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We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical 13 Cα chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: (a) in terms of the expected differences between experimental and posterior predicted values; (b) in terms of the leave-one-out cross-validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.

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Here we propose that the upper bound marginal stability of proteins is a universal property that includes macro-molecular complexes and is not affected by molecular changes such as mutations and post-translational modifications. We theorize that its existence is a consequence of Afinsen's thermodynamic hypothesis rather than a result of an evolutionary process. This result enables us to conjecture that neutral evolution should also be, with respect to protein stability, a universal phenomenon.

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In the present work, we explore three different approaches for the computation of the one-bond spin-spin coupling constants (SSCC) 1JCαH in proteins: density functional theory (DFT) calculations, a Karplus-like equation, and Gaussian process regression. The main motivation of this work is to select the best method for fast and accurate computation of the 1JCαH SSCC, for its use in everyday applications in protein structure validation, refinement, and/or determination. Our initial results showed a poor agreement between the DFT-computed and observed 1JCαH SSCC values. Further analysis leads us to the understanding that the model chosen for the DFT computations is inappropriate and that more complex models will require a higher, if not prohibitively, computational cost. Finally, we show that the Karplus-like equation and Gaussian Process regression provide faster and more accurate results than DFT-based calculations.

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The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical 13C' chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental 13C' chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C' chemical shifts.

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BACKGROUND: South America faces strong environmental pressures as a result of agriculture and infrastructure expansion and also of demographic growth, demanding immediate action to preserve natural assets by establishing protected areas. Currently, 7.1% of the (sub)continent is under strict conservation categories (I to IV, IUCN), but the spatial distribution of these 1.3 × 106 km2 is poorly understood. We evaluated the representation of nature within the networks of protected areas, map conservation priorities and assess demographic, economic or geopolitical causes of existing protection patterns. METHODS: We characterized nature representation by looking at two components: the extent and the equality of protection. The first refers to the fraction of territory under protection, while the second refers to the homogeneity in the distribution along natural conditions of this protected fraction. We characterized natural conditions by either 113 biogeographical units (specifically, ecoregions) or a series of limited and significant climatic, topographic and edaphic traits. We analyzed representation every ten years since 1960 at national and continental levels. In the physical approach, histograms allowed us to map the degree of conservation priorities. Finally, we ranked the importance of different economic or geopolitical variables driving the observed distributions with a random forest technique. RESULTS: Nature representation varied across countries in spite of its priority in conservation agendas. In Brazil, Peru and Argentina there are still natural conditions with no formal protection, while in Bolivia and Venezuela, protected areas incorporate the natural diversity in a more balanced manner. As protected networks have increased their extent, so did their equality across and within countries over time. Our maps revealed as top continental priorities the southern temperate, subhumid and fertile lowland environments, and other country-specific areas. Protection extent was generally driven by a low population density and isolation, while other variables like distance to frontiers, were relevant only locally (e.g., in Argentina). DISCUSSION: Our description of the spatial distribution of protected areas can help societies and governments to improve the allocation of conservation efforts. We identified the main limitations that future conservation efforts will face, as protection was generally driven by the opportunities provided by low population density and isolation. From a methodological perspective, the physical approach reveals new properties of protection and provides tools to explore nature representation at different spatial, temporal and conceptual levels, complementing the traditional ones based on biodiversity or biogeographical attributes.

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There is abundant theoretical evidence indicating that a mirror image of Protein A may occur during the protein folding process. However, as to whether such mirror image exists in solution is an unsolved issue. Here we provide outline of an experimental design aimed to detect the mirror image of Protein A in solution. The proposal is based on computational simulations indicating that the use of a mutant of protein A, namely Q10H, could be used to detect the mirror image conformation in solution. Our results indicate that the native conformation of the protein A should have a pKa, for the Q10H mutant, at ≈6.2, while the mirror-image conformation should have a pKa close to ≈7.3. Naturally, if all the population is in the native state for the Q10H mutant, the pKa should be ≈6.2, while, if all are in the mirror-image state, it would be ≈7.3, and, if it is a mixture, the pKa should be largerthan 6.2, presumably in proportion to the mirror population. In addition, evidence is provided indicating the tautomeric distribution of H10 must also change between the native and mirror conformations. Although this may not be completely relevant for the purpose of determining whether the protein A mirror image exists in solution, it could provide valuable information to validate the pKa findings. We hope this proposal will foster experimental work on this problem either by direct application of our proposed experimental design or serving as inspiration and motivation for other experiments.

