Pharmacokinetic properties of a compound are important in drug discovery and development. These properties are most often estimated from the structural properties of a compound with a structural–activity relationship (QSAR) approach. Rapid advances in molecular pharmacology have characterized a number of important proteins that shape the pharmacokinetic profile of a compound. Previous studies have shown that molecular docking, which is capable of analyzing compound–protein interactions, could be applied to make a categorical estimation of a pharmacokinetic property. The present study focused on the binding affinity of human serum albumin (HSA) as an example to show that docking descriptors might also be useful to estimate the exact value of a pharmacokinetic property. A previously reported dataset containing 94 compounds with log KHSA values was analyzed. A support vector regression model based on the docking descriptors was able to approximate the observed log KHSA in the training and validation dataset with an R2 = 0.79. This accuracy was comparable to known QSAR models based on compound descriptors. In this case study, it was shown that an account of protein flexibility is essential to calculate informative docking descriptors for use in the quantitative estimation of log KHSA.Graphical abstractHighlights► Docking result descriptors can be used to estimate binding affinities. ► Descriptors need to summarize docking results from multiple protein conformations. ► Ligand flexibility is less of an issue in affinity estimations from docking results.

Natural products (NPs) have been widely used in traditional medicines and are a valuable source for new drug discovery. However, insufficient knowledge about their molecular mechanisms has limited the scope of their application and hindered the effort to design new drugs from their synergistic action strategies. Thus far, a systematic study of all NP ingredients in a traditional medicine recipe remains impractical. However encouraging results have begun to appear illustrating synergies between several principle active ingredients. In this work, we propose the use of structure activity relationship (SAR) to identify potential active ingredients in natural products, with the aim to facilitate experimental and computational characterizations of their therapeutic mechanisms and synergies. We call this approach the bioactive natural compound-likeness (BNC-likeness) approach, drawing a parallel to the concept of drug-likeness. In cross-validations and independent example tests, our approach displayed 90–92% sensitivity and 85–90% specificity, suggesting its practical usefulness. We also showed that BNC-like compounds were not just drug-like NP ingredients. BNC-like compounds and drug-like chemicals may share different structural characteristics. Therefore, BNC-likeness is a helpful novel conception inviting dedicated research.

G protein-coupled receptors (GPCRs) are the largest and most versatile family of transmembrane receptors in the cell, occupying the highest hierarchical positions in the regulation of many physiological processes. Although they have been extensively studied in a number of model insects, there have been few investigations of GPCRs in large Lepidopterans, such as Bombyx mori, an organism that provides a means to perform detailed tissue expression analyses, which may help to characterize GPCRs and their ligands. In addition, B. mori, also known as the silkworm, is an insect of substantial economic importance, due to its use in silk production and traditional medicines. In this work, we computationally identified 90 putative GPCRs in B. mori, 33 of which represent novel proteins. These GPCRs were annotated and compared with their homologs in Drosophila melanogaster and Anopheles gambiae. Phylogenetics analyses of the GPCRs from these three insects showed that GPCRs may easily duplicate or disappear during insect evolution, especially in the neuropeptide and protein hormone receptor subfamily. Interestingly, we observed a decrease in the quantity and diversity of the stress-tolerance gene, Methuselah, in B. mori, which may be related to its long history of domestication. Moreover, the presence of many Bombyx-specific GPCRs suggests that neither Drosophila nor Anopheles is good representatives for the GPCRs in the Class Insecta.

In this work, we report a case of episodic sitewise positive selection acting on the highly conserved SRP protein Ffh in Actinobacteria. An elevated non-synonymous to synonymous mutation ratio (ω) was observed along the non-terminal branches of the species tree, which contained 11 Actinobacteria species, where positively selected residues were frequently observed within the signal sequence-binding domain. Together with the estimated lineage-specific ω ratios for several core components in the major protein translocation systems, our data suggest that this positive selection might be partially driven by the diversification of signal sequences.