改进化合物-蛋白质相互作用预测,专注于以多模式张量融合策略的内模式和跨模式动态
Meng Wang1, Jianmin Wang2, Jianxin Ji1
1Department of Biostatistics, Harbin Medical University, Harbin 150081, China.
Computational and structural biotechnology journal
|November 11, 2024
概括
一个新的框架,MMTF-CPI,通过有效地从多种数据类型中学习来增强化合物-蛋白相互作用 (CPI) 的预测. 这种方法显著改善了药物发现,并确定了潜在的癌症疗法.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 识别新型化合物-蛋白相互作用 (CPI) 对药物发现和目标识别至关重要.
- 现有的多式联运方法难以捕捉模式内和模式间的动态,限制了预测的准确性.
- 需要先进的计算框架来提高CPI预测性能.
研究的目的:
- 为预测化合物-蛋白相互作用 (CPI) 提出一种新的多式张量融合框架,命名为MMTF-CPI.
- 为了提高预测性能,增强 intra-modality 和 inter-modality 动态的学习.
- 验证该框架的有效性,并探索其在药物发现和标识中的应用.
主要方法:
- 开发了MMTF-CPI,一个包含三个单模式学习模块 (结构,异质网络,转录概况) 的框架.
- 实现了一个张量融合模块,以有效地整合不同模式的信息.
- 利用一个预测模块来预测化合物-蛋白相互作用.
主要成果:
- 与最先进的多式联运方法相比,MMTF-CPI在七个不同的数据集中表现出更高的性能.
- 与其他聚变技术相比,张量聚变模块显著提高了预测性能.
- 案例研究证实了MMTF-CPI在识别潜在药物点方面的实际实用性.
结论:
- MMTF-CPI代表了CPI预测计算方法的重大进步.
- 该框架能够学习复杂的跨模式动态,为药物发现提供了强大的工具.
- MMTF-CPI成功地确定了治疗乳腺癌和非小细胞肺癌的候选化合物,突出了其治疗潜力.
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