140 research outputs found
EFFICIENT CONSTRUCTION OF DISORDERED PROTEIN ENSEMBLES IN A BAYESIAN FRAMEWORK WITH OPTIMAL SELECTION OF CONFORMATIONS
No full textConstructing an accurate model for the thermally accessible states of an Intrinsically Disordered Protein (IDP) is a fundamental problem in structural biology. This problem requires one to consider a large number of conformations in order to ensure that the model adequately represents the range of structures that the protein can adopt. Typically, one samples a wide range of structures in an attempt to obtain an ensemble that agrees with some pre-specified set of experimental data. However, models that contain more structures than the available experimental restraints are problematic as the large number of degrees of freedom in the ensemble leads to considerable uncertainty in the final model. We introduce a computationally efficient algorithm called Variational Bayesian Weighting with Structure Selection (VBWSS) for constructing a model for the ensemble of an IDP that contains a minimal number of conformations and, simultaneously, provides estimates for the uncertainty in properties calculated from the model. The algorithm is validated using reference ensembles and applied to construct an ensemble for the 140-residue IDP, monomeric α- synuclein.National Institutes of Health (U.S.) (Grant 5R21NS063185-02
Machine Learning Improves Risk Stratification After Acute Coronary Syndrome
No full textThe accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care. Commonly used risk metrics have been based on logistic regression models that incorporate aspects of the medical history, presenting signs and symptoms, and lab values. More sophisticated methods, such as Artificial Neural Networks (ANN), form an attractive platform to build risk metrics because they can easily incorporate disparate pieces of data, yielding classifiers with improved performance. Using two cohorts consisting of patients admitted with a non-ST-segment elevation acute coronary syndrome, we constructed an ANN that identifies patients at high risk of cardiovascular death (CVD). The ANN was trained and tested using patient subsets derived from a cohort containing 4395 patients (Area Under the Curve (AUC) 0.743) and validated on an independent holdout set containing 861 patients (AUC 0.767). The ANN 1-year Hazard Ratio for CVD was 3.72 (95% confidence interval 1.04-14.3) after adjusting for the TIMI Risk Score, left ventricular ejection fraction, and B-type natriuretic peptide. A unique feature of our approach is that it captures small changes in the ST segment over time that cannot be detected by visual inspection. These findings highlight the important role that ANNs can play in risk stratification
Constructing ensembles for intrinsically disordered proteins
No full textThe relatively flat energy landscapes associated with intrinsically disordered proteins makes modeling these systems especially problematic. A comprehensive model for these proteins requires one to build an ensemble consisting of a finite collection of structures, and their corresponding relative stabilities, which adequately capture the range of accessible states of the protein. In this regard, methods that use computational techniques to interpret experimental data in terms of such ensembles are an essential part of the modeling process. In this review, we critically assess the advantages and limitations of current techniques and discuss new methods for the validation of these ensembles
Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge
No full textThis paper describes novel fully automated techniques for analyzing large amounts of cardiovascular data. In contrast to traditional medical expert systems our techniques incorporate no a priori knowledge about disease states. This facilitates the discovery of unexpected events. We start by transforming continuous waveform signals into symbolic strings derived directly from the data. Morphological features are used to partition heart beats into clusters by maximizing the dynamic time-warped sequence-aligned separation of clusters. Each cluster is assigned a symbol, and the original signal is replaced by the corresponding sequence of symbols. The symbolization process allows us to shift from the analysis of raw signals to the analysis of sequences of symbols. This discrete representation reduces the amount of data by several orders of magnitude, making the search space for discovering interesting activity more manageable. We describe techniques that operate in this symbolic domain to discover rhythms, transient patterns, abnormal changes in entropy, and clinically significant relationships among multiple streams of physiological data. We tested our techniques on cardiologist-annotated ECG data from forty-eight patients. Our process for labeling heart beats produced results that were consistent with the cardiologist supplied labels 98.6 of the time, and often provided relevant finer-grained distinctions. Our higher level analysis techniques proved effective at identifying clinically relevant activity not only from symbolized ECG streams, but also from multimodal data obtained by symbolizing ECG and other physiological data streams. Using no prior knowledge, our analysis techniques uncovered examples of ventricular bigeminy and trigeminy, ectopic atrial rhythms with aberrant ventricular conduction, paroxysmal atrial tachyarrhythmias, atrial fibrillation, and pulsus paradoxus.Center for Integration of Medicine and Innovative TechnologyMIT Project OxygenBurroughs Wellcome FundHarvard University--MIT Division of Health Sciences and Technolog
Protein Structure along the Order–Disorder Continuum
