Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

Towards a Light-mediated Gene Therapy for the Eye using Caged Ethinylestradiol and the Inducible Cre/lox System.

Increasingly, retinal pathologies are being treated with virus-mediated gene therapies. To be able to target viral transgene expression specifically to the pathological regions of the retina with light, we established an in vivo photoactivated gene expression paradigm for retinal tissue. Based on the inducible Cre/lox system, we discovered that ethinylestradiol is a suitable alternative to Tamoxifen as ethinylestradiol is more amenable to modification with photosensitive protecting compounds, i.e., "caging." Identification of ethinylestradiol as a ligand for the mutated human estradiol receptor was supported by in silico binding studies showing the reduced binding of caged ethinylestradiol. Caged ethinylestradiol was injected into the eyes of double transgenic GFAP-CreERT2 mice with a Cre-dependent tdTomato reporter transgene followed by irradiation with light of 450 nm. Photoactivation significantly increased retinal tdTomato expression compared to controls. We thus demonstrated a first step towards the development of a targeted, light-mediated gene therapy for the eyes.

Distinct Conformations of SARS-CoV-2 Omicron Spike Protein and Its Interaction with ACE2 and Antibody.

Since November 2021, Omicron has been the dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant that causes the coronavirus disease 2019 (COVID-19) and has continuously impacted human health. Omicron sublineages are still increasing and cause increased transmission and infection rates. The additional 15 mutations on the receptor binding domain (RBD) of Omicron spike proteins change the protein conformation, enabling the Omicron variant to evade neutralizing antibodies. For this reason, many efforts have been made to design new antigenic variants to induce effective antibodies in SARS-CoV-2 vaccine development. However, understanding the different states of Omicron spike proteins with and without external molecules has not yet been addressed. In this review, we analyze the structures of the spike protein in the presence and absence of angiotensin-converting enzyme 2 (ACE2) and antibodies. Compared to previously determined structures for the wildtype spike protein and other variants such as alpha, beta, delta, and gamma, the Omicron spike protein adopts a partially open form. The open-form spike protein with one RBD up is dominant, followed by the open-form spike protein with two RBD up, and the closed-form spike protein with the RBD down. It is suggested that the competition between antibodies and ACE2 induces interactions between adjacent RBDs of the spike protein, which lead to a partially open form of the Omicron spike protein. The comprehensive structural information of Omicron spike proteins could be helpful for the efficient design of vaccines against the Omicron variant.

Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor.

The United States is experiencing the most profound and devastating opioid crisis in history, with the number of deaths involving opioids, including prescription and illegal opioids, continuing to climb over the past two decades. This severe public health issue is difficult to combat as opioids remain a crucial treatment for pain, and at the same time, they are also highly addictive. Opioids act on the opioid receptor, which in turn activates its downstream signaling pathway that eventually leads to an analgesic effect. Among the four types of opioid receptors, the µ subtype is primarily responsible for the analgesic cascade. This review describes available 3D structures of the µ opioid receptor in the protein data bank and provides structural insights for the binding of agonists and antagonists to the receptor. Comparative analysis on the atomic details of the binding site in these structures was conducted and distinct binding interactions for agonists, partial agonists, and antagonists were observed. The findings in this article deepen our understanding of the ligand binding activity and shed some light on the development of novel opioid analgesics which may improve the risk benefit balance of existing opioids.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

A comprehensive rat transcriptome built from large scale RNA-seq-based annotation.

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors.

Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.

Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms.

Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential endocrine-disrupting effects. Quantitative structure-activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure-activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.

Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors.

Endocrine-disrupting chemicals (EDCs) can inadvertently interact with 12 classic nuclear receptors (NRs) that disrupt the endocrine system and cause adverse effects. There is no widely accepted understanding about what structural features make thousands of EDCs able to activate different NRs as well as how these structural features exert their functions and induce different outcomes at the cellular level. This paper applies the hierarchical characteristic fragment methodology and high-throughput screening molecular docking to comprehensively explore the structural and functional features of EDCs for the 12 NRs based on more than 7000 chemicals from curated datasets. EDCs share three levels of key fragments. The primary and secondary fragments are associated with the binding of EDCs to four groups of receptors: steroidal nuclear receptors (SNRs, including androgen, estrogen, glucocorticoid, mineralocorticoid, and progesterone), retinoic acid receptors, thyroid hormone receptors, and vitamin D receptors. The tertiary fragments determine the activity type by interacting with two key locations in the ligand-binding domains of NRs (N-H5-H3-C and N-H7-H11-C for SNRs and N-H5-H5'-H2'-H3-C and N-H6'-H11-C for non-SNRs). The resulting compiled structural fragments of EDCs together with elucidated compound NR binding modes provide a framework for understanding the interactions between EDCs and NRs, facilitating faster and more accurate screening of EDCs for multiple NRs in the future.

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations.

Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17ß-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.

Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food.

