Structural investigation of inhibitor designs targeting 3-dehydroquinate dehydratase from the shikimate pathway of <em>Mycobacterium tuberculosis</em>

International audienceThe shikimate pathway is essential in Mycobacterium tuberculosis and its absence in humans makes the enzymes of this pathway potential drug targets. In this report, we provide structural insights into ligand and inhibitor binding to 3-dehydroquinate dehydratase (dehydroquinase) from Mycobacterium tuberculosis (MtDHQase), the third enzyme of the shikimate pathway. The enzyme has been crystallized in complex with its reaction product, 3-dehydroshikimate, and with six different competitive inhibitors. The inhibitor 2,3-anhydroquinate mimics the flattened enol/enolate reaction intermediate and serves as an anchor molecule for four of the inhibitors investigated. MtDHQase also forms a complex with citrazinic acid, a planar analog of the reaction product. The structure of MtDHQase in complex with a 2,3-anhydroquinate moiety attached to a biaryl group shows that this group extends to an active site subpocket inducing significant structural re-arrangement. The flexible extensions of inhibitors designed to form π-stacking interactions with the catalytic Tyr24 residue have been investigated. The high resolution crystal structures of the MtDHQase complexes provide structural evidence for the role of the loop residues 19-24 in MtDHQase ligand binding and catalytic mechanism and provide rationale for the design and efficacy of inhibitors

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