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In recent years, the detection of proteins by using bare graphene oxide (GO) to quench the fluorescence of fluorescein-labeled aptamers has been reported. However, the proteins can be adsorbed on the surface of bare GO to prevent the sensitivity from further being improved. In order to solve this problem, polyethylene glycol (PEG)-protected GO was used to prevent the proteins using thrombin as an example from nonspecific binding. The detection limit was improved compared to bare GO under the optimized ratio of GO to PEG concentration. The results show that our method is a promising technique for the detection of proteins.

MoS2, a family member of transition-metal dichalcogenides, has shown highly attractive superiority for detection arising from its unique physical and chemical properties.

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Aging shows a decline in overall physical function, and cellular senescence is the powerful catalyst leading to aging. Considering that aging will be accompanied with the emergence of various aging-related diseases, research on new antiaging drugs is still valuable. Extracellular vesicles (EVs), as tools for intercellular communication, are important components of the senescence-associated secretory phenotype (SASP), and they can play pathological roles in the process of cellular senescence. In addition, EVs are similar to their original cells in functions. Therefore, EVs derived from pathological tissues or body fluids may be closely related to the progression of diseases and become potential biomarkers, while those from healthy cells may have therapeutic effects. Moreover, EVs are satisfactory drug carriers. At present, numerous studies have supported the idea that engineered EVs could improve drug targeting ability and utilization efficiency. Here, we summarize the characteristics of EVs and cellular senescence and focus on the diagnostic and therapeutic potential of EVs in various aging-related diseases, including Alzheimer disease, osteoporosis, cardiovascular disease, diabetes mellitus and its complications, and skin aging.

Peptide-mediated interactions are crucial to a variety of functions in the living cell and are estimated to be involved in up to 40 % of all cellular processes. Fast and reliable inference of such interactions is fundamentally important for our understanding and, then, reconstruction of complete virtual interactomics involved in a specific cell, tissue or organism. In the current study, we performed structure-level characterization, modeling and prediction of protein-peptide recognition specificity and stability in a high-throughput manner. To achieve this, the classical chemometrics methodology quantitative structure-activity relationship (QSAR), which is traditionally applied to small-molecule entities such as drug compounds and environmental chemicals, was employed to statistically correlate structure features with binding affinities for a panel of structure-solved, affinity-known protein-peptide complexes compiled from the PDB database and literatures. In the standard QSAR procedure, various structural descriptors including physicochemical, geometrical and constitutional parameters that characterize diverse aspects of protein-peptide interaction property were derived from the biomacromolecular complex structure architecture, and these descriptors were then correlated with experimentally measured affinities by using the partial least squares (PLS) regression and Gaussian process (GP) in conjunction with genetic algorithm (GA) variable selection. The nonlinear GA/GP method was found to perform much well as compared to linear GA/PLS modeling, suggesting that the protein-peptide interaction system is highly complicated that may involve strong noise and interactive effect. The optimal GA/GP model revealed that the interface size and solvent effect play a critical role in protein-peptide binding, and other properties such as peptide length and flexibility also contribute significantly to the binding. A further test on 2,018 human amphiphysin SH3 domain-binding peptides demonstrated that the purposed QSAR modeling procedure is very fast and effective, which can thus be readily used to perform proteome-wide inference of peptide-mediated interactions.

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