Results of the average <i>E</i>_av of the minimum values of <i>E</i>_Q(x) obtained from 100 independent runs in the effective model estimation of the quantum Heisenberg model.

<p>(a) <i>E</i>_av as a function of <i>N</i>_s obtained from the random search method (red circles), the steepest descent method (yellow circles), the Monte Carlo method (green circles), and the Bayesian optimization (blue circles). (b) <i>E</i>_av as a function of <i>N</i>_s obtained from the random search method (RS) (red circles), the Bayesian optimization using <i>f</i>_EI(<b>x</b>) (BO) (blue circles), the random search method with the steepest descent method (RS+SD) (red diamonds), and the Bayesian optimization with the steepest descent method (BO+SD) (blue diamonds). In the steepest descent method, 50 updates are performed after RS or BO.</p

Results of the average <i>E</i>_av of the minimum values of <i>E</i>_C(x) obtained from 100 independent runs in the effective model estimation of the classical Ising model.

<p>(a) <i>E</i>_av as a function of <i>N</i>_s, which is the number of sampling points on <i>E</i>_C(<b>x</b>), obtained from the random search method (red circles), the steepest descent method (yellow circles), the Monte Carlo method (green circles), and the Bayesian optimization (blue circles). (b) <i>E</i>_av as a function of <i>N</i>_s obtained from the random search method (RS) (red circles), the Bayesian optimization using <i>f</i>_LCB(<b>x</b>) with <i>κ</i> = 20 (BO) (blue circles), the random search method with the steepest descent method (RS+SD) (red diamonds), and the Bayesian optimization with the steepest descent method (BO+SD) (Blue diamonds). Dashed lines connect the initial <i>E</i>_av (circle point) by only RS or BO and the obtained <i>E</i>_av (diamond point) by performing the steepest descent method with 50 updates after RS or BO.</p

Bayesian optimization for computationally extensive probability distributions - Fig 1

<p>(a) Lattice and types of exchange interactions considered in the classical Ising model defined by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193785#pone.0193785.e023" target="_blank">Eq (15)</a>. (b) Inputted magnetization curve {<i>m</i>^ex(<i>H_l</i>)}_{<i>l</i>=1,…,<i>L</i>} with <i>L</i> = 200 where (<i>x</i>₁, <i>x</i>₂, <i>x</i>₃) = (−1.0, −0.5, 0.3) are used for a temperature <i>T</i> = 3.0.</p

Temperature-Dependent Regioselectivity of Nucleophilic Aromatic Photosubstitution. Evidence That Activation Energy Controls Reactivity

Irradiation (λ > 330 nm) of 2-chloro-4-nitroanisole (<b>1</b>) at 25 °C in aqueous NaOH forms three substitution photoproducts: 2-methoxy-5-nitrophenol (<b>2</b>), 2-chloro-4-nitrophenol (<b>3</b>), and 3-chloro-4-methoxyphenol (<b>4</b>), in chemical yields of 69.2%, 14.3%, and 16.5%. The activation energies for the elementary steps from the triplet state at 25 °C were determined to be 1.8, 2.4, and 2.7 kcal/mol, respectively. The chemical yields of each of the three products were determined for exhaustive irradiations at 0, 35, and 70 °C. The variation with temperature of the experimental yields is reproduced almost exactly by the yields calculated with the Arrhenius equation. This indicates that activation energy is the fundamental property related to regioselectivity in nucleophilic aromatic photosubstitution of the S_N2 Ar* type. The many methods proposed for predicting regioselectivity in reactions of this type have had limited success and have not been related to activation energy

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization

To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation

Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space

Increasing the variety of antimicrobial peptides is crucial in meeting the global challenge of multi-drug-resistant bacterial pathogens. While several deep-learning-based peptide design pipelines are reported, they may not be optimal in data efficiency. High efficiency requires a well-compressed latent space, where optimization is likely to fail due to numerous local minima. We present a multi-objective peptide design pipeline based on a discrete latent space and D-Wave quantum annealer with the aim of solving the local minima problem. To achieve multi-objective optimization, multiple peptide properties are encoded into a score using non-dominated sorting. Our pipeline is applied to design therapeutic peptides that are antimicrobial and non-hemolytic at the same time. From 200 000 peptides designed by our pipeline, four peptides proceeded to wet-lab validation. Three of them showed high anti-microbial activity, and two are non-hemolytic. Our results demonstrate how quantum-based optimizers can be taken advantage of in real-world medical studies

