A remote sensing method for mapping alpine grasslines based on graph-cut.

Climate change has induced substantial shifts in vegetation boundaries such as alpine treelines and shrublines, with widespread ecological and climatic influences. However, spatial and temporal changes in the upper elevational limit of alpine grasslands ("alpine grasslines") are still poorly understood due to lack of field observations and remote sensing estimates. In this study, taking the Tibetan Plateau as an example, we propose a novel method for automatically identifying alpine grasslines from multi-source remote sensing data and determining their positions at 30-m spatial resolution. We first identified 2895 mountains potentially having alpine grasslines. On each mountain, we identified a narrow area around the upper elevational limit of alpine grasslands where the alpine grassline was potentially located. Then, we used linear discriminant analysis to adaptively generate from Landsat reflectance features a synthetic feature that maximized the difference between vegetated and unvegetated pixels in each of these areas. After that, we designed a graph-cut algorithm to integrate the advantages of the Otsu and Canny approaches, which was used to determine the precise position of the alpine grassline from the synthetic feature image. Validation against alpine grasslines visually interpreted from a large number of high-spatial-resolution images showed a high level of accuracy (R2 , .99 and .98; mean absolute error, 22.6 and 36.2 m, vs. drone and PlanetScope images, respectively). Across the Tibetan Plateau, the alpine grassline elevation ranged from 4038 to 5380 m (5th-95th percentile), lower in the northeast and southeast and higher in the southwest. This study provides a method for remotely sensing alpine grasslines for the first-time at large scale and lays a foundation for investigating their responses to climate change.

Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000-2016 on the Tibetan Plateau.

Climate warming on the Tibetan Plateau tends to induce an uphill shift of temperature isolines. Observations and process-based models have both shown that climate warming has resulted in an increase in vegetation greenness on the Tibetan Plateau in recent decades. However, it is unclear whether the uphill shift of temperature isolines has caused greenness isolines to shift upward and whether the two shifts match each other. Our analysis of satellite observed vegetation greenness during the growing season (May-Sep) and gridded climate data for 2000-2016 documented a substantial mismatch between the elevational shifts of greenness and temperature isolines. This mismatch is probably associated with a lagging response of greenness to temperature change and with the elevational gradient of greenness. The lagging response of greenness may be associated with water limitation, resources availability, and acclimation. This lag may weaken carbon sequestration by Tibetan ecosystems, given that greenness is closely related to primary carbon uptake and ecosystem respiration increases exponentially with temperature. We also found that differences in terrain slope angle accounted for large spatial variations in the elevational gradient of greenness and thus the velocity of elevational shifts of greenness isolines and the sensitivity of elevational shifts of greenness isolines to temperature, highlighting the role of terrain effects on the elevational shifts of greenness isolines. The mismatches and the terrain effect found in this study suggest that there is potentially large micro-topographical difference in response and acclimation/adaptation of greenness to temperature changes in plants. More widespread in situ measurements and fine-resolution remote sensing observations and fine-gridded climate data are required to attribute the mismatch to specific environmental drivers and ecological processes such as vertical changes in community structure, plant physiology, and distribution of species.

Structural Prediction of the Dimeric Form of the Mammalian Translocator Membrane Protein TSPO: A Key Target for Brain Diagnostics.

Positron emission tomography (PET) radioligands targeting the human translocator membrane protein (TSPO) are broadly used for the investigations of neuroinflammatory conditions associated with neurological disorders. Structural information on the mammalian protein homodimers-the suggested functional state of the protein-is limited to a solid-state nuclear magnetic resonance (NMR) study and to a model based on the previously-deposited solution NMR structure of the monomeric mouse protein. Computational studies performed here suggest that the NMR-solved structure in the presence of detergents is not prone to dimer formation and is furthermore unstable in its native membrane environment. We, therefore, propose a new model of the functionally-relevant dimeric form of the mouse protein, based on a prokaryotic homologue. The model, fully consistent with solid-state NMR data, is very different from the previous predictions. Hence, it provides, for the first time, structural insights into this pharmaceutically-important target which are fully consistent with experimental data.

Role of Extracellular Loops and Membrane Lipids for Ligand Recognition in the Neuronal Adenosine Receptor Type 2A: An Enhanced Sampling Simulation Study.

Human G-protein coupled receptors (GPCRs) are important targets for pharmaceutical intervention against neurological diseases. Here, we use molecular simulation to investigate the key step in ligand recognition governed by the extracellular domains in the neuronal adenosine receptor type 2A (hA2AR), a target for neuroprotective compounds. The ligand is the high-affinity antagonist (4-(2-(7-amino-2-(furan-2-yl)-[1,2,4]triazolo[1,5-a][1,3,5]triazin-5-ylamino)ethyl)phenol), embedded in a neuronal membrane mimic environment. Free energy calculations, based on well-tempered metadynamics, reproduce the experimentally measured binding affinity. The results are consistent with the available mutagenesis studies. The calculations identify a vestibular binding site, where lipids molecules can actively participate to stabilize ligand binding. Bioinformatic analyses suggest that such vestibular binding site and, in particular, the second extracellular loop, might drive the ligand toward the orthosteric binding pocket, possibly by allosteric modulation. Taken together, these findings point to a fundamental role of the interaction between extracellular loops and membrane lipids for ligands' molecular recognition and ligand design in hA2AR.

