Understanding Toll-like Receptor Modulation Through Machine Learning

Toll-like receptors (TLRs) represent one of the most fascinating and currently most widely studied immunologic targets, due to their crucial role in forming the first barrier in immune response. The structurally conserved TLRs consist of ten human subtypes (TLR1-TLR10), with a structurally broad range of natural ligands, including lipids, peptides, and ribonucleic acid (RNA), which challenges the rational design of drug-like TLR ligands. Therefore, despite their enormous therapeutic potential as powerful regulators of inflammatory pathways, only few TLR modulators (e.g., Imiquimod) are currently in clinical use. Since no complete and up-to-date repository for known TLR modulators is currently available, we carefully collected and manually curated data to create a Toll-like receptor database (TollDB), the first database which includes all reported small organic druglike molecules targeting TLRs and detailed pharmacological assay conditions used for their characterization. TollDB is freely accessible via https://tolldb.drug-design.de and provides three different search possibilities including a ligand-centered simple search, an advanced search that can retrieve information on biological assays and a structure search. Currently, TollDB contains 4925 datapoints describing 2155 compounds tested in 36 assay types using 553 different assay conditions. Among all the 2155 compounds, 1278 are not reported as TLR ligands by ChEMBL database. Users can retrieve information about the measured inactives and multi-target TLR ligands from TollDB. After statistical analysis for TollDB, we compared the chemical space covered by compounds in TollDB to that covered by the compounds in DrugBank. Next, we explored the matched molecular pairs (MMPs) and activity cliffs, then used docking to explain the activity cliffs between MMPs. After a thorough analysis of the entire database, we used a selected dataset from TollDB to train machine learning models to distinguish active ligands for different subtypes. These validated models can be used for prioritizing hits from virtual screening for chemical synthesis or for biological testing. The curated database can be directly used in many ways, for example, as a validation dataset for pharmacophore model evaluation, as a virtual screening library for drug repurposing or as reference for pharmacological assay design. TollDB represents a unique and useful resource for various research fields such as medicinal chemistry, immunology, computational biology and promotes the use of artificial intelligence in modern drug design campaigns.Toll-like Rezeptoren (TLRs) sind aufgrund ihrer entscheidenden Rolle bei der Bildung der ersten Barriere der Immunantwort eines der faszinierendsten und derzeit am häufigsten untersuchten immunologischen Ziele. Die strukturell konservierten TLRs weisen zehn menschliche Subtypen (TLR1-TLR10) auf. Sie umfassen ein breites strukturelles Spektrum natürlicher Liganden, einschließlich Lipiden, Peptiden und RNA, was das rationale Design von arzneimittelähnlichen TLR-Liganden herausfordernd macht. Daher werden derzeit trotz ihres enormen therapeutischen Potenzials als starke Regulatoren von Entzündungswegen nur wenige TLR-Modulatoren (z. B. Imiquimod) klinisch eingesetzt. Da derzeit keine vollständiges und aktuelles Respository für bekannte TLR Modulatoren verfügbar ist, haben wir sorgfältig Daten gesammelt und manuell überprüft, um eine Toll-like-Rezeptor-Datenbank TollDB zu erstellen. Diese Datenbank enthält alle uns bekannten kleinen organischen arzneimittelähnlichen Moleküle mit detaillierten pharmakologischen Testbedingungen, die für ihre Charakterisierung verwendet wurden. TollDB ist unter https://tolldb.drug-design.de frei zugänglich und bietet drei verschiedene Suchmöglichkeiten, darunter eine Liganden zentrierte einfache Suche, eine erweiterte Suche, mit der Informationen zu biologischen Assays abgerufen werden können, und eine strukturelle Suche. Derzeit enthält TollDB 4925 Datenpunkte, die 2155 Verbindungen beschreiben, die in 36 in vitro Testtypen unter Verwendung von 553 verschiedenen Testbedingungen getestet wurden. Von allen 2155 Verbindungen sind 1278 nicht in der ChEMBL Datenbank enthalten. Benutzer können bei der TollDB auch Informationen zu den gemessenen inaktiven und Multi-Target-TLR-Liganden erhalten. Nach der statistischen Analyse für TollDB haben wir den von den Verbindungen in TollDB abgedeckten chemischen Raum mit dem von den Verbindungen in DrugBank abgedeckten verglichen. Wir haben die matched molecular pairs und activity cliffs untersucht. Nachdem wir ein umfassendes Verständnis der Daten in der TollDB erlangt haben, haben wir die Daten verwendet, um Modelle für maschinelles Lernen zu trainieren, um aktive Liganden für verschiedene Subtypen zu identifizieren. Diese validierten Modelle können zur Priorisierung von Treffern aus dem virtuellen Screening zur Synthese oder zum Testen verwendet werden. Zusammenfassend kann die Datenbank in vielen Aspekten direkt verwendet werden, beispielsweise als Validierungsdatensatz für die Bewertung eines Pharmakophormodells, als virtuelle Screening-Bibliothek für die Umfunktionierung von Arzneimitteln oder als Referenzsubstanz, für das Design des pharmakologischen Assays. TollDB stellt eine einzigartige und nützliche Ressource für verschiedene Forschungsbereiche wie medizinische Chemie, Immunologie und Computerbiologie dar und fördert den Einsatz künstlicher Intelligenz in modernen Wirkstoffdesign

