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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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相关实验视频

Updated: Jun 6, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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SIGMAP:一种可解释的人工智能工具,用于SIGMA-1受体亲和力预测.

Maria Cristina Lomuscio1, Nicola Corriero2, Vittoria Nanna2

  • 1Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica (DiMePRe-J), Università degli Studi di Bari Aldo Moro Piazza Giulio Cesare, 11, Policlinico 70124 Bari Italy.

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概括

开发用于预测西格玛-1受体 (S1R) 调节器的计算工具对于治疗神经退行性疾病和癌症至关重要. 一种新的机器学习模型实现了高精度,有助于药物发现.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算化学和化学信息学
  • 药物发现和药物化学
  • 药理学中的人工智能

背景情况:

  • 西格玛-1受体 (S1R) 调节器对神经退行,癌症和COVID-19等病毒感染具有治疗潜力.
  • 精确的S1R亲和力的*in silico*预测对于高效的药物设计至关重要.
  • 现有的方法需要改进,以便可靠地识别S1R调节器.

研究的目的:

  • 开发和验证准确的计算模型来预测S1R调节器活动.
  • 为药物化学家创建一个用户友好的平台,以帮助合理的药物设计.
  • 利用可解释AI (XAI) 来更好地理解预测模型决策.

主要方法:

  • 从ChEMBL v33.3.中提取了25000多个S1R小分子生物活性数据点的精选数据集.
  • 25个分类器被训练使用五个不同的分子指纹和各种机器学习算法.
  • 应用了可解释的AI技术,包括SHAP和对比解释,以解释模型预测.

主要成果:

  • 大多数开发的分类器都表现出良好的预测性能.
  • 使用摩根指纹的支向量机器,表现最好的模型实现了0.90.9的AUC.
  • 开发了一个用户友好的网络平台,SIGMAP,以提供访问最好的预测模型.

结论:

  • 开发的计算模型,特别是表现最好的SVM模型,在预测S1R亲和力方面表现出很高的准确性.
  • 结合XAI的SIGMAP平台为药物化学家提供了一个有价值的工具,用于合理设计新型S1R调节器.
  • 这种方法有助于发现S1R相关疾病的新疗法.