Identification of sites phosphorylated by the vaccinia virus B1R kinase in viral protein H5R

Background: Vaccinia virus gene B1R encodes a erine/threonine protein kinase. In vitro this protein kinase phosphorylates ribosomal proteins Sa and S2 and vaccinia virus protein H5R, proteins that become phosphorylated during infection. Nothing is known about the sites phosphorylated on these proteins or the general substrate specificity of the kinase. The work described is the first to address these questions. Results: Vaccinia virus protein H5R was phosphorylated by the B1R protein kinase in vitro, digested with V8 protease, and phosphopeptides separated by HPLC. The N-terminal sequence of one radioactively labelled phosphopeptide was determined and found to correspond to residues 81-87 of the protein, with Thr-84 and Thr-85 being phosphorylated. A synthetic peptide based on this region of the protein was shown to be a substrate for the B1R protein kinase, and the extent of phosphorylation was substantially decreased if either Thr residue was replaced by an Ala. Conclusions: We have identified the first phosphorylation site for the vaccinia virus B1R protein kinase. This gives important information about the substrate-specificity of the enzyme, which differs from that of other known protein kinases. It remains to be seen whether the same site is phosphorylated in vivo

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