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English(EN) Computational Modeling of Antibody-Antigen Complexes: PLM-Based and MSA-Based Approaches

新的计算方法提升抗体-抗原复合物建模能力

研究人员开发了新的计算方法来改进抗体-抗原复合物的建模,解决了与一般蛋白质-蛋白质相互作用相比存在的性能差距。该研究探索了使用蛋白质语言模型(PLMs)进行抗体结构预测,在CDR-H3准确性方面取得了良好结果。然而,单一序列PLMs在没有共进化信号的情况下难以进行复合物预测。为克服这一问题,研究团队引入了MSA精炼和收敛感知循环技术,这些技术在无需重新训练模型的情况下,增强了AlphaFold3在抗体-抗原复合物预测方面的基线性能。 AI

影响 增强了药物发现和治疗性抗体合理设计的计算工具。

排序理由 该集群包含一篇研究论文,详细介绍了用于抗体-抗原复合物建模的新计算方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的计算方法提升抗体-抗原复合物建模能力

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该集群包含一篇研究论文,详细介绍了用于抗体-抗原复合物建模的新计算方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiao Luo ·

    抗体-抗原复合物的计算建模:基于PLM和MSA的方法

    arXiv:2605.28886v1 Announce Type: cross Abstract: Antibodies play a central role in the immune response by specifically recognizing and neutralizing antigens, and therapeutic antibodies have become major drugs for cancer and autoimmune diseases. However, their discovery still rel…