Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.

Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature- and template-based strategies could improve the prediction of these key residues in proteins. Nevertheless, traditional hybrid algorithms have been surpassed by recently developed deep learning-based methods, and the possibility of integrating deep learning- and template-based approaches to improve performance remains to be explored. To address these issues, we developed a novel structure-based integrative algorithm called NABind that can accurately predict DNA- and RNA-binding residues. A deep learning module was built based on the diversified sequence and structural descriptors and edge aggregated graph attention networks, while a template module was constructed by transforming the alignments between the query and its multiple templates into features for supervised learning. Furthermore, the stacking strategy was adopted to integrate the above two modules for improving prediction performance. Finally, a post-processing module dependent on the random walk algorithm was proposed to further correct the integrative predictions. Extensive evaluations indicated that our approach could not only achieve excellent performance on both native and predicted structures but also outperformed existing hybrid algorithms and recent deep learning methods. The NABind server is available at http://liulab.hzau.edu.cn/NABind/.

Pharmacogenomic Cluster Analysis of Lung Cancer Cell Lines Provides Insights into Preclinical Model Selection in NSCLC.

Human lung cell lines are utilized widely for investigating tumor biology, experimental therapy, anticancer drug screening and biomarkers identification. However, the consistency of drug responses of these established cell lines and non-small cell lung cancer (NSCLC) is uncertain. In this study, we assessed the drug response consistency between lung cell lines and NSCLC tumors in The Cancer Genome Atlas by hierarchical clustering using copy number variations in driver genes, and profiled the molecular patterns and correlations in cell lines. We found that some frequently used cell lines of NSCLC subtypes were not clustered with their matched subtypes of tumor. Mutation profiles in the oxidative stress response and squamous differentiation pathway in lung cell lines were in concordance with lung squamous cell carcinoma. Furthermore, lung cell lines and tumors in the same sub-cluster had very similar responses to certain drugs but some were inconsistent, suggesting that clustering through copy number variation data could capture part of the suitability of lung cell lines. The analysis of these results could aid investigators in evaluating drug response models and eventually enabling personalized treatment recommendations for individual patients with NSCLC.

Dendritic CsSnI3 for Efficient and Flexible Near-Infrared Perovskite Light-Emitting Diodes.

All-inorganic and lead-free CsSnI3 is emerging as one of the most promising candidates for near-infrared perovskite light-emitting diodes (NIR Pero-LEDs), which find practical applications including facial recognition, biomedical apparatus, night vision camera, and Light Fidelity. However, in the CsSnI3 -based Pero-LEDs, the holes injection is significantly higher than that of electrons, resulting in unbalanced charge injection, undesired exciton dissipation, and poor device performance. Herein, it is proposed to manage charge injection and recombination behavior by tuning the interface area of perovskite and charge-transporter. A dendritic CsSnI3 structure is prepared on the hole-transporter, only making a bottom contact with the hole-transporter and exposing all other available crystal surfaces to the electron-transporter. In other words, the interface area of perovskite/electron-transporter is significantly higher than that of perovskite/hole-transporter. Moreover, the embedding interface of perovskite/electron-transporter can spatially confine holes and electrons, increasing the radiation recombination. By taking advantage of the dendritic structure, efficient lead-free NIR Pero-LEDs are achieved with a record external quantum efficiency (EQE) of 5.4%. More importantly, the dendritic structure shows great superiorities in flexible devices, for there is almost no morphology change after 2000-cycles of bends, and the fabricated Pero-LEDs can keep 93.4% of initial EQEs after 50-cycles of bends.

IdentPMP: identification of moonlighting proteins in plants using sequence-based learning models.

