荷兰癌症研究所Anastassis Perrakis和Robbie P. Joosten共同合作，近期取得重要工作进展。他们研究开发了AlphaFill工具，可以用配体和辅因子丰富原有的AlphaFold模型。相关研究成果2022年11月24在线发表于《自然—方法学》杂志上。

研究人员开发了AlphaFill，这是一种使用序列和结构相似性将“缺失”小分子和离子从实验确定的结构“移植”到预测蛋白质模型的算法。该算法在实验结构上得到了成功验证。在995411个AlphaFold模型上共执行了12029789次移植，并与相关的验证指标一起在alphafill.eu数据库中提供，这是一个帮助科学家提出新假设和设计目标实验的资源。

据介绍，基于人工智能的蛋白质结构预测方法对生物分子科学产生了变革性的影响。然而，AlphaFold蛋白质结构数据库中预测的蛋白质模型都缺乏分子结构或功能所必需的小分子坐标：血红蛋白缺乏结合血红素；锌指基序缺乏结构完整性所需的锌离子；金属蛋白酶缺乏催化所需的金属离子；对生物功能重要的配体也不存在；没有ADP或ATP与任何ATP酶或激酶结合。

附：英文原文

Title: AlphaFill: enriching AlphaFold models with ligands and cofactors

Author: Hekkelman, Maarten L., de Vries, Ida, Joosten, Robbie P., Perrakis, Anastassis

Issue&Volume: 2022-11-24

Abstract: Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecules, essential for molecular structure or function: hemoglobin lacks bound heme; zinc-finger motifs lack zinc ions essential for structural integrity and metalloproteases lack metal ions needed for catalysis. Ligands important for biological function are absent too; no ADP or ATP is bound to any of the ATPases or kinases. Here we present AlphaFill, an algorithm that uses sequence and structure similarity to ‘transplant’ such ‘missing’ small molecules and ions from experimentally determined structures to predicted protein models. The algorithm was successfully validated against experimental structures. A total of 12,029,789 transplants were performed on 995,411 AlphaFold models and are available together with associated validation metrics in the alphafill.eu databank, a resource to help scientists make new hypotheses and design targeted experiments.

DOI: 10.1038/s41592-022-01685-y

Source: https://www.nature.com/articles/s41592-022-01685-y