Crystal structures of bacterial Small Multidrug Resistance transporter EmrE in complex with structurally diverse substrates

Proteins from the bacterial small multidrug resistance (SMR) family are proton-coupled exporters of diverse antiseptics and antimicrobials, including polyaromatic cations and quaternary ammonium compounds. The transport mechanism of the Escherichia coli transporter, EmrE, has been studied extensively, but a lack of high-resolution structural information has impeded a structural description of its molecular mechanism. Here we apply a novel approach, multipurpose crystallization chaperones, to solve several structures of EmrE, including a 2.9 Å structure at low pH without substrate. We report five additional structures in complex with structurally diverse transported substrates, including quaternary phosphonium, quaternary ammonium, and planar polyaromatic compounds. These structures show that binding site tryptophan and glutamate residues adopt different rotamers to conform to disparate structures without requiring major rearrangements of the backbone structure. Structural and functional comparison to Gdx-Clo, an SMR protein that transports a much narrower spectrum of substrates, suggests that in EmrE, a relatively sparse hydrogen bond network among binding site residues permits increased sidechain flexibility.

The Concept of Floating Shipyard

Shipyards are known as a land-based facility that ships steer to for docking and repair. This study represents an introductory conceptual study for a new principle of developing a Floating shipyard: changing the phenomena of a fixed site shipyard into a self-propelled floating shipyard. This Floating Shipyard can travel and conduct dry-docking activities at the client's location or even lift the client's vessel and travel to the F-Yard extension, taking advantage of having comprehensive repair or conversion. The arising challenges to this floating shipyard, such as lay-offs, restructuring, and environmental legislation, requires a balanced solution. The solution of all this relies on adapting talent management and competitive production tools during this massive undergoing change in its backbone structure. The Floating shipyard, here named "F-Yard", changes the mindset of dry-dock. This is because F-Yard travels to the client's location or is capable of pick the client's unit nearby the cargo destination, where it can complete the required dry-dock or repair. The cutthroat advantage of the F-Yard comes from self-propelled, where the other approaches depend on others for mobilizing and anchoring from one location to another. In addition, F-Yard could serve other industries, due to its fully equipped workshops, that able to serve different market needs such as oil and gas and renewable energy...etc. These open the door for a variety of business scope. F-Yard depends on front-end engineering and marketing teams to optimize its route and sort the supply chain requirements.

Estimating the Designability of Protein Structures

The total number of amino acid sequences that can fold to a target protein structure, known as "designability", is a fundamental property of proteins that contributes to their structure and function robustness. The highly designable structures always have higher thermodynamic stability, mutational stability, fast folding, regular secondary structures, and tertiary symmetries. Although it has been studied on lattice models for very short chains by exhaustive enumeration, it remains a challenge to estimate the designable quantitatively for real proteins. In this study, we designed a new deep neural network model that samples protein sequences given a backbone structure using sequential Monte Carlo method. The sampled sequences with proper weights were used to estimate the designability of several real proteins. The designed sequences were also tested using the latest AlphaFold2 and RoseTTAFold to confirm their foldabilities. We report this as the first study to estimate the designability of real proteins.

Computational data analysis shows that key developments towards the periodic system occurred in the 1840s

The periodic system arose from knowledge about substances, which constitute the chemical space. Despite the importance of this interplay, little is known about how the expanding space affected the system. Here we show, by analysing the space between 1800 and 1869, how the periodic system evolved until its formulation. We found that after an unstable period culminating around 1826, the system began to converge to a backbone structure, unveiled in the 1860s, which was clearly evident in the 1840s. Hence, contrary to the belief that the ``ripe moment'' to formulate the system was in the 1860s, it was in the 1840s. The evolution of the system is marked by the rise of organic chemistry in the first quarter of the nineteenth-century, which prompted the recognition of relationships among main group elements and obscured some of transition metals, which explains why the formulators of the periodic system struggled accommodating them. We also introduced an algorithm to adjust the chemical space according to different sets of atomic weights, which allowed for estimating the resulting periodic systems of chemists using one or the other nineteenth-century atomic weights. These weights produce orderings of the elements very similar to that of 1869, while providing different similarity relationships among the elements, therefore producing different periodic systems. By analysing these systems, from Dalton up to Mendeleev, we found that Gmelin's atomic weights of 1843 produce systems remarkably similar to that of 1869, a similarity that was reinforced by the atomic weights on the years to come.

Improved antibody structure prediction by deep learning of side chain conformations

Antibody engineering is becoming increasingly popular in the medical field for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering our method particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of our model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.

Crystal structure of the complex of DNA with the C-terminal domain of TYE7 from Saccharomyces cerevisiae

TYE7, a bHLH (basic helix–loop–helix) transcription factor from Saccharomyces cerevisiae, is involved in the regulation of many genes, including glycolytic genes. Meanwhile, accumulating evidence indicates that TYE7 also functions as a cyclin and is linked to sulfur metabolism. Here, the structure of TYE7 (residues 165–291) complexed with its specific DNA was determined by X-ray crystallography. Structural analysis and comparison revealed that His185 and Glu189 are conserved in base recognition. However, Arg193 is also involved in base recognition in the structures that were compared. In the structure in this study, Arg193 in chain A has two conformations and makes a salt bridge with the phosphate backbone structure. In addition, a series of corresponding electrophoretic mobility shift assays were performed to better understand the DNA-binding mechanism of the bHLH domain of TYE7.