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An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories.

ABSTRACT: BACKGROUND:Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produce a huge amount of folding simulation data. Hence there is a pressing need to be able to interpret and identify novel folding features from them. RESULTS:In this paper, we model each folding trajectory as a multi-dimensional curve. We then develop an effective multiple curve comparison (MCC) algorithm, called the enhanced partial order (EPO) algorithm, to extract features from a set of diverse folding trajectories, including both successful and unsuccessful simulation runs. The EPO algorithm addresses several new challenges presented by comparing high dimensional curves coming from folding trajectories. A detailed case study on miniprotein Trp-cage 1 demonstrates that our algorithm can detect similarities at rather low level, and extract biologically meaningful folding events. CONCLUSION:The EPO algorithm is general and applicable to a wide range of applications. We demonstrate its generality and effectiveness by applying it to aligning multiple protein structures with low similarities. For user's convenience, we provide a web server for the algorithm at http://db.cse.ohio-state.edu/EPO.

SUBMITTER: Sun H

PROVIDER: S-EPMC2571979 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories.

Sun Hong H Ferhatosmanoglu Hakan H Ota Motonori M Wang Yusu Y

BMC bioinformatics 20080818

<h4>Background</h4>Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produce a huge amount of folding simulation data. Hence there is a pressing need to be able to interpret and identify novel folding features from them.<h4>Results</h4>In this paper, we model each folding trajectory as a multi-dimensional curve. We then develop an effective multiple curve comparison (MCC) algorithm, cal ...[more]

PMID: 18710565

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Project description:IntroductionConsensus guidelines recommend targeting a vancomycin area under the curve to minimum inhibitory concentration (AUC24 :MIC) ratio of 400-600 to improve therapeutic success and reduce nephrotoxicity. Although guidelines specify either Bayesian software or first-order equations may be used to estimate AUC24 , there are currently no large studies directly comparing these methods.ObjectiveTo compare calculated vancomycin AUC24 using first-order equations with two-drug concentrations at steady state to Bayesian two- and one-concentration estimations.MethodsThis was a single-center, retrospective cohort study of 978 adult hospitalized patients receiving intravenous vancomycin between 2017 and 2019. Patients were included if they received at least 72 h of vancomycin and had two-serum drug concentrations obtained. AUC24 was calculated using first-order analytic (linear), Bayesian two-concentration, and Bayesian one-concentration methods for each patient. The InsightRx™ software platform was used to calculate Bayesian AUC24 . Pearson's correlation and clinical agreement (based on AUC24 classified as subtherapeutic, therapeutic, or supratherapeutic) were used to assess agreement between methods. Bland-Altman plots were used to assess mean difference (MD) and 95% limits of agreement (LOA).ResultsExcellent agreement was observed between linear and Bayesian two-concentration methods (r = 0.963, clinical agreement = 87.4%) and Bayesian two-concentration and one-concentration methods (r = 0.931, clinical agreement = 88.5%); however, a degree of variability was noted with 95% LOA -99 to 76 (MD = -11.5 mg*h/L) and -92 to 113 (MD = -10.4 mg*h/L), for the respective comparisons. The agreement between linear and Bayesian one-concentration approaches was less than prior comparisons (r = 0.823, clinical agreement = 76.8%) and demonstrated the greatest amount of variability with 95% LOA -197 to 153 (MD = -21.9 mg*h/L).ConclusionsLinear and Bayesian two-concentration methods demonstrated high-level agreement with acceptable variability and may be considered comparable to estimate vancomycin AUC24 . As linear and Bayesian one-concentration methods demonstrated significant variability and suboptimal agreement, concerns exist surrounding the interchangeability of these methods in clinical practice, particularly at higher extremes of AUC24 .

Dataset Information

An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories.

Publications

An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories.

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