Partition-based differentially private synthetic data generation

Private synthetic data sharing is preferred as it keeps the distribution and nuances of original data compared to summary statistics. The state-of-the-art methods adopt a select-measure-generate paradigm, but measuring large domain marginals still results in much error and allocating privacy budget iteratively is still difficult. To address these issues, our method employs a partition-based approach that effectively reduces errors and improves the quality of synthetic data, even with a limited privacy budget. Results from our experiments demonstrate the superiority of our method over existing approaches. The synthetic data produced using our approach exhibits improved quality and utility, making it a preferable choice for private synthetic data sharing

Planning Emergency Shelters for Urban Disasters: A Multi-Level Location–Allocation Modeling Approach

In recent years, cities are threatened by various natural hazards. Planning emergency shelters in advance is an effective approach to reducing the damage caused by disasters and ensuring the safety of residents. Thus, providing the optimal layout of urban emergency shelters is an important stage of disaster management and an act of humanitarian logistics. In order to study the optimal layout of emergency shelters in small mountain cities, this paper constructs multi-level location models for different grades of emergency shelters so as to minimize the travel and construction costs and maximize the coverage rate. Specifically, the actual service of emergency shelters is determined using Geographic Information System (GIS) software and Weighted Voronoi Diagram (WVD) models under the limitation of site capacity, and the space layout is adjusted through combining the actual urban land with the construction position. In this paper, the Jianchuan county seat at Yunnan Province, China, was considered as a case study to illustrate the models of emergency shelters in which the feasibility of the presented models is verified. The proposed research methods and models have provided theoretical basis and a benchmark for the optimal layout of emergency shelters in other small mountain cities

PP2A Mediated AMPK Inhibition Promotes HSP70 Expression in Heat Shock Response

BACKGROUND: Under stress, AMP-activated protein kinase (AMPK) plays a central role in energy balance, and the heat shock response is a protective mechanism for cell survival. The relationship between AMPK activity and heat shock protein (HSP) expression under stress is unclear. METHODOLOGY/PRINCIPAL FINDINGS: We found that heat stress induced dephosphorylation of AMPKα subunit (AMPKα) in various cell types from human and rodent. In HepG2 cells, the dephosphorylation of AMPKα under heat stress in turn caused dephosphorylation of acetyl-CoA carboxylase and upregulation of phosphoenolpyruvate carboxykinase, two downstream targets of AMPK, confirming the inhibition of AMPK activity by heat stress. Treatment of HepG2 cells with phosphatase 2A (PP2A) inhibitor okadaic acid or inhibition of PP2A expression by RNA interference efficiently reversed heat stress-induced AMPKα dephosphorylation, suggesting that heat stress inhibited AMPK through activation of PP2A. Heat stress- and other HSP inducer (CdCl(2), celastrol, MG132)-induced HSP70 expression could be inhibited by AICAR, an AMPK specific activator. Inhibition of AMPKα expression by RNA interference reversed the inhibitory effect of AICAR on HSP70 expression under heat stress. These results indicate that AMPK inhibition under stress contribute to HSP70 expression. Mechanistic studies showed that activation of AMPK by AICAR had no effect on heat stress-induced HSF1 nuclear translocation, phosphorylation and binding with heat response element in the promoter region of HSP70 gene, but significantly decreased HSP70 mRNA stability. CONCLUSIONS/SIGNIFICANCE: These results demonstrate that during heat shock response, PP2A mediated AMPK inhibition upregulates HSP70 expression at least partially through stabilizing its mRNA, which suggests a novel mechanism for HSP induction under stress

