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Showing 1501 to 1504 of 5186 entries
Published on September 10, 2021
3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds.
Abstract: The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.
Published on September 9, 2021
Investigation of structural analogs of hydroxychloroquine for SARS-CoV-2 main protease (Mpro): A computational drug discovery study.
Abstract: The main protease (Mpro) is the key enzyme of nCOVID-19 and plays a decisive role that makes it an attractive drug target. Multiple analysis of crystal structures reveals the presence of W1, W2, and W3 water locations in the active site pocket of Mpro; W1 and W2 are unstable and are weakly bonded with protein in comparison to W3 of Mpro-native. So, we adopt the water displacement method to occupy W1 or W2 sites by triggering HCQ or its analogs to inactivate the enzyme. Virtual screening is employed to find out best analogs of HCQ, molecular docking is used for water displacement from catalytic region of Mpro, and finally, MD simulations are conducted for validation of these findings. The docking study reveals that W1 and W2 are occupied by respective atoms of ZINC28706440 whereas W2 by HCQ and indacaterol. Finally, MD results demonstrate (i) HCQ occupies W1 and W2 positions, but its analogs (indacaterol and ZINC28706440) are inadequate to retain either W1 or W2 (ii) His41 and Asp187 are stabilized by W3 in Mpro-native and His41, Cys145 and HCQ by W7 in ZINC28706440, and W4, W5, and W6 make water mediated bridge between indacaterol with His41. The structural, dynamical, and thermodynamic (WFP and J value) profiling parameters suggest that W3, W4, and W7 are prominent in their corresponding positions in comparison with W5 and W6. The final results conclude that ZINC28706440 may act as a best analog of HCQ with acceptable physico-chemical and toxicological scores and may further be synthesized for experimental validation.
Published on September 9, 2021
Accelerating antibiotic discovery through artificial intelligence.
Abstract: By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.
Published on September 3, 2021
DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding.
Abstract: BACKGROUND: Prediction of the drug-target interaction (DTI) is a critical step in the drug repurposing process, which can effectively reduce the following workload for experimental verification of potential drugs' properties. In recent studies, many machine-learning-based methods have been proposed to discover unknown interactions between drugs and protein targets. A recent trend is to use graph-based machine learning, e.g., graph embedding to extract features from drug-target networks and then predict new drug-target interactions. However, most of the graph embedding methods are not specifically designed for DTI predictions; thus, it is difficult for these methods to fully utilize the heterogeneous information of drugs and targets (e.g., the respective vertex features of drugs and targets and path-based interactive features between drugs and targets). RESULTS: We propose a DTI prediction method DTI-HeNE (DTI based on Heterogeneous Network Embedding), which is specifically designed to cope with the bipartite DTI relations for generating high-quality embeddings of drug-target pairs. This method splits a heterogeneous DTI network into a bipartite DTI network, multiple drug homogeneous networks and target homogeneous networks, and extracts features from these sub-networks separately to better utilize the characteristics of bipartite DTI relations as well as the auxiliary similarity information related to drugs and targets. The features extracted from each sub-network are integrated using pathway information between these sub-networks to acquire new features, i.e., embedding vectors of drug-target pairs. Finally, these features are fed into a random forest (RF) model to predict novel DTIs. CONCLUSIONS: Our experimental results show that, the proposed DTI network embedding method can learn higher-quality features of heterogeneous drug-target interaction networks for novel DTIs discovery.
Published on September 3, 2021
Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.
Abstract: Identification of drug-target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.
Published on September 3, 2021
FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases.
Abstract: MOTIVATION: Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. RESULTS: The use of a Semantic Web framework on biological data allows us to apply ontological based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. AVAILABILITY: A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM knowledge graph, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Published on September 2, 2021
ExonSkipAD provides the functional genomic landscape of exon skipping events in Alzheimer's disease.
