Multi-site programmable functionalization of alkenes via controllable alkene isomerization.

Direct and selective functionalization of hydrocarbon chains is a fundamental problem in synthetic chemistry. Conventional functionalization of C=C double bonds and C(sp3)-H bonds provides some solutions, but site diversity remains an issue. The merging of alkene isomerization with (oxidative) functionalization provides an ideal method for remote functionalization, which would provide more opportunities for site diversity. However, the reported functionalized sites are still limited and focus on a specific terminal position and internal site; new site-selective functionalization, including multi-functionalization, remains a largely unmet challenge. Here we describe a palladium-catalysed aerobic oxidative method for the multi-site programmable functionalization, involving the C=C double bond and multiple C(sp3)-H bonds, of terminal olefins via a strategy that controls the reaction sequence between alkene isomerization and oxidative functionalization. Specifically, 1-acetoxylation (anti-Markovnikov), 2-acetoxylation, 1,2-diacetoxylation and 1,2,3-triacetoxylation have been realized, accompanied by controllable remote alkenylation. This method enables available terminal olefins from petrochemical feedstocks to be readily converted into unsaturated alcohols and polyalcohols and particularly into different monosaccharides and C-glycosides.

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.

Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.