Measuring Urban Spatial Activity Structures: A Comparative Analysis

Abstract: Human activity recognition has been of interest in the field of urban planning. This paper established a general framework by which expected human activity intensity (HAI) measured by the built environment and factual HAI measured by the Baidu thermal chart were estimated and comparatively analyzed so as to identify abnormal human activities in Hanghzou, China. Three elements of the built environment (i.e., residential density, road connectivity, and land-use mixing degree) from multi-source data with high precision are selected to assess the expected HAI. Results indicate Hangzhou has evolved into a polycentric city with three urban clusters. In addition, a significant positive correlation exists between the two types of HAIs. However, there are areas with spatial mismatches, particularly in the “urban village” and new towns, suggesting human activities are not equally distributed all over the city. Research implications, limitations, and future research needs are discussed

High-fiber-diet-related metabolites improve neurodegenerative symptoms in patients with obesity with diabetes mellitus by modulating the hippocampal–hypothalamic endocrine axis

ObjectiveThrough transcriptomic and metabolomic analyses, this study examined the role of high-fiber diet in obesity complicated by diabetes and neurodegenerative symptoms.MethodThe expression matrix of high-fiber-diet-related metabolites, blood methylation profile associated with pre-symptomatic dementia in elderly patients with type 2 diabetes mellitus (T2DM), and high-throughput single-cell sequencing data of hippocampal samples from patients with Alzheimer's disease (AD) were retrieved from the Gene Expression Omnibus (GEO) database and through a literature search. Data were analyzed using principal component analysis (PCA) after quality control and data filtering to identify different cell clusters and candidate markers. A protein–protein interaction network was mapped using the STRING database. To further investigate the interaction among high-fiber-diet-related metabolites, methylation-related DEGs related to T2DM, and single-cell marker genes related to AD, AutoDock was used for semi-flexible molecular docking.ResultBased on GEO database data and previous studies, 24 marker genes associated with high-fiber diet, T2DM, and AD were identified. Top 10 core genes include SYNE1, ANK2, SPEG, PDZD2, KALRN, PTPRM, PTPRK, BIN1, DOCK9, and NPNT, and their functions are primarily related to autophagy. According to molecular docking analysis, acetamidobenzoic acid, the most substantially altered metabolic marker associated with a high-fiber diet, had the strongest binding affinity for SPEG.ConclusionBy targeting the SPEG protein in the hippocampus, acetamidobenzoic acid, a metabolite associated with high-fiber diet, may improve diabetic and neurodegenerative diseases in obese people

Sum Rate Analysis of MU-MIMO with a 3D MIMO Base Station Exploiting Elevation Features

Although the three-dimensional (3D) channel model considering the elevation factor has been used to analyze the performance of multiuser multiple-input multiple-output (MU-MIMO) systems, less attention is paid to the effect of the elevation variation. In this paper, we elaborate the sum rate of MU-MIMO systems with a 3D base station (BS) exploiting different elevations. To illustrate clearly, we consider a high-rise building scenario. Due to the floor height, each floor corresponds to an elevation. Therefore, we can analyze the sum rate performance for each floor and discuss its effect on the performance of the whole building. This work can be seen as the first attempt to analyze the sum rate performance for high-rise buildings in modern city and used as a reference for infrastructure

Next generation 3D pharmacophore modeling

3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond