Ille Alexander M, Markosian Christopher, Burley Stephen K, Pasqualini Renata, Arap Wadih
Rutgers Cancer Institute, Newark, NJ, USA.
Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA.
bioRxiv. 2025 Jan 4:2024.12.03.626727. doi: 10.1101/2024.12.03.626727.
Protein structure prediction artificial intelligence/machine learning (AI/ML) approaches has sparked substantial research interest in structural biology and adjacent disciplines. More recently, AlphaFold2 (AF2) has been adapted for the prediction of multiple structural conformations in addition to single-state structures. This novel avenue of research has focused on proteins (typically 50 residues in length or greater), while multi-conformation prediction of shorter peptides has not yet been explored in this context. Here, we report AF2-based structural conformation prediction of a total of 557 peptides (ranging in length from 10 to 40 residues) for a benchmark dataset with corresponding nuclear magnetic resonance (NMR)-determined conformational ensembles. structure predictions were accompanied by structural comparison analyses to assess prediction accuracy. We found that the prediction of conformational ensembles for peptides with AF2 varied in accuracy NMR data, with average root-mean-square deviation (RMSD) among structured regions under 2.5 Å and average root-mean-square fluctuation (RMSF) differences under 1.5 Å. Our results reveal notable capabilities of AF2-based structural conformation prediction for peptides but also underscore the necessity for interpretation discretion.
蛋白质结构预测的人工智能/机器学习（AI/ML）方法引发了结构生物学及相关学科的大量研究兴趣。最近，除了单态结构外，AlphaFold2（AF2）已被用于预测多种结构构象。这条新的研究途径主要聚焦于蛋白质（通常长度为50个残基或更长），而在此背景下，较短肽段的多构象预测尚未得到探索。在此，我们报告了基于AF2对总共557个肽段（长度从10到40个残基不等）进行的结构构象预测，该预测针对的是具有相应核磁共振（NMR）确定的构象集合的基准数据集。结构预测还伴随着结构比较分析以评估预测准确性。我们发现，利用AF2对肽段构象集合的预测准确性因NMR数据而异，结构化区域的平均均方根偏差（RMSD）在2.5 Å以下，平均均方根波动（RMSF）差异在1.5 Å以下。我们的结果揭示了基于AF2的肽段结构构象预测的显著能力，但也强调了解释时谨慎判断的必要性。
