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.

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.

Sequence Design Using RNAstructure.

RNA is present in all domains of life. It was once thought to be solely involved in protein expression, but recent advances have revealed its crucial role in catalysis and gene regulation through noncoding RNA. With a growing interest in exploring RNAs with specific structures, there is an increasing focus on designing RNA structures for in vivo and in vitro experimentation and for therapeutics. The development of RNA secondary structure prediction methods has also spurred the growth of RNA design software. However, there are challenges to designing RNA sequences that meet secondary structure requirements. One major challenge is that the secondary structure design problem is likely NP-hard, making it computationally intensive. Another issue is that objective functions need to consider the folding ensemble of RNA molecules to avoid off target structures. In this chapter, we provide protocols for two software tools from the RNAstructure package: "Design" for structured RNA sequence design and "orega" for unstructured RNA sequence design.

SHAPE Probing to Screen Computationally Designed RNA.

RNA design is a major challenge for the future development of synthetic biology and RNA-based therapy. The development of efficient and accurate RNA design pipelines is based on trial and error strategies. The fast progression of such algorithms requires assaying the properties of many RNA sequences in a short time frame. High throughput RNA structure chemical probing technologies such as SHAPE-MaP allow for assaying RNA structure and interaction rapidly and at a very large scale. However, the promiscuity of the designed sequences that may differ only by one nucleotide requires special care. In addition, it necessitates the analysis and evaluation of many experimental results that may reveal to be very tedious. Here we propose an experimental and analytical workflow that eases the screening of thousands of designed RNA sequences at once. In particular, we have developed shapemap tools a customized software suite available at https://github.com/sargueil-citcom/shapemap-tools .

Datasets for Benchmarking RNA Design Algorithms.

RNA molecules play vital roles in many biological processes, such as gene regulation or protein synthesis. The adoption of a specific secondary and tertiary structure by RNA is essential to perform these diverse functions, making RNA a popular tool in bioengineering therapeutics. The field of RNA design responds to the need to develop novel RNA molecules that possess specific functional attributes. In recent years, computational tools for predicting RNA sequences with desired folding characteristics have improved and expanded. However, there is still a lack of well-defined and standardized datasets to assess these programs. Here, we present a large dataset of internal and multibranched loops extracted from PDB-deposited RNA structures that encompass a wide spectrum of design difficulties. Furthermore, we conducted benchmarking tests of widely utilized open-source RNA design algorithms employing this dataset.

QSAR: Using the Past to Study the Present.

Quantitative structure-activity relationships (QSAR) is a method for predicting the physical and biological properties of small molecules; it is in use in industry and public services. However, as any scientific method, it is challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. To answer the question whether QSAR, by exploiting available knowledge, can build new knowledge, the chapter reviews QSAR methods in search of a QSAR epistemology. QSAR stands on tree pillars, i.e., biological data, chemical knowledge, and modeling algorithms. Usually the biological data, resulting from good experimental practice, are taken as a true picture of the world; chemical knowledge has scientific bases; so if a QSAR model is not working, blame modeling. The role of modeling in developing scientific theories, and in producing knowledge, is so analyzed. QSAR is a mature technology and is part of a large body of in silico methods and other computational methods. The active debate about the acceptability of the QSAR models, about the way to communicate them, and the explanation to provide accompanies the development of today QSAR models. An example about predicting possible endocrine-disrupting chemicals (EDC) shows the many faces of modern QSAR methods.

Development, Use, and Validation of (Q)SARs for Predicting Genotoxicity and Carcinogenicity: Experiences from Italian National Institute of Health Activities.

In silico approaches are now increasingly accepted in several areas of toxicology to rapidly assess chemical hazard without the need for animal testing. Among in silico tools, quantitative and qualitative structure-activity approaches ((Q)SARs) are the most typically applied methods to predict hazard in the absence of experimental data. This paper provides an overview of different protocols that can be applied while dealing with (Q)SARs in different scenarios, namely, (Q)SAR development, use, and validation. Examples of protocols adopted in the three scenarios are reported, derived from the authors' experience in working at the Predictive Toxicology unit of the Italian National Institute of Health, focusing on the endpoints of carcinogenicity and genotoxicity.The illustrated activities are in line with the Institute's mission, the main center of research, control, and technical-scientific advice on public health in Italy.

MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first update. We also developed its version for android, being the first free app capable of predicting biological activities. MolPredictX is available for free at https://www.molpredictX.ufpb.br/ , and its mobile application version can be obtained from Google Play.

PFAS-Biomolecule Interactions: Case Study Using Asclepios Nodes and Automated Workflows in KNIME for Drug Discovery and Toxicology.

The Asclepios suite of KNIME nodes represents an innovative solution for conducting cheminformatics and computational chemistry tasks, specifically tailored for applications in drug discovery and computational toxicology. This suite has been developed using open-source and publicly accessible software. In this chapter, we introduce and explore the Asclepios suite through the lens of a case study. This case study revolves around investigating the interactions between per- and polyfluorinated alkyl substances (PFAS) and biomolecules, such as nuclear receptors. The objective is to characterize the potential toxicity of PFAS and gain insights into their chemical mode of action at the molecular level. The Asclepios KNIME nodes have been designed as versatile tools capable of addressing a wide range of computational toxicology challenges. Furthermore, they can be adapted and customized to accomodate the specific needs of individual users, spanning various domains such as nanoinformatics, biomedical research, and other related applications. This chapter provides an in-depth examination of the technical underpinnings and foundations of these tools. It is accompanied by a practical case study that demonstrates the utilization of Asclepios nodes in a computational toxicology investigation. This showcases the extendable functionalities that can be applied in diverse computational chemistry contexts. By the end of this chapter, we aim for readers to have a comprehensive understanding of the effectiveness of the Asclepios node functions. These functions hold significant potential for enhancing a wide spectrum of cheminformatics applications.

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.

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.

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.

Methods for Genome-Wide Chromatin Interaction Analysis.

Recent analyses revealed the essential function of chromatin structure in maintaining and regulating genomic information. Advancements in microscopy, nuclear structure observation techniques, and the development of methods utilizing next-generation sequencers (NGSs) have significantly progressed these discoveries. Methods utilizing NGS enable genome-wide analysis, which is challenging with microscopy, and have elucidated concepts of important chromatin structures such as a loop structure, a domain structure called topologically associating domains (TADs), and compartments. In this chapter, I introduce chromatin interaction techniques using NGS and outline the principles and features of each method.

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.