Bacteria and archaea possessing the hgcAB gene pair methylate inorganic mercury (Hg) to form highly toxic methylmercury. HgcA consists of a corrinoid binding domain and a transmembrane domain, and ...HgcB is a dicluster ferredoxin. However, their detailed structure and function have not been thoroughly characterized. We modeled the HgcAB complex by combining metagenome sequence data mining, coevolution analysis, and Rosetta structure calculations. In addition, we overexpressed HgcA and HgcB in Escherichia coli, confirmed spectroscopically that they bind cobalamin and 4Fe-4S clusters, respectively, and incorporated these cofactors into the structural model. Surprisingly, the two domains of HgcA do not interact with each other, but HgcB forms extensive contacts with both domains. The model suggests that conserved cysteines in HgcB are involved in shuttling Hg
, methylmercury, or both. These findings refine our understanding of the mechanism of Hg methylation and expand the known repertoire of corrinoid methyltransferases in nature.
Nowadays, the magneto-ellipsometry technique is considered as a promising tool for studying nanostructures. It leads to a great demand of both designing set-ups for conducting experiments and ...developing approaches to data processing. The later one is a problem in framework of in situ analysis as it would be useful to have an approach to data analysis which is reliable, quick and reasonably easy. This work continues our previous study of layered nanostructures by means of magneto-ellipsometry technique and logically generalizes the approach to magneto-ellipsometry data analysis for the multi-layered model use. As a result, the algorithm with detailed description of necessary formulae is presented.
The two widely accepted mechanisms of the insulator-metal Mott-Hubbard transitions which have been considered up until now are driven by the band-filling or bandwidth effects. We found a different ...mechanism of the Mott-Hubbard insulator-metal transition, which is controlled instead by the changes in the Mott-Hubbard energy U. In contrast to the changes in the bandwidth W in the 'bandwidth control' scenario or to the variations of the band-filling n parameter in the 'band-filling' scenario, a dramatic decrease in the Mott-Hubbard energy U plays the key role in this mechanism. We have experimentally observed this type of the insulator metal transition in the transition metal oxide BiFeO{sub 3}. The decrease in the Mott-Hubbard energy is caused by the high-spin-low-spin crossover in the electronic d shell of 3d transition metal ion Fe{sup 3+} with d{sup 5} configuration under high pressure. The pressure-induced spin crossover in BiFeO{sub 3} was investigated and confirmed by synchrotron x-ray diffraction, nuclear forward scattering, and x-ray emission methods. The insulator-metal transition at the same pressures was found by the optical absorption and dc resistivity measurements.
The problem of predicting a protein's 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like alphafold have achieved remarkable ...performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models' accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a protein's structure from only its primary sequence by learning an approximate biophysical energy function. We provide evidence that alphafold has learned such an energy function, and uses coevolution data to solve the global search problem of finding a low-energy conformation. We demonstrate that alphafold'slearned energy function can be used to rank the quality of candidate protein structures with state-of-the-art accuracy, without using any coevolution data. Finally, we explore several applications of this energy function, including the prediction of protein structures without multiple sequence alignments.
Residue pairs that directly coevolve in protein families are generally close in protein 3D structures. Here we study the exceptions to this general trend—directly coevolving residue pairs that are ...distant in protein structures—to determine the origins of evolutionary pressure on spatially distant residues and to understand the sources of error in contact-based structure prediction. Over a set of 4,000 protein families, we find that 25% of directly coevolving residue pairs are separated by more than 5 Å in protein structures and 3% by more than 15 Å. The majority (91%) of directly coevolving residue pairs in the 5–15 Å range are found to be in contact in at least one homologous structure—these exceptions arise from structural variation in the family in the region containing the residues. Thirty-five percent of the exceptions greater than 15 Å are at homo-oligomeric interfaces, 19% arise from family structural variation, and 27% are in repeat proteins likely reflecting alignment errors. Of the remaining long-range exceptions (<1% of the total number of coupled pairs), many can be attributed to close interactions in an oligomeric state. Overall, the results suggest that directly coevolving residue pairs not in repeat proteins are spatially proximal in at least one biologically relevant protein conformation within the family; we find little evidence for direct coupling between residues at spatially separated allosteric and functional sites or for increased direct coupling between residue pairs on putative allosteric pathways connecting them.
The electronic structure, transport and optical properties of thin films of Mn
4
Si
7
and Mn
17
Si
30
higher manganese silicides (HMS) with the Nowotny “chimney-ladder” crystal structure are ...investigated using different experimental techniques and density functional theory calculations. Formation of new Mn
17
Si
30
compound through selective solid-state reaction synthesis proposed and its crystal structure is reported for the first time, the latter belonging to
I
-
42d
. Absorption measurements show that both materials demonstrate direct interband transitions around 0.9 eV, while the lowest indirect transitions are observed close to 0.4 eV. According to ab initio calculations, ideally structured Mn
17
Si
30
is a degenerate n-type semiconductor; however, the Hall measurements on the both investigated materials reveal their p-type conductivity and degenerate nature. Such a shift of the Fermi level is attributed to introduction of silicon vacancies in accordance with our DFT calculations and optical characteristics in low photon energy range (0.076–0.4 eV). The Hall mobility for Mn
17
Si
30
thin film was found to be 25 cm
2
/V s at
T
= 77 K, being the highest among all HMS known before. X-ray photoelectron spectroscopy discloses a presence of plasmon satellites in the Mn
4
Si
7
and Mn
17
Si
30
valence band spectra. Experimental permittivity spectra for the Mn
4
Si
7
and Mn
17
Si
30
compounds in a wide range (0.076–6.54 eV) also indicate degenerate nature of both materials and put more emphasis upon the intrinsic relationship between lattice defects and optical properties.
Within the framework of a realistic multiband p−d model, we derived an effective Hamiltonian to describe the exchange interaction effects near the spin crossover in magnetic Mott-Hubbard insulators ...under pressure. It is shown that the single-ion mechanism of spin crossover under the change in the crystal field does not lead to a thermodynamic phase transition; however, at T=0 a quantum phase transition appears. It has been found that the cooperativity leads to a modification of the quantum phase transition to a first-order phase transition and the appearance of metastable states of the system. The pressure-temperature phase diagram has been obtained to describe the magnetization and high-spin population near the spin crossover of Mott's insulators with d6 ions.
Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical ...processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.
Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms ...from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
Solid-state reaction processes in Fe/Si multilayer nanofilms have been studied in situ by the methods of transmission electron microscopy and electron diffraction in the process of heating from room ...temperature up to 900ºС at a heating rate of 8-10ºС/min. The solid-state reaction between the nanolayers of iron and silicon has been established to begin at 350-450ºС increasing with the thickness of the iron layer.
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