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Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.

ABSTRACT: Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.

SUBMITTER: Hsin KY

PROVIDER: S-EPMC3877102 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.

Hsin Kun-Yi KY Ghosh Samik S Kitano Hiroaki H

PloS one 20131231 12

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learni ...[more]

PMID: 24391846

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Project description:Serotonin (5-HT) receptors are proteins involved in various neurological and biological processes, such as aggression, anxiety, appetite, cognition, learning, memory, mood, sleep, and thermoregulation. They are commonly associated with drug abuse and addiction due to their importance as targets for various pharmaceutical and recreational drugs. However, due to a high sequence similarity/identity among 5-HT receptors and the unavailability of the 3D structure of the different 5-HT receptor, no report was available so far regarding the systematical comparison of the key and selective residues involved in the binding pocket, making it difficult to design subtype-selective serotonergic drugs. In this work, we first built and validated three-dimensional models for all 5-HT receptors based on the existing crystal structures of 5-HT1B, 5-HT2B, and 5-HT2C. Then, we performed molecular docking studies between 5-HT receptors agonists/inhibitors and our 3D models. The results from docking were consistent with the known binding affinities of each model. Sequentially, we compared the binding pose and selective residues among 5-HT receptors. Our results showed that the affinity variation could be potentially attributed to the selective residues located in the binding pockets. Moreover, we performed MD simulations for 12 5-HT receptors complexed with ligands; the results were consistent with our docking results and the reported data. Finally, we carried out off-target prediction and blood-brain barrier (BBB) prediction for Captagon using our established hallucinogen-related chemogenomics knowledgebase and in-house computational tools, with the hope to provide more information regarding the use of Captagon. We showed that 5-HT2C, 5-HT5A, and 5-HT7 were the most promising targets for Captagon before metabolism. Overall, our findings can provide insights into future drug discovery and design of medications with high specificity to the individual 5-HT receptor to decrease the risk of addiction and prevent drug abuse.

Project description:BackgroundFufang Honghua Buji (FHB) granules, have proven efficacy against vitiligo in long-term clinical practice. However, its major active chemical components and molecular mechanisms of action remain unknown. The purpose of this study was to confirm the molecular mechanism of FHB's therapeutic effect on vitiligo utilizing network pharmacology, molecular docking, and molecular dynamics simulation prediction, as well as experimental verification.MethodsTraditional Chinese Medicine Systems Pharmacology (TCMSP) and HERB databases were used to obtain the chemical composition and action targets of FHB. Online Mendelian Inheritance in Man (OMIM), DrugBank, DisGeNET, GeneCards, and Therapeutic Target Database (TTD) databases were applied to screen for vitiligo-related targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed through the Matascape database. Molecular docking and dynamics simulation methods were for the analysis of the binding sites and binding energies between the FHB's active components and the targets. Finally, a vitiligo mouse model was created, and the therapeutic effect and molecular mechanism of action of FHB were validated using enzyme linked immunosorbent assay (ELISA), western blot (WB), and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Additionally, hematoxylin-eosin staining (HE) and blood biochemical assays were conducted to assess the biosafety of FHB.ResultThe screening of chemical composition and targets suggested that 94 genetic targets of FHB were associated with vitiligo. The bioinformatics analysis suggested that luteolin, quercetin, and wogonin may be major active components, and nuclear factor-kappa B p65 subunit (RELA), signal transducer, and activator of transcription (STAT) 3 and RAC-alpha serine/threonine-protein kinase (AKT) 1 may be potential targets of FHB-vitiligo therapy. Molecular docking and dynamics simulation further demonstrated that luteolin, quercetin, and wogonin all bound best to STAT3. Through experimental verification, FHB has been demonstrated to alleviate the pathogenic characteristics of vitiligo mice, suppress the JAK-STAT signaling pathway, reduce inflammation, and increase melanogenesis. The in vivo safety evaluation experiments also demonstrated the non-toxicity of FHB.ConclusionsFHB exerts anti-inflammatory and melanogenesis-promoting effects via the effect of multi-component on multi-target, among which the JAK-STAT pathway is a validated FHB-vitiligo target, providing new ideas and clues for the development of vitiligo therapy.

Dataset Information

Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.

Publications

Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.

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