提高蛋白质聚合预测:利用图形卷积网络和主动学习进行统一分析
Jiwon Sun1, JunHo Song1, Juo Kim1
1School of Mechanical Engineering, Soongsil University 369 Sangdo-ro, Dongjak-gu Seoul 06978 Republic of Korea kmin.min@ssu.ac.kr.
RSC advances
|October 4, 2024
概括
这项研究开发了一个图形卷积网络 (GCN) 模型来预测蛋白质聚合 (PA) 倾向,实现高精度. 积极学习方法进一步提高了识别容易聚合的蛋白质的效率.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 蛋白质聚合 (PA) 与神经退行性疾病 (如阿尔茨海默氏症和帕金森症) 有关.
- 了解PA需要洞察聚合倾向区域 (APR) 和结构互动.
- 计算方法,特别是机器学习,为PA实验研究提供了有效的替代方案.
研究的目的:
- 为准确的蛋白质聚合 (PA) 评分预测开发一个图形卷积网络 (GCN) 模型.
- 利用来自蛋白质数据库 (PDB) 和AlphaFold2.0的扩展数据集来改进模型训练.
- 评估一种积极学习策略,以有效识别具有高PA倾向的蛋白质.
主要方法:
- 使用从PDB和AlphaFold2.0.0获得的增强数据集构建了一个GCN模型.
- 使用AGGRESCAN3D 2.0计算PA倾向,并通过分离多多链来精制PDB数据.
- 在序列相似性比较后,从AlphaFold2.0中整合了22,774个Homo sapiens序列.
主要成果:
- 经过训练的GCN模型实现了0.9849的高确定系数 (R2) 和0.0381的低平均绝对误差 (MAE),用于PA预测.
- 积极学习方法表现出卓越的表现,预期改善的MAE为0.0291.
- 通过探索仅29%的搜索空间,积极学习识别了99%的目标蛋白.
结论:
- 开发的GCN模型显示了预测蛋白质聚合易感性的显著前景.
- 积极学习策略提高了识别容易聚合的蛋白质的效率.
- 这项工作推进了用于PA预测的计算工具,在疾病诊断和治疗中具有潜在的应用.
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