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仅使用阳性样本预测细菌蛋白化合物相互作用

Ki-Hwa Kim¹, Avinash Yaganapu², Sai Kosaraju³

¹Genome-based BioIT Convergence Institute, Asan, Republic of Korea.

Bioinformatics (Oxford, England)

|February 18, 2026

概括

一个新的正无标记 (PU) 学习框架,BIN-PU,通过生成伪标签,有效地预测细菌化合物-蛋白相互作用 (CPI). 这通过克服现有的CPI模型的局限性来推动药物发现和生物催化.

科学领域:

生物化学和生物信息学
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:

对化合物-蛋白相互作用 (CPI) 的准确预测对于制药和化学工程应用至关重要.
由于缺乏负相互作用数据,现有的CPI模型对于细菌系统是不够的.
细菌细胞染色体P450 (CYP) 相互作用特别有趣.

研究的目的:

开发一种用于预测细菌化合物-蛋白相互作用 (CPI) 的新框架.
为了应对细菌CPI预测中有限的负样本的挑战.
提高深度学习模型在细菌中CPI预测的准确性和适用性.

主要方法:

提出了一个名为BIN-PU的正非标记 (PU) 学习框架.
实施了一种策略,从已知的积极相互作用中生成伪阳性和负性标签.
开发了一个加权的正损失函数,以优先考虑真正正的样本.
在细菌CYP数据上使用各种CPI骨干模型验证了BIN-PU.

主要成果:

与现有的PU模型相比,BIN-PU在仅使用阳性样本预测CPI方面表现优异.
该框架显示了多种细菌蛋白数据集的可重复性,包括人类CYP和未经处理的数据.
实验验证证证实了BIN-PU对未经理的CYP数据的CPI预测的准确性.

结论:

BIN-PU为预测细菌化合物-蛋白相互作用 (CPI) 提供了重大进展.
该框架增强了深度学习模型在生物相互作用任务中的预测能力.
BIN-PU为涉及细菌蛋白的药物发现和生物催化研究开辟了新的途径.

关键词:

化合物-蛋白质相互作用这是一种细菌性CYP.生物催化剂生物催化剂深度学习是一种深度学习.积极的未标记的学习.

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