BACKGROUND: A moonlighting protein refers to a protein that can perform two or more functions. Since the current moonlighting protein prediction tools mainly focus on the proteins in animals and microorganisms, and there are differences in the cells and proteins between animals and plants, these may cause the existing tools to predict plant moonlighting proteins inaccurately. Hence, the availability of a benchmark data set and a prediction tool specific for plant moonlighting protein are necessary. METHODS: This study used some protein feature classes from the data set constructed in house to develop a web-based prediction tool. In the beginning, we built a data set about plant protein and reduced redundant sequences. We then performed feature selection, feature normalization and feature dimensionality reduction on the training data. Next, machine learning methods for preliminary modeling were used to select feature classes that performed best in plant moonlighting protein prediction. This selected feature was incorporated into the final plant protein prediction tool. After that, we compared five machine learning methods and used grid searching to optimize parameters, and the most suitable method was chosen as the final model. RESULTS: The prediction results indicated that the eXtreme Gradient Boosting (XGBoost) performed best, which was used as the algorithm to construct the prediction tool, called IdentPMP (Identification of Plant Moonlighting Proteins). The results of the independent test set shows that the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUC) of IdentPMP is 0.43 and 0.68, which are 19.44% (0.43 vs. 0.36) and 13.33% (0.68 vs. 0.60) higher than state-of-the-art non-plant specific methods, respectively. This further demonstrated that a benchmark data set and a plant-specific prediction tool was required for plant moonlighting protein studies. Finally, we implemented the tool into a web version, and users can use it freely through the URL: http://identpmp.aielab.net/.

dbMCS: A Database for Exploring the Mutation Markers of Anti-Cancer Drug Sensitivity.

The identification of mutation markers and the selection of appropriate treatment for patients with specific genome mutations are important steps in the development of targeted therapies and the realization of precision medicine for human cancers. To investigate the baseline characteristics of drug sensitivity markers and develop computational methods of mutation effect prediction, we presented a manually curated online-based database of mutation Markers for anti-Cancer drug Sensitivity (dbMCS). Currently, dbMCS contains 1271 mutations and 4427 mutation-disease-drug associations (3151 and 1276 for sensitivity and resistance, respectively) with their PubMed indexed articles. By comparing the mutations in dbMCS with the putative neutral polymorphisms, we investigated the characteristics of drug sensitivity markers. We found that the mutation markers tend to significantly impact on high-conservative regions both in DNA sequences and protein domains. And some of them presented pleiotropic effects depending on the tumor context, appearing concurrently in the sensitivity and resistance categories. In addition, we preliminarily explored the machine learning-based methods for identifying mutation markers of anti-cancer drug sensitivity and produced optimistic results, which suggests that a reliable dataset may provide new insights and essential clues for future cancer pharmacogenomics studies. dbMCS is available at http://bioinfo.aielab.cc/dbMCS/.

Effect of Pharmaceutical Excipients on Intestinal Absorption of Metformin via Organic Cation-Selective Transporters.

Growing evidence has shown that some pharmaceutical excipients can act on drug transporters. The present study was aimed at investigating the effects of 13 commonly used excipients on the intestinal absorption of metformin (MTF) and the underlying mechanisms using Caco-2 cells and an ex vivo mouse non-everted gut sac model. First, the uptake of MTF in Caco-2 cells was markedly inhibited by nonionic excipients including Solutol HS 15, polysorbate 20, polysorbate 40, polysorbate 60, polysorbate 80, and crospovidone. Second, transport profile studies showed that MTF was taken up via multiple cation-selective transporters, among which a novel pyrilamine-sensitive proton-coupled organic cation (H+/OC+) antiporter played a key role. Third, Solutol HS 15, polysorbate 40, and polysorbate 60 showed cis-inhibitory effects on the uptake of either pyrilamine (prototypical substrate of the pyrilamine-sensitive H+/OC+ antiporter) or 1-methyl-4-phenylpyridinium (substrate of traditional cation-selective transporters including OCTs, MATEs, PMAT, SERT, and THTR-2), indicating that their suppression on MTF uptake is due to the synergistic inhibition toward multiple influx transporters. Finally, the pH-dependent mouse intestinal absorption of MTF was significantly decreased by Solutol HS 15, polysorbate 20, polysorbate 40, polysorbate 60, polysorbate 80, and pyrilamine. In conclusion, this study revealed that a novel transport process mediated by the pyrilamine-sensitive H+/OC+ antiporter contributes to the intestinal absorption of MTF in conjunction with the traditional cation-selective transporters. Mechanistic understanding of the interaction of excipients with cation-selective transporters can improve the formulation design and clinical application of cationic drugs.

Mahuang Decoction Antagonizes Acute Liver Failure via Modulating Tricarboxylic Acid Cycle and Amino Acids Metabolism.

