Bitter taste receptor activation by cholesterol and an intracellular tastant.

Bitter taste sensing is mediated by type 2 taste receptors (TAS2Rs (also known as T2Rs)), which represent a distinct class of G-protein-coupled receptors1. Among the 26 members of the TAS2Rs, TAS2R14 is highly expressed in extraoral tissues and mediates the responses to more than 100 structurally diverse tastants2-6, although the molecular mechanisms for recognizing diverse chemicals and initiating cellular signalling are still poorly understood. Here we report two cryo-electron microscopy structures for TAS2R14 complexed with Ggust (also known as gustducin) and Gi1. Both structures have an orthosteric binding pocket occupied by endogenous cholesterol as well as an intracellular allosteric site bound by the bitter tastant cmpd28.1, including a direct interaction with the α5 helix of Ggust and Gi1. Computational and biochemical studies validate both ligand interactions. Our functional analysis identified cholesterol as an orthosteric agonist and the bitter tastant cmpd28.1 as a positive allosteric modulator with direct agonist activity at TAS2R14. Moreover, the orthosteric pocket is connected to the allosteric site via an elongated cavity, which has a hydrophobic core rich in aromatic residues. Our findings provide insights into the ligand recognition of bitter taste receptors and suggest activities of TAS2R14 beyond bitter taste perception via intracellular allosteric tastants.

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.

This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.

GAIT: Gene expression Analysis for Interval Time.

Motivation: Despite the potential usefulness, the association analysis of gene expression with interval times of two events has been hampered because the occurrence of events can be censored and the conventional survival analysis is not suitable to handle two censored events. However, the recent advances of multivariate survival analysis considering multiple censored events together provide an unprecedented chance for this problem. Based on such advances, we have developed a software tool, GAIT, for the association analysis of gene expression with interval time of two events. Results: The performance of GAIT was demonstrated by simulation studies and the real data analysis. The result indicates the usefulness of GAIT in a wide range of biomedical applications. Availability and implementation: http://cdal.korea.ac.kr/GAIT/index.html. Supplementary information: Supplementary data are available at Bioinformatics online.

Prioritizing Disease Diagnosis in Neonatal Cohorts through Multivariate Survival Analysis: A Nonparametric Bayesian Approach.

Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence Analysis (CEPA), which is a nonparametric Bayesian approach suitable for understanding the precedence relationships in censored multivariate events. CEPA aims to analyze the precedence relationships between events to predict subsequent occurrences effectively. We applied CEPA to neonatal data from the National Health Insurance Service, identifying the precedence relationships among the seven most commonly diagnosed diseases categorized by the International Classification of Diseases. This analysis revealed a typical diagnostic sequence, starting with respiratory diseases, followed by skin, infectious, digestive, ear, eye, and injury-related diseases. Furthermore, simulation studies were conducted to demonstrate CEPA suitability for censored multivariate datasets compared to traditional models. The performance accuracy reached 76% for uniform distribution and 65% for exponential distribution, showing superior performance in all four tested environments. Therefore, the statistical approach based on CEPA enhances our understanding of disease interrelationships beyond competitive methodologies. By identifying disease precedence with CEPA, we can preempt subsequent disease occurrences and propose a healthcare system based on these relationships.

AlphaFold2 structures guide prospective ligand discovery.

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.

AlphaFold2 structures template ligand discovery.

AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.

Discovering potential pathways between type 2 diabetes mellitus and diabetic retinopathy: A big data analysis of the South Korean National Sample Cohort.

Diabetes mellitus, a prevalent metabolic disorder, is associated with a multitude of complications that necessitate vigilant management post-diagnosis. A notable complication, diabetic retinopathy, could lead to intense ocular injury, including vision impairment and blindness, due to the impact of the disease. Studying the transition from diabetes to diabetic retinopathy is paramount for grasping and halting the progression of complications. In this study, we examine the statistical correlation between type 2 diabetes mellitus and retinal disorders classified elsewhere, ultimately proposing a comprehensive disease network. The National Sample Cohort of South Korea, containing approximately 1 million samples and primary diagnoses based on the International Statistical Classification of Diseases and Related Health Problems 10th Revision classification, was utilized for this retrospective analysis. The diagnoses of both conditions displayed a statistically significant correlation with a chi-square test value of P < .001, and the t test for the initial diagnosis date also yielded a P < .001 value. The devised network, comprising 27 diseases and 142 connections, was established through statistical evaluations. This network offers insight into potential pathways leading to diabetic retinopathy and intermediary diseases, encouraging medical researchers to further examine various risk factors associated with these connections.

CTIVA: Censored time interval variable analysis.

Traditionally, datasets with multiple censored time-to-events have not been utilized in multivariate analysis because of their high level of complexity. In this paper, we propose the Censored Time Interval Analysis (CTIVA) method to address this issue. It estimates the joint probability distribution of actual event times in the censored dataset by implementing a statistical probability density estimation technique on the dataset. Based on the acquired event time, CTIVA investigates variables correlated with the interval time of events via statistical tests. The proposed method handles both categorical and continuous variables simultaneously-thus, it is suitable for application on real-world censored time-to-event datasets, which include both categorical and continuous variables. CTIVA outperforms traditional censored time-to-event data handling methods by 5% on simulation data. The average area under the curve (AUC) of the proposed method on the simulation dataset exceeds 0.9 under various conditions. Further, CTIVA yields novel results on National Sample Cohort Demo (NSCD) and proteasome inhibitor bortezomib dataset, a real-world censored time-to-event dataset of medical history of beneficiaries provided by the National Health Insurance Sharing Service (NHISS) and National Center for Biotechnology Information (NCBI). We believe that the development of CTIVA is a milestone in the investigation of variables correlated with interval time of events in presence of censoring.

Predicting medical specialty from text based on a domain-specific pre-trained BERT.

BACKGROUND: Owing to the prevalence of the coronavirus disease (COVID-19), coping with clinical issues at the individual level has become important to the healthcare system. Accordingly, precise initiation of treatment after a hospital visit is required for expedited processes and effective diagnoses of outpatients. To achieve this, artificial intelligence in medical natural language processing (NLP), such as a healthcare chatbot or a clinical decision support system, can be suitable tools for an advanced clinical system. Furthermore, support for decisions on the medical specialty from the initial visit can be helpful. MATERIALS AND METHODS: In this study, we propose a medical specialty prediction model from patient-side medical question text based on pre-trained bidirectional encoder representations from transformers (BERT). The dataset comprised pairs of medical question texts and labeled specialties scraped from a website for the medical question-and-answer service. The model was fine-tuned for predicting the required medical specialty labels among 27 labels from medical question texts. To demonstrate the feasibility, we conducted experiments on a real-world dataset and elaborately evaluated the predictive performance compared with four deep learning NLP models through cross-validation and test set evaluation. RESULTS: The proposed model showed improved performance compared with competitive models in terms of overall specialties. In addition, we demonstrate the usefulness of the proposed model by performing case studies for visualization applications. CONCLUSION: The proposed model can benefit hospital patient management and reasonable recommendations for specialties for patients.