楽器の音を写す擬音語（２） : 近世・近現代

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Diazotization of kynurenine by acidified nitrite secreted from indoleamine 2,3-dioxygenase-expressing myeloid dendritic cells

Indoleamine 2,3-dioxygenase (IDO)-initiated tryptophan metabolism along the kynurenine (Kyn) pathway regulates T-cell responses in some dendritic cells (DC) such as plasmacytoid DC. A Kyn assay using HPLC showed that samples were frequently deproteinized with trichloroacetic acid (TCA). In the present study, bone marrow derived myeloid DC (BMDC) were differentiated from mouse bone marrow cells with GM-CSF. CpG oligodeoxynucleotides (CpG) induced the expression of IDO protein with NO production in BMDC cultured for 24 hr. The concentrations of Kyn in the culture supernatants were not increased by stimulation with CpG but rather decreased by based on the Kyn assay after deproteinization with TCA. The level of Kyn exogenously added into the cell-free culture supernatant of BMDC stimulated with CpG was severely decreased by deproteinization with TCA but not methanol, and the decrease was prevented when BMDC was stimulated with CpG in the presence of a NOS inhibitor. Under acidic conditions, Kyn reacted with nitrite produced by BMDC, and generated a new compound that was not detected by Ehrlich reagent reacting with the aromatic amino residue of Kyn. An analysis by mass spectrometry showed the new compound to be a diazotization form of Kyn. In conclusion, the deproteinization of samples by acidic treatment should be avoided for the Kyn assay when NO is produced.journal articl

Upper Bound on the Decay τ→μγ from the Belle Detector

journal articl

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery

Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as graphs and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom’s information. Common aggregation operators, such as averaging, result in a loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted nonlinearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in β-lactam antibiotics. The ability of our approach to rank the atoms’ importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery

Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as graphs and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom’s information. Common aggregation operators, such as averaging, result in a loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted nonlinearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in β-lactam antibiotics. The ability of our approach to rank the atoms’ importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level

Specificity ξ as a function of mismatch <i>d</i> and flexibility, <i>k.</i>

<p>Colors denote various values of rigidity <i>k</i> (in units of <i>k</i>_B<i>T</i>/Ų, legend). (A) For targets that differ by Δ = 3 Å the specificity is optimal at a nonzero mismatch. As the ligand becomes more rigid the optimal mismatch tends to zero as <i>d₀</i>∼<i>k^−1/2</i>. (B) For competing targets with similar structure, Δ = 0, the specificity resembles a rectangular window centered on zero mismatch. The width of this window also decreases as <i>k^−1/2</i>. (C) The specificity when only matched complexes are functional, <i>r = 0</i>, increases exponentially with the mismatch as ξ∼exp(<i>k</i>·Δ·<i>d</i>). The parameters of the plot are <i>N</i> = 15, <i>m</i> = 2, ε = 2 <i>k</i>_B<i>T</i>, <i>r</i> = 0.1, <i>g</i> = 1 Å, <i>f</i> = 15 <i>k</i>_B<i>T</i>.</p

Competition between two rigid targets.

<p>The ligand (white) is interconverting within an ensemble of conformations while interacting with two rigid competitors, <i>A</i> and <i>B</i> (green and orange), characterized by different structures. Non-specific binding energy may lead to the formation of functional complexes in which the target and the ligand are not exactly matched. The unmatched complexes may also be functional but their product formation rates, ν<i>_um</i>, may differ from these of the matched complexes, ν<i>_m</i>. The specificity of the ligand, that is its ability to discriminate between <i>A</i> and <i>B</i>, depends on the ligand flexibility, the structural mismatch between its native state and the correct target and on the structural difference between the competing targets.</p

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery

Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as graphs and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom’s information. Common aggregation operators, such as averaging, result in a loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted nonlinearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in β-lactam antibiotics. The ability of our approach to rank the atoms’ importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery

Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as graphs and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom’s information. Common aggregation operators, such as averaging, result in a loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted nonlinearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in β-lactam antibiotics. The ability of our approach to rank the atoms’ importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level

General molecular recognition scheme.

<p>Both the ligand (white) and the target (green) are interconverting between an ensemble of conformations denoted by indices, <i>a_i</i> and <i>A_i</i>, respectively. All the different conformations may interact and as a result, a variety of complexes is formed. In some of them the target and the ligand are perfectly matched, for example <i>a_iA_i</i> and <i>a_jA_j</i>, and in some there is only partial fit, for example <i>a_iA_j</i> and <i>a_jA_i</i>. The rate of product formation depends on the concentrations of the complexes, which depend on <i>K_ij</i>, and on the functionality of each complex, which depends on the turnover numbers, υ<i>_ij</i>. In a similar fashion, the different ligand conformations, <i>a_i</i>, may interact with competing target conformations <i>B_i</i> and thus catalyze incorrect product.</p