Dataset Information

Hot spots in a network of functional sites.

ABSTRACT: It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8-58%, C 84-95%, P 5-19% and A 81-92% on unbound structures and S 8-51%, C 97-99%, P 14-50%, A 94-97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.

SUBMITTER: Ozbek P

PROVIDER: S-EPMC3759471 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Hot spots in a network of functional sites.

Ozbek Pemra P Soner Seren S Haliloglu Turkan T

PloS one 20130902 9

It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8-58%, C 84-95%, P 5-19% and A 81-92% on unbound structures and S 8-51%, C 97-99%, P 14-50%, A 94-97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and ac ...[more]

PMID: 24023934

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Project description:BACKGROUND:Overuse and misuse of prescription opioids have become signi?cant public health burdens in the United States. About 11.5 million people are estimated to have misused prescription opioids for nonmedical purposes in 2016. This has led to a signi?cant number of drug overdose deaths in the United States. Previous studies have examined spatiotemporal clusters of opioid misuse, but they have been restricted to circular shaped regions. OBJECTIVE:The goal of this study was to identify spatiotemporal hot spots of opioid users and opioid prescription claims using Medicare data. METHODS:We examined spatiotemporal clusters with signi?cantly higher number of bene?ciaries and rate of prescriptions for opioids using Medicare payment data from the Centers for Medicare & Medicaid Services. We used network scan statistics to detect signi?cant clusters with arbitrary shapes, the Kulldorff scan statistic to examine the signi?cant clusters for each year (2013, 2014, and 2015) and an expectation-based version to examine the signi?cant clusters relative to past years. Regression analysis was used to characterize the demographics of the counties that are a part of any signi?cant cluster, and data mining techniques were used to discover the specialties of the anomalous providers. RESULTS:We examined anomalous spatial clusters with respect to opioid prescription claims and bene?ciary counts and found some common patterns across states: the counties in the most anomalous clusters were fairly stable in 2014 and 2015, but they have shrunk from 2013. In Virginia, a higher percentage of African Americans in a county lower the odds of the county being anomalous in terms of opioid beneficiary counts to about 0.96 in 2015. For opioid prescription claim counts, the odds were 0.92. This pattern was consistent across the 3 states and across the 3 years. A higher number of people in the county with access to Medicaid increased the odds of the county being in the anomalous cluster to 1.16 in both types of counts in Virginia. A higher number of people with access to direct purchase of insurance plans decreased the odds of a county being in an anomalous cluster to 0.85. The expectation-based scan statistic, which captures change over time, revealed different clusters than the Kulldorff statistic. Providers with an unusually high number of opioid beneficiaries and opioid claims include specialties such as physician's assistant, nurse practitioner, and family practice. CONCLUSIONS:Our analysis of the Medicare claims data provides characteristics of the counties and provider specialties that have higher odds of being anomalous. The empirical analysis identifies highly refined spatial hot spots that are likely to encounter prescription opioid misuse and overdose. The methodology is generic and can be applied to monitor providers and their prescription behaviors in regions that are at a high risk of abuse.

Dataset Information

Hot spots in a network of functional sites.

Publications

Hot spots in a network of functional sites.

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