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BACKGROUND: Protected areas, regarded today as a cornerstone of nature conservation, result from an array of multiple motivations and opportunities. We explored at global and regional levels the current distribution of protected areas along biophysical, human, and biological gradients, and assessed to what extent protection has pursued (i) a balanced representation of biophysical environments, (ii) a set of preferred conditions (biological, spiritual, economic, or geopolitical), or (iii) existing opportunities for conservation regardless of any representation or preference criteria. METHODS: We used histograms to describe the distribution of terrestrial protected areas along biophysical, human, and biological independent gradients and linear and non-linear regression and correlation analyses to describe the sign, shape, and strength of the relationships. We used a random forest analysis to rank the importance of different variables related to conservation preferences and opportunity drivers, and an evenness metric to quantify representativeness. RESULTS: We find that protection at a global level is primarily driven by the opportunities provided by isolation and a low population density (variable importance = 34.6 and 19.9, respectively). Preferences play a secondary role, with a bias towards tourism attractiveness and proximity to international borders (variable importance = 12.7 and 3.4, respectively). Opportunities shape protection strongly in "North America & Australia-NZ" and "Latin America & Caribbean," while the importance of the representativeness of biophysical environments is higher in "Sub-Saharan Africa" (1.3 times the average of other regions). DISCUSSION: Environmental representativeness and biodiversity protection are top priorities in land conservation agendas. However, our results suggest that they have been minor players driving current protection at both global and regional levels. Attempts to increase their relevance will necessarily have to recognize the predominant opportunistic nature that the establishment of protected areas has had until present times.

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Post-translational modifications of proteins expand the diversity of the proteome by several orders of magnitude and have a profound effect on several biological processes. Their detection by experimental methods is not free of limitations such as the amount of sample needed or the use of destructive procedures to obtain the sample. Certainly, new approaches are needed and, therefore, we explore here the feasibility of using (13)C chemical shifts of different nuclei to detect methylation, acetylation and glycosylation of protein residues by monitoring the deviation of the (13)C chemical shifts from the expected (mean) experimental value of the non-modified residue. As a proof-of-concept, we used (13)C chemical shifts, computed at the DFT-level of theory, to test this hypothesis. Moreover, as a validation test of this approach, we compare our theoretical computations of the (13)Cε chemical-shift values against existing experimental data, obtained from NMR spectroscopy, for methylated and acetylated lysine residues with good agreement within ∼1 ppm. Then, further use of this approach to select the most suitable (13)C-nucleus, with which to determine other modifications commonly seen, such as methylation of arginine and glycosylation of serine, asparagine and threonine, shows encouraging results.

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Glycans are key molecules in many physiological and pathological processes. As with other molecules, like proteins, visualization of the 3D structures of glycans adds valuable information for understanding their biological function. Hence, here we introduce Azahar, a computing environment for the creation, visualization and analysis of glycan molecules. Azahar is implemented in Python and works as a plugin for the well known PyMOL package (Schrodinger in The PyMOL molecular graphics system, version 1.3r1, 2010). Besides the already available visualization and analysis options provided by PyMOL, Azahar includes 3 cartoon-like representations and tools for 3D structure caracterization such as a comformational search using a Monte Carlo with minimization routine and also tools to analyse single glycans or trajectories/ensembles including the calculation of radius of gyration, Ramachandran plots and hydrogen bonds. Azahar is freely available to download from http://www.pymolwiki.org/index.php/Azahar and the source code is available at https://github.com/agustinaarroyuelo/Azahar .

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Knowledge of the three-dimensional structures of glycans and glycoproteins is useful for a full understanding of molecular processes in which glycans are involved, such as antigen-recognition and virus infection, to name a few. Among the ubiquitous nuclei in glycan molecules, the (13)C nucleus is an attractive candidate for computation of theoretical chemical shifts at the quantum chemical level of theory to validate and determine glycan structures. For this purpose, it is important to determine, first, which carbons can be used as probes to sense conformational changes and, second, all factors that affect the computation of the shielding, at the density functional theory (DFT) level of theory, of those carbons. To answer such questions, we performed a series of analyses on low-energy conformations, obtained by sampling the glycosidic torsional angles (ϕ, ψ) every 10°, of 12 disaccharides. Our results provide evidence that: (i) the carbons that participate in the glycosidic linkage are the most sensitive probes with which to sense conformational changes of disaccharides; (ii) the rotation of the hydroxyl groups closest to the glycosidic linkage significantly affects the computation of the shieldings of the carbons that participate in the glycosidic linkage; (iii) it is not possible to obtain the shieldings of one disaccharide from the computed values of a different disaccharide or from those disaccharides that differ in the anomeric state; and (iv) a proper basis set distribution, a functional, and a step size, with which to sample the conformational space, are necessary to compute shieldings accurately and rapidly.

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