Toward Increasing the Credibility of RNA Design.

In the problem of RNA design, also known as inverse folding, RNA sequences are predicted that achieve the desired secondary structure at the lowest possible free energy and under certain constraints. The designed sequences have applications in synthetic biology and RNA-based nanotechnologies. There are also known cases of the successful use of inverse folding to discover previously unknown noncoding RNAs. Several computational methods have been dedicated to the problem of RNA design. They differ by algorithm and additional parameters, e.g., those determining the goal function in the sequence optimization process. Users can obtain many promising RNA sequences quite easily. The more difficult issue is to critically evaluate them and select the most favorable and reliable sequence that form1s the expected RNA structure. The latter problem is addressed in this paper. We propose an RNA design protocol extended to include sequence evaluation, for which a 3D structure is used. Experiments show that the accuracy of RNA design can be improved by adding a 3D structure prediction and analysis step.

gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics. gRNAde is a graph neural network that uses an SE (3) equivariant encoder-decoder framework for generating RNA sequences conditioned on backbone structures where the identities of the bases are unknown. We demonstrate the utility of gRNAde for fixed-backbone re-design of existing RNA structures of interest from the PDB, including riboswitches, aptamers, and ribozymes. gRNAde is more accurate in terms of native sequence recovery while being significantly faster compared to existing physics-based tools for 3D RNA inverse design, such as Rosetta.

The Potential of Molecular Docking for Predictive Toxicology.

Molecular modeling techniques are widely used in medicinal chemistry for the study of biological targets, the rational design of new drugs, or the investigation of their mechanism of action.They are also applied in toxicology to identify chemical potential harmful effects.Molecular docking is a computational technique to predict the ligand binding mode and evaluate the interaction energy with a biological target.This chapter describes a computational workflow to predict possible endocrine disruptors on peroxisome proliferator-activated receptor alpha (PPARα), a nuclear receptor involved in glucose and lipid metabolism. The analyzed compounds are food contact chemicals, natural or synthetic substances intentionally added to food or released from the package or during production or technological processes.

Conversion of albumin into a BODIPY-like photosensitizer by a flick reaction, tumor accumulation and photodynamic therapy.

The accumulation of photosensitizers (PSs) in lesion sites but not in other organs is an important challenge for efficient image guiding in photodynamic therapy. Cancer cells are known to express a significant number of albumin-binding proteins that take up albumin as a nutrient source. Here, we converted albumin to a novel BODIPY-like PS by generating a tetrahedral boron environment via a flick reaction. The formed albumin PS has almost the same 3-dimensional structural feature as free albumin because binding occurs at Sudlow Site 1, which is located in the interior space of albumin. An i.v. injection experiment in tumor-bearing mice demonstrated that the human serum albumin PS effectively accumulated in cancer tissue and, more surprisingly, albumin PS accumulated much more in the cancer tissue than in the liver and kidneys. The albumin PS was effective at killing tumor cells through the generation of reactive oxygen species under light irradiation. The crystal structure of the albumin PS was fully elucidated by X-ray crystallography; thus, further tuning of the structure will lead to novel physicochemical properties of the albumin PS, suggesting its potential in biological and clinical applications.

Impact of fermentation by Saccharomyces Cerevisiae on the macronutrient and in vitro digestion characteristics of Chinese noodles.

This study examined the impact of live bread yeast (Saccharomyces cerevisiae) on the nutritional characteristics of Asian dried noodles. Micronutrient analysis of fermented noodles revealed a 6.9% increase in the overall amino acid content, a 37.1% increase in the vitamin B content and a 63.0% decrease in the phytic acid level. Molecular weight analysis of starch and protein contents revealed moderate decrease in the fermented noodles. The in vitro digestion of fermented noodles showed a slightly faster initial acidification, four-fold decrease in the initial shear viscosity (from 8.85 to 1.94 Pa·s). The initial large food particle count (>2 mm diameter) was 19.5% lower in the fermented noodles. The fermented noodles contained slightly higher free sugar content (73.5 mg g-1 noodle) during the gastric digestion phase. The overall nutrition and digestion results indicate nutritional improvement and digestion-easing attributes in the fermented noodles.

Binding and distribution regularity of water molecules in high-moisture textured Antarctic krill (Euphausia superba) meat.

