) Fisher, Charles Kenneth; Huang, Austin; Stultz, CollinThe characterization of intrinsically disordered proteins is challenging because accurate models of these systems require a description of both their thermally accessible conformers and the associated relative stabilities or weights. These structures and weights are typically chosen such that calculated ensemble averages agree with some set of prespecified experimental measurements; however, the large number of degrees of freedom in these systems typically leads to multiple conformational ensembles that are degenerate with respect to any given set of experimental observables. In this work we demonstrate that estimates of the relative stabilities of conformers within an ensemble are often incorrect when one does not account for the underlying uncertainty in the estimates themselves. Therefore, we present a method for modeling the conformational properties of disordered proteins that estimates the uncertainty in the weights of each conformer. The Bayesian weighting (BW) formalism incorporates information from both experimental data and theoretical predictions to calculate a probability density over all possible ways of weighting the conformers in the ensemble. This probability density is then used to estimate the values of the weights. A unique and powerful feature of the approach is that it provides a built-in error measure that allows one to assess the accuracy of the ensemble. We validate the approach using reference ensembles constructed from the five-residue peptide met-enkephalin and then apply the BW method to construct an ensemble of the K18 isoform of the tau protein. Using this ensemble, we indentify a specific pattern of long-range contacts in K18 that correlates with the known aggregation properties of the sequence.Publication Searching for the Nik Operon: How a Ligand-Responsive Transcription Factor Hunts for its DNA Binding Site(American Chemical Society, 2010) Phillips, Christine M.; Stultz, Collin; Drennan, Catherine L.Transcription factors regulate a wide variety of genes in the cell and play a crucial role in maintaining cellular homeostasis. A major unresolved issue is how transcription factors find their specific DNA binding sequence in the vast expanse of the cell and how they do so at rates that appear faster than the diffusion limit. Here, we relate an atomic-detail model that has been developed to describe the transcription factor NikR’s mechanism of DNA binding to the broader theories of how transcription factors find their binding sites on DNA. NikR is the nickel regulatory transcription factor for many bacteria, and NikR from Escherichia coli is one of the best studied ligand-mediated transcription factors. For the E. coli NikR protein, there is a wide variety of structural, biochemical, and computational studies that provide significant insight into the NikR−DNA binding mechanism. We find that the two models, the atomic-level model for E. coli NikR and the cellular model for transcription factors in general, are in agreement, and the details laid out by the NikR system may lend additional credence to the current models for transcription factors searching for DNA.Publication Fibronectin Unfolding Revisited: Modeling Cell Traction-Mediated Unfolding of the Tenth Type-III Repeat(Public Library of Science, 2008) Gee, Elaine Pei-San; Ingber, Donald; Stultz, CollinFibronectin polymerization is essential for the development and repair of the extracellular matrix. Consequently, deciphering the mechanism of fibronectin fibril formation is of immense interest. Fibronectin fibrillogenesis is driven by cell-traction forces that mechanically unfold particular modules within fibronectin. Previously, mechanical unfolding of fibronectin has been modeled by applying tensile forces at the N- and C-termini of fibronectin domains; however, physiological loading is likely focused on the solvent-exposed RGD loop in the 10th type-III repeat of fibronectin (10FNIII), which mediates binding to cell-surface integrin receptors. In this work we used steered molecular dynamics to study the mechanical unfolding of 10FNIII under tensile force applied at this RGD site. We demonstrate that mechanically unfolding 10FNIII by pulling at the RGD site requires less work than unfolding by pulling at the N- and C- termini. Moreover, pulling at the N- and C-termini leads to 10FNIII unfolding along several pathways while pulling on the RGD site leads to a single exclusive unfolding pathway that includes a partially unfolded intermediate with exposed hydrophobic N-terminal β-strands – residues that may facilitate fibronectin self-association. Additional mechanical unfolding triggers an essential arginine residue, which is required for high affinity binding to integrins, to move to a position far from the integrin binding site. This cell traction-induced conformational change may promote cell detachment after important partially unfolded kinetic intermediates are formed. These data suggest a novel mechanism that explains how cell-mediated forces promote fibronectin fibrillogenesis and how cell surface integrins detach from newly forming fibrils. This process enables cells to bind and unfold additional fibronectin modules – a method that propagates matrix assembly.