Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO)

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The “Continuous Automated Model EvaluatiOn (CAMEO)” platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4-day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross-validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO—either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo-oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods.     

Molecular simulation of the preferential adsorption of CH4 and CO2 in middle-rank coal

The adsorption of the CO₂/CH₄ mixture in coal affects the CO₂-enhanced coalbed methane recovery project. To gain a better understanding of CH₄ and CO₂ interaction with middle-rank coal, we developed a molecular concept with support for the sorption of CH₄ and CO₂ on Ximing-8 coal (XM-8) (1.8% vitrinite reflectance). A XM-8 coal model was built by using molecular dynamic (MD) simulations. The molecular simulations were established by the Grand Canonical Monte Carlo and MD methods to study the effects of the temperature, pressure, and species bulk mole fraction on the pure component adsorption isotherms, isosteric heat and adsorption selectivity. It turns out that the CO₂ selectivity decreases as the pressure and its own bulk mole fraction increases, but it increases as temperature increases, and the selectivity values are not always greater than 1. The interactions between the small molecules and XM-8 were determined by using density functional theory. It was found that the interactions between the CO₂ and XM-8 surface is greater, particularly for the heteroatoms than CH₄. The adsorption selectivity and interaction were simultaneously used to reveal that the advantageously substituted range is high temperature, low pressure and a high content of heteroatoms.     

Evaluation of the combination mode and features of p38 MAPK inhibitors: construction of different pharmacophore models and molecular docking

Abnormal expression of tumour necrosis factor-α (TNF-α) can lead to various pathological reactions, such as arthritis, psoriasis, krone disease, etc. p38 mitogen-activated protein kinase (p38 MAPK) is an important signal transduction enzyme that plays important roles in influencing the release of intracellular TNF-α factor. It is very meaningful to study the targeting kinase with specific inhibitors in the treatment of related diseases. In order to achieve a deeper insight, it is necessary to analyse the structural characteristics and the action mode of the p38 MAPK inhibitors in the active site. In the study, a ligand-based common feature pharmacophore model and the receptor structure-based pharmacophore model were constructed, respectively. Their common chemical features consisted of the hydrophobic groups (H) and the hydrogen bond acceptors (A), and kept the consistency of spatial structure distribution. Then, the molecular docking and molecular dynamics simulation were performed with the eight training set compounds. The binding characteristics of molecules binding were described in the topological region of the active site. Finally, the structure–activity relationship (SAR) was obtained by analysing docking results with the different pharmacophore models. This research leads to the proposal of an interaction model in the p38 MAPK active site and provides guidance for the screening and design of more potent and selective p38 MAPK inhibitors.     

Synthetic biology and metabolic engineering of actinomycetes for natural product discovery

Actinomycetes are one of the most valuable sources of natural products with industrial and medicinal importance. After more than half a century of exploitation, it has become increasingly challenging to find novel natural products with useful properties as the same known compounds are often repeatedly re-discovered when using traditional approaches. Modern genome mining approaches have led to the discovery of new biosynthetic gene clusters, thus indicating that actinomycetes still harbor a huge unexploited potential to produce novel natural products. In recent years, innovative synthetic biology and metabolic engineering tools have greatly accelerated the discovery of new natural products and the engineering of actinomycetes. In the first part of this review, we outline the successful application of metabolic engineering to optimize natural product production, focusing on the use of multi-omics data, genome-scale metabolic models, rational approaches to balance precursor pools, and the engineering of regulatory genes and regulatory elements. In the second part, we summarize the recent advances of synthetic biology for actinomycetal metabolic engineering including cluster assembly, cloning and expression, CRISPR/Cas9 technologies, and chassis strain development for natural product overproduction and discovery. Finally, we describe new advances in reprogramming biosynthetic pathways through polyketide synthase and non-ribosomal peptide synthetase engineering. These new developments are expected to revitalize discovery and development of new natural products with medicinal and other industrial applications.     

