β-Di-substituted α-amino acids(AAs) contain adjacent carbon stereogenic centers and pose considerable synthetic challenge. Complementary to the conventional synthesis strategies based on the transformation of existi...β-Di-substituted α-amino acids(AAs) contain adjacent carbon stereogenic centers and pose considerable synthetic challenge. Complementary to the conventional synthesis strategies based on the transformation of existing functional groups, we envisioned these molecules could be quickly accessed via selective functionalization of sp3 hybridized C-H bonds on the side chains of common α-AA precursors. We report a readily applicable method to prepare β-alkynyl α-amino acids via Pd-catalyzed diastereoselective C(sp3)-H alkynylation of common α-amino acids precursors with acetylene bromide.展开更多
A copper-catalyzedα-selective C–H trifluoromethylation of acrylamides with TMSCF3 is described.A wide range of arenes and heteroarenes at theβ-position of acrylamides are compatible with the reaction,affording the ...A copper-catalyzedα-selective C–H trifluoromethylation of acrylamides with TMSCF3 is described.A wide range of arenes and heteroarenes at theβ-position of acrylamides are compatible with the reaction,affording the corresponding(E)-trifluoromethylated products in moderate to good yields.The reaction proceeded fast and can be completed within 30 min.展开更多
Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate predi...Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.展开更多
基金supported by the Pennsylvania State University and Natural Science Foundation(CAREER CHE-1055795)
文摘β-Di-substituted α-amino acids(AAs) contain adjacent carbon stereogenic centers and pose considerable synthetic challenge. Complementary to the conventional synthesis strategies based on the transformation of existing functional groups, we envisioned these molecules could be quickly accessed via selective functionalization of sp3 hybridized C-H bonds on the side chains of common α-AA precursors. We report a readily applicable method to prepare β-alkynyl α-amino acids via Pd-catalyzed diastereoselective C(sp3)-H alkynylation of common α-amino acids precursors with acetylene bromide.
基金the National Natural Science Foundation of China(Nos.21472211,21502212,21772211)Youth Innovation Promotion Association CAS(Nos.2014229 and2018293)+1 种基金Institutes for Drug Discovery and Development,Chinese Academy of Sciences(No.CASIMM 0120163006)Science and Technology Commission of Shanghai Municipality(No.17JC1405000)for financial support
文摘A copper-catalyzedα-selective C–H trifluoromethylation of acrylamides with TMSCF3 is described.A wide range of arenes and heteroarenes at theβ-position of acrylamides are compatible with the reaction,affording the corresponding(E)-trifluoromethylated products in moderate to good yields.The reaction proceeded fast and can be completed within 30 min.
文摘Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.
