李翛然
(2025-09-29 01:04):
#paper Predicting protein-protein interactions in the human proteome doi.org/10.1126/science.adt1630 David baker发表了一项由美国德克萨斯大学西南医学中心领衔的重大研究成果,研究团队成功构建了全球最全面的人类蛋白质相互作用(PPI)预测模型。该工作整合了30PB基因组数据,结合深度学习技术,系统鉴定了17,849组高置信度蛋白质互作关系,其中包括3,631组全新互作,为疾病机制解析及药物研发提供了重要分子蓝图。
研究突破性地开发了omicMSA技术,显著增强共进化信号分析的灵敏度,并利用AlphaFold数据库训练新型网络RF2-PPI,实现较传统方法20倍的预测速度提升及90%的准确率。
Science,
2025-9-25.
DOI: 10.1126/science.adt1630
Predicting protein-protein interactions in the human proteome
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Abstract:
Protein-protein interactions (PPI) are essential for biological function. Coevolutionary analysis and deep learning (DL) based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches have had limited success for the more complex human proteome. We overcame this challenge by enhancing the coevolutionary signals with 7-fold deeper multiple sequence alignments harvested from 30 petabytes of unassembled genomic data and developing a new DL network trained on augmented datasets of domain-domain interactions from 200 million predicted protein structures. We systematically screened 200 million human protein pairs and predicted 17,849 interactions with an expected precision of 90%, of which 3,631 interactions were not identified in previous experimental screens. Three-dimensional models of these predicted interactions provide numerous hypotheses about protein function and mechanisms of human diseases.
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