Persistent Organic Pollutants (POPs) are a serious food safety concern due to their persistence and toxic effects. To promote food safety and protect human health, it is important to understand the sources of POPs and how to minimize human exposure to these contaminants. The POPs Program within the U.S. Food and Drug Administration (FDA), manually evaluates congener patterns of POPs-contaminated samples and sometimes compares the finding to other previously analyzed samples with similar patterns. This manual comparison is time consuming and solely depends on human expertise. To improve the efficiency of this evaluation, we developed software to assist in identifying potential sources of POPs contamination by detecting similarities between the congener patterns of a contaminated sample and potential environmental source samples. Similarity scores were computed and used to rank potential source samples. The software has been tested on a diverse set of incurred samples by comparing results from the software with those from human experts. We demonstrated that the software provides results consistent with human expert observation. This software also provided the advantage of reliably evaluating an increased sample lot which increased overall efficiency.

Applicability Domains Enhance Application of PPARγ Agonist Classifiers Trained by Drug-like Compounds to Environmental Chemicals.

Peroxisome proliferator activator receptor gamma (PPARγ) agonist activity of chemicals is an indicator of concerned health conditions such as fatty liver and obesity. In silico screening PPARγ agonists based on quantitative structure-activity relationship (QSAR) models could serve as an efficient and pragmatic strategy. Owing to the broad research interests in discovery of PPARγ-targeted drugs, a large amount of PPARγ agonist activity data has been produced in the field of medicinal chemistry, facilitating development of robust QSAR models. In this study, random forest classifiers were developed based on the binary-category data transformed from the heterogeneous PPARγ agonist activity data of drug-like compounds. Coupling with applicability domains, capability of the established classifiers for predicting environmental chemicals was evaluated using two external data sets. Our results demonstrated that applicability domains could enhance application of the developed classifiers to predict environmental PPARγ agonists.

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model.

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.

Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor.

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.

Similarities and differences between variants called with human reference genome HG19 or HG38.

BACKGROUND: Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed. METHODS: We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs. RESULTS: The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected). CONCLUSION: A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38.

After publication of this supplement article.

dnAQET: a framework to compute a consolidated metric for benchmarking quality of de novo assemblies.

BACKGROUND: Accurate de novo genome assembly has become reality with the advancements in sequencing technology. With the ever-increasing number of de novo genome assembly tools, assessing the quality of assemblies has become of great importance in genome research. Although many quality metrics have been proposed and software tools for calculating those metrics have been developed, the existing tools do not produce a unified measure to reflect the overall quality of an assembly. RESULTS: To address this issue, we developed the de novo Assembly Quality Evaluation Tool (dnAQET) that generates a unified metric for benchmarking the quality assessment of assemblies. Our framework first calculates individual quality scores for the scaffolds/contigs of an assembly by aligning them to a reference genome. Next, it computes a quality score for the assembly using its overall reference genome coverage, the quality score distribution of its scaffolds and the redundancy identified in it. Using synthetic assemblies randomly generated from the latest human genome build, various builds of the reference genomes for five organisms and six de novo assemblies for sample NA24385, we tested dnAQET to assess its capability for benchmarking quality evaluation of genome assemblies. For synthetic data, our quality score increased with decreasing number of misassemblies and redundancy and increasing average contig length and coverage, as expected. For genome builds, dnAQET quality score calculated for a more recent reference genome was better than the score for an older version. To compare with some of the most frequently used measures, 13 other quality measures were calculated. The quality score from dnAQET was found to be better than all other measures in terms of consistency with the known quality of the reference genomes, indicating that dnAQET is reliable for benchmarking quality assessment of de novo genome assemblies. CONCLUSIONS: The dnAQET is a scalable framework designed to evaluate a de novo genome assembly based on the aggregated quality of its scaffolds (or contigs). Our results demonstrated that dnAQET quality score is reliable for benchmarking quality assessment of genome assemblies. The dnQAET can help researchers to identify the most suitable assembly tools and to select high quality assemblies generated.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era.

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

Quantum chemical simulations revealed the toxicokinetic mechanisms of organic phosphorus flame retardants catalyzed by P450 enzymes.

The metabolic fate and toxicokinetics of organic phosphorus flame retardants catalyzed by cytochrome P450 enzymes (CYPs) are here investigated by in silico simulations, leveraging an active center model to mimic the CYPs, triphenyl phosphate (TPHP), tris(2-butoxyethyl) phosphate and tris(1,3-dichloro-2-propyl) phosphate as substrates. Our calculations elucidated key main pathways and predicted products, which were corroborated by current in vitro data. Results showed that alkyl OPFRs are eliminated faster than aryl and halogenated alkyl-substituted OPFRs. In addition, we discovered a proton shuttle pathway for aryl hydroxylation of TPHP and P = O bond-assisted H-transfer mechanisms (rather than nonenzymatic hydrolysis) that lead to O-dealkylation/dearylation of phosphotriesters.

Deep learning for predicting toxicity of chemicals: a mini review.

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.