Evaluation of Spike Protein Epitopes by Assessing the Dynamics of Humoral Immune Responses in Moderate COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike protein (S) of SARS-CoV-2 is a major target for diagnosis and vaccine development because of its essential role in viral infection and host immunity. Currently, time-dependent responses of humoral immune system against various S protein epitopes are poorly understood. In this study, enzyme-linked immunosorbent assay (ELISA), peptide microarray, and antibody binding epitope mapping (AbMap) techniques were used to systematically analyze the dynamic changes of humoral immune responses against the S protein in a small cohort of moderate COVID-19 patients who were hospitalized for approximately two months after symptom onset. Recombinant truncated S proteins, target S peptides, and random peptides were used as antigens in the analyses. The assays demonstrated the dynamic IgM- and IgG recognition and reactivity against various S protein epitopes with patient-dependent patterns. Comprehensive analysis of epitope distribution along the spike gene sequence and spatial structure of the homotrimer S protein demonstrated that most IgM- and IgG-reactive peptides were clustered into similar genomic regions and were located at accessible domains. Seven S peptides were generally recognized by IgG antibodies derived from serum samples of all COVID-19 patients. The dynamic immune recognition signals from these seven S peptides were comparable to those of the entire S protein or truncated S1 protein. This suggested that the humoral immune system recognized few conserved S protein epitopes in most COVID-19 patients during the entire duration of humoral immune response after symptom onset. Furthermore, in this cohort, individual patients demonstrated stable immune recognition to certain S protein epitopes throughout their hospitalization period. Therefore, the dynamic characteristics of humoral immune responses to S protein have provided valuable information for accurate diagnosis and immunotherapy of COVID-19 patients.

A long-term 0.01° surface air temperature dataset of Tibetan Plateau.

The surface air temperature (Ta) dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean Ta and 3-hourly instantaneous Ta. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 2000 to 2015. Spatial dimension of data: 73°E-106°E, 40°N-23°N. The Ta with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.

In silico properties characterization of water-soluble γ-cyclodextrin bi-capped C60 complex: free energy and geometrical insights for stability and solubility.

Cyclodextrin-related host-guest encapsulation is pivotal to modulate the solubility of C60, thereby promoting its potential therapeutic applications. Here we present a computational study on γ-cyclodextrin bi-capped C60 complex, probing characteristics for all the possible stoichiometry in aqueous solution. The potential of mean force (PMF) delineating the association process was computed, while the geometrical features of corresponding thermodynamically-favored stoichiometry are captured by molecular dynamics simulations, which provides insightful explanations to previous experimental and computational results. PMF partitioning indicates that intermolecular van der Waals dispersion forces are essential for molecular recognition and self-assembly, and the hydrogen-bonding interactions play a key role in dissolving the complex in water.

Binding of the Antagonist Caffeine to the Human Adenosine Receptor hA2AR in Nearly Physiological Conditions.

Lipid composition may significantly affect membrane proteins function, yet its impact on the protein structural determinants is not well understood. Here we present a comparative molecular dynamics (MD) study of the human adenosine receptor type 2A (hA(2A)R) in complex with caffeine--a system of high neuro-pharmacological relevance--within different membrane types. These are POPC, mixed POPC/POPE and cholesterol-rich membranes. 0.8-µs MD simulations unambiguously show that the helical folding of the amphipathic helix 8 depends on membrane contents. Most importantly, the distinct cholesterol binding into the cleft between helix 1 and 2 stabilizes a specific caffeine-binding pose against others visited during the simulation. Hence, cholesterol presence (~33%-50% in synaptic membrane in central nervous system), often neglected in X-ray determination of membrane proteins, affects the population of the ligand binding poses. We conclude that including a correct description of neuronal membranes may be very important for computer-aided design of ligands targeting hA(2A)R and possibly other GPCRs.

Recognition of cello-oligosaccharides by CBM17 from Clostridium cellulovorans : molecular dynamics simulation.

The basic function of carbohydrate binding module (CBM) is believed to enhance local concentration of glycosidases on the carbohydrate molecule, and thus facilitates the subsequent degradation of carbohydrate. Full understanding of the recognition mechanism of carbohydrates by CBM can be helpful to enhance the enzyme activity. In this work, the detailed recognition specificity of two soluble cello-oligosaccharide substrates, cellotetraose and cellohexaose, by a family 17 CBM from Clostridium cellulovorans was investigated by molecular dynamics simulation. Calculated binding free energies using molecular mechanics/generalized Born and surface area (MM/GBSA) approach are in excellent agreement with experimental values. Overall, based on the decomposition of total binding free energy, nonpolar terms are shown to have favorable contributions to the binding, while polar interactions make unfavorable contributions, no matter significant hydrogen bond network is formed between substrate and protein. On the basis of computational alanine scanning and per-residue free energy decomposition, Trp88 and Trp135 are shown to be two most important residues in the cellohexaose binding mainly via hydrophobic interactions. The calculated subtotal contributions for those polar residues, D54, R92, Q129, and N185, can compare very well with experimental data.

pH-Dependent reactivity for glycyl-L-tyrosine in carboxypeptidase-A-catalyzed hydrolysis.

The dipeptide glycyl-L-tyrosine (GY) can be either a substrate for carboxypeptidase A (CPA) or an inhibitor, depending on pH. In this work, we investigate the pH-dependent reactivity of this dipeptide in CPA-catalyzed hydrolysis using a combined quantum mechanical and molecular mechanical method. It is shown that the monoionic form of the dipeptide, prevalent at high pH, chelates the active site zinc ion, rendering the enzyme inactive. This inhibitory form is consistent with an earlier X-ray structure of the CPA-GY complex. On the other hand, the prevailing di-ionic form of the dipeptide at low pH was found to undergo hydrolysis via a nucleophilic mechanism, leading to an acyl-enzyme complex. The stability of this reaction intermediate is consistent with previous low-temperature solid-state NMR results. The calculated overall free-energy barrier of 20.1 kcal/mol is in excellent agreement with the experimental value of 19.9 kcal/mol.