High-moisture extrusion technique with the advantage of high efficiency and low energy consumption is a promising strategy for processing Antarctic krill meat. Consequently, this study aimed to prepare high-moisture textured Antarctic krill meat (HMTAKM) with a rich fiber structure at different water contents (53 %, 57 %, and 61 %) and to reveal the binding and distribution regularity of water molecules, which is closely related to the fiber structure of HMTAKM and has been less studied. The hydrogen-bond network results indicated the presence of at least two or more types of water molecules with different hydrogen bonds. Increasing the water content of HMTAKM promoted the formation of hydrogen bonds between the water molecules and protein molecules, leading to the transition of the ß-sheet to the α-helix. These findings offer a novel viable processing technique for Antarctic krill and a new understanding of the fiber formation of high-moisture textured proteins.

Effects of degree of milling on bran layer structure, physicochemical properties and cooking quality of brown rice.

Effects of varying degree of milling (DOM) (0-22%) on the bran layer structure, physicochemical properties, and cooking quality of brown rice were explored. As the DOM increased, bran degree, protein, lipid, dietary fiber, amylose, mineral elements, and color parameters (a* and b* values) of milled rice decreased while starch and L* value increased. Microscopic fluorescence images showed that the pericarp, combined seed coat-nucellus layer, and aleurone layer were removed in rice processed at DOM of 6.6%, 9.2%, and 15.4%, respectively. The pasting properties, thermal properties, and palatability of rice increased as the DOM increased. Principal component and correlation analysis indicated that excessive milling lead to a decline in nutritional value of rice with limited impact on enhancing palatability. Notably, when parts of aleurone cell wall were retained, rice samples exhibited high cooking and sensory properties. It serves as a potential guide to the production of moderately milled rice.

Exploring the conformational landscape through rotational spectroscopy and computational modelling: The tunneling dynamics in 2,6-diethylphenol.

Phenol and some of its derivatives exhibit interesting tunneling motions consisting of two groups of transitions separated by a few hundred MHz. Recently, one of its derivatives, 2,6-di-tert-butylphenol, has shown additional hyperfine tunneling components, the origin of which remains unclear. In this work, another member of the family, 2,6-diethylphenol, has been investigated through its rotational spectrum. The jet-cooled broadband chirped-pulse Fourier transform microwave spectra in the 2-8 GHz frequency region revealed the presence of two conformers. The comparison with the equilibrium structure obtained by computational calculations at the B3LYP-D3(BJ)/Def2-TZVP level validates the structural determination and the orientation of the lateral ethyl groups. Additional observation of all the singly-substituted 13C isotopologues for the most stable ones allowed the determination of the substitution structure by means of the Kraitchman equations. Both conformers exhibited tunneling that was reproduced using an advanced 1D model, which provides an estimate of the barrier height for both conformers.

Two three-dimensional coordination polymers as fluorescence probes for the detection of nitrobenzene, tetracycline, fluazinam and their application in green pepper.

Two coordination polymers (CPs), [Zn5(L)2(phen)5](1) and [Cd2(HL)(2,2-bpy)(H2O)3](2), were synthesized by using 2',3,3',5,5'-Diphenyl ether pentacarboxylic acid (H5L), phenanthroline (phen), and 2,2'-bipyridine (2,2'-bpy) under hydrothermal conditions. The L5- ligand adopts the µ6-к2: к2: к1: к1: к1: к1 mode in 1 and the µ5-к2: к2: к2: к2: к1 mode in 2. Sensing experiments show that 1 and 2 are fluorescence probes with high sensitivity and rapid detection of nitro explosives, antibiotics, and pesticides. In order to verify the ability of 2 to detect FLU in actual samples, we performed a spiked recovery experiment in green pepper water. The spiked recoveries were 97.77-101.18 %. Interestingly, because H5L is not completely deprotonated in 2, there is abundant hydrogen bonding, which makes the fluorescence quenching rate higher and the detection limit lower. The possible fluorescence quenching mechanism of 1 and 2 can be explained by their UV-VIS absorption spectra and orbital energy levels.

Characterization and function of particulate organic matter: Evidence from lakes undergoing ecological restoration.