Acute liver failure (ALF) is a serious clinical disorder with high fatality rates. Mahuang decoction (MHD), a well-known traditional Chinese medicine, has multiple pharmacological effects, such as anti-inflammation, anti-allergy, anti-asthma, and anti-hyperglycemia. In this study, we investigated the protective effect of MHD against ALF. In the lipopolysaccharide and D-galactosamine (LPS/D-GalN)-induced ALF mouse model, the elevated activities of the serum alanine and aspartate transaminases as well as the liver pathological damage were markedly alleviated by MHD. Subsequently, a metabolomics study based on the ultrahigh performance liquid chromatograph coupled with Q Exactive Orbitrap mass spectrometry was carried to clarify the therapeutic mechanisms of MHD against ALF. A total of 36 metabolites contributing to LPS/D-GalN-induced ALF were identified in the serum samples, among which the abnormalities of 27 metabolites were ameliorated by MHD. The analysis of metabolic pathways revealed that the therapeutic effects of MHD are likely due to the modulation of the metabolic disorders of tricarboxylic acid (TCA) cycle, retinol metabolism, tryptophan metabolism, arginine and proline metabolism, nicotinate and nicotinamide metabolism, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan synthesis, as well as cysteine and methionine metabolism. This study demonstrated for the first time that MHD exerted an obvious protective effect against ALF mainly through the regulation of TCA cycle and amino acid metabolism, highlighting the importance of metabolomics to investigate the drug-targeted metabolic pathways.

Adsorption of RE3+ from aqueous solutions by bayberry tannin immobilized on chitosan.

Bayberry tannin immobilized on chitosan (CS-BT) was successfully prepared, and its adsorption performance was studied for aqueous solutions of rare earth ions. The as-prepared absorbents were characterized by Fourier transform infrared spectrometry and scanning electron microscopy. The equilibrium adsorption capacity was achieved in approximately 30 min. The adsorption process of CS-BT for Nd3+ was well fitted with a Freundlich model and the kinetics followed the pseudo-second-order rate equation. The maximum adsorption capacity for Nd3+ was 133.72 mg/g and dynamic adsorption characteristics of single ion (La3+, Ce3+, Nd3+) were investigated. The solution concentration was less than 30 mg/L when effluent volume was approximately 800 mL. Subsequently, the adsorbent column was desorbed by HNO3 solution. There was no significant loss of adsorption capacity after three cycles of regeneration, showing a satisfactory recyclability. Furthermore, CS-BT exhibited excellent dynamic adsorption performance of two mixed ions (La3+/Ce3+, La3+/Nd3+, Ce3+/Nd3+) and three mixed ions (La3+/Ce3+/Nd3+). The competitive adsorption capacity was La3+ < Ce3+ < Nd3+. The results indicate that the adsorption selectivity of column adsorption could provide a theoretical basis for the adsorption and separation of light rare earth ions. Therefore, this efficient adsorbent shows promising potential for the treatment of industrial wastewater.

Exploration of Deregulated Long Non-Coding RNAs in Association with Hepatocarcinogenesis and Survival.

Long non-coding RNAs (lncRNAs) are larger than 200 nucleotides in length and pervasively expressed across the genome. An increasing number of studies indicate that lncRNA transcripts play integral regulatory roles in cellular growth, division, differentiation and apoptosis. Deregulated lncRNAs have been observed in a variety of human cancers, including hepatocellular carcinoma (HCC). We determined the expression profiles of 90 lncRNAs for 65 paired HCC tumor and adjacent non-tumor tissues, and 55 lncRNAs were expressed in over 90% of samples. Eight lncRNAs were significantly down-regulated in HCC tumor compared to non-tumor tissues (p < 0.05), but no lncRNA achieved statistical significance after Bonferroni correction for multiple comparisons. Within tumor tissues, carrying more aberrant lncRNAs (6-7) was associated with a borderline significant reduction Cancers 2015, 7 1848 in survival (HR = 8.5, 95% CI: 1.0-72.5). The predictive accuracy depicted by the AUC was 0.93 for HCC survival when using seven deregulated lncRNAs (likelihood ratio test p = 0.001), which was similar to that combining the seven lncRNAs with tumor size and treatment (AUC = 0.96, sensitivity = 87%, specificity = 87%). These data suggest the potential association of deregulated lncRNAs with hepatocarcinogenesis and HCC survival.