Artemyriantholidimers A-G, undescribed guaiane-type sesquiterpenoid dimers from Artemisia myriantha and their antihepatoma activities.

Artemyriantholidimers A-G (1-7), seven undescribed guaiane-type sesquiterpenoid dimers (GSDs), and 27 known compounds (8-34) were isolated from Artemisia myriantha (Asteraceae). Their structures and relative configurations were elucidated based on the comprehensive analyses of UV, IR, MS, NMR data, quantum chemical NMR calculations with DP4+ probability analyses, and the absolute configurations were elucidated by ECD calculations. The undescribed GSDs (1-7) were presumably formed via Diels-Alder reactions, and compounds 5-7 were rare GSDs with α-configuration of H-6'.

Artemordosins A-L, cadinane-monoterpene heterodimers and sesquiterpenoids with antihepatoma activity from Artemisia ordosica.

Two unprecedented sesquiterpene and monoterpene heterodimers and ten previously undescribed sesquiterpenoids, artemordosins A-L (1-12), as well as ten known sesquiterpenoids (13-22), were obtained from Artemisia ordosica. Their structures were elucidated based on comprehensive analyses of NMR, IR, HRESIMS, GIAO NMR calculations with DP4+ probability analysis, and ECD calculations. Notably, artemordosins A and B (1 and 2) were the first examples of cadinane-monoterpene dimers, and artemordosin A (1) was a cadinane-myrceane heterodimer with a 6/6/6/6 ring system formed by [4 + 2] cycloaddition, while artemordosin B (2) was a 4,5-seco-cadinane-artemisane dimer connected through a C-5-O-C-4' linkage.

Compound-protein interaction prediction based on heterogeneous network reveals potential antihepatoma agents.

Despite the development of an increasing number of multi-kinase and immune checkpoint inhibitors for hepatocellular carcinoma (HCC), improvement in cancer survival remains limited due to their similar structures and targets. Natural products (NPs) maintain diverse structures and activities and are important sources of drug discovery. Currently, most of active NPs exhibit ambiguous binding targets and mechanisms.

Artemyriantholides A-K, guaiane-type sesquiterpenoid dimers from Artemisia myriantha var. pleiocephala and their antihepatoma activity.

Artemyriantholides A-K (1-11) as well as 14 known compounds (12-25) were isolated from Artemisia myriantha var. pleiocephala (Asteraceae). The structures and absolute configuration of compounds 2 and 8-9 were confirmed by the single crystal X-ray diffraction analyses, and the others were elucidated by MS, NMR spectral data and electronic circular dichroism calculations.

Computational probing protein-protein interactions targeting small molecules.

Motivation: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects.

Network predicting drug's anatomical therapeutic chemical code.

Motivation: Discovering drug's Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug's potential ATC-codes by computational approaches.

Results: Here, we introduce drug-target network to computationally predict drug's ATC-codes and propose a novel method named NetPredATC.

Kernel-based data fusion improves the drug-protein interaction prediction.

Proteins are involved in almost every action of every organism by interacting with other small molecules including drugs. Computationally predicting the drug-protein interactions is particularly important in speeding up the process of developing novel drugs. To borrow the information from existing drug-protein interactions, we need to define the similarity among proteins and the similarity among drugs.

Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation.

Background: With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized.

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

Background: Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes.

Prediction of enzyme subfamily class via pseudo amino acid composition by incorporating the conjoint triad feature.

Predicting enzyme subfamily class is an imbalance multi-class classification problem due to the fact that the number of proteins in each subfamily makes a great difference. In this paper, we focus on developing the computational methods specially designed for the imbalance multi-class classification problem to predict enzyme subfamily class. We compare two support vector machine (SVM)-based methods for the imbalance problem, AdaBoost algorithm with RBFSVM (SVM with RBF kernel) and SVM with arithmetic mean (AM) offset (AM-SVM) in enzyme subfamily classification.

Prediction of palmitoylation sites using the composition of k-spaced amino acid pairs.

Palmitoylation is an important hydrophobic protein modification activity that participates many cellular processes, including signaling, neuronal transmission, membrane trafficking and so on. So it is an important problem to identify palmitoylated proteins and the corresponding sites. Comparing with the expensive and time-consuming biochemical experiments, the computational methods have attracted much attention due to their good performances in predicting palmitoylation sites.