Tomoshi Kameda

Carbon nanotubes (CNTs) exhibit exceptional properties, including high thermal conductivity, mechanical flexibility, and near-infrared luminescence. These properties can be further tuned through CNT surface functionalization. Aryl diazonium salts are widely used for such functionalization because they react under mild conditions. Although the CNT functionalization efficiency depends on the solvent, the factors contributing to these solvent effects remain insufficiently understood. This study systematically investigated the effect of five representative organic solvents—dimethylformamide, isopropyl alcohol, tetrahydrofuran, N-methyl-2-pyrrolidone, and dimethyl sulfoxide (DMSO)—on the degree of CNT functionalization using aryl diazonium reagents. The degree of functionalization was evaluated using photoluminescence spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy. The solvent effect on CNT functionalization was attributable to three factors: CNT dispersibility, the chemical stability of the aryl diazonium reagent, and the binding affinity of the reagent to CNTs; the relative contributions depend on the reagent concentration. Among the solvents tested, DMSO was the most effective for CNT functionalization over a wide range of reagent concentrations. Furthermore, a homogeneous CNT–protein composite film exhibiting near-infrared photoluminescence was fabricated using DMSO. These findings provide mechanistic insights into solvent effects in diazonium-based CNT functionalization and offer guidelines for solvent selection in CNT functionalization and CNT-based composite fabrication.

Protein condensation regulates cellular metabolism. Although many enzymes are highly structured proteins, the molecular mechanisms underlying their condensation remain poorly understood. Here, we report the discovery and characterization of Corynebacterium glutamicum citrate synthase (CgCS) condensates in relation to extracellular glutamate overproduction. CgCS, which does not possess typical intrinsically disordered sequences, formed droplet-like condensates in C. glutamicum. CgCS condensates were observed in growing cells and dispersed in stationary phase cells. Condensates were not formed for the CgCS variants that lacked a catalytic residue, and the degree of condensation was correlated with catalytic activity. Oxaloacetate, a substrate of CS, was a crucial factor affecting condensation. Both substrates (oxaloacetate and acetyl-CoA) affected the saturation concentration of CgCS in a catalytic residue-dependent manner. We suggest that conformational heterogeneity generated through catalytic events is important for condensation, i.e., catalysis-dependent condensation. This study provides insights into the mechanisms underlying condensation of enzymes lacking intrinsically disordered sequences.

Post-translational modifications (PTMs) of histones play a pivotal role in chromatin condensation, with increasingly more evidence pointing to the involvement of liquid-liquid phase separation (LLPS). Here, we report the significant impact of the acetylation site of the N-terminal histone H3 peptide on LLPS with nucleosomal-linker DNA. In our model system, wherein the synthesized H3 peptide and DNA undergo LLPS, acetylation of the H3 peptide significantly inhibits LLPS, with the effects varying markedly depending on the acetylation site; acetylation near the H3 peptide end more strongly inhibits LLPS than acetylation near the H3 peptide center. Through experiments and molecular dynamics (MD) simulations, we show that this phenomenon arises from multiple contributing factors, including differences in the thermal stability of DNA, hydrophobic effects, and charge distribution within the H3 peptide/DNA complexes, which together promote the formation of intercomplex networks. Our findings not only provide fundamental insights that link LLPS-mediated chromatin condensation to biological phenomena depending on the acetylation sites but also suggest novel avenues for site-specific chemical modulation to regulate LLPS, which may, in turn, inspire new strategies in biotechnological applications and materials design.

