Dataset Information

Polypharmacology of Berberine Based on Multi-Target Binding Motifs.

ABSTRACT: Background: Polypharmacology is emerging as the next paradigm in drug discovery. However, considerable challenges still exist for polypharmacology modeling. In this study, we developed a rational design to identify highly potential targets (HPTs) for polypharmacological drugs, such as berberine. Methods and Results: All the proven co-crystal structures locate berberine in the active cavities of a redundancy of aromatic, aliphatic, and acidic residues. The side chains from residues provide hydrophobic and electronic interactions to aid in neutralization for the positive charge of berberine. Accordingly, we generated multi-target binding motifs (MBM) for berberine, and established a new mathematical model to identify HPTs based on MBM. Remarkably, the berberine MBM was embodied in 13 HPTs, including beta-secretase 1 (BACE1) and amyloid-?1-42 (A?1-42). Further study indicated that berberine acted as a high-affinity BACE1 inhibitor and prevented A?1-42 aggregation to delay the pathological process of Alzheimer's disease. Conclusion: Here, we proposed a MBM-based drug-target space model to analyze the underlying mechanism of multi-target drugs against polypharmacological profiles, and demonstrated the role of berberine in Alzheimer's disease. This approach can be useful in derivation of rules, which will illuminate our understanding of drug action in diseases.

SUBMITTER: Chu M

PROVIDER: S-EPMC6066535 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Polypharmacology of Berberine Based on Multi-Target Binding Motifs.

Chu Ming M Chen Xi X Wang Jing J Guo Likai L Wang Qianqian Q Gao Zirui Z Kang Jiarui J Zhang Mingbo M Feng Jinqiu J Guo Qi Q Li Binghua B Zhang Chengrui C Guo Xueyuan X Chu Zhengyun Z Wang Yuedan Y

Frontiers in pharmacology 20180724

<b>Background:</b> Polypharmacology is emerging as the next paradigm in drug discovery. However, considerable challenges still exist for polypharmacology modeling. In this study, we developed a rational design to identify highly potential targets (HPTs) for polypharmacological drugs, such as berberine. <b>Methods and Results:</b> All the proven co-crystal structures locate berberine in the active cavities of a redundancy of aromatic, aliphatic, and acidic residues. The side chains from residues ...[more]

PMID: 30087614

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Project description:In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment. Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.

Dataset Information

Polypharmacology of Berberine Based on Multi-Target Binding Motifs.

Publications

Polypharmacology of Berberine Based on Multi-Target Binding Motifs.

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