Particulate organic matter (POM) plays a crucial role in the organic composition of lakes; however, its characteristics remain poorly understood. This study aimed to characterize the structure and composition of POM in Lake Baiyangdian using many kinds of techniques and investigate the effects of different extracted forms of POM on water quality. The suspended particulate matter in the lake had complex compositions, with its components primarily derived from aquatic plants and their detritus. The organic matter content of the suspended particulate matter was relatively high (organic carbon content 27.29-145.94 g/kg) for the sum of three extractable states (water-extracted organic matter [WEOM], humic acid, and fulvic acid) and one stable bound state (humin). Spatial distribution analysis revealed that the POM content in the water increased from west to east, which was consistent with the water flow pattern influenced by the Baiyangdian water diversion project. Fluorescence spectroscopy analysis of the WEOM showed three prominent peaks with excitation/emission wavelengths similar to those of dissolved organic matter peaks. These peaks were potentially initial products of POM conversion into dissolved organic matter. Furthermore, the intensity of the WEOM fluorescence peak (total fluorescence peak intensity) was negatively correlated with the inorganic nitrogen concentration in water (p < 0.01), while the intensity of the HA fluorescence peak showed a positive correlation with the inorganic nitrogen concentration (p < 0.01). This suggested that exogenous organic matter inputs led to the diffusion of alkaline dissolved nitrogen from sediment into water, while degradation processes of aquatic plant debris contributed to the decrease in inorganic nitrogen concentrations in the water column. These findings enhance our understanding of POM characteristics in shallow lakes and the role of POM in shallow lake ecosystems.

The structure-function relationships and techno-functions of ß-conglycinin.

ß-conglycinin (ß-CG) is a prominent storage protein belonging to the globulin family in soybean (Glycine max) seeds. Along with other soybean proteins, it serves as an important source of essential amino acids and high-quality nutrition. However, the digestibility and nutritional value of ß-CG are key factors affecting the nutritional profile of soy-based foods. The heterotrimeric, secondary, and quaternary structures of ß-CG, particularly the spatial arrangement of its α, α', and ß subunits, influence its functional properties. Considering these aspects, ß-CG emerges as a significant protein with diverse applications in the food and health sectors. Therefore, this review explores ß-CG's composition, structure, function, health implications, and industrial uses. Salient discussions are presented on its molecular structure, nutrition, digestibility, allergenicity, and techno-functions including emulsification, solubility, gelling, and structure-function complexities. Overall, the multifaceted potential of ß-CG in the healthcare sector and the food industry is evident.

Micro-C Analysis Workflow Using Pairtools and Juicer.

Three-dimensional (3D) chromosome structures are closely related to various chromosomal functions, and deep analysis of the structures is crucial for the elucidation of the functions. In recent years, chromosome conformation capture (3C) techniques combined with next-generation sequencing analysis have been developed to comprehensively reveal 3D chromosome structures. Micro-C is one such method that can detect the structures at nucleosome resolution. In this chapter, I provide a basic method for Micro-C analysis. I present and discuss a series of data analyses ranging from mapping to basic downstream analyses, including loop detection.

Step-by-Step Protocol to Generate Hi-C Contact Maps Using the rfy_hic2 Pipeline.

The Hi-C method has emerged as an indispensable tool for analyzing the 3D organization of the genome, becoming increasingly accessible and frequently utilized in chromatin research. To effectively leverage 3D genomics data obtained through advanced technologies, it is crucial to understand what processes are undertaken and what aspects require special attention within the bioinformatics pipeline. This protocol aims to demystify the Hi-C data analysis process for field newcomers. In a step-by-step manner, we describe how to process Hi-C data, from the initial sequencing of the Hi-C library to the final visualization of Hi-C contact data as heatmaps. Each step of the analysis is clearly explained to ensure an understanding of the procedures and their objectives. By the end of this chapter, readers will be equipped with the knowledge to transform raw Hi-C reads into informative visual representations, facilitating a deeper comprehension of the spatial genomic structures critical to cellular functions.

Analysis and Visualization of Multiple Hi-C and Micro-C Data with CustardPy.

Three-dimensional (3D) genome structure plays crucial roles in biological processes and disease pathogenesis. Hi-C and Micro-C, well-established methods for 3D genome analysis, can identify a variety of 3D genome structures. However, selecting appropriate pipelines and tools for the analysis and setting up the required computing environment can sometimes pose challenges. To address this, we have introduced CustardPy, a Docker-based pipeline specifically designed for 3D genome analysis. CustardPy is designed to compare and evaluate multiple samples and wraps several existing tools to cover the entire workflow from FASTQ mapping to visualization. In this chapter, we demonstrate how to analyze and visualize Hi-C data using CustardPy and introduce several 3D genome features observed in Hi-C data.