Endocrine therapy is the mainstay for estrogen receptor (ER) α-positive breast cancer (BC), yet many patients display acquired resistance. We then screened natural compounds using human ERα-positive BC cells and identified perillyl alcohol (POH), a monoterpene from perilla, that reduces ERα protein levels. Chemoproteome analysis using POH-immobilized nanomagnetic beads revealed adenine nucleotide translocase 2 (ANT2), a mitochondrial inner membrane protein, as a direct target of POH. Molecular dynamics (MD) simulations predicted POH binding to the central pore of ANT2, which functions in ATP transport. ANT2 depletion reduced ERα levels, and public datasets indicate that high ANT2 expression correlates with poor prognosis in ERα-positive BC. POH also inhibited the growth of Tamoxifen- and Fulvestrant-resistant BC cells. RNA sequencing showed that fatty acid elongation-related genes were upregulated in Fulvestrant-resistant cells but downregulated by ANT2 depletion. Both ANT2 depletion and POH treatment led to the accumulation of intracellular lipid droplets in Fulvestrant-resistant cells, consistent with impaired fatty acid elongation. Finally, in silico screening using MD simulations identified venetoclax and nystatin as potential ANT2 pore binders. Both compounds reduced ERα levels in ERα-positive BC cells and increased lipid droplet formation in Fulvestrant-resistant cells. These findings highlight ANT2 as a druggable target against endocrine-resistant BC.

Carbon nanotubes (CNTs) exhibit exceptional properties, including high thermal conductivity and flexibility, and biologically transparent near-infrared luminescence. These properties can be further tuned by introducing functional groups onto CNT surfaces. Aryl diazonium salts are widely used for such functionalization because they react under mild conditions, typically in organic solvents. However, the effect of solvent on the efficiency of CNT functionalization remains unclear. This study systematically investigated the effect of organic solvents on the degree of CNT functionalization using 4-carboxybenzenediazonium tetrafluoroborate as the aryl diazonium reagent. The solvents tested were dimethylformamide (DMF), isopropyl alcohol (IPA), tetrahydrofuran (THF), dimethyl sulfoxide (DMSO), and 1-methyl-2-pyrrolidone (NMP). The degree of functionalization was found to depend on the solvent type, reflecting their combined effects on CNT dispersion and aryl diazonium salt stability. These insights enabled the fabrication of a CNT–protein composite film that exhibited near-infrared emission derived from CNT functional groups under white LED irradiation. The present findings provide general guidelines for CNT surface modification and interface design in CNT-based applications.

Accurately predicting the structures of RNA–protein complexes remains a major challenge. Recently, machine learning-based methods such as AlphaFold3 and RosettaFoldNA have been proposed. However, most conventional approaches rely on docking simulations to generate candidate structures, which are then identified as accurate using various methods. This study presents a method that integrates specialized molecular dynamics simulations and machine learning (ML) techniques to identify the correct structure among many docking poses. First, steered molecular dynamics simulations are performed to estimate the stability of the candidate structures. The simulation data then serve as the training data for a ML model, which classifies the results as either correct or incorrect. Next, the candidates predicted as correct are narrowed down using thermodynamic simulations and ML methods. Findings indicated that candidate structures could be classified as correct or incorrect with an accuracy of 0.934 in the RNA–protein docking simulation results. Additionally, we used AlphaFold3 to predict 15 RNA–protein complexes that Zou’s group categorized as difficult, medium or easy category. Subsequently, our method classified these binding structures as correct or incorrect, with accuracies of 0.80, 0.92 and 0.96, respectively. Thus, our method is powerful for accurately predicting the structures of RNA–protein complexes.

L-Menthol, valued for its aroma, cooling properties, and biological activity, is often produced as a mixture with d-menthol, which negatively impacts taste and odor. To improve the purity of industrial l-menthol production, we engineered Burkholderia cepacia lipase (BCL) to improve its stereoselectivity. While wild-type BCL achieves only 98% enantiomeric excess, we used molecular dynamics simulations of BCL bound to menthol acetate, combined with computational rational design method (MSPER), to identify key mutation sites. We experimentally confirmed that five mutations that can effectively improve the enantiomeric excess. In particular, Q88A increased enantiomeric excess to 99.4% and productivity from 14.5% to 49.9%. Q88G exhibited the highest productivity at 54.0% with 99.3% optical purity. These BCL mutants offer a greener, more efficient approach to high-purity l-menthol production, contributing to more sustainable chemical processes.