A theory of Plasma Membrane Calcium Pump stimulation and activity

The ATP-driven Plasma Membrane Calcium pump or Ca(2+)-ATPase (PMCA) is characterized by a high affinity to calcium and a low transport rate compared to other transmembrane calcium transport proteins. It plays a crucial role for calcium extrusion from cells. Calmodulin is an intracellular calcium buffering protein which is capable in its Ca(2+) liganded form of stimulating the PMCA by increasing both the affinity to calcium and the maximum calcium transport rate. We introduce a new model of this stimulation process and derive analytical expressions for experimental observables in order to determine the model parameters on the basis of specific experiments. We furthermore develop a model for the pumping activity. The pumping description resolves the seeming contradiction of the Ca(2+):ATP stoichiometry of 1:1 during a translocation step and the observation that the pump binds two calcium ions at the intracellular site. The combination of the calcium pumping and the stimulation model correctly describes PMCA function. We find that the processes of calmodulin-calcium complex attachment to the pump and of stimulation have to be separated. Other PMCA properties are discussed in the framework of the model. The presented model can serve as a tool for calcium dynamics simulations and provides the possibility to characterize different pump isoforms by different type-specific parameter sets.Comment: 24 pages, 6 figure

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press