StoPred:使.

Quancheng Liu1, Chunxiang Peng2, Wei Zheng3,1

  • 1Gilbert S Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, 48109-2218, MI, U.S..

Research square
|November 24, 2025
PubMed
概括

使用蛋白质语言模型和图表注意力网络,StoPred准确地预测了蛋白质复合体静态度. 这种新的方法增强了对同质和异质复合体的预测,克服了现有的计算方法的局限性.

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