在蛋白质-蛋白质亲和力预测中微调Boltz-2
On fine-tuning Boltz-2 for protein-protein affinity prediction
AI总结：
本文研究了在蛋白质-蛋白质亲和力预测中微调Boltz-2的表现，发现其在小规模和大规模数据集上均不如基于序列的方法，但结合结构和序列嵌入可提升性能。
摘要：
准确预测蛋白质-蛋白质结合亲和力对于理解分子相互作用和设计药物至关重要。我们采用Boltz-2，一种最先进的基于结构的蛋白质-配体亲和力预测器，用于蛋白质-蛋白质亲和力回归，并在两个数据集TCR3d和PPB-affinity上进行了评估。尽管具有较高的结构准确性，Boltz-2-PPI在小规模和大规模数据集上均不如基于序列的替代方法表现。将Boltz-2-PPI的嵌入与基于序列的嵌入结合，可以产生互补的改进，尤其是在较弱的序列模型上，这表明基于序列和结构的模型学习了不同的信号。我们的结果反映了与使用结构数据训练相关的已知偏差，并表明当前基于结构的表示方法尚未针对高效的亲和力预测进行优化。
英文摘要：
Accurate prediction of protein-protein binding affinity is vital for understanding molecular interactions and designing therapeutics. We adapt Boltz-2, a state-of-the-art structure-based protein-ligand affinity predictor, for protein-protein affinity regression and evaluate it on two datasets, TCR3d and PPB-affinity. Despite high structural accuracy, Boltz-2-PPI underperforms relative to sequence-based alternatives in both small- and larger-scale data regimes. Combining embeddings from Boltz-2-PPI with sequence-based embeddings yields complementary improvements, particularly for weaker sequence models, suggesting different signals are learned by sequence- and structure-based models. Our results echo known biases associated with training with structural data and suggest that current structure-based representations are not primed for performant affinity prediction.
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