美国洛斯阿拉莫斯国家实验室Thomas C. Terwilliger团队近期取得重要工作进展，他们研究利用隐式实验信息改进AlphaFold建模。相关工作2022年10月20日在线发表于《自然—方法学》杂志上。

研究人员假设，通过隐含地包括新的实验信息，如密度图，一个模型的更大部分可以被准确预测，这可能会协同改善模型中没有被机器学习或实验单独完全解决的部分。研究人员开发了一个迭代程序，其中AlphaFold模型是在实验密度图的基础上自动重建的，重建的模型被用作新的AlphaFold预测的模板。

研究人员表明，包括实验信息在内的预测改进超过了由实验数据指导的简单重建所获得的改进。AlphaFold的密度建模程序已经被纳入到一个自动解释晶体学和冷冻电镜图像的程序中。

据介绍，像AlphaFold和RoseTTAFold这样的机器学习预测算法可以创建非常精确的蛋白质模型，但这些模型都有一些预测的区域置信度低或准确性差。

附：英文原文

Title: Improved AlphaFold modeling with implicit experimental information

Author: Terwilliger, Thomas C., Poon, Billy K., Afonine, Pavel V., Schlicksup, Christopher J., Croll, Tristan I., Milln, Claudia, Richardson, Jane. S., Read, Randy J., Adams, Paul D.

Issue&Volume: 2022-10-20

Abstract: Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.

DOI: 10.1038/s41592-022-01645-6

Source: https://www.nature.com/articles/s41592-022-01645-6