Tribolium castaneum as a whole‐animal screening system for the detection and characterization of neuroprotective substances

Parkinson's disease (PD) is a movement disorder caused by the progressive loss of dopaminergic neurons. Natural antioxidants and plant extracts with neuroprotective properties offer a promising new therapeutic approach for PD patients, but a suitable large‐scale screening system is required for their discovery and preclinical analysis. Here we used the red flour beetle (Tribolium castaneum ) as a whole‐animal screening system for the detection and characterization of neuroprotective substances. Paraquat was added to the diet of adult beetles to induce PD‐like symptoms, which were quantified using a novel positive geotaxis behavioral assay. These paraquat‐induced behavioral changes were reduced in beetles fed on diets supplemented with l‐ dihydroxyphenylalanine, ascorbic acid, curcumin, hempseed flour, or the Chinese herb gou‐teng. T. castaneum is, therefore, a valuable model for the screening of neuroprotective substances in chemical libraries and plant extracts and could be developed as a model for the preclinical testing of therapeutic candidates for the treatment of neurodegenerative diseases, such as PD.     

Context‐dependent biotic interactions control plant abundance across altitudinal environmental gradients

Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context‐dependent across abiotic gradients. For example, plant–plant interactions can grade from competition to facilitation over temperature gradients. We used a hierarchical Bayesian framework to predict the abundances of 12 plant species across a mountain landscape and test hypotheses on the context‐dependency of biotic interactions over abiotic gradients. We combined field‐based estimates of six biotic interactions (foliar herbivory and pathogen damage, fungal root colonization, fossorial mammal disturbance, plant cover and plant diversity) with abiotic data on climate and soil depth, nutrients and moisture. All biotic interactions were significantly context‐dependent along temperature gradients. Results supported the stress gradient hypothesis: as abiotic stress increased, the strength or direction of the relationship between biotic variables and plant abundance generally switched from negative (suggesting suppressed plant abundance) to positive (suggesting facilitation/mutualism). For half of the species, plant cover was the best predictor of abundance, suggesting that the prior focus on plant–plant interactions is well‐justified. Explicitly incorporating the context‐dependency of biotic interactions generated novel hypotheses about drivers of plant abundance across abiotic gradients and may improve the accuracy of niche models.     

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

Impact of apolipoprotein A1- or lecithin:cholesterol acyltransferase-deficiency on white adipose tissue metabolic activity and glucose homeostasis in mice

High density lipoprotein (HDL) has attracted the attention of biomedical community due to its well-documented role in atheroprotection. HDL has also been recently implicated in the regulation of islets of Langerhans secretory function and in the etiology of peripheral insulin sensitivity. Indeed, data from numerous studies strongly indicate that the functions of pancreatic β-cells, skeletal muscles and adipose tissue could benefit from improved HDL functionality. To better understand how changes in HDL structure may affect diet-induced obesity and type 2 diabetes we aimed at investigating the impact of Apoa1 or Lcat deficiency, two key proteins of peripheral HDL metabolic pathway, on these pathological conditions in mouse models. We report that universal deletion of apoa1 or lcat expression in mice fed western-type diet results in increased sensitivity to body-weight gain compared to control C57BL/6 group. These changes in mouse genome correlate with discrete effects on white adipose tissue (WAT) metabolic activation and plasma glucose homeostasis. Apoa1-deficiency results in reduced WAT mitochondrial non-shivering thermogenesis. Lcat-deficiency causes a concerted reduction in both WAT oxidative phosphorylation and non-shivering thermogenesis, rendering lcat^?/? mice the most sensitive to weight gain out of the three strains tested, followed by apoa1^?/? mice. Nevertheless, only apoa1^?/? mice show disturbed plasma glucose homeostasis due to dysfunctional glucose-stimulated insulin secretion in pancreatic β-islets and insulin resistant skeletal muscles. Our analyses show that both apoa1^?/? and lcat^?/? mice fed high-fat diet have no measurable Apoa1 levels in their plasma, suggesting no direct involvement of Apoa1 in the observed phenotypic differences among groups.