Co-evolution of quaternary organization and novel RNA tertiary interactions revealed in the crystal structure of a bacterial protein–RNA toxin–antitoxin system

Genes encoding toxin–antitoxin (TA) systems are near ubiquitous in bacterial genomes and they play key roles in important aspects of bacterial physiology, including genomic stability, formation of persister cells under antibiotic stress, and resistance to phage infection. The CptIN locus from Eubacterium rectale is a member of the recently-discovered Type III class of TA systems, defined by a protein toxin suppressed by direct interaction with a structured RNA antitoxin. Here, we present the crystal structure of the CptIN protein–RNA complex to 2.2 Å resolution. The structure reveals a new heterotetrameric quaternary organization for the Type III TA class, and the RNA antitoxin bears a novel structural feature of an extended A-twist motif within the pseudoknot fold. The retention of a conserved ribonuclease active site as well as traits normally associated with TA systems, such as plasmid maintenance, implicates a wider functional role for Type III TA systems. We present evidence for the co-variation of the Type III component pair, highlighting a distinctive evolutionary process in which an enzyme and its substrate co-evolve

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 research.Peer reviewe