Toward Determining ATPase Mechanism in ABC Transporters: Development of the Reaction Path–Force Matching QM/MM Method

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters are ubiquitous ATP-dependent membrane proteins involved in translocations of a wide variety of substrates across cellular membranes. To understand the chemomechanical coupling mechanism as well as functional asymmetry in these systems, a quantitative description of how ABC transporters hydrolyze ATP is needed. Complementary to experimental approaches, computer simulations based on combined quantum mechanical and molecular mechanical (QM/MM) potentials have provided new insights into the catalytic mechanism in ABC transporters. Quantitatively reliable determination of the free energy requirement for enzymatic ATP hydrolysis, however, requires substantial statistical sampling on QM/MM potential. A case study shows that brute force sampling of ab initio QM/MM (AI/MM) potential energy surfaces is computationally impractical for enzyme simulations of ABC transporters. On the other hand, existing semiempirical QM/MM (SE/MM) methods, although affordable for free energy sampling, are unreliable for studying ATP hydrolysis. To close this gap, a multiscale QM/MM approach named reaction path-force matching (RP-FM) has been developed. In RP-FM, specific reaction parameters for a selected SE method are optimized against AI reference data along reaction paths by employing the force matching technique. The feasibility of the method is demonstrated for a proton transfer reaction in the gas phase and in solution. The RP-FM method may offer a general tool for simulating complex enzyme systems such as ABC transporters

Improving Patient Safety through Medical Alert Management: An Automated Decision Tool to Reduce Alert Fatigue

Drug safety alerts, a feature of electronic medical records (EMRs), are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, there has also been increased understanding that alert fatigue, a state in which users become overwhelmed and unresponsive to alerts in general, is a threat to patient safety. In this paper, we seek to mitigate alert fatigue by filtering superfluous alerts. We design a method of predicting alert overrides based on past alert override rate, range in override rate, and sample size. Using a dataset from a large pediatric network, we retroactively test and validate our method. For the test implementation, alerts are filtered with 91–96% accuracy, depending on the parameter values selected. By filtering these alerts, we reduce alert fatigue and allow users to refocus resources to potentially vital alerts, reducing the occurrence of adverse drug events

Improving patient safety through medical alert management: an automated decision tool to reduce alert fatigue.

Drug safety alerts, a feature of electronic medical records (EMRs), are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, there has also been increased understanding that alert fatigue, a state in which users become overwhelmed and unresponsive to alerts in general, is a threat to patient safety. In this paper, we seek to mitigate alert fatigue by filtering superfluous alerts. We design a method of predicting alert overrides based on past alert override rate, range in override rate, and sample size. Using a dataset from a large pediatric network, we retroactively test and validate our method. For the test implementation, alerts are filtered with 91-96% accuracy, depending on the parameter values selected. By filtering these alerts, we reduce alert fatigue and allow users to refocus resources to potentially vital alerts, reducing the occurrence of adverse drug events