Extensive molecular dynamics simulations reveal that the interactions between proteins and poly(ethylene glycol) (PEG) can be described in terms of the surface composition of the proteins. PEG molecules accumulate around non-polar residues while avoiding the polar ones. A solvent-accessible-surface-area model of protein adsorption accurately fits a large set of data on the composition of the protein corona of poly(ethylene glycol)- and poly(phosphoester)-coated nanoparticles recently obtained by label-free proteomic mass spectrometry.

A self-consistent mean-field theory (SCMFT) study on the phase behaviors of AB-diblock copolymer solutions is presented. All of the possible thermodynamically stable ordered structures verified by experiments are considered and the phase diagrams are generated. This work mainly focuses on the cases of selective solvents. The results show that a series of phase transitions occurs upon dilution. In particular, large windows of lamellae þ cylinder coexistence are located for the cases of highly asymmetric diblock copolymers in strongly selective solvents, which is in agreement with previous experimental observations. The transitions between the "inverted" fcc structure and the "inverted" bcc structure are also observed. The calculation indicates that the copolymer-solvent interaction plays important roles in these transitions.

In this review, we summarize the recent interplay between proteomics and research on TCM, ranging from exploration of the medicinal materials to the biological basis of TCM concepts, and from pathological studies to pharmacological investigations. We show that proteomic analyses provide preliminary biological evidence of the promises in TCM, and the integration of proteomics with other omics and bioinformatics offers a comprehensive methodology to address the complications of TCM. Expert commentary: Currently, only limited information can be obtained regarding TCM issues and thus more work is required to resolve the ambiguity. As such, more collaborations between proteomics and other techniques (other omics, network pharmacology, etc.) are essential for deciphering the underlying biological basis in TCM topics.

We present a paradigm, combining chemical profiling, absorbed components detection in plasma and network analysis, for investigating the pharmacology of combination drugs and complex formulae. On the one hand, the composition of the formula is investigated comprehensively via mass spectrometry analysis, followed by pharmacological studies of the fractions as well as the plasma concentration testing for the ingredients. On the other hand, both the candidate target proteins and the effective ingredients of the formula are predicted via analyzing the corresponding networks. The most probable active compounds can then be identified by combining the experimental results with the network analysis. In order to illustrate the performance of the paradigm, we apply it to the Danggui-Jianzhong formula (DJF) from traditional Chinese medicine (TCM) and predict 4 probably active ingredients, 3 of which are verified experimentally to display anti-platelet activity, i.e., (Z)-Ligustilide, Licochalcone A and Pentagalloylglucose. Moreover, the 3-compound formulae composed of these 3 chemicals show better anti-platelet activity than DJF. In addition, the paradigm predicts the association between these 3 compounds and COX-1, and our experimental validation further shows that such association comes from the inhibitory effects of the compounds on the activity of COX-1.