A novel class of polymers and oligomers of chiral folding chirality has been designed and synthesized, showing structurally compacted triple-column/multiple-layer frameworks. Both uniformed and differentiated aromatic chromophoric units were successfully constructed between naphthyl piers of this framework. Screening monomers, catalysts, and catalytic systems led to the success of asymmetric catalytic Suzuki-Miyaura polycouplings. Enantio- and diastereochemistry were unambiguously determined by X-ray structural analysis and concurrently by comparison with a similar asymmetric induction by the same catalyst in the asymmetric synthesis of a chiral three-layered product. The resulting chiral polymers exhibit intense fluorescence activity in a solid form and solution under specific wavelength irradiation.
more »
« less
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
-
more » « less
Abstract Motivation Human immunodeficiency virus type 1 (HIV-1) genome integration is closely related to clinical latency and viral rebound. In addition to human DNA sequences that directly interact with the integration machinery, the selection of HIV integration sites has also been shown to depend on the heterogeneous genomic context around a large region, which greatly hinders the prediction and mechanistic studies of HIV integration.
Results We have developed an attention-based deep learning framework, named DeepHINT, to simultaneously provide accurate prediction of HIV integration sites and mechanistic explanations of the detected sites. Extensive tests on a high-density HIV integration site dataset showed that DeepHINT can outperform conventional modeling strategies by automatically learning the genomic context of HIV integration from primary DNA sequence alone or together with epigenetic information. Systematic analyses on diverse known factors of HIV integration further validated the biological relevance of the prediction results. More importantly, in-depth analyses of the attention values output by DeepHINT revealed intriguing mechanistic implications in the selection of HIV integration sites, including potential roles of several DNA-binding proteins. These results established DeepHINT as an effective and explainable deep learning framework for the prediction and mechanistic study of HIV integration.
Availability and implementation DeepHINT is available as an open-source software and can be downloaded from https://github.com/nonnerdling/DeepHINT.
Supplementary information Supplementary data are available at Bioinformatics online.
