The Natural Products and Pharmacological Biodiversity of Brown Algae from the Genus Dictyopteris

Genus Dictyopteris is an important genus among marine seaweeds and is excessively distributed and known by its ocean smell due to its secondary metabolites including C11-hydrocarbons and sulfur compounds. This chemical feature is responsible for its interesting biological properties. This review detected the literature from 1959 to 2021 on the genus Dictyopteris and revealed the secondary metabolites, together with biological activities of the genus Dictyopteris to create the base for additional studies on its clinical and pharmaceutical applications. Resumen. El género Dictyopteris es un género importante entre las algas marinas y está excesivamente distribuido y conocido por su olor a océano debido a sus metabolitos secundarios que incluyen hidrocarburos C11 y compuestos de azufre. Esta característica química es responsable de sus interesantes propiedades biológicas. Esta revisión detectó la literatura de 1959 a 2021 sobre el género Dictyopteris y reveló los metabolitos secundarios, junto con las actividades biológicas del género Dictyopteris, para crear la base para estudios adicionales sobre sus aplicaciones clínicas y farmacéuticas.

A histone H3K4me1-specific binding protein is required for siRNA accumulation and DNA methylation at a subset of loci targeted by RNA-directed DNA methylation

In plants, RNA-directed DNA methylation (RdDM) is a well-known de novo DNA methylation pathway that involves two plant-specific RNA polymerases, Pol IV and Pol V. In this study, we discovered and characterized an RdDM factor, RDM15. Through DNA methylome and genome-wide siRNA analyses, we show that RDM15 is required for RdDM-dependent DNA methylation and siRNA accumulation at a subset of RdDM target loci. We show that RDM15 contributes to Pol V-dependent downstream siRNA accumulation and interacts with NRPE3B, a subunit specific to Pol V. We also show that the C-terminal tudor domain of RDM15 specifically recognizes the histone 3 lysine 4 monomethylation (H3K4me1) mark. Structure analysis of RDM15 in complex with the H3K4me1 peptide showed that the RDM15 tudor domain specifically recognizes the monomethyllysine through an aromatic cage and a specific hydrogen bonding network; this chemical feature-based recognition mechanism differs from all previously reported monomethyllysine recognition mechanisms. RDM15 and H3K4me1 have similar genome-wide distribution patterns at RDM15-dependent RdDM target loci, establishing a link between H3K4me1 and RDM15-mediated RdDM in vivo. In summary, we have identified and characterized a histone H3K4me1-specific binding protein as an RdDM component, and structural analysis of RDM15 revealed a chemical feature-based lower methyllysine recognition mechanism.

A novel histone H3K4me1-specific binding protein is required for siRNA accumulation and DNA methylation at a subset of loci targeted by RNA-directed DNA methylation

In plants, RNA-directed DNA methylation (RdDM) is a well-known de novo DNA methylation pathway that involves two plant-specific RNA polymerases, Pol IV and Pol V. In this study, we discovered and characterized a new RdDM factor, RDM15. Through DNA methylome and genome wide siRNA analyses, we show that RDM15 is required for RdDM-dependent DNA methylation and siRNA accumulation at a subset of RdDM target loci. We show that RDM15 contributes to Pol V-dependent downstream siRNA accumulation, and interacts with NRPE3B, a subunit specific to Pol V. We also show that the C-terminal tudor domain of RDM15 specifically recognizes the histone 3 lysine 4 monomethylation (H3K4me1) mark. Structure analysis of RDM15 in complex with the H3K4me1 peptide showed that the RDM15 tudor domain specifically recognizes the monomethyllysine through an aromatic cage and a specific hydrogen bonding network; this chemical feature-based recognition mechanism differs from all previously reported lower methyllysine recognition mechanisms. RDM15 and H3K4me1 have similar genome-wide distribution patterns at RDM15-dependent RdDM target loci, establishing a link between H3K4me1 and RDM15-mediated RdDM in vivo. In summary, we have identified and characterized a histone H3K4me1-specific binding protein as a new RdDM component, and our structural analysis of RDM15 revealed a new type of chemical feature-based lower methyllysine recognition mechanism.

EnzyMine: a comprehensive database for enzyme function annotation with enzymatic reaction chemical feature

Addition of chemical structural information in enzymatic reactions has proven to be significant for accurate enzyme function prediction. However, such chemical data lack systematic feature mining and hardly exist in enzyme-related databases. Therefore, global mining of enzymatic reactions will offer a unique landscape for researchers to understand the basic functional mechanisms of natural bioprocesses and facilitate enzyme function annotation. Here, we established a new knowledge base called EnzyMine, through which we propose to elucidate enzymatic reaction features and then link them with sequence and structural annotations. EnzyMine represents an advanced database that extends enzyme knowledge by incorporating reaction chemical feature strategies, strengthening the connectivity between enzyme and metabolic reactions. Therefore, it has the potential to reveal many new metabolic pathways involved with given enzymes, as well as expand enzyme function annotation. Database URL: http://www.rxnfinder.org/enzymine/

Theoretical Prediction of Dual-Potency Anti-Tumor Agents: Combination of Oxoplatin with Other FDA-Approved Oncology Drugs

Although Pt(II)-based drugs are widely used to treat cancer, very few molecules have been approved for routine use in chemotherapy due to their side-effects on healthy tissues. A new approach to reducing the toxicity of these drugs is generating a prodrug by increasing the oxidation state of the metallic center to Pt(IV), a less reactive form that is only activated once it enters a cell. We used theoretical tools to combine the parent Pt(IV) prodrug, oxoplatin, with the most recent FDA-approved anti-cancer drug set published by the National Institute of Health (NIH). The only prerequisite imposed for the latter was the presence of one carboxylic group in the structure, a chemical feature that ensures a link to the coordination sphere via a simple esterification procedure. Our calculations led to a series of bifunctional prodrugs ranked according to their relative stabilities and activation profiles. Of all the designed molecules, the combination of oxoplatin with aminolevulinic acid as the bioactive ligand emerged as the most promising strategy by which to design enhanced dual-potency oncology drugs.

The chemical feature of Miras in 47 Tuc based on the WINERED spectra

Variable stars are good stellar tracers. Among various variables, Miras have long periods and are at the evolutionary phase of asymptotic giant branch. Their low effective temperatures lead to a difficulty to determine their chemical composition that since plenty of molecular bands exist in their spectra which even blocks the identifition of metallic lines. However, molecular features are less common in near-infrared (NIR) compared with other wavelength ranges. Here we take advantage of the high-resolution (R ~ 28, 000) spectra obtained with WINERED, which is a NIR spectrograph covering the wavelength range of 0.91–1.35 μm, to analyze and determine the chemical abundances of three Miras in the Galactic globular cluster 47 Tuc (NGC 104). Steps of data reduction and analysis, as well as part of the preliminary results, are briefly shown.

Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.