PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

Effect of a brief scenario-tailored educational program on parents' risk knowledge, perceptions, and decisions to administer prescribed opioids: a randomized controlled trial.

ABSTRACT: This randomized, controlled trial evaluated whether a brief educational program (ie, Scenario-Tailored Opioid Messaging Program [STOMP]) would improve parental opioid risk knowledge, perceptions, and analgesic efficacy; ensure safe opioid use decisions; and impact prescription opioid use after surgery. Parent-child dyads (n = 604) who were prescribed an opioid for short-term use were randomized to routine instruction (Control) or routine plus STOMP administered preoperatively. Baseline and follow-up surveys assessed parents' awareness and perceived seriousness of adverse opioid effects, and their analgesic efficacy. Parents' decisions to give an opioid in hypothetical scenarios and total opioid doses they gave to children at home were assessed at follow-up. Scenario-Tailored Opioid Messaging Program parents gained enhanced perceptions of opioid-related risks over time, whereas Controls did not; however, risk perceptions did not differ between groups except for addiction risk. Scenario-Tailored Opioid Messaging Program parents exhibited marginally greater self-efficacy compared to Controls (mean difference vs controls = 0.58 [95% confidence interval 0.08-1.09], P = 0.023). Scenario-Tailored Opioid Messaging Program parents had a 53% lower odds of giving an opioid in an excessive sedation scenario (odds ratio 0.47 [95% confidence interval 0.28-0.78], P = 0.003), but otherwise made similar scenario-based opioid decisions. Scenario-Tailored Opioid Messaging Program was not associated with total opioid doses administered at home. Instead, parents' analgesic efficacy and pain-relief preferences explained 7%, whereas child and surgical factors explained 22% of the variance in opioid doses. Scenario-tailored education enhanced parents' opioid risk knowledge, perceptions, and scenario-based decision-making. Although this may inform later situation-specific decision-making, our research did not demonstrate an impact on total opioid dosing, which was primarily driven by surgical and child-related factors.