Image Analysis Protocol for DNA/RNA/Immunofluorescence (IF)-seqFISH Data.

Imaging-based spatial multi-omics technologies facilitate the analysis of higher-order genomic structures, gene transcription, and the localization of proteins and posttranslational modifications (PTMs) at the single-allele level, thereby enabling detailed observations of biological phenomena, including transcription machinery within cells and tissues. This chapter details the principles of such technologies, with a focus on DNA/RNA/immunofluorescence (IF) sequential fluorescence in situ hybridization (seqFISH). A comprehensive step-by-step protocol for image analysis is provided, covering image preprocessing, spot detection, and data visualization. For practical application, complete Jupyter Notebook codes are made available on GitHub ( https://github.com/Ochiai-Lab/seqFISH_analysis ).

Extracting Chromosome Structural Information as One-Dimensional Metrics and Integrating Them with Epigenomics.

Hi-C is a powerful method for obtaining genome-wide chromosomal structural information. The typical Hi-C analysis utilizes a two-dimensional (2D) contact matrix, which poses challenges for quantitative comparisons, visualizations, and integrations across multiple datasets. Here, we present a protocol for extracting one-dimensional (1D) features from chromosome structure data by HiC1Dmetrics. Leveraging these 1D features enables integrated analysis of Hi-C and epigenomic data.

Exploring Cohesin-Related Multiomics Information in the Web Browser.

Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.

Review of Core-shell structure zeolite-based catalysts for NOx emission control.

Nitrogen oxides (NOx) from diesel engine exhaust, is one of the major sources of environmental pollution. Currently, selective catalytic reduction with ammonia (NH3-SCR) is considered to be the most effective protocol for reducing NOx emissions. Nowadays, zeolite-based NH3-SCR catalysts have been industrialized and widespread used in this field. Nevertheless, with the increasingly stringent environmental regulations and implementation of the requirement of "zero emission" of diesel engine exhaust, it is extremely urgent to prepare catalysts with superior NH3-SCR activity and exceptional resistance to poisons (SO2, alkali metals, hydrocarbons, etc.). Core-shell structure zeolite-based catalysts (CSCs) have shown great promise in NH3-SCR of NOx in recent years by virtue of its relatively higher low-temperature activity, broader operation temperature window and outstanding resistance to poisons. This review mainly focuses on the recent progress of CSCs for NH3-SCR of NOx with three extensively investigated SSZ-13, ZSM-5, Beta zeolites as cores. The reaction mechanisms of resistance to sulfur poisoning, alkali metal poisoning, hydrocarbon poisoning, and hydrothermal aging are summarized. Moreover, the important role of interfacial effect between core and shell in the reaction of NH3-SCR was clarified. Finally, the future development and application outlook of CSCs are prospected.

Sequence Design for RNA-RNA Interactions.

The design of RNA sequences with desired structural properties presents a challenging computational problem with promising applications in biotechnology and biomedicine. Most regulatory RNAs function by forming RNA-RNA interactions, e.g., in order to regulate mRNA expression. It is therefore natural to consider problems where a sequence is designed to form a desired RNA-RNA interaction and switch between structures upon binding. This contribution demonstrates the use of the Infrared framework to design interacting sequences. Specifically, we consider the regulation of the rpoS mRNA by the sRNA DsrA and design artificial 5 ' UTRs that place a downstream protein coding gene under control of DsrA. The design process is explained step by step in a Jupyter notebook, accompanied by Python code. The text discusses setting up design constraints for sampling sequences in Infrared, computing quality measures, constructing a suitable cost function, as well as the optimization procedure. We show that not only thermodynamic but also kinetic folding features can be relevant. Kinetics of interaction formation can be estimated efficiently using the RRIkinDP tool, and the chapter explains how to include kinetic folding features from RRIkinDP directly in the cost function. The protocol implemented in our Jupyter notebook can easily be extended to consider additional requirements or adapted to novel design scenarios.

Riboswitch Design Using MODENA.

In silico design of artificial riboswitches is a challenging and intriguing task. Since experimental approaches such as in vitro selection are time-consuming processes, computational tools that guide riboswitch design are desirable to accelerate the design process. In this chapter, we describe the usage of the MODENA web server to design ON riboswitches on the basis of a multi-objective genetic algorithm and RNA secondary structure prediction.