I-PINE web server: an integrative probabilistic NMR assignment system for proteins.

Various methods for understanding the structural and dynamic properties of proteins rely on the analysis of their NMR chemical shifts. These methods require the initial assignment of NMR signals to particular atoms in the sequence of the protein, a step that can be very time-consuming. The probabilistic interaction network of evidence (PINE) algorithm for automated assignment of backbone and side chain chemical shifts utilizes a Bayesian probabilistic network model that analyzes sequence data and peak lists from multiple NMR experiments. PINE, which is one of the most popular and reliable automated chemical shift assignment algorithms, has been available to the protein NMR community for longer than a decade. We announce here a new web server version of PINE, called Integrative PINE (I-PINE), which supports more types of NMR experiments than PINE (including three-dimensional nuclear Overhauser enhancement and four-dimensional J-coupling experiments) along with more comprehensive visualization of chemical shift based analysis of protein structure and dynamics. The I-PINE server is freely accessible at http://i-pine.nmrfam.wisc.edu . Help pages and tutorial including browser capability are available at: http://i-pine.nmrfam.wisc.edu/instruction.html . Sample data that can be used for testing the web server are available at: http://i-pine.nmrfam.wisc.edu/examples.html .

Historical Controls in Randomized Clinical Trials: Opportunities and Challenges.

Randomized control trials (RCTs) with placebo are the gold standard for determining efficacy of novel pharmaceutical treatments. Since their inception, over 75 years ago, researchers have amassed a large body of underutilized data on outcomes in the placebo control arms of these trials. Although rare disease indications have used these historical placebo data as synthetic controls to reduce burden on patients and accelerate drug discovery, broad use of historical controls is in its infancy. Large-scale historical placebo data could be leveraged to benefit both drug developers and patients if warehoused and made more available to guide trial design and analysis. Here, we examine challenges in utilizing historical controls related to heterogeneity in trial design, outcome ascertainment, patient characteristics, and unmeasured pharmacogenomic effects. We then discuss the advantages and disadvantages of current approaches and propose a path forward to broader use of historical controls in RCTs.