Biologically inert gases play important roles in the biological functionality of proteins. However, researchers lack a full understanding of the effects of these gases since they are very chemically stable only weakly absorbed by biological tissues. By combining X-ray fluorescence, particle sizing and molecular dynamics (MD) simulations, this work shows that the aggregation of these inert gases near the hydrophobic active cavity of pepsin should lead to protein deactivation. Micro X-ray fluorescence spectra show that a pepsin solution can contain a high concentration of Xe or Kr after gassing, and that the gas concentrations decrease quickly with degassing time. Biological activity experiments indicate a reversible deactivation of the protein during this gassing and degassing. Meanwhile, the nanoparticle size measurements reveal a higher number of "nanoparticles" in gas-containing pepsin solution, also supporting the possible interaction between inert gases and the protein. Further, MD simulations indicate that gas molecules can aggregate into a tiny bubble shape near the hydrophobic active cavity of pepsin, suggesting a mechanism for reducing their biological function.

Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.

Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.

The interaction among proteins is essential in all life activities, and it is the basis of all the metabolic activities of the cells. By studying the protein-protein interactions (PPIs), people can better interpret the function of protein, decoding the phenomenon of life, especially in the design of new drugs with great practical value. Although many high-throughput techniques have been devised for large-scale detection of PPIs, these methods are still expensive and time-consuming. For this reason, there is a much-needed to develop computational methods for predicting PPIs at the entire proteome scale. In this article, we propose a new approach to predict PPIs using Rotation Forest (RF) classifier combine with matrix-based protein sequence. We apply the Position-Specific Scoring Matrix (PSSM), which contains biological evolution information, to represent protein sequences and extract the features through the two-dimensional Principal Component Analysis (2DPCA) algorithm. The descriptors are then sending to the rotation forest classifier for classification. We obtained 97.43% prediction accuracy with 94.92% sensitivity at the precision of 99.93% when the proposed method was applied to the PPIs data of yeast. To evaluate the performance of the proposed method, we compared it with other methods in the same dataset, and validate it on an independent datasets. The results obtained show that the proposed method is an appropriate and promising method for predicting PPIs.

A series of biodegradable Mg-Cu alloys is designed to induce osteogenesis, stimulate angiogenesis, and provide long-lasting antibacterial performance at the same time. The Mg-Cu alloys with precipitated Mg2Cu intermetallic phases exhibit accelerated degradation in the physiological environment due to galvanic corrosion and the alkaline environment combined with Cu release endows the Mg-Cu alloys with prolonged antibacterial effects. In addition to no cytotoxicity towards HUVECs and MC3T3-E1 cells, the Mg-Cu alloys, particularly Mg-0.03Cu, enhance the cell viability, alkaline phosphatase activity, matrix mineralization, collagen secretion, osteogenesis-related gene and protein expressions of MC3T3-E1 cells, cell proliferation, migration, endothelial tubule forming, angiogenesis-related gene, and protein expressions of HUVECs compared to pure Mg. The favorable osteogenesis and angiogenesis are believed to arise from the release of bioactive Mg and Cu ions into the biological environment and the biodegradable Mg-Cu alloys with osteogenesis, angiogenesis, and long-term antibacterial ability are very promising in orthopedic applications.