No full textThermal fluctuations cause proteins to adopt an ensemble of conformations wherein the relative stability of the different ensemble members is determined by the topography of the underlying energy landscape. “Folded” proteins have relatively homogeneous ensembles, while “unfolded” proteins have heterogeneous ensembles. Hence, the labels “folded” and “unfolded” represent attempts to provide a qualitative characterization of the extent of structural heterogeneity within the underlying ensemble. In this work, we introduce an information-theoretic order parameter to quantify this conformational heterogeneity. We demonstrate that this order parameter can be estimated in a straightforward manner from an ensemble and is applicable to both unfolded and folded proteins. In addition, a simple formula for approximating the order parameter directly from crystallographic B factors is presented. By applying these metrics to a large sample of proteins, we show that proteins span the full range of the order–disorder axis.National Institutes of Health (U.S.) (NIH Grant 5R21NS063185-02
A Structure-free Method for Quantifying Conformational Flexibility in proteins
No full textAll proteins sample a range of conformations at physiologic temperatures and this inherent flexibility enables them to carry out their prescribed functions. A comprehensive understanding of protein function therefore entails a characterization of protein flexibility. Here we describe a novel approach for quantifying a protein’s flexibility in solution using small-angle X-ray scattering (SAXS) data. The method calculates an effective entropy that quantifies the diversity of radii of gyration that a protein can adopt in solution and does not require the explicit generation of structural ensembles to garner insights into protein flexibility. Application of this structure-free approach to over 200 experimental datasets demonstrates that the methodology can quantify a protein’s disorder as well as the effects of ligand binding on protein flexibility. Such quantitative descriptions of protein flexibility form the basis of a rigorous taxonomy for the description and classification of protein structure.Massachusetts Institute of Technology (Steve G. and Renee Finn Faculty Innovation Fellowship)Swiss National Science Foundation (Early Postdoc.Mobility Fellowship
Explaining the Structural Plasticity of α-Synuclein
No full textGiven that α-synuclein has been implicated in the pathogenesis of several neurodegenerative disorders, deciphering the structure of this protein is of particular importance. While monomeric α-synuclein is disordered in solution, it can form aggregates rich in cross-β structure, relatively long helical segments when bound to micelles or lipid vesicles, and a relatively ordered helical tetramer within the native cell environment. To understand the physical basis underlying this structural plasticity, we generated an ensemble for monomeric α-synuclein using a Bayesian formalism that combines data from NMR chemical shifts, RDCs, and SAXS with molecular simulations. An analysis of the resulting ensemble suggests that a non-negligible fraction of the ensemble (0.08, 95% confidence interval 0.03–0.12) places the minimal toxic aggregation-prone segment in α-synuclein, NAC(8–18), in a solvent exposed and extended conformation that can form cross-β structure. Our data also suggest that a sizable fraction of structures in the ensemble (0.14, 95% confidence interval 0.04–0.23) contains long-range contacts between the N- and C-termini. Moreover, a significant fraction of structures that contain these long-range contacts also place the NAC(8–18) segment in a solvent exposed orientation, a finding in contrast to the theory that such long-range contacts help to prevent aggregation. Lastly, our data suggest that α-synuclein samples structures with amphipathic helices that can self-associate via hydrophobic contacts to form tetrameric structures. Overall, these observations represent a comprehensive view of the unfolded ensemble of monomeric α-synuclein and explain how different conformations can arise from the monomeric protein.United States. National Institutes of Health (5R21NS063185-02
Modeling Intrinsically Disordered Proteins with Bayesian Statistics
No full textThe 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.National Institutes of Health (U.S.) (NIH Grant 5R21NS063185-02
Structural Basis of Low-Affinity Nickel Binding to the Nickel-Responsive Transcription Factor NikR from Escherichia coli
No full textEscherichia 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.National Institutes of Health (U.S.) (grant GM69857
Computers in Cardiology, 2009
No full textInformation in electrocardiographic (ECG) signals is widely believed to have value in the short-term prediction of arrhythmias. This study evaluates the use of morphologic variability (MV), a recently proposed metric measuring subtle variability in the shape of ECG signals over long periods, to risk stratify patients for arrhythmia following non-ST-elevation acute coronary syndrome (NSTEACS). We assessed the relationship between MV and the composite occurrence of ventricular tachycardia and cardiac pause (150 events) in 2302 patients admitted post-NSTEACS. On univariate analysis, high MV was strongly associated with the short-term occurrence of arrhythmias (HR 2.02; 95% CI 1.42-2.87; p < 0.001). The relationship between MV and arrhythmias was consistent even after adjusting for other risk variables (adjusted HR 2.04; 95% CI 1.37 - 3.03; p < 0.001). Our results suggest that MV may be a clinically useful tool for identifying patients at an increased short-term risk of serious arrhythmias in the setting of NSTEACS.Center for Integration of Medicine and Innovative TechnologyHarvard University--MIT Division of Health Sciences and TechnologyIndustrial Technology Research Institute (ITRI
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