Effect of the protein ligand in DMSO reductase studied by computational methods

The DMSO reductase family is the largest and most diverse family of mononuclear molybdenum oxygen-atom-transfer proteins. Their active sites contain a Mo ion coordinated to two molybdopterin ligands, one oxo group in the oxidised state, and one additional, often protein-derived ligand. We have used density-functional theory to evaluate how the fourth ligand (serine, cysteine, selenocysteine, OH−, O2–, SH−, or S2–) affects the geometries, reaction mechanism, reaction energies, and reduction potentials of intermediates in the DMSO reductase reaction. Our results show that there are only small changes in the geometries of the reactant and product states, except from the elongation of the Mo[sbnd]X bond as the ionic radius of X[dbnd]O, S, Se increases. The five ligands with a single negative charge gave an identical two-step reaction mechanism, in which DMSO first binds to the reduced active site, after which the S[sbnd]O bond is cleaved, concomitantly with the transfer of two electrons from Mo in a rate-determining second transition state. The five models gave similar activation energies of 69–85 kJ/mol, with SH− giving the lowest barrier. In contrast, the O2– and S2– ligands gave much higher activation energies (212 and 168 kJ/mol) and differing mechanisms (a more symmetric intermediate for O2– and a one-step reaction without any intermediate for S2–). The high activation energies are caused by a less exothermic reaction energy, 13–25 kJ/mol, and by a more stable reactant state owing to the strong Mo[sbnd]O2– or Mo[sbnd]S2– bonds

Exploration of H2 binding to the [NiFe]-hydrogenase active site with multiconfigurational density functional theory

The combination of density functional theory (DFT) with a multiconfigurational wave function is an efficient way to include dynamical correlation in calculations with multiconfiguration self-consistent field wave functions. These methods can potentially be employed to elucidate reaction mechanisms in bio-inorganic chemistry, where many other methods become either too computationally expensive or too inaccurate. In this paper, a complete active space (CAS) short-range DFT (CAS-srDFT) hybrid was employed to investigate a bio-inorganic system, namely H2 binding to the active site of [NiFe] hydrogenase. This system was previously investigated with coupled-cluster (CC) and multiconfigurational methods in form of cumulant-approximated second-order perturbation theory, based on the density matrix renormalization group (DMRG). We find that it is more favorable for H2 to bind to Ni than to Fe, in agreement with previous CC and DMRG calculations. The accuracy of CAS-srDFT is comparable to both CC and DMRG, despite that much smaller active spaces were employed. This enhanced efficiency at smaller active spaces shows that CAS-srDFT can become a useful method for bio-inorganic chemistry.Comment: 22 page

Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.Comment: 15 pages, 12 figure

Robust Representation Learning for Unreliable Partial Label Learning

Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. However, this idealistic assumption may not always hold due to potential annotation inaccuracies, meaning the ground-truth may not be present in the candidate label set. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels, often resulting in a sub-optimal performance with existing methods. To address this challenge, we propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively. Concurrently, we propose a dual strategy that combines KNN-based candidate label set correction and consistency-regularization-based label disambiguation to refine label quality and enhance the ability of representation learning within the URRL framework. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art PLL methods on various datasets with diverse degrees of unreliability and ambiguity. Furthermore, we provide a theoretical analysis of our approach from the perspective of the expectation maximization (EM) algorithm. Upon acceptance, we pledge to make the code publicly accessible

Qubit Reset with a Shortcut-to-Isothermal Scheme

Landauer's principle shows that the minimum energy cost to reset a classical bit in a bath with temperature $T$ is $k_{B}T\ln2$ in the infinite time. However, the task to reset the bit in finite time has posted a new challenge, especially for quantum bit (qubit) where both the operation time and controllability are limited. We design a shortcut-to-isothermal scheme to reset a qubit in finite time $\tau$ with limited controllability. The energy cost is minimized with the optimal control scheme with and without nonholonomic constraint. This optimal control scheme can provide a reference to realize qubit reset with minimum energy cost for the limited time.Comment: 8 pages, 7 figure

Roles of the spiA gene from Salmonella enteritidis in biofilm formation and virulence

Salmonella enteritidis has emerged as one of the most important food-borne pathogens for humans, and the formation of biofilms by this species may improve its resistance to disadvantageous conditions. The spiA gene of Salmonella typhimurium is essential for its virulence in host cells. However, the roles of the spiA gene in biofilm formation and virulence of S. enteritidis remain unclear. In this study we constructed a spiA gene mutant with a suicide plasmid. Phenotypic and biological analysis revealed that the mutant was similar to the wild-type strain in growth rate, morphology, and adherence to and invasion of epithelial cells. However, the mutant showed reduced biofilm formation in a quantitative microtitre assay and by scanning electron microscopy, and significantly decreased curli production and intracellular proliferation of macrophages during the biofilm phase. In addition, the spiA mutant was attenuated in a mouse model in both the exponential growth and biofilm phases. These data indicate that the spiA gene is involved in both biofilm formation and virulence of S. enteritidis