Harmalkar Ameya, Lyskov Sergey, Gray Jeffrey J
Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, United States.
Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, United States.
Elife. 2025 May 27;13:RP94029. doi: 10.7554/eLife.94029.
Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases (Yin et al., 2022). In this work, we combine AF as a structural template generator with a physics-based replica exchange docking algorithm to better sample conformational changes. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AF confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol to complete a robust in silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions, including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 63% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (20% success rate), AlphaRED demonstrates a success rate of 43%. This new strategy demonstrates the success possible by integrating deep learning-based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at https://github.com/Graylab/AlphaRED.
尽管AlphaFold（AF）最近在蛋白质序列到结构预测领域取得了突破，但对蛋白质界面进行建模和预测蛋白质复合物结构仍然具有挑战性，特别是当一个或两个结合伙伴发生显著构象变化时。先前的研究表明，AF-multimer（AFm）在仅高达43%的情况下能够预测准确的蛋白质复合物（Yin等人，2022年）。在这项工作中，我们将AF作为结构模板生成器与基于物理的副本交换对接算法相结合，以更好地对构象变化进行采样。使用包含254个具有未结合和结合结构的可用蛋白质靶点的精选集合，我们首先证明AF置信度度量（pLDDT）可重新用于估计多聚体的蛋白质灵活性和对接准确性。我们将这些指标纳入我们的ReplicaDock 2.0协议中，以完成一个强大的计算机模拟流程，用于准确的蛋白质复合物结构预测。AlphaRED（由AlphaFold启动的副本交换对接）成功对接了AF预测失败的案例，包括对接基准集5.5中的97个失败案例。AlphaRED对63%的基准靶点生成了符合蛋白质-蛋白质相互作用预测评估（CAPRI）可接受质量或更高质量的预测。此外，在对AFm具有挑战性的抗原-抗体靶点子集上（成功率为20%），AlphaRED的成功率为43%。这种新策略证明了将基于进化信息训练的深度学习架构与基于物理的增强采样相结合可能取得的成功。该流程可在https://github.com/Graylab/AlphaRED上获取。