Inhibition of the H3K4 methyltransferase SET7/9 ameliorates peritoneal fibrosis

<div><p>Transforming growth factor-β1 (TGF-β1) is a major mediator of peritoneal fibrosis and reportedly affects expression of the H3K4 methyltransferase, SET7/9. SET7/9-induced H3K4 mono-methylation (H3K4me1) critically activates transcription of fibrosis-related genes. In this study, we examined the effect of SET7/9 inhibition on peritoneal fibrosis in mice and in human peritoneal mesothelial cells (HPMCs). We also examined SET7/9 expression in nonadherent cells isolated from the effluent of peritoneal dialysis (PD) patients. Murine peritoneal fibrosis was induced by intraperitoneal injection of methylglyoxal (MGO) into male C57/BL6 mice over 21 days. Sinefungin, a SET7/9 inhibitor, was administered subcutaneously just before MGO injection (10 mg/kg). SET7/9 expression was elevated in both MGO-injected mice and nonadherent cells isolated from the effluent of PD patients. SET7/9 expression was positively correlated with dialysate/plasma ratio of creatinine in PD patients. Sinefungin was shown immunohistochemically to suppress expression of mesenchymal cells and collagen deposition, accompanied by decreased H3K4me1 levels. Peritoneal equilibration tests showed that sinefungin attenuated the urea nitrogen transport rate from plasma and the glucose absorption rate from the dialysate. <i>In vitro</i>, sinefungin suppressed TGF-β1-induced expression of fibrotic markers and inhibited H3K4me1. These findings suggest that inhibiting the H3K4 methyltransferase SET7/9 ameliorates peritoneal fibrosis.</p></div

Sinefungin inhibited the expression of H3K4me1 but not that of TGF-β1 in mice with peritoneal fibrosis.

<p>(A) Typical H3K4me1 levels in peritoneal tissue of control mice, MGO-injected mice treated with vehicle only, and MGO-injected mice treated with sinefungin (immunohistochemical [IHC] stain, ×200). (B) Numbers of H3K4me1-positive (H3K4me1^<b>+</b>) cells in the 3 groups of mice. (C) Typical TGF-β1 expression in peritoneal tissue of control mice, MGO-injected mice treated with vehicle only and MGO-injected mice treated with sinefungin (IHC stain, ×200). (D) Numbers of TGF-β1-positive (TGF-β1^<b>+</b>) cells in the 3 groups of mice. The quantitative data are presented as dot plots in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196844#pone.0196844.s003" target="_blank">S3 Fig</a>. (E) Two-color immunohistochemical staining showing localization of H3K4me1 (blue-gray) and collagens I (brown). (F) The concentration of TGF-β1 protein in mouse PD effluent was quantitated by ELISA. Scale Bar = 200 μm. Data are means ± S.D. *, <i>P</i> < 0.05 (one-way ANOVA followed by <i>post hoc</i> test using <i>t</i> test with Bonferroni correction; <i>n</i> = 5 mice per group).</p

SET7/9 expression was elevated in methylglyoxal (MGO)-injected mice, and was associated with the level of functional impairment of the peritoneal membrane in PD patients’ effluents.

<p>(A) Immunohistochemical analyses of SET7/9 expression in peritoneal tissues of control- and MGO-injected mice (×200). (B) Numbers of SET7/9-positive (SET7/9^<b>+</b>) cells in mice with or without peritoneal MGO injection (<i>n</i> = 5 for both groups). (C) SET7/9 protein expression in nonadherent cells from human PD effluents was confirmed by Western blotting. Panel: typical results. GAPDH was used as an internal control. Full-length blots are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196844#pone.0196844.s001" target="_blank">S1 Fig</a>. (D) Relative levels of SET7/9 protein expression. Controls: HPMCs from non-PD patients; PD patients: nonadherent cells isolated from PD effluent of patients who had undergone PD for ≥ 1 year. (E) Correlation between SET7/9 protein expression of nonadherent cells and the dialysate/plasma (D/P) ratio of creatinine (Cr) in PD patients (<i>n</i> = 12). Scale Bar = 200 μm. Data are means ± S.D. *, <i>P</i> < 0.05 (Student’s <i>t</i> test or Spearman’s rank correlation coefficient).</p

Sinefungin suppressed expression of extracellular matrix (ECM)-associated genes and H3K4me1 level at <i>Col1A2</i> promoters.

<p>Quantitative real-time polymerase chain reaction (PCR) analysis of mRNA expression of (A) <i>ACTA2</i> (<i>α-SMA</i>), (B) <i>Col1A2</i>, (C) <i>CTGF</i> and (D) <i>PAI-1</i> in HPMCs (standardized to glyceraldehyde 3-phosphate dehydrogenase [<i>GAPDH</i>]). (E) Representative chromatin immunoprecipitation (ChIP) assay of the binding of the H3K4me1 protein (H3K4me1-Ab) to <i>Col1A2</i> promoters in HPMCs. Negative control: mouse immunoglobulin G (IgG). Full-length gels are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196844#pone.0196844.s005" target="_blank">S5 Fig</a>. Data are means ± S.D. *, <i>P</i> < 0.05 (one-way ANOVA followed by the <i>post hoc</i> test using <i>t</i> test with Bonferroni correction; <i>n</i> = 5 samples per group).</p