Zhang Chengyun, Wang Wentong, Zhu Ning, Cao Zhigang, Wu Yaling, Mao Qingyi, Zhu Cheng, Zhang Chenhao, Guo Jingjing, Duan Hongliang
Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Henan Institute of Advanced Technology, Zhengzhou 450003, China.
J Chem Inf Model. 2025 Sep 22;65(18):9777-9789. doi: 10.1021/acs.jcim.5c01393. Epub 2025 Aug 28.
Despite the revolutionary impact of AlphaFold3 on structural biology, this model's capability in predicting noncanonical cyclic peptides remains unexplored. Given the clinical significance of cyclic peptides containing unnatural residues as a therapeutic modality, we present the first systematic evaluation of AlphaFold3 for this class of molecules. To facilitate benchmarking, we developed an automated input pipeline to streamline AlphaFold3 predictions for cyclic peptides. Our study aims to (1) quantify the hierarchical accuracy (all atoms, Cα atoms, and atoms of unnatural residue levels) of AlphaFold3 in predicting both noncanonical cyclic peptide monomers and complexes, (2) assess the reliability of AlphaFold3's confidence metrics, (3) evaluate the influence of multiple sequence alignment and structural templates, and (4) identify systematic biases in AlphaFold3's predictions. Based on these analyses, we provide practical guidelines for applying AlphaFold3 in cyclic peptide structure prediction to facilitate the related research of bioactive cyclic peptides.
尽管AlphaFold3对结构生物学产生了革命性影响，但其在预测非经典环肽方面的能力仍未得到探索。鉴于含有非天然残基的环肽作为一种治疗方式具有临床意义，我们首次对AlphaFold3针对这类分子进行了系统评估。为便于进行基准测试，我们开发了一个自动化输入流程，以简化AlphaFold3对环肽的预测。我们的研究旨在：（1）量化AlphaFold3在预测非经典环肽单体和复合物时的分层准确性（所有原子、Cα原子以及非天然残基水平的原子）；（2）评估AlphaFold3置信度指标的可靠性；（3）评估多序列比对和结构模板的影响；（4）识别AlphaFold3预测中的系统偏差。基于这些分析，我们提供了在环肽结构预测中应用AlphaFold3的实用指南，以促进生物活性环肽的相关研究。