Abstract: Exon skipping (ES), the most common alternative splicing event, has been reported to contribute to diverse human diseases due to the loss of functional domains/sites or frameshifting of the open reading frame (ORF) and noticed as therapeutic targets. Accumulating transcriptomic studies of aging brains show the splicing disruption is a widespread hallmark of neurodegenerative diseases such as Alzheimer's disease (AD). Here, we built ExonSkipAD, the ES annotation database aiming to provide a resource/reference for functional annotation of ES events in AD and identify therapeutic targets in exon units. We identified 16 414 genes that have ~156 K, ~ 69 K, ~ 231 K ES events from the three representative AD cohorts of ROSMAP, MSBB and Mayo, respectively. For these ES events, we performed multiple functional annotations relating to ES mechanisms or downstream. Specifically, through the functional feature retention studies followed by the open reading frames (ORFs), we identified 275 important cellular regulators that might lose their cellular regulator roles due to exon skipping in AD. ExonSkipAD provides twelve categories of annotations: gene summary, gene structures and expression levels, exon skipping events with PSIs, ORF annotation, exon skipping events in the canonical protein sequence, 3'-UTR located exon skipping events lost miRNA-binding sites, SNversus in the skipped exons with a depth of coverage, AD stage-associated exon skipping events, splicing quantitative trait loci (sQTLs) in the skipped exons, correlation with RNA-binding proteins, and related drugs & diseases. ExonSkipAD will be a unique resource of transcriptomic diversity research for understanding the mechanisms of neurodegenerative disease development and identifying potential therapeutic targets in AD. Significance AS the first comprehensive resource of the functional genomics of the alternative splicing events in AD, ExonSkipAD will be useful for many researchers in the fields of pathology, AD genomics and precision medicine, and pharmaceutical and therapeutic researches.
Published on September 2, 2021
Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.
Abstract: Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.
Published on September 2, 2021
Human melanocyte development and melanoma dedifferentiation at single-cell resolution.
Abstract: In humans, epidermal melanocytes are responsible for skin pigmentation, defence against ultraviolet radiation and the deadliest common skin cancer, melanoma. Although there is substantial overlap in melanocyte development pathways between different model organisms, species-dependent differences are frequent and the conservation of these processes in human skin remains unresolved. Here, we used a single-cell enrichment and RNA-sequencing pipeline to study human epidermal melanocytes directly from the skin, capturing transcriptomes across different anatomical sites, developmental age, sexes and multiple skin tones. We uncovered subpopulations of melanocytes that exhibit anatomical site-specific enrichment that occurs during gestation and persists through adulthood. The transcriptional signature of the volar-enriched subpopulation is retained in acral melanomas. Furthermore, we identified human melanocyte differentiation transcriptional programs that are distinct from gene signatures generated from model systems. Finally, we used these programs to define patterns of dedifferentiation that are predictive of melanoma prognosis and response to immune checkpoint inhibitor therapy.
Published on September 2, 2021
A machine learning framework for predicting drug-drug interactions.
Abstract: Understanding drug-drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to elucidate the molecular mechanisms underlying drug-drug interactions while preserving rational biological interpretability is a challenging task in computational modeling for drug discovery. In this study, we attempt to investigate drug-drug interactions via the associations between genes that two drugs target. For this purpose, we propose a simple f drug target profile representation to depict drugs and drug pairs, from which an l2-regularized logistic regression model is built to predict drug-drug interactions. Furthermore, we define several statistical metrics in the context of human protein-protein interaction networks and signaling pathways to measure the interaction intensity, interaction efficacy and action range between two drugs. Large-scale empirical studies including both cross validation and independent test show that the proposed drug target profiles-based machine learning framework outperforms existing data integration-based methods. The proposed statistical metrics show that two drugs easily interact in the cases that they target common genes; or their target genes connect via short paths in protein-protein interaction networks; or their target genes are located at signaling pathways that have cross-talks. The unravelled mechanisms could provide biological insights into potential adverse drug reactions of co-prescribed drugs.
Showing 1501 to 1504 of 5186 entries