QuartataWeb: Integrated Chemical–Protein-Pathway Mapping for Polypharmacology and Chemogenomics

Li, Hongchun; Pei, Fen; Taylor, D. Lansing; Bahar, İvet

doi:10.1093/bioinformatics/btaa210

Cited by 26 publications

(32 citation statements)

References 16 publications

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“…In this study, our QSP analysis led to the identification of 1,837 experimentally verified (known) and 368 computationally predicted (new) interactions between these autophagy modulators and their 993 known and 12 new target proteins, involved in 294 pathways. Many new interactions predicted using our machine learning method through QuartataWeb interface [28] were found to be consistent with recent experiments. For example, an interaction between fluphenazine and dopamine receptor D3 which was not reported in DrugBank (and therefore not included in our input dataset) was predicted with high confidence.…”

Section: Discussionsupporting

confidence: 86%

“…The set of modulators consists of 174 activators, 31 inhibitors, and 20 compounds acting as dualmodulators. We identified 1,831 known interactions between these drugs and their targets, and predicted 368 novel interactions using our probabilistic matrix factorization (PMF)-based application programming interface [26][27][28]. Of these predictions, 75 were consistent with recently published experimental data (not yet deposited in DrugBank [25]).…”

Section: Introductionsupporting

confidence: 54%

“…For example, matrix factorizationbased machine learning algorithms have applied to the prediction of novel drug-target interactions [26,27,91]. Pathway/network analysis tools have been developed to study drug effects [28,[92][93][94].…”

Section: Discussionmentioning

confidence: 99%

“…We further analyzed the distribution of pathways for each category of autophagy modulators using QuartataWeb [28]. Supplementary Table S6 presents the list of pathways with high enrichment scores (p-value < 0.05), including 63 targeted by activators, 24 targeted by inhibitors, and 106 targeted by dual-modulators.…”

Section: Functional Analysis Of the Targets Reveals Enriched Pathwaysmentioning

confidence: 99%

“…New drug-target interactions were predicted using our PMF-based machine learning algorithm [26][27][28]. We represented the known interactions between drugs and targets as a sparse matrix .…”

Section: Drug-target Interaction Predictionmentioning

confidence: 99%

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Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis

Shi

Pei

Perlmutter

et al. 2020

Preprint

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Autophagy plays an essential role in cell survival/death and functioning. Modulation of autophagy has been recognized as a promising therapeutic strategy against diseases/disorders associated with uncontrolled growth or accumulation of biomolecular aggregates, organelles or cells including those caused by cancer, aging, neurodegeneration, and liver diseases such as α1antitrypsin deficiency. Numerous pharmacological agents that enhance or suppress autophagy have been discovered. However, their molecular mechanisms of action are far from clear. Here we collected a set of 225 autophagy modulators and carried out a comprehensive quantitative systems pharmacology (QSP) analysis of their targets using both existing databases and predictions made by our machine learning algorithm. Autophagy modulators include several highly promiscuous drugs (e.g. artenimol and olanzapine acting as activator, fostamatinib as inhibitor, or melatonin as dual-modulator), as well as selected drugs uniquely targeting specific proteins (~30% of modulators). They are mediated by three layers of regulation: (i) pathways involving core autophagy-related (ATG) proteins such as mTOR, AKT, and AMPK; (ii) upstream signaling events that regulates the activity of ATG pathways such as calcium-, cAMP-, and MAPK-signaling pathways; and (iii) transcription factors regulating the expression ATG proteins such as TFEB, TFE3, HIF-1, FoxO, and NF-κB. Our results suggest that PKA serves as a linker bridging between various signal transduction events and autophagy. These new insights contribute to a better assessment of the mechanism of action of autophagy modulators as well as their side effects, development of novel polypharmacological strategies, and identification of drug repurposing opportunities.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionsupporting

confidence: 54%

Section: Discussionmentioning

confidence: 99%

Section: Functional Analysis Of the Targets Reveals Enriched Pathwaysmentioning

confidence: 99%

Section: Drug-target Interaction Predictionmentioning

confidence: 99%

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Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis

Shi

Pei

Perlmutter

et al. 2020

Preprint

Self Cite

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Identification of a novel six‐gene signature with potential prognostic and therapeutic value in cervical cancer

Shi

Guo

et al. 2021

Cancer Medicine

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Introduction Cervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection. Materials Differentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six‐gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co‐expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six‐gene signature. Results We discovered 178 overlapping DEGs in three microarray datasets and established a six‐gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six‐gene signature was closely associated with the immune response and tumour immune microenvironment. The six‐gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer. Conclusion Our study established a novel six‐gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six‐gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six‐gene signature have the potential to become novel drug targets.

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Multidisciplinary Advances Address the Challenges in Developing Drugs against Transient Receptor Potential Channels to Treat Metabolic Disorders

Wang

2023

ChemMedChem

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Transient receptor potential (TRP) channels are cation channels that regulate key physiological and pathological processes in response to a broad range of stimuli. Moreover, they systemically regulate the release of hormones, metabolic homeostasis, and complications of diabetes, which positions them as promising therapeutic targets to combat metabolic disorders. Nevertheless, there are significant challenges in the design of TRP ligands with high potency and durability. Herein we summarize the four challenges as hydrophobicity, selectivity, mono‐target therapy, and interspecies discrepancy. We present 1134 TRP ligands with diversified modes of TRP‐ligand interaction and provide a detailed discussion of the latest strategies, especially cryogenic electron microscopy (cryo‐EM) and computational methods. We propose solutions to address the challenges with a critical analysis of advances in membrane partitioning, polypharmacology, biased agonism, and biochemical screening of transcriptional modulators. They are fueled by the breakthrough from cryo‐EM, chemoinformatics and bioinformatics. The discussion is aimed to shed new light on designing next‐generation drugs to treat obesity, diabetes and its complications, with optimal hydrophobicity, higher mode selectivity, multi‐targeting and consistent activities between human and rodents.

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QuartataWeb: Integrated Chemical–Protein-Pathway Mapping for Polypharmacology and Chemogenomics

Cited by 26 publications

References 16 publications

Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis

Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis

Identification of a novel six‐gene signature with potential prognostic and therapeutic value in cervical cancer

Multidisciplinary Advances Address the Challenges in Developing Drugs against Transient Receptor Potential Channels to Treat Metabolic Disorders

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