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使用基于深度学习的分子对接和AlphaFold预测的蛋白质结构发现新型抗精神病药物的靶点

Target Discovery Using Deep Learning-Based Molecular Docking and Predicted Protein Structures With AlphaFold for Novel Antipsychotics.

作者信息

Kim Yangsik, Kim Seyong

机构信息

Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea.

Mental Health Rearch Institute, National Center for Mental Health, Seoul, Republic of Korea.

出版信息

Psychiatry Investig. 2023 Jun;20(6):504-514. doi: 10.30773/pi.2022.0343. Epub 2023 May 30.

DOI:10.30773/pi.2022.0343
PMID:37248690
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10307917/
Abstract

OBJECTIVE

New drugs are needed to treat antipsychotic-resistant schizophrenia, especially those with clozapine-resistant schizophrenia. Atypical antipsychotics have predominantly 5-HT2A and dopaminergic antagonism, but also require investigation of other receptors.

METHODS

In this study, the binding affinities between clozapine, olanzapine, and quetiapine with neuropharmacological, immunological, and metabolic receptors were measured using GNINA (Deep Learning Based Molecular Docking) and AlphaFold (Predicted Protein Structures).

RESULTS

Through this study, it was determined that these antipsychotics showed high binding affinity to a variety of receptors, such as CB2, 5-HT1BR, NPYR4, and CCR5. Cyclosporin A and everolimus which show high affinities with those receptors could be used for the development of new antipsychotic drugs based on these drugs.

CONCLUSION

In the future, the method used in this study will be applied to the development of new antipsychotic drugs, including drug repositioning, and to the discovery of the pathophysiology of schizophrenia.

摘要

目的

需要新的药物来治疗对抗精神病药物耐药的精神分裂症,尤其是对氯氮平耐药的精神分裂症。非典型抗精神病药物主要具有5-羟色胺2A和多巴胺能拮抗作用,但也需要研究其他受体。

方法

在本研究中,使用GNINA(基于深度学习的分子对接)和AlphaFold(预测蛋白质结构)测量了氯氮平、奥氮平和喹硫平与神经药理学、免疫学和代谢受体之间的结合亲和力。

结果

通过本研究确定,这些抗精神病药物对多种受体表现出高结合亲和力,如CB2、5-羟色胺1B受体、神经肽Y受体4和趋化因子受体5。与这些受体具有高亲和力的环孢素A和依维莫司可用于基于这些药物开发新的抗精神病药物。

结论

未来,本研究中使用的方法将应用于新抗精神病药物的开发,包括药物重新定位,以及精神分裂症病理生理学的发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/10307917/78d74225886e/pi-2022-0343f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/10307917/11b63836411c/pi-2022-0343f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/10307917/29ef3361309d/pi-2022-0343f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/10307917/78d74225886e/pi-2022-0343f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/10307917/11b63836411c/pi-2022-0343f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/10307917/29ef3361309d/pi-2022-0343f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/10307917/78d74225886e/pi-2022-0343f3.jpg

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Impaired prepulse inhibition in mice with IRSp53 deletion in modulatory neurotransmitter neurons including dopamine, acetylcholine, oxytocin, and serotonin.
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Highly accurate protein structure prediction with AlphaFold.利用 AlphaFold 进行高精度蛋白质结构预测。
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