Substrate control in stereoselective lanthionine biosynthesis.

Enzymes are typically highly stereoselective catalysts that enforce a reactive conformation on their native substrates. We report here a rare example in which the substrate controls the stereoselectivity of an enzyme-catalysed Michael-type addition during the biosynthesis of lanthipeptides. These natural products contain thioether crosslinks formed by a cysteine attack on dehydrated Ser and Thr residues. We demonstrate that several lanthionine synthetases catalyse highly selective anti-additions in which the substrate (and not the enzyme) determines whether the addition occurs from the re or si face. A single point mutation in the peptide substrate completely inverted the stereochemical outcome of the enzymatic modification. Quantum mechanical calculations reproduced the experimentally observed selectivity and suggest that conformational restraints imposed by the amino-acid sequence on the transition states determine the face selectivity of the Michael-type cyclization

Extreme rainfall and snowfall alter responses of soil respiration to nitrogen fertilization : a 3-year field experiment

Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 23 (2017): 3403-3417, doi:10.1111/gcb.13620.Extreme precipitation is predicted to be more frequent and intense accompanying global warming, and may have profound impacts on soil respiration (Rs) and its components, i.e., autotrophic (Ra) and heterotrophic (Rh) respiration. However, how natural extreme rainfall or snowfall events affect these fluxes are still lacking, especially under nitrogen (N) fertilization. In this study, extreme rainfall and snowfall events occurred during a 3-year field experiment, allowing us to examine their effects on the response of Rs, Rh and Ra to N supply. In normal rainfall years of 2011/2012 and 2012/2013, N fertilization significantly stimulated Rs by 23.9% and 10.9%, respectively. This stimulation was mainly due to the increase of Ra because of N-induced increase in plant biomass. In the record wet year of 2013/2014, however, Rs was independent on N supply because of the inhibition effect of the extreme rainfall event. Compared with those in other years, Rh and Ra were reduced by 36.8% and 59.1%, respectively, which were likely related to the anoxic stress on soil microbes and decreased photosynthates supply. Although N supply did not affect annual Rh, the response ratio (RR) of Rh flux to N fertilization decreased firstly during growing season, increased in nongrowing season and peaked during spring thaw in each year. Nongrowing season Rs and Rh contributed 5.5–16.4% to their annual fluxes, and were higher in 2012/2013 than other years due to the extreme snowfall inducing higher soil moisture during spring thaw. The RR of nongrowing season Rs and Rh decreased in years with extreme snowfall or rainfall compared to those in normal years. Overall, our results highlight the significant effects of extreme precipitation on responses of Rs and its components to N fertilization, which should be incorporated into models to improve the prediction of carbon-climate feedbacks.This research was funded by the Chinese Academy of Sciences (XDB15020100) and the National Natural Science Foundation of China (31561143011).2017-12-2

Smoking-Related DNA Alkylation Events Are Mapped at Single-Nucleotide Resolution

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Entity Alignment

This open access book systematically investigates the topic of entity alignment, which aims to detect equivalent entities that are located in different knowledge graphs. Entity alignment represents an essential step in enhancing the quality of knowledge graphs, and hence is of significance to downstream applications, e.g., question answering and recommender systems. Recent years have witnessed a rapid increase in the number of entity alignment frameworks, while the relationships among them remain unclear. This book aims to fill that gap by elaborating the concept and categorization of entity alignment, reviewing recent advances in entity alignment approaches, and introducing novel scenarios and corresponding solutions. Specifically, the book includes comprehensive evaluations and detailed analyses of state-of-the-art entity alignment approaches and strives to provide a clear picture of the strengths and weaknesses of the currently available solutions, so as to inspire follow-up research. In addition, it identifies novel entity alignment scenarios and explores the issues of large-scale data, long-tail knowledge, scarce supervision signals, lack of labelled data, and multimodal knowledge, offering potential directions for future research. The book offers a valuable reference guide for junior researchers, covering the latest advances in entity alignment, and a valuable asset for senior researchers, sharing novel entity alignment scenarios and their solutions. Accordingly, it will appeal to a broad audience in the fields of knowledge bases, database management, artificial intelligence and big data

Mechanistic studies of lanthipeptide biosynthesis

Natural products and natural product derivatives have been the leading source of pharmaceutical compounds since the initial application of modern medicine. To fight the increasing occurrence of drug resistance and to reach the ultimate goal of personalized drugs for treating complicated symptoms, novel natural products are demanded. Lanthipeptides are ribosomal peptides with post-translationally incorporated thioether crosslinks named lanthionine. This family of natural products has garnered substantial attention during the past few decades due to their favorable biological activities and the potential for engineering. This thesis focuses on a subclass of lanthipeptides, the class II compounds, for which the synthesis of dehydroamino acids and the formation of thioether linkages are carried out by a bifunctional lanthionine synthetase. Chapter 2 presents the structural characterization of the enterococcal cytolysin, a lanthipeptide tightly linked to Enterococcus faecalis virulence. The stereoselectivity of lanthionine synthesis is discussed in chapters 3 and 4. A non-canonical configuration of lanthionines was discovered in a few lanthipeptides and the selective synthesis of the unusual stereochemistry was found to be induced by the peptide sequence rather than the lanthionine synthetase. Such a substrate-controlled stereoselectivity is rarely identified in naturally occurring enzymatic processes. Although it has been more than ten years since the first in vitro reconstitution of a class II lanthionine synthetase, no structural information was available for this class of proteins before the study presented in chapter 5 with respect to the cytolysin synthetase CylM. Unexpectedly, the CylM dehydratase domain resembles the catalytic core of lipid kinases despite the absence of notable sequence homology. Mutagenesis study of CylM provides further insights into the mechanism of the modification process. The maturation of lanthipeptides typically requires a proteolytic step that removes the leader peptide from the modified precursor peptides. Characterization of two peptidases involved in the synthesis of lichenicidin and cytolysin, described in chapters 6 and 7, provides mechanistic insights into these subtilisin-like proteins and reveals their potential as sequence-specific proteases. In addition, structural elucidation of four prochlorosins, a set of lanthipeptides synthesized by a highly substrate-tolerant synthetase ProcM in marine bacteria, is included as chapter 8. The unveiled structural information of these lanthipeptides and the biochemical studies with respect to the biosynthetic process described in this thesis may assist the genome mining and synthetic biology efforts towards novel lanthipeptides for therapeutic purposes and other applications

Collective Entity Alignment via Adaptive Features

Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current solutions treat entities independently and fail to take into account the interdependence between entities. To fill this gap, we propose a collective EA framework. We first employ three representative features, i.e., structural, semantic and string signals, which are adapted to capture different aspects of the similarity between entities in heterogeneous KGs. In order to make collective EA decisions, we formulate EA as the classical stable matching problem, which is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority.Comment: ICDE2