Computational models evaluating the impact of sieve plates and radial water exchange on phloem pressure gradients

The sugar conducting phloem in angiosperms is a high resistance pathway made up of sieve elements bounded by sieve plates. The high resistance generated by sieve plates may be a trade‐off for promoting quick sealing in the event of injury. However, previous modeling efforts have demonstrated a wide variation in the contribution of sieve plates towards total sieve tube resistance. In the current study, we generated high resolution scanning electron microscope images of sieve plates from balsam poplar and integrated them into a mathematical model using Comsol Multiphysics software. We found that sieve plates contribute upwards of 85% towards total sieve tube resistance. Utilizing the Navier–Stokes equations, we found that oblong pores may create over 50% more resistance in comparison with round pores of the same area. Although radial water flows in phloem sieve tubes have been previously considered, their impact on alleviating pressure gradients has not been fully studied. Our novel simulations find that radial water flow can reduce pressure requirements by half in comparison with modeled sieve tubes with no radial permeability. We discuss the implication that sieve tubes may alleviate pressure requirements to overcome high resistances by regulating their membrane permeability along the entire transport pathway.     

Estimation of aboveground biomass in conserved areas of Stipa tenacissima L. stands in the high steppes of western Algeria by mean of the Landsat 8 imagery‐based vegetation indices

The main aim of this paper was to evaluate the use of OLI spectral data as a tool to assess the steppe vegetation in a conservation context. The field sampling was conducted for two specific areas of treatment (a) an exclosure area and (b) a free grazing area. After testing several vegetation indices (VIs), the optimal results were obtained for the Normalised Difference Vegetation Index (NDVI)‐based aboveground biomass model with r² = 0.61 and r² = 0.72 for total and perennial biomass, respectively. No difference between observed and predicted total and perennial biomass was found (p = 0.700 and p = 0.306, respectively). The comparison between the two treatments using the field sampling revealed a significant difference on total plant cover (p = 0.016) and total biomass (p = 0.005), with a plant cover of 50.6% and a biomass of 325.771 kg dry matter per hectare (kg DM ha^?1) on average in grazed area and 66.9%, 1,407.869 kg DM ha^?1 in exclosure. Finally, a concordance is noted between the results obtained by the NDVI‐based biomass model and the field sampling‐based biomass.     

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.     

Structural modeling of protein complexes: Current capabilities